Machine-learning approach expands the repertoire of anti-CRISPR protein families

[Translated article] Introducing artificial intelligence to hospital pharmacy departments

Artificial intelligence is a broad concept that includes the study of the ability of computers to perform tasks that would normally require the intervention of human intelligence. By exploiting large volumes of healthcare data, Artificial intelligence algorithms can identify patterns and predict outcomes, which can help healthcare organizations and their professionals make better decisions and achieve better results. Machine learning, deep learning, neural networks, or natural language processing are among the most important methods, allowing systems to learn and improve from data without the need for explicit programming. Artificial intelligence has been introduced in biomedicine, accelerating processes, improving accuracy and efficiency, and improving patient care. By using Artificial intelligence algorithms and machine learning, hospital pharmacists can analyze a large volume of patient data, including medical records, laboratory results, and medication profiles, aiding them in identifying potential drug-drug interactions, assessing the safety and efficacy of medicines, and making informed recommendations. Artificial intelligence integration will improve the quality of pharmaceutical care, optimize processes, promote research, deploy open innovation, and facilitate education. Hospital pharmacists who master Artificial intelligence will play a crucial role in this transformation.

Approaching artificial intelligence to Hospital Pharmacy

Artificial intelligence (AI) is a broad concept that includes the study of the ability of computers to perform tasks that would normally require the intervention of human intelligence. By exploiting large volumes of healthcare data, artificial intelligence algorithms can identify patterns and predict outcomes, which can help healthcare organizations and their professionals make better decisions and achieve better results. Machine learning, deep learning, neural networks or natural language processing are among the most important methods, allowing systems to learn and improve from data without the need for explicit programming. AI has been introduced in biomedicine, accelerating processes, improving safety and efficiency, and improving patient care. By using AI algorithms and Machine Learning, hospital pharmacists can analyze a large volume of patient data, including medical records, laboratory results, and medication profiles, aiding them in identifying potential drug-drug interactions, assessing the safety and efficacy of medicines, and making informed recommendations. AI integration will improve the quality of pharmaceutical care, optimize processes, promote research, deploy open innovation, and facilitate education. Hospital pharmacists who master AI will play a crucial role in this transformation.

Machine intelligence accelerated design of conductive MXene aerogels with programmable properties

Designing ultralight conductive aerogels with tailored electrical and mechanical properties is critical for various applications. Conventional approaches rely on iterative, time-consuming experiments across a vast parameter space. Herein, an integrated workflow is developed to combine collaborative robotics with machine learning to accelerate the design of conductive aerogels with programmable properties. An automated pipetting robot is operated to prepare 264 mixtures of Ti3C2Tx MXene, cellulose, gelatin, and glutaraldehyde at different ratios/loadings. After freeze-drying, the aerogels' structural integrity is evaluated to train a support vector machine classifier. Through 8 active learning cycles with data augmentation, 162 unique conductive aerogels are fabricated/characterized via robotics-automated platforms, enabling the construction of an artificial neural network prediction model. The prediction model conducts two-way design tasks: (1) predicting the aerogels' physicochemical properties from fabrication parameters and (2) automating the inverse design of aerogels for specific property requirements. The combined use of model interpretation and finite element simulations validates a pronounced correlation between aerogel density and compressive strength. The model-suggested aerogels with high conductivity, customized strength, and pressure insensitivity allow for compression-stable Joule heating for wearable thermal management.

Regulatory sequence-based discovery of anti-defense genes in archaeal viruses

In silico identification of viral anti-CRISPR proteins (Acrs) has relied largely on the guilt-by-association method using known Acrs or anti-CRISPR associated proteins (Acas) as the bait. However, the low number and limited spread of the characterized archaeal Acrs and Aca hinders our ability to identify Acrs using guilt-by-association. Here, based on the observation that the few characterized archaeal Acrs and Aca are transcribed immediately post viral infection, we hypothesize that these genes, and many other unidentified anti-defense genes (ADG), are under the control of conserved regulatory sequences including a strong promoter, which can be used to predict anti-defense genes in archaeal viruses. Using this consensus sequence based method, we identify 354 potential ADGs in 57 archaeal viruses and 6 metagenome-assembled genomes. Experimental validation identified a CRISPR subtype I-A inhibitor and the first virally encoded inhibitor of an archaeal toxin-antitoxin based immune system. We also identify regulatory proteins potentially akin to Acas that can facilitate further identification of ADGs combined with the guilt-by-association approach. These results demonstrate the potential of regulatory sequence analysis for extensive identification of ADGs in viruses of archaea and bacteria.

Inhibitors of bacterial immune systems: discovery, mechanisms and applications

To contend with the diversity and ubiquity of bacteriophages and other mobile genetic elements, bacteria have developed an arsenal of immune defence mechanisms. Bacterial defences include CRISPR-Cas, restriction-modification and a growing list of mechanistically diverse systems, which constitute the bacterial 'immune system'. As a response, bacteriophages and mobile genetic elements have evolved direct and indirect mechanisms to circumvent or block bacterial defence pathways and ensure successful infection. Recent advances in methodological and computational approaches, as well as the increasing availability of genome sequences, have boosted the discovery of direct inhibitors of bacterial defence systems. In this Review, we discuss methods for the discovery of direct inhibitors, their diverse mechanisms of action and perspectives on their emerging applications in biotechnology and beyond.

Exploiting activation and inactivation mechanisms in type I-C CRISPR-Cas3 for genome-editing applications

Type I CRISPR-Cas systems utilize the RNA-guided Cascade complex to identify matching DNA targets and the nuclease-helicase Cas3 to degrade them. Among the seven subtypes, type I-C is compact in size and highly active in creating large-sized genome deletions in human cells. Here, we use four cryoelectron microscopy snapshots to define its RNA-guided DNA binding and cleavage mechanisms in high resolution. The non-target DNA strand (NTS) is accommodated by I-C Cascade in a continuous binding groove along the juxtaposed Cas11 subunits. Binding of Cas3 further traps a flexible bulge in NTS, enabling NTS nicking. We identified two anti-CRISPR proteins AcrIC8 and AcrIC9 that strongly inhibit Neisseria lactamica I-C function. Structural analysis showed that AcrIC8 inhibits PAM recognition through allosteric inhibition, whereas AcrIC9 achieves so through direct competition. Both Acrs potently inhibit I-C-mediated genome editing and transcriptional modulation in human cells, providing the first off-switches for type I CRISPR eukaryotic genome engineering.

Interrogating two extensively self-targeting Type I CRISPR-Cas systems in Xanthomonas albilineans reveals distinct anti-CRISPR proteins that block DNA degradation

CRISPR-Cas systems store fragments of invader DNA as spacers to recognize and clear those same invaders in the future. Spacers can also be acquired from the host's genomic DNA, leading to lethal self-targeting. While self-targeting can be circumvented through different mechanisms, natural examples remain poorly explored. Here, we investigate extensive self-targeting by two CRISPR-Cas systems encoding 24 self-targeting spacers in the plant pathogen Xanthomonas albilineans. We show that the native I-C and I-F1 systems are actively expressed and that CRISPR RNAs are properly processed. When expressed in Escherichia coli, each Cascade complex binds its PAM-flanked DNA target to block transcription, while the addition of Cas3 paired with genome targeting induces cell killing. While exploring how X. albilineans survives self-targeting, we predicted putative anti-CRISPR proteins (Acrs) encoded within the bacterium's genome. Screening of identified candidates with cell-free transcription-translation systems and in E. coli revealed two Acrs, which we named AcrIC11 and AcrIF12Xal, that inhibit the activity of Cas3 but not Cascade of the respective system. While AcrF12Xal is homologous to AcrIF12, AcrIC11 shares sequence and structural homology with the anti-restriction protein KlcA. These findings help explain tolerance of self-targeting through two CRISPR-Cas systems and expand the known suite of DNA degradation-inhibiting Acrs.

Anti-CRISPR Proteins and Their Application to Control CRISPR Effectors in Mammalian Systems

CRISPR-Cas effectors are powerful tools for genome and transcriptome targeting and editing. Naturally, these protein-RNA complexes are part of the microbial innate immune system, which emerged from the evolutionary arms race between microbes and phages. This coevolution has also given rise to so-called anti-CRISPR (Acr) proteins that counteract the CRISPR-Cas adaptive immunity. Acrs constitutively block cognate CRISPR-Cas effectors, e.g., by interfering with guide RNA binding, target DNA/RNA recognition, or target cleavage. In addition to their important role in microbiology and evolution, Acrs have recently gained particular attention for being useful tools and switches to regulate or fine-tune the activity of CRISPR-Cas effectors. Due to their commonly small size, high inhibition potency, and structural and mechanistic versatility, Acrs offer a wide range of potential applications for controlling CRISPR effectors in heterologous systems, including mammalian cells.Here, we review the diverse applications of Acrs in mammalian cells and organisms and discuss the underlying engineering strategies. These applications include (i) persistent blockage of CRISPR-Cas function to create write-protected cells, (ii) reduction of CRISPR-Cas off-target editing, (iii) focusing CRISPR-Cas activity to specific cell types and tissues, (iv) spatiotemporal control of CRISPR effectors based on engineered, opto-, or chemogenetic Acrs, and (v) the use of Acrs for selective binding and detection of CRISPR-Cas effectors in complex samples. We will also highlight potential future applications of Acrs in a biomedical context and point out present challenges that need to be overcome on the way.

Identification of an anti-CRISPR protein that inhibits the CRISPR-Cas type I-B system in Clostridioides difficile

IMPORTANCE

Clostridioides difficile is the widespread anaerobic spore-forming bacterium that is a major cause of potentially lethal nosocomial infections associated with antibiotic therapy worldwide. Due to the increase in severe forms associated with a strong inflammatory response and higher recurrence rates, a current imperative is to develop synergistic and alternative treatments for C. difficile infections. In particular, phage therapy is regarded as a potential substitute for existing antimicrobial treatments. However, it faces challenges because C. difficile has highly active CRISPR-Cas immunity, which may be a specific adaptation to phage-rich and highly crowded gut environment. To overcome this defense, C. difficile phages must employ anti-CRISPR mechanisms. Here, we present the first anti-CRISPR protein that inhibits the CRISPR-Cas defense system in this pathogen. Our work offers insights into the interactions between C. difficile and its phages, paving the way for future CRISPR-based applications and development of effective phage therapy strategies combined with the engineering of virulent C. difficile infecting phages.

Mobilizable plasmids drive the spread of antimicrobial resistance genes and virulence genes in Klebsiella pneumoniae

BACKGROUND

Klebsiella pneumoniae is a notorious clinical pathogen and frequently carries various plasmids, which are the main carriers of antimicrobial resistance and virulence genes. In comparison to self-transmissible conjugative plasmids, mobilizable plasmids have received much less attention due to their defects in conjugative elements. However, the contribution of mobilizable plasmids to the horizontal transfer of antimicrobial resistance genes and virulence genes of K. pneumoniae remains unclear. In this study, the transfer, stability, and cargo genes of the mobilizable plasmids of K. pneumoniae were examined via genetic experiments and genomic analysis.

METHODS

Carbapenem-resistant (CR) plasmid pHSKP2 and multidrug-resistant (MDR) plasmid pHSKP3 of K. pneumoniae HS11286, virulence plasmid pRJF293 of K. pneumoniae RJF293 were employed in conjugation assays to assess the transfer ability of mobilizable plasmids. Mimic mobilizable plasmids and genetically modified plasmids were constructed to confirm the cotransfer models. The plasmid morphology was evaluated through XbaI and S1 nuclease pulsed-field gel electrophoresis and/or complete genome sequencing. Mobilizable plasmid stability in transconjugants was analyzed via serial passage culture. In addition, in silico genome analysis of 3923 plasmids of 1194 completely sequenced K. pneumoniae was performed to investigate the distribution of the conjugative elements, the cargo genes, and the targets of the CRISPR-Cas system. The mobilizable MDR plasmid and virulence plasmid of K. pneumoniae were investigated, which carry oriT but lack other conjugative elements.

RESULTS

Our results showed that mobilizable MDR and virulence plasmids carrying oriT but lacking the relaxase gene were able to cotransfer with a helper conjugative CR plasmid across various Klebsiella and Escherichia coli strains. The transfer and stability of mobilizable plasmids rather than conjugative plasmids were not interfered with by the CRISPR-Cas system of recipient strains. According to the in silico analysis, the mobilizable plasmids carry about twenty percent of acquired antimicrobial resistance genes and more than seventy-five percent of virulence genes in K. pneumoniae.

CONCLUSIONS

Our work observed that a mobilizable MDR or virulence plasmid that carries oriT but lacks the relaxase genes transferred with the helper CR conjugative plasmid and mobilizable plasmids escaped from CRISPR-Cas defence and remained stable in recipients. These results highlight the threats of mobilizable plasmids as vital vehicles in the dissemination of antibiotic resistance and virulence genes in K. pneumoniae.

Bacteriophages suppress CRISPR-Cas immunity using RNA-based anti-CRISPRs

Many bacteria use CRISPR-Cas systems to combat mobile genetic elements, such as bacteriophages and plasmids1. In turn, these invasive elements have evolved anti-CRISPR proteins to block host immunity2,3. Here we unveil a distinct type of CRISPR-Cas Inhibition strategy that is based on small non-coding RNA anti-CRISPRs (Racrs). Racrs mimic the repeats found in CRISPR arrays and are encoded in viral genomes as solitary repeat units4. We show that a prophage-encoded Racr strongly inhibits the type I-F CRISPR-Cas system by interacting specifically with Cas6f and Cas7f, resulting in the formation of an aberrant Cas subcomplex. We identified Racr candidates for almost all CRISPR-Cas types encoded by a diverse range of viruses and plasmids, often in the genetic context of other anti-CRISPR genes5. Functional testing of nine candidates spanning the two CRISPR-Cas classes confirmed their strong immune inhibitory function. Our results demonstrate that molecular mimicry of CRISPR repeats is a widespread anti-CRISPR strategy, which opens the door to potential biotechnological applications6.

The structure of AcrIC9 revealing the putative inhibitory mechanism of AcrIC9 against the type IC CRISPR-Cas system

CRISPR-Cas systems are known to be part of the bacterial adaptive immune system that provides resistance against intruders such as viruses, phages and other mobile genetic elements. To combat this bacterial defense mechanism, phages encode inhibitors called Acrs (anti-CRISPR proteins) that can suppress them. AcrIC9 is the most recently identified member of the AcrIC family that inhibits the type IC CRISPR-Cas system. Here, the crystal structure of AcrIC9 from Rhodobacter capsulatus is reported, which comprises a novel fold made of three central antiparallel β-strands surrounded by three α-helixes, a structure that has not been detected before. It is also shown that AcrIC9 can form a dimer via disulfide bonds generated by the Cys69 residue. Finally, it is revealed that AcrIC9 directly binds to the type IC cascade. Analysis and comparison of its structure with structural homologs indicate that AcrIC9 belongs to DNA-mimic Acrs that directly bind to the cascade complex and hinder the target DNA from binding to the cascade.

CRISPR-Cas-Based Adaptive Immunity Mediates Phage Resistance in Periodontal Red Complex Pathogens

Periodontal diseases are polymicrobial immune-inflammatory diseases that can severely destroy tooth-supporting structures. The critical bacteria responsible for this destruction include red complex bacteria such as Porphoromonas gingivalis, Tanerella forsythia and Treponema denticola. These organisms have developed adaptive immune mechanisms against bacteriophages/viruses, plasmids and transposons through clustered regularly interspaced short palindromic repeats (CRISPR) and their associated proteins (Cas). The CRISPR-Cas system contributes to adaptive immunity, and this acquired genetic immune system of bacteria may contribute to moderating the microbiome of chronic periodontitis. The current research examined the role of the CRISPR-Cas system of red complex bacteria in the dysbiosis of oral bacteriophages in periodontitis. Whole-genome sequences of red complex bacteria were obtained and investigated for CRISPR using the CRISPR identification tool. Repeated spacer sequences were analyzed for homologous sequences in the bacteriophage genome and viromes using BLAST algorithms. The results of the BLAST spacer analysis for T. denticola spacers had a 100% score (e value with a bacillus phage), and the results for T. forsthyia and P. gingivalis had a 56% score with a pectophage and cellulophage (e value: 0.21), respectively. The machine learning model of the identified red complex CRISPR sequences predicts with area an under the curve (AUC) accuracy of 100 percent, indicating phage inhibition. These results infer that red complex bacteria could significantly inhibit viruses and phages with CRISPR immune sequences. Therefore, the role of viruses and bacteriophages in modulating sub-gingival bacterial growth in periodontitis is limited or questionable.

Exploiting Activation and Inactivation Mechanisms in Type I-C CRISPR-Cas3 for Genome Editing Applications

Type I CRISPR-Cas systems utilize the RNA-guided Cascade complex to identify matching DNA targets, and the nuclease-helicase Cas3 to degrade them. Among seven subtypes, Type I-C is compact in size and highly active in creating large-sized genome deletions in human cells. Here we use four cryo-electron microscopy snapshots to define its RNA-guided DNA binding and cleavage mechanisms in high resolution. The non-target DNA strand (NTS) is accommodated by I-C Cascade in a continuous binding groove along the juxtaposed Cas11 subunits. Binding of Cas3 further traps a flexible bulge in NTS, enabling efficient NTS nicking. We identified two anti-CRISPR proteins AcrIC8 and AcrIC9, that strongly inhibit N. lactamica I-C function. Structural analysis showed that AcrIC8 inhibits PAM recognition through direct competition, whereas AcrIC9 achieves so through allosteric inhibition. Both Acrs potently inhibit I-C-mediated genome editing and transcriptional modulation in human cells, providing the first off-switches for controllable Type I CRISPR genome engineering.

Advances in the Applications of Bioinformatics and Chemoinformatics

Chemoinformatics involves integrating the principles of physical chemistry with computer-based and information science methodologies, commonly referred to as "in silico techniques", in order to address a wide range of descriptive and prescriptive chemistry issues, including applications to biology, drug discovery, and related molecular areas. On the other hand, the incorporation of machine learning has been considered of high importance in the field of drug design, enabling the extraction of chemical data from enormous compound databases to develop drugs endowed with significant biological features. The present review discusses the field of cheminformatics and proposes the use of virtual chemical libraries in virtual screening methods to increase the probability of discovering novel hit chemicals. The virtual libraries address the need to increase the quality of the compounds as well as discover promising ones. On the other hand, various applications of bioinformatics in disease classification, diagnosis, and identification of multidrug-resistant organisms were discussed. The use of ensemble models and brute-force feature selection methodology has resulted in high accuracy rates for heart disease and COVID-19 diagnosis, along with the role of special formulations for targeting meningitis and Alzheimer's disease. Additionally, the correlation between genomic variations and disease states such as obesity and chronic progressive external ophthalmoplegia, the investigation of the antibacterial activity of pyrazole and benzimidazole-based compounds against resistant microorganisms, and its applications in chemoinformatics for the prediction of drug properties and toxicity-all the previously mentioned-were presented in the current review.

In Silico Approaches for Prediction of Anti-CRISPR Proteins

Numerous viruses infecting bacteria and archaea encode CRISPR-Cas system inhibitors, known as anti-CRISPR proteins (Acr). The Acrs typically are highly specific for particular CRISPR variants, resulting in remarkable sequence and structural diversity and complicating accurate prediction and identification of Acrs. In addition to their intrinsic interest for understanding the coevolution of defense and counter-defense systems in prokaryotes, Acrs could be natural, potent on-off switches for CRISPR-based biotechnological tools, so their discovery, characterization and application are of major importance. Here we discuss the computational approaches for Acr prediction. Due to the enormous diversity and likely multiple origins of the Acrs, sequence similarity searches are of limited use. However, multiple features of protein and gene organization have been successfully harnessed to this end including small protein size and distinct amino acid compositions of the Acrs, association of acr genes in virus genomes with genes encoding helix-turn-helix proteins that regulate Acr expression (Acr-associated proteins, Aca), and presence of self-targeting CRISPR spacers in bacterial and archaeal genomes containing Acr-encoding proviruses. Productive approaches for Acr prediction also involve genome comparison of closely related viruses, of which one is resistant and the other one is sensitive to a particular CRISPR variant, and "guilt by association" whereby genes adjacent to a homolog of a known Aca are identified as candidate Acrs. The distinctive features of Acrs are employed for Acr prediction both by developing dedicated search algorithms and through machine learning. New approaches will be needed to identify novel types of Acrs that are likely to exist.

Anti-CRISPR Discovery: Using Magnets to Find Needles in Haystacks

CRISPR-Cas immune systems in bacteria and archaea protect against viral infection, which has spurred viruses to develop dedicated inhibitors of these systems called anti-CRISPRs (Acrs). Like most host-virus arms races, many diverse examples of these immune and counter-immune proteins are encoded by the genomes of bacteria, archaea, and their viruses. For the case of Acrs, it is almost certain that just a small minority of nature's true diversity has been described. In this review, I discuss the various approaches used to identify these Acrs and speculate on the future for Acr discovery. Because Acrs can determine infection outcomes in nature and regulate CRISPR-Cas activities in applied settings, they have a dual importance to both host-virus conflicts and emerging biotechnologies. Thus, revealing the largely hidden world of Acrs should provide important lessons in microbiology that have the potential to ripple far beyond the field.

CRISPR-Cas provides limited phage immunity to a prevalent gut bacterium in gnotobiotic mice

Many bacteria and archaea harbor the adaptive CRISPR-Cas system, which stores small nucleotide fragments from previous invasions of nucleic acids via viruses or plasmids. This molecular archive blocks further invaders carrying identical or similar nucleotide sequences. However, few of these systems have been confirmed experimentally to be active in gut bacteria. Here, we demonstrate experimentally that the type I-C CRISPR-Cas system of the prevalent gut bacterium Eggerthella lenta can specifically target and cleave foreign DNA in vitro by using a plasmid transformation assay. We also show that the CRISPR-Cas system acquires new immunities (spacers) from the genome of a virulent E. lenta phage using traditional phage assays in vitro but also in vivo using gnotobiotic (GB) mice. Both high phage titer and an increased number of spacer acquisition events were observed when E. lenta was exposed to a low multiplicity of infection in vitro, and three phage genes were found to contain protospacer hotspots. Fewer new spacer acquisitions were detected in vivo than in vitro. Longitudinal analysis of phage-bacteria interactions showed sustained coexistence in the gut of GB mice, with phage abundance being approximately one log higher than the bacteria. Our findings show that while the type I-C CRISPR-Cas system is active in vitro and in vivo, a highly virulent phage in vitro was still able to co-exist with its bacterial host in vivo. Taken altogether, our results suggest that the CRISPR-Cas defense system of E. lenta provides only partial immunity in the gut.

AcrPred: A hybrid optimization with enumerated machine learning algorithm to predict Anti-CRISPR proteins

CRISPR-Cas, as a tool for gene editing, has received extensive attention in recent years. Anti-CRISPR (Acr) proteins can inactivate the CRISPR-Cas defense system during interference phase, and can be used as a potential tool for the regulation of gene editing. In-depth study of Anti-CRISPR proteins is of great significance for the implementation of gene editing. In this study, we developed a high-accuracy prediction model based on two-step model fusion strategy, called AcrPred, which could produce an AUC of 0.952 with independent dataset validation. To further validate the proposed model, we compared with published tools and correctly identified 9 of 10 new Acr proteins, indicating the strong generalization ability of our model. Finally, for the convenience of related wet-experimental researchers, a user-friendly web-server AcrPred (Anti-CRISPR proteins Prediction) was established at http://lin-group.cn/server/AcrPred, by which users can easily identify potential Anti-CRISPR proteins.

Non-canonical inhibition strategies and structural basis of anti-CRISPR proteins targeting type I CRISPR-Cas systems

Mobile genetic elements (MGEs) such as bacteriophages and their host prokaryotes are trapped in an eternal battle against each other. To cope with foreign infection, bacteria and archaea have evolved multiple immune strategies, out of which CRISPR-Cas system is up to now the only discovered adaptive system in prokaryotes. Despite the fact that CRISPR-Cas system provides powerful and delicate protection against MGEs, MGEs have also evolved anti-CRISPR proteins (Acrs) to counteract the CRISPR-Cas immune defenses. To date, 46 families of Acrs targeting type I CRISPR-Cas system have been characterized, out of which structure information of 21 families have provided insights on their inhibition strategies. Here, we review the non-canonical inhibition strategies adopted by Acrs targeting type I CRISPR-Cas systems based on their structure information by incorporating the most recent advances in this field, and discuss our current understanding and future perspectives. The delicate interplay between type I CRISPR-Cas systems and their Acrs provides us with important insights into the ongoing fierce arms race between prokaryotic hosts and their predators.

Phylogenetic Analysis of Anti-CRISPR and Member Addition in the Families

CRISPR-Cas is a widespread anti-viral adaptive immune system in the microorganisms. Viruses living in bacteria or some phages carry anti-CRISPR proteins to evade immunity by CRISPR-Cas. The anti-CRISPR proteins are prevalent in phages capable of lying dormant in a CRISPR-carrying host, while their orthologs frequently found in virulent phages. Here, we propose a probabilistic strategy of ancestral sequence reconstruction (ASR) and Hidden Markov Model (HMM) profile search to fish out sequences of anti-CRISPR proteins from environmental metagenomic, human microbiome metagenomic, human microbiome reference genome, and NCBI's non-redundant databases. Our results revealed that the metagenome database dark matter might contain anti-CRISPR encoding genes.

Functional ecology of bacteriophages in the environment

Bacteriophages are as ubiquitous as their bacterial hosts and often more abundant. Understanding how bacteriophages control their bacterial host populations requires a number of different approaches. Bacteriophages can control bacterial populations through lysis, drive evolution of bacterial immunity systems through infection, provide a conduit for horizontal gene transfer and alter host metabolism by carriage of auxiliary metabolic genes. Understanding and quantifying how bacteriophages drive these processes, requires both technological developments to take measurements in situ, and laboratory-based studies to understand mechanisms. Technological advances have allowed quantification of the number of infected cells in situ, revealing far-lower levels than expected. Understanding how observations in laboratory conditions relate to what occurs in the environment, and experimental confirmation of the predicted function of phage genes from observations in environmental omics data, remains challenging.

Ecology and evolution of phages encoding anti-CRISPR proteins

CRISPR-Cas are prokaryotic defence systems that provide protection against invasion by mobile genetic elements (MGE), including bacteriophages. MGE can overcome CRISPR-Cas defences by encoding anti-CRISPR (Acr) proteins. These proteins are produced in the early stages of the infection and inhibit the CRISPR-Cas machinery to allow phage replication. While research on Acr has mainly focused on their discovery, structure and mode of action, and their applications in biotechnology, the impact of Acr on the ecology of MGE as well as on the coevolution with their bacterial hosts only begins to be unravelled. In this review, we summarise our current understanding on the distribution of anti-CRISPR genes in MGE, the ecology of phages encoding Acr, and their coevolution with bacterial defence mechanisms. We highlight the need to use more diverse and complex experimental models to better understand the impact of anti-CRISPR in MGE-host interactions.

Mechanisms regulating the CRISPR-Cas systems

The CRISPR-Cas (Clustered Regularly Interspaced Short Palindromic Repeats- CRISPR associated proteins) is a prokaryotic system that enables sequence specific recognition and cleavage of nucleic acids. This is possible due to cooperation between CRISPR array which contains short fragments of DNA called spacers that are complimentary to the targeted nucleic acid and Cas proteins, which take part in processes of: acquisition of new spacers, processing them into their functional form as well as recognition and cleavage of targeted nucleic acids. The primary role of CRISPR-Cas systems is to provide their host with an adaptive and hereditary immunity against exogenous nucleic acids. This system is present in many variants in both Bacteria and Archea. Due to its modular structure, and programmability CRISPR-Cas system become attractive tool for modern molecular biology. Since their discovery and implementation, the CRISPR-Cas systems revolutionized areas of gene editing and regulation of gene expression. Although our knowledge on how CRISPR-Cas systems work has increased rapidly in recent years, there is still little information on how these systems are controlled and how they interact with other cellular mechanisms. Such regulation can be the result of both auto-regulatory mechanisms as well as exogenous proteins of phage origin. Better understanding of these interaction networks would be beneficial for optimization of current and development of new CRISPR-Cas-based tools. In this review we summarize current knowledge on the various molecular mechanisms that affect activity of CRISPR-Cas systems.

Recent advances in machine learning applications in metabolic engineering

Metabolic engineering encompasses several widely-used strategies, which currently hold a high seat in the field of biotechnology when its potential is manifesting through a plethora of research and commercial products with a strong societal impact. The genomic revolution that occurred almost three decades ago has initiated the generation of large omics-datasets which has helped in gaining a better understanding of cellular behavior. The itinerary of metabolic engineering that has occurred based on these large datasets has allowed researchers to gain detailed insights and a reasonable understanding of the intricacies of biosystems. However, the existing trail-and-error approaches for metabolic engineering are laborious and time-intensive when it comes to the production of target compounds with high yields through genetic manipulations in host organisms. Machine learning (ML) coupled with the available metabolic engineering test instances and omics data brings a comprehensive and multidisciplinary approach that enables scientists to evaluate various parameters for effective strain design. This vast amount of biological data should be standardized through knowledge engineering to train different ML models for providing accurate predictions in gene circuits designing, modification of proteins, optimization of bioprocess parameters for scaling up, and screening of hyper-producing robust cell factories. This review briefs on the premise of ML, followed by mentioning various ML methods and algorithms alongside the numerous omics datasets available to train ML models for predicting metabolic outcomes with high-accuracy. The combinative interplay between the ML algorithms and biological datasets through knowledge engineering have guided the recent advancements in applications such as CRISPR/Cas systems, gene circuits, protein engineering, metabolic pathway reconstruction, and bioprocess engineering. Finally, this review addresses the probable challenges of applying ML in metabolic engineering which will guide the researchers toward novel techniques to overcome the limitations.

Comprehensive Review on the Use of Artificial Intelligence in Ophthalmology and Future Research Directions

BACKGROUND

Having several applications in medicine, and in ophthalmology in particular, artificial intelligence (AI) tools have been used to detect visual function deficits, thus playing a key role in diagnosing eye diseases and in predicting the evolution of these common and disabling diseases. AI tools, i.e., artificial neural networks (ANNs), are progressively involved in detecting and customized control of ophthalmic diseases. The studies that refer to the efficiency of AI in medicine and especially in ophthalmology were analyzed in this review.

MATERIALS AND METHODS

We conducted a comprehensive review in order to collect all accounts published between 2015 and 2022 that refer to these applications of AI in medicine and especially in ophthalmology. Neural networks have a major role in establishing the demand to initiate preliminary anti-glaucoma therapy to stop the advance of the disease.

RESULTS

Different surveys in the literature review show the remarkable benefit of these AI tools in ophthalmology in evaluating the visual field, optic nerve, and retinal nerve fiber layer, thus ensuring a higher precision in detecting advances in glaucoma and retinal shifts in diabetes. We thus identified 1762 applications of artificial intelligence in ophthalmology: review articles and research articles (301 pub med, 144 scopus, 445 web of science, 872 science direct). Of these, we analyzed 70 articles and review papers (diabetic retinopathy (N = 24), glaucoma (N = 24), DMLV (N = 15), other pathologies (N = 7)) after applying the inclusion and exclusion criteria.

CONCLUSION

In medicine, AI tools are used in surgery, radiology, gynecology, oncology, etc., in making a diagnosis, predicting the evolution of a disease, and assessing the prognosis in patients with oncological pathologies. In ophthalmology, AI potentially increases the patient's access to screening/clinical diagnosis and decreases healthcare costs, mainly when there is a high risk of disease or communities face financial shortages. AI/DL (deep learning) algorithms using both OCT and FO images will change image analysis techniques and methodologies. Optimizing these (combined) technologies will accelerate progress in this area.

AcaFinder: Genome Mining for Anti-CRISPR-Associated Genes

Anti-CRISPR (Acr) proteins are encoded by (pro)viruses to inhibit their host's CRISPR-Cas systems. Genes encoding Acr and Aca (Acr associated) proteins often colocalize to form acr-aca operons. Here, we present AcaFinder as the first Aca genome mining tool. AcaFinder can (i) predict Acas and their associated acr-aca operons using guilt-by-association (GBA); (ii) identify homologs of known Acas using an HMM (Hidden Markov model) database; (iii) take input genomes for potential prophages, CRISPR-Cas systems, and self-targeting spacers (STSs); and (iv) provide a standalone program (https://github.com/boweny920/AcaFinder) and a web server (http://aca.unl.edu/Aca). AcaFinder was applied to mining over 16,000 prokaryotic and 142,000 gut phage genomes. After a multistep filtering, 36 high-confident new Aca families were identified, which is three times that of the 12 known Aca families. Seven new Aca families were from major human gut bacteria (Bacteroidota, Actinobacteria, and Fusobacteria) and their phages, while most known Aca families were from Proteobacteria and Firmicutes. A complex association network between Acrs and Acas was revealed by analyzing their operonic colocalizations. It appears very common in evolution that the same aca genes can recombine with different acr genes and vice versa to form diverse acr-aca operon combinations. IMPORTANCE At least four bioinformatics programs have been published for genome mining of Acrs since 2020. In contrast, no bioinformatics tools are available for automated Aca discovery. As the self-transcriptional repressor of acr-aca operons, Aca can be viewed as anti-anti-CRISPRs, with great potential in the improvement of CRISPR-Cas technology. Although all the 12 known Aca proteins contain a conserved helix-turn-helix (HTH) domain, not all HTH-containing proteins are Acas. However, HTH-containing proteins with adjacent Acr homologs encoded in the same genetic operon are likely Aca proteins. AcaFinder implements this guilt-by-association idea and the idea of using HMMs of known Acas for homologs into one software package. Applying AcaFinder in screening prokaryotic and gut phage genomes reveals a complex acr-aca operonic colocalization network between different families of Acrs and Acas.

PreAcrs: a machine learning framework for identifying anti-CRISPR proteins

BACKGROUND

Anti-CRISPR proteins are potent modulators that inhibit the CRISPR-Cas immunity system and have huge potential in gene editing and gene therapy as a genome-editing tool. Extensive studies have shown that anti-CRISPR proteins are essential for modifying endogenous genes, promoting the RNA-guided binding and cleavage of DNA or RNA substrates. In recent years, identifying and characterizing anti-CRISPR proteins has become a hot and significant research topic in bioinformatics. However, as most anti-CRISPR proteins fall short in sharing similarities to those currently known, traditional screening methods are time-consuming and inefficient. Machine learning methods could fill this gap with powerful predictive capability and provide a new perspective for anti-CRISPR protein identification.

RESULTS

Here, we present a novel machine learning ensemble predictor, called PreAcrs, to identify anti-CRISPR proteins from protein sequences directly. Three features and eight different machine learning algorithms were used to train PreAcrs. PreAcrs outperformed other existing methods and significantly improved the prediction accuracy for identifying anti-CRISPR proteins.

CONCLUSIONS

In summary, the PreAcrs predictor achieved a competitive performance for predicting new anti-CRISPR proteins in terms of accuracy and robustness. We anticipate PreAcrs will be a valuable tool for researchers to speed up the research process. The source code is available at: https://github.com/Lyn-666/anti_CRISPR.git .

Molecular basis of dual anti-CRISPR and auto-regulatory functions of AcrIF24

CRISPR-Cas systems are adaptive immune systems in bacteria and archaea that provide resistance against phages and other mobile genetic elements. To fight against CRISPR-Cas systems, phages and archaeal viruses encode anti-CRISPR (Acr) proteins that inhibit CRISPR-Cas systems. The expression of acr genes is controlled by anti-CRISPR-associated (Aca) proteins encoded within acr-aca operons. AcrIF24 is a recently identified Acr that inhibits the type I-F CRISPR-Cas system. Interestingly, AcrIF24 was predicted to be a dual-function Acr and Aca. Here, we elucidated the crystal structure of AcrIF24 from Pseudomonas aeruginosa and identified its operator sequence within the regulated acr-aca operon promoter. The structure of AcrIF24 has a novel domain composition, with wing, head and body domains. The body domain is responsible for recognition of promoter DNA for Aca regulatory activity. We also revealed that AcrIF24 directly bound to type I-F Cascade, specifically to Cas7 via its head domain as part of its Acr mechanism. Our results provide new molecular insights into the mechanism of a dual functional Acr-Aca protein.

Development and application of CRISPR-based genetic tools in Bacillus species and Bacillus phages

Recently, the clustered regularly interspaced short palindromic repeats (CRISPR) system has been developed into a precise and efficient genome editing tool. Since its discovery as an adaptive immune system in prokaryotes, it has been applied in many different research fields including biotechnology and medical sciences. The high demand for rapid, highly efficient and versatile genetic tools to thrive in bacteria-based cell factories accelerates this process. This review mainly focuses on significant advancements of the CRISPR system in Bacillus subtilis, including the achievements in gene editing, and on problems still remaining. Next, we comprehensively summarize this genetic tool's up-to-date development and utilization in other Bacillus species, including B. licheniformis, B. methanolicus, B. anthracis, B. cereus, B. smithii and B. thuringiensis. Furthermore, we describe the current application of CRISPR tools in phages to increase Bacillus hosts' resistance to virulent phages and phage genetic modification. Finally, we suggest potential strategies to further improve this advanced technique and provide insights into future directions of CRISPR technologies for rendering Bacillus species cell factories more effective and more powerful.

Widespread repression of anti-CRISPR production by anti-CRISPR-associated proteins

Many bacteria use CRISPR-Cas systems to defend against invasive mobile genetic elements (MGEs). In response, MGEs have developed strategies to resist CRISPR-Cas, including the use of anti-CRISPR (Acr) proteins. Known acr genes may be followed in an operon by a putative regulatory Acr-associated gene (aca), suggesting the importance of regulation. Although ten families of helix-turn-helix (HTH) motif containing Aca proteins have been identified (Aca1-10), only three have been tested and shown to be transcriptional repressors of acr-aca expression. The AcrIIA1 protein (a Cas9 inhibitor) also contains a functionally similar HTH containing repressor domain. Here, we identified and analysed Aca and AcrIIA1 homologs across all bacterial genomes. Using HMM models we found aca-like genes are widely distributed in bacteria, both with and without known acr genes. The putative promoter regions of acr-aca operons were analysed and members of each family of bacterial Aca tested for regulatory function. For each Aca family, we predicted a conserved inverted repeat binding site within a core promoter. Promoters containing these sites directed reporter expression in E. coli and were repressed by the cognate Aca protein. These data demonstrate that acr repression by Aca proteins is widely conserved in nature.

Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space

We introduce an unsupervised clustering algorithm to improve training efficiency and accuracy in predicting energies using molecular-orbital-based machine learning (MOB-ML). This work determines clusters via the Gaussian mixture model (GMM) in an entirely automatic manner and simplifies an earlier supervised clustering approach [ J. Chem. Theory Comput. 2019, 15, 6668] by eliminating both the necessity for user-specified parameters and the training of an additional classifier. Unsupervised clustering results from GMM have the advantages of accurately reproducing chemically intuitive groupings of frontier molecular orbitals and exhibiting improved performance with an increasing number of training examples. The resulting clusters from supervised or unsupervised clustering are further combined with scalable Gaussian process regression (GPR) or linear regression (LR) to learn molecular energies accurately by generating a local regression model in each cluster. Among all four combinations of regressors and clustering methods, GMM combined with scalable exact GPR (GMM/GPR) is the most efficient training protocol for MOB-ML. The numerical tests of molecular energy learning on thermalized data sets of drug-like molecules demonstrate the improved accuracy, transferability, and learning efficiency of GMM/GPR over other training protocols for MOB-ML, i.e., supervised regression clustering combined with GPR (RC/GPR) and GPR without clustering. GMM/GPR also provides the best molecular energy predictions compared with ones from the literature on the same benchmark data sets. With a lower scaling, GMM/GPR has a 10.4-fold speedup in wall-clock training time compared with scalable exact GPR with a training size of 6500 QM7b-T molecules.

A versatile active learning workflow for optimization of genetic and metabolic networks

Optimization of biological networks is often limited by wet lab labor and cost, and the lack of convenient computational tools. Here, we describe METIS, a versatile active machine learning workflow with a simple online interface for the data-driven optimization of biological targets with minimal experiments. We demonstrate our workflow for various applications, including cell-free transcription and translation, genetic circuits, and a 27-variable synthetic CO₂-fixation cycle (CETCH cycle), improving these systems between one and two orders of magnitude. For the CETCH cycle, we explore 10²⁵ conditions with only 1,000 experiments to yield the most efficient CO₂-fixation cascade described to date. Beyond optimization, our workflow also quantifies the relative importance of individual factors to the performance of a system identifying unknown interactions and bottlenecks. Overall, our workflow opens the way for convenient optimization and prototyping of genetic and metabolic networks with customizable adjustments according to user experience, experimental setup, and laboratory facilities.

Optimization of biological networks is often limited by wet lab labor and cost, and the lack of convenient computational tools. Here, aimed at democratization and standardization, the authors describe METIS, a modular and versatile active machine learning workflow with a simple online interface for the optimization of biological target functions with minimal experimental datasets.

Three families of Asgard archaeal viruses identified in metagenome-assembled genomes

Asgardarchaeota harbour many eukaryotic signature proteins and are widely considered to represent the closest archaeal relatives of eukaryotes. Whether similarities between Asgard archaea and eukaryotes extend to their viromes remains unknown. Here we present 20 metagenome-assembled genomes of Asgardarchaeota from deep-sea sediments of the basin off the Shimokita Peninsula, Japan. By combining a CRISPR spacer search of metagenomic sequences with phylogenomic analysis, we identify three family-level groups of viruses associated with Asgard archaea. The first group, verdandiviruses, includes tailed viruses of the class Caudoviricetes (realm Duplodnaviria); the second, skuldviruses, consists of viruses with predicted icosahedral capsids of the realm Varidnaviria; and the third group, wyrdviruses, is related to spindle-shaped viruses previously identified in other archaea. More than 90% of the proteins encoded by these viruses of Asgard archaea show no sequence similarity to proteins encoded by other known viruses. Nevertheless, all three proposed families consist of viruses typical of prokaryotes, providing no indication of specific evolutionary relationships between viruses infecting Asgard archaea and eukaryotes. Verdandiviruses and skuldviruses are likely to be lytic, whereas wyrdviruses potentially establish chronic infection and are released without host cell lysis. All three groups of viruses are predicted to play important roles in controlling Asgard archaea populations in deep-sea ecosystems.

Machine learning approaches for biomolecular, biophysical, and biomaterials research

A fluent conversation with a virtual assistant, person-tailored news feeds, and deep-fake images created within seconds-all those things that have been unthinkable for a long time are now a part of our everyday lives. What these examples have in common is that they are realized by different means of machine learning (ML), a technology that has fundamentally changed many aspects of the modern world. The possibility to process enormous amount of data in multi-hierarchical, digital constructs has paved the way not only for creating intelligent systems but also for obtaining surprising new insight into many scientific problems. However, in the different areas of biosciences, which typically rely heavily on the collection of time-consuming experimental data, applying ML methods is a bit more challenging: Here, difficulties can arise from small datasets and the inherent, broad variability, and complexity associated with studying biological objects and phenomena. In this Review, we give an overview of commonly used ML algorithms (which are often referred to as "machines") and learning strategies as well as their applications in different bio-disciplines such as molecular biology, drug development, biophysics, and biomaterials science. We highlight how selected research questions from those fields were successfully translated into machine readable formats, discuss typical problems that can arise in this context, and provide an overview of how to resolve those encountered difficulties.

Anti-CRISPR prediction using deep learning reveals an inhibitor of Cas13b nucleases

As part of the ongoing bacterial-phage arms race, CRISPR-Cas systems in bacteria clear invading phages whereas anti-CRISPR proteins (Acrs) in phages inhibit CRISPR defenses. Known Acrs have proven extremely diverse, complicating their identification. Here, we report a deep learning algorithm for Acr identification that revealed an Acr against type VI-B CRISPR-Cas systems. The algorithm predicted numerous putative Acrs spanning almost all CRISPR-Cas types and subtypes, including over 7,000 putative type IV and VI Acrs not predicted by other algorithms. By performing a cell-free screen for Acr hits against type VI-B systems, we identified a potent inhibitor of Cas13b nucleases we named AcrVIB1. AcrVIB1 blocks Cas13b-mediated defense against a targeted plasmid and lytic phage, and its inhibitory function principally occurs upstream of ribonucleoprotein complex formation. Overall, our work helps expand the known Acr universe, aiding our understanding of the bacteria-phage arms race and the use of Acrs to control CRISPR technologies.

Anti-CRISPRdb v2.2: an online repository of anti-CRISPR proteins including information on inhibitory mechanisms, activities and neighbors of curated anti-CRISPR proteins

We previously released the Anti-CRISPRdb database hosting anti-CRISPR proteins (Acrs) and associated information. Since then, the number of known Acr families, types, structures and inhibitory activities has accumulated over time, and Acr neighbors can be used as a candidate pool for screening Acrs in further studies. Therefore, we here updated the database to include the new available information. Our newly updated database shows several improvements: (i) it comprises more entries and families because it includes both Acrs reported in the most recent literatures and Acrs obtained via performing homologous alignment; (ii) the prediction of Acr neighbors is integrated into Anti-CRISPRdb v2.2, and users can identify novel Acrs from these candidates; and (iii) this version includes experimental information on the inhibitory strength and stage for Acr-Cas/Acr-CRISPR pairs, motivating the development of tools for predicting specific inhibitory abilities. Additionally, a parameter, the rank of codon usage bias (CUBRank), was proposed and provided in the new version, which showed a positive relationship with predicted result from AcRanker; hence, it can be used as an indicator for proteins to be Acrs. CUBRank can be used to estimate the possibility of genes occurring within genome island-a hotspot hosting potential genes encoding Acrs. Based on CUBRank and Anti-CRISPRdb, we also gave the first glimpse for the emergence of Acr genes (acrs).

DATABASE URL

http://guolab.whu.edu.cn/anti-CRISPRdb.

Structural basis of cyclic oligoadenylate binding to the transcription factor Csa3 outlines cross talk between type III and type I CRISPR systems

RNA interference by type III CRISPR systems results in the synthesis of cyclic oligoadenylate (cOA) second messengers, which are known to bind and regulate various CARF domain-containing nuclease receptors. The CARF domain-containing Csa3 family of transcriptional factors associated with the DNA-targeting type I CRISPR systems regulate expression of various CRISPR and DNA repair genes in many prokaryotes. In this study, we extend the known receptor repertoire of cOA messengers to include transcriptional factors by demonstrating specific binding of cyclic tetra-adenylate (cA4) to Saccharolobus solfataricus Csa3 (Csa3Sso). Our 2.0-Å resolution X-ray crystal structure of cA4-bound full-length Csa3Sso reveals the binding of its CARF domain to an elongated conformation of cA4. Using cA4 binding affinity analyses of Csa3Sso mutants targeting the observed Csa3Sso•cA4 structural interface, we identified a Csa3-specific cA4 binding motif distinct from a more widely conserved cOA-binding CARF motif. Using a rational surface engineering approach, we increased the cA4 binding affinity of Csa3Sso up to ∼145-fold over the wildtype, which has potential applications for future second messenger-driven CRISPR gene expression and editing systems. Our in-solution Csa3Sso structural analysis identified cA4-induced allosteric and asymmetric conformational rearrangement of its C-terminal winged helix-turn-helix effector domains, which could potentially be incompatible to DNA binding. However, specific in vitro binding of the purified Csa3Sso to its putative promoter (PCas4a) was found to be cA4 independent, suggesting a complex mode of Csa3Sso regulation. Overall, our results support cA4-and Csa3-mediated cross talk between type III and type I CRISPR systems.

AI in health and medicine

Artificial intelligence (AI) is poised to broadly reshape medicine, potentially improving the experiences of both clinicians and patients. We discuss key findings from a 2-year weekly effort to track and share key developments in medical AI. We cover prospective studies and advances in medical image analysis, which have reduced the gap between research and deployment. We also address several promising avenues for novel medical AI research, including non-image data sources, unconventional problem formulations and human-AI collaboration. Finally, we consider serious technical and ethical challenges in issues spanning from data scarcity to racial bias. As these challenges are addressed, AI's potential may be realized, making healthcare more accurate, efficient and accessible for patients worldwide.

CRISPR-Cas is associated with fewer antibiotic resistance genes in bacterial pathogens

The acquisition of antibiotic resistance (ABR) genes via horizontal gene transfer (HGT) is a key driver of the rise in multidrug resistance amongst bacterial pathogens. Bacterial defence systems per definition restrict the influx of foreign genetic material, and may therefore limit the acquisition of ABR. CRISPR-Cas adaptive immune systems are one of the most prevalent defences in bacteria, found in roughly half of bacterial genomes, but it has remained unclear if and how much they contribute to restricting the spread of ABR. We analysed approximately 40 000 whole genomes comprising the full RefSeq dataset for 11 species of clinically important genera of human pathogens, including Enterococcus, Staphylococcus, Acinetobacter and Pseudomonas. We modelled the association between CRISPR-Cas and indicators of HGT, and found that pathogens with a CRISPR-Cas system were less likely to carry ABR genes than those lacking this defence system. Analysis of the mobile genetic elements (MGEs) targeted by CRISPR-Cas supports a model where this host defence system blocks important vectors of ABR. These results suggest a potential 'immunocompromised' state for multidrug-resistant strains that may be exploited in tailored interventions that rely on MGEs, such as phages or phagemids, to treat infections caused by bacterial pathogens. This article is part of the theme issue 'The secret lives of microbial mobile genetic elements'.

Anti-CRISPR proteins as a therapeutic agent against drug-resistant bacteria

The continuous deployment of various antibiotics to treat multiple serious bacterial infections leads to multidrug resistance among the bacterial population. It has failed the standard treatment strategies through different antibacterial agents and serves as a significant threat to public health worldwide at devastating levels. The discovery of anti-CRISPR proteins catches the interest of researchers around the world as a promising therapeutic agent against drug-resistant bacteria. Anti-CRISPR proteins are known to inhibit bacterial CRISPR-Cas defense systems in multiple possible ways. The CRISPR-Cas nucleoprotein assembly provides adaptive immunity in bacteria against diverse categories of phage infections. Parallelly, phages also try to break the CRISPR-Cas barrier by producing anti-CRISPR proteins, leading to growth inhibition and bacterial lysis. This review begins with a brief description of the bacterial CRISPR-Cas system, followed by a detailed portrayal of anti-CRISPR proteins, including their discovery and evolution, mechanism of action, regulation of expression, and potential applications in the healthcare sector as an alternative therapeutic strategy to combat severe bacterial infections.

Atomic-scale insights into allosteric inhibition and evolutional rescue mechanism of Streptococcus thermophilus Cas9 by the anti-CRISPR protein AcrIIA6

CRISPR-Cas systems are prokaryotic adaptive immunity against invading phages and plasmids. Phages have evolved diverse protein inhibitors of CRISPR-Cas systems, called anti-CRISPR (Acr) proteins, to neutralize this CRISPR machinery. In response, bacteria have co-evolved Cas variants to escape phage's anti-CRISPR strategies, called anti-anti-CRISPR systems. Here we explore the anti-CRISPR allosteric inhibition and anti-anti-CRISPR rescue mechanisms between Streptococcus thermophilus Cas9 (St1Cas9) and the anti-CRISPR protein AcrIIA6 at the atomic level, by generating mutants of key residues in St1Cas9. Extensive unbiased molecular dynamics simulations show that the functional motions of St1Cas9 in the presence of AcrIIA6 differ substantially from those of St1Cas9 alone. AcrIIA6 binding triggers a shift of St1Cas9 conformational ensemble towards a less catalytically competent state; this state significantly compromises protospacer adjacent motif (PAM) recognition and nuclease activity by altering interdependently conformational dynamics and allosteric signals among nuclease domains, PAM-interacting (PI) regions, and AcrIIA6 binding motifs. Via in vitro DNA cleavage assays, we further elucidate the rescue mechanism of efficiently escaping AcrIIA6 inhibition harboring St1Cas9 triple mutations (G993K/K1008M/K1010E) in the PI domain and identify the evolutionary landscape of such mutational escape within species. Our results provide mechanistic insights into Acr proteins as natural brakes for the CRISPR-Cas systems and a promising potential for the design of allosteric Acr peptidomimetics.

Type II anti-CRISPR proteins as a new tool for synthetic biology

The clustered regularly interspaced short palindromic repeats (CRISPR)-Cas (CRISPR-associated proteins) system represents, in prokaryotes, an adaptive and inheritable immune response against invading DNA. The discovery of anti-CRISPR proteins (Acrs), which are inhibitors of CRISPR-Cas, mainly encoded by phages and prophages, showed a co-evolution history between prokaryotes and phages. In the past decade, the CRISPR-Cas systems together with the corresponding Acrs have been turned into a genetic-engineering tool. Among the six types of CRISPR-Cas characterized so far, type II CRISPR-Cas system is the most popular in biotechnology. Here, we discuss about the discovery, the reported inhibitory mechanisms, and the applications in both gene editing and gene transcriptional regulation of type II Acrs. Moreover, we provide insights into future potential research and feasible applications.

Challenges and Opportunities for Clustered Regularly Interspaced Short Palindromic Repeats Based Molecular Biosensing

Clustered regularly interspaced short palindromic repeats, CRISPR, has recently emerged as a powerful molecular biosensing tool for nucleic acids and other biomarkers due to its unique properties such as collateral cleavage nature, room temperature reaction conditions, and high target-recognition specificity. Numerous platforms have been developed to leverage the CRISPR assay for ultrasensitive biosensing applications. However, to be considered as a new gold standard, several key challenges for CRISPR molecular biosensing must be addressed. In this paper, we briefly review the history of biosensors, followed by the current status of nucleic acid-based detection methods. We then discuss the current challenges pertaining to CRISPR-based nucleic acid detection, followed by the recent breakthroughs addressing these challenges. We focus upon future advancements required to enable rapid, simple, sensitive, specific, multiplexed, amplification-free, and shelf-stable CRISPR-based molecular biosensors.

Microbial Arsenal of Antiviral Defenses - Part I

Bacteriophages or phages are viruses that infect bacterial cells (for the scope of this review we will also consider viruses that infect Archaea). Constant threat of phage infection is a major force that shapes evolution of the microbial genomes. To withstand infection, bacteria had evolved numerous strategies to avoid recognition by phages or to directly interfere with phage propagation inside the cell. Classical molecular biology and genetic engineering have been deeply intertwined with the study of phages and host defenses. Nowadays, owing to the rise of phage therapy, broad application of CRISPR-Cas technologies, and development of bioinformatics approaches that facilitate discovery of new systems, phage biology experiences a revival. This review describes variety of strategies employed by microbes to counter phage infection, with a focus on novel systems discovered in recent years. First chapter covers defense associated with cell surface, role of small molecules, and innate immunity systems relying on DNA modification.

Engineered Bacteriophage Therapeutics: Rationale, Challenges and Future

The current problems with increasing bacterial resistance to antibacterial therapies, resulting in a growing frequency of incurable bacterial infections, necessitates the acceleration of studies on antibacterials of a new generation that could offer an alternative to antibiotics or support their action. Bacteriophages (phages) can kill antibiotic-sensitive as well as antibiotic-resistant bacteria, and thus are a major subject of such studies. Their efficacy in curing bacterial infections has been demonstrated in in vivo experiments and in the clinic. Unlike antibiotics, phages have a narrow range of specificity, which makes them safe for commensal microbiota. However, targeting even only the most clinically relevant strains of pathogenic bacteria requires large collections of well characterized phages, whose specificity would cover all such strains. The environment is a rich source of diverse phages, but due to their complex relationships with bacteria and safety concerns, only some naturally occurring phages can be considered for therapeutic applications. Still, their number and diversity make a detailed characterization of all potentially promising phages virtually impossible. Moreover, no single phage combines all the features required of an ideal therapeutic agent. Additionally, the rapid acquisition of phage resistance by bacteria may make phages already approved for therapy ineffective and turn the search for environmental phages of better efficacy and new specificity into an endless race. An alternative strategy for acquiring phages with desired properties in a short time with minimal cost regarding their acquisition, characterization, and approval for therapy could be based on targeted genome modifications of phage isolates with known properties. The first example demonstrating the potential of this strategy in curing bacterial diseases resistant to traditional therapy is the recent successful treatment of a progressing disseminated Mycobacterium abscessus infection in a teenage patient with the use of an engineered phage. In this review, we briefly present current methods of phage genetic engineering, highlighting their advantages and disadvantages, and provide examples of genetically engineered phages with a modified host range, improved safety or antibacterial activity, and proven therapeutic efficacy. We also summarize novel uses of engineered phages not only for killing pathogenic bacteria, but also for in situ modification of human microbiota to attenuate symptoms of certain bacterial diseases and metabolic, immune, or mental disorders.

Resistance and Adaptation of Bacteria to Non-Antibiotic Antibacterial Agents: Physical Stressors, Nanoparticles, and Bacteriophages

Antimicrobial resistance is a significant threat to human health worldwide, forcing scientists to explore non-traditional antibacterial agents to support rapid interventions and combat the emergence and spread of drug resistant bacteria. Many new antibiotic-free approaches are being developed while the old ones are being revised, resulting in creating unique solutions that arise at the interface of physics, nanotechnology, and microbiology. Specifically, physical factors (e.g., pressure, temperature, UV light) are increasingly used for industrial sterilization. Nanoparticles (unmodified or in combination with toxic compounds) are also applied to circumvent in vivo drug resistance mechanisms in bacteria. Recently, bacteriophage-based treatments are also gaining momentum due to their high bactericidal activity and specificity. Although the number of novel approaches for tackling the antimicrobial resistance crisis is snowballing, it is still unclear if any proposed solutions would provide a long-term remedy. This review aims to provide a detailed overview of how bacteria acquire resistance against these non-antibiotic factors. We also discuss innate bacterial defense systems and how bacteriophages have evolved to tackle them.

Thousands of previously unknown phages discovered in whole-community human gut metagenomes

BACKGROUND

Double-stranded DNA bacteriophages (dsDNA phages) play pivotal roles in structuring human gut microbiomes; yet, the gut virome is far from being fully characterized, and additional groups of phages, including highly abundant ones, continue to be discovered by metagenome mining. A multilevel framework for taxonomic classification of viruses was recently adopted, facilitating the classification of phages into evolutionary informative taxonomic units based on hallmark genes. Together with advanced approaches for sequence assembly and powerful methods of sequence analysis, this revised framework offers the opportunity to discover and classify unknown phage taxa in the human gut.

RESULTS

A search of human gut metagenomes for circular contigs encoding phage hallmark genes resulted in the identification of 3738 apparently complete phage genomes that represent 451 putative genera. Several of these phage genera are only distantly related to previously identified phages and are likely to found new families. Two of the candidate families, "Flandersviridae" and "Quimbyviridae", include some of the most common and abundant members of the human gut virome that infect Bacteroides, Parabacteroides, and Prevotella. The third proposed family, "Gratiaviridae," consists of less abundant phages that are distantly related to the families Autographiviridae, Drexlerviridae, and Chaseviridae. Analysis of CRISPR spacers indicates that phages of all three putative families infect bacteria of the phylum Bacteroidetes. Comparative genomic analysis of the three candidate phage families revealed features without precedent in phage genomes. Some "Quimbyviridae" phages possess Diversity-Generating Retroelements (DGRs) that generate hypervariable target genes nested within defense-related genes, whereas the previously known targets of phage-encoded DGRs are structural genes. Several "Flandersviridae" phages encode enzymes of the isoprenoid pathway, a lipid biosynthesis pathway that so far has not been known to be manipulated by phages. The "Gratiaviridae" phages encode a HipA-family protein kinase and glycosyltransferase, suggesting these phages modify the host cell wall, preventing superinfection by other phages. Hundreds of phages in these three and other families are shown to encode catalases and iron-sequestering enzymes that can be predicted to enhance cellular tolerance to reactive oxygen species.

CONCLUSIONS

Analysis of phage genomes identified in whole-community human gut metagenomes resulted in the delineation of at least three new candidate families of Caudovirales and revealed diverse putative mechanisms underlying phage-host interactions in the human gut. Addition of these phylogenetically classified, diverse, and distinct phages to public databases will facilitate taxonomic decomposition and functional characterization of human gut viromes. Video abstract.

Incorporating Machine Learning into Established Bioinformatics Frameworks

The exponential growth of biomedical data in recent years has urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling the automatic feature extraction, selection, and generation of predictive models, these methods can be used to efficiently study complex biological systems. Machine learning techniques are frequently integrated with bioinformatic methods, as well as curated databases and biological networks, to enhance training and validation, identify the best interpretable features, and enable feature and model investigation. Here, we review recently developed methods that incorporate machine learning within the same framework with techniques from molecular evolution, protein structure analysis, systems biology, and disease genomics. We outline the challenges posed for machine learning, and, in particular, deep learning in biomedicine, and suggest unique opportunities for machine learning techniques integrated with established bioinformatics approaches to overcome some of these challenges.