Porin Associates with Tom22 to Regulate the Mitochondrial Protein Gate Assembly.

Mitochondria import nearly all of their resident proteins from the cytosol, and the TOM complex functions as their entry gate. The TOM complex undergoes a dynamic conversion between the majority population of a three-channel gateway ("trimer") and the minor population that lacks Tom22 and has only two Tom40 channels ("dimer"). Here, we found that the porin Por1 acts as a sink to bind newly imported Tom22. This Por1 association thereby modulates Tom22 integration into the TOM complex, guaranteeing formation of the functional trimeric TOM complex. Por1 sequestration of Tom22 dissociated from the trimeric TOM complex also enhances the dimeric TOM complex, which is preferable for the import of TIM40/MIA-dependent proteins into mitochondria. Furthermore, Por1 appears to contribute to cell-cycle-dependent variation of the functional trimeric TOM complex by chaperoning monomeric Tom22, which arises from the cell-cycle-controlled variation of phosphorylated Tom6.

The TAM, a Translocation and Assembly Module for protein assembly and potential conduit for phospholipid transfer.

The assembly of ß-barrel proteins into the bacterial outer membrane is an essential process enabling the colonization of new environmental niches. The TAM was discovered as a module of the ß-barrel protein assembly machinery; it is a heterodimeric complex composed of an outer membrane protein (TamA) bound to an inner membrane protein (TamB). The TAM spans the periplasm, providing a scaffold through the peptidoglycan layer and catalyzing the translocation and assembly of ß-barrel proteins into the outer membrane. Recently, studies on another membrane protein (YhdP) have suggested that TamB might play a role in phospholipid transport to the outer membrane. Here we review and re-evaluate the literature covering the experimental studies on the TAM over the past decade, to reconcile what appear to be conflicting claims on the function of the TAM.

RIL-seq reveals extensive involvement of small RNAs in virulence and capsule regulation in hypervirulent Klebsiella pneumoniae.

Hypervirulent Klebsiella pneumoniae (hvKp) can infect healthy individuals, in contrast to classical strains that commonly cause nosocomial infections. The recent convergence of hypervirulence with carbapenem-resistance in K. pneumoniae can potentially create 'superbugs' that are challenging to treat. Understanding virulence regulation of hvKp is thus critical. Accumulating evidence suggest that posttranscriptional regulation by small RNAs (sRNAs) plays a role in bacterial virulence, but it has hardly been studied in K. pneumoniae. We applied RIL-seq to a prototypical clinical isolate of hvKp to unravel the Hfq-dependent RNA-RNA interaction (RRI) network. The RRI network is dominated by sRNAs, including predicted novel sRNAs, three of which we validated experimentally. We constructed a stringent subnetwork composed of RRIs that involve at least one hvKp virulence-associated gene and identified the capsule gene loci as a hub target where multiple sRNAs interact. We found that the sRNA OmrB suppressed both capsule production and hypermucoviscosity when overexpressed. Furthermore, OmrB base-pairs within kvrA coding region and partially suppresses translation of the capsule regulator KvrA. This agrees with current understanding of capsule as a major virulence and fitness factor. It emphasizes the intricate regulatory control of bacterial phenotypes by sRNAs, particularly of genes critical to bacterial physiology and virulence.

A noncanonical chaperone interacts with drug efflux pumps during their assembly into bacterial outer membranes.

Bacteria have membrane-spanning efflux pumps to secrete toxic compounds ranging from heavy metal ions to organic chemicals, including antibiotic drugs. The overall architecture of these efflux pumps is highly conserved: with an inner membrane energy-transducing subunit coupled via an adaptor protein to an outer membrane conduit subunit that enables toxic compounds to be expelled into the environment. Here, we map the distribution of efflux pumps across bacterial lineages to show these proteins are more widespread than previously recognised. Complex phylogenetics support the concept that gene cassettes encoding the subunits for these pumps are commonly acquired by horizontal gene transfer. Using TolC as a model protein, we demonstrate that assembly of conduit subunits into the outer membrane uses the chaperone TAM to physically organise the membrane-embedded staves of the conduit subunit of the efflux pump. The characteristics of this assembly pathway have impact for the acquisition of efflux pumps across bacterial species and for the development of new antimicrobial compounds that inhibit efflux pump function.

Structure of the mitochondrial import gate reveals distinct preprotein paths.

The translocase of the outer mitochondrial membrane (TOM) is the main entry gate for proteins1-4. Here we use cryo-electron microscopy to report the structure of the yeast TOM core complex5-9 at 3.8-Å resolution. The structure reveals the high-resolution architecture of the translocator consisting of two Tom40 ß-barrel channels and α-helical transmembrane subunits, providing insight into critical features that are conserved in all eukaryotes1-3. Each Tom40 ß-barrel is surrounded by small TOM subunits, and tethered by two Tom22 subunits and one phospholipid. The N-terminal extension of Tom40 forms a helix inside the channel; mutational analysis reveals its dual role in early and late steps in the biogenesis of intermembrane-space proteins in cooperation with Tom5. Each Tom40 channel possesses two precursor exit sites. Tom22, Tom40 and Tom7 guide presequence-containing preproteins to the exit in the middle of the dimer, whereas Tom5 and the Tom40 N extension guide preproteins lacking a presequence to the exit at the periphery of the dimer.

Bacteriophages evolve enhanced persistence to a mucosal surface.

The majority of viruses within the gut are obligate bacterial viruses known as bacteriophages (phages). Their bacteriotropism underscores the study of phage ecology in the gut, where they modulate and coevolve with gut bacterial communities. Traditionally, these ecological and evolutionary questions were investigated empirically via in vitro experimental evolution and, more recently, in vivo models were adopted to account for physiologically relevant conditions of the gut. Here, we probed beyond conventional phage-bacteria coevolution to investigate potential tripartite evolutionary interactions between phages, their bacterial hosts, and the mammalian gut mucosa. To capture the role of the mammalian gut, we recapitulated a life-like gut mucosal layer using in vitro lab-on-a-chip devices (to wit, the gut-on-a-chip) and showed that the mucosal environment supports stable phage-bacteria coexistence. Next, we experimentally coevolved lytic phage populations within the gut-on-a-chip devices alongside their bacterial hosts. We found that while phages adapt to the mucosal environment via de novo mutations, genetic recombination was the key evolutionary force in driving mutational fitness. A single mutation in the phage capsid protein Hoc-known to facilitate phage adherence to mucus-caused altered phage binding to fucosylated mucin glycans. We demonstrated that the altered glycan-binding phenotype provided the evolved mutant phage a competitive fitness advantage over its ancestral wild-type phage in the gut-on-a-chip mucosal environment. Collectively, our findings revealed that phages-in addition to their evolutionary relationship with bacteria-are able to evolve in response to a mammalian-derived mucosal environment.

Importing mitochondrial proteins: machineries and mechanisms.

Most mitochondrial proteins are synthesized on cytosolic ribosomes and must be imported across one or both mitochondrial membranes. There is an amazingly versatile set of machineries and mechanisms, and at least four different pathways, for the importing and sorting of mitochondrial precursor proteins. The translocases that catalyze these processes are highly dynamic machines driven by the membrane potential, ATP, or redox reactions, and they cooperate with molecular chaperones and assembly complexes to direct mitochondrial proteins to their correct destinations. Here, we discuss recent insights into the importing and sorting of mitochondrial proteins and their contributions to mitochondrial biogenesis.

DeepVF: a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy.

Virulence factors (VFs) enable pathogens to infect their hosts. A wealth of individual, disease-focused studies has identified a wide variety of VFs, and the growing mass of bacterial genome sequence data provides an opportunity for computational methods aimed at predicting VFs. Despite their attractive advantages and performance improvements, the existing methods have some limitations and drawbacks. Firstly, as the characteristics and mechanisms of VFs are continually evolving with the emergence of antibiotic resistance, it is more and more difficult to identify novel VFs using existing tools that were previously developed based on the outdated data sets; secondly, few systematic feature engineering efforts have been made to examine the utility of different types of features for model performances, as the majority of tools only focused on extracting very few types of features. By addressing the aforementioned issues, the accuracy of VF predictors can likely be significantly improved. This, in turn, would be particularly useful in the context of genome wide predictions of VFs. In this work, we present a deep learning (DL)-based hybrid framework (termed DeepVF) that is utilizing the stacking strategy to achieve more accurate identification of VFs. Using an enlarged, up-to-date dataset, DeepVF comprehensively explores a wide range of heterogeneous features with popular machine learning algorithms. Specifically, four classical algorithms, including random forest, support vector machines, extreme gradient boosting and multilayer perceptron, and three DL algorithms, including convolutional neural networks, long short-term memory networks and deep neural networks are employed to train 62 baseline models using these features. In order to integrate their individual strengths, DeepVF effectively combines these baseline models to construct the final meta model using the stacking strategy. Extensive benchmarking experiments demonstrate the effectiveness of DeepVF: it achieves a more accurate and stable performance compared with baseline models on the benchmark dataset and clearly outperforms state-of-the-art VF predictors on the independent test. Using the proposed hybrid ensemble model, a user-friendly online predictor of DeepVF (http://deepvf.erc.monash.edu/) is implemented. Furthermore, its utility, from the user's viewpoint, is compared with that of existing toolkits. We believe that DeepVF will be exploited as a useful tool for screening and identifying potential VFs from protein-coding gene sequences in bacterial genomes.

DeepBL: a deep learning-based approach for in silico discovery of beta-lactamases.

Beta-lactamases (BLs) are enzymes localized in the periplasmic space of bacterial pathogens, where they confer resistance to beta-lactam antibiotics. Experimental identification of BLs is costly yet crucial to understand beta-lactam resistance mechanisms. To address this issue, we present DeepBL, a deep learning-based approach by incorporating sequence-derived features to enable high-throughput prediction of BLs. Specifically, DeepBL is implemented based on the Small VGGNet architecture and the TensorFlow deep learning library. Furthermore, the performance of DeepBL models is investigated in relation to the sequence redundancy level and negative sample selection in the benchmark dataset. The models are trained on datasets of varying sequence redundancy thresholds, and the model performance is evaluated by extensive benchmarking tests. Using the optimized DeepBL model, we perform proteome-wide screening for all reviewed bacterium protein sequences available from the UniProt database. These results are freely accessible at the DeepBL webserver at http://deepbl.erc.monash.edu.au/.

AcrHub: an integrative hub for investigating, predicting and mapping anti-CRISPR proteins.

Anti-CRISPR (Acr) proteins naturally inhibit CRISPR-Cas adaptive immune systems across bacterial and archaeal domains of life. This emerging field has caused a paradigm shift in the way we think about the CRISPR-Cas system, and promises a number of useful applications from gene editing to phage therapy. As the number of verified and predicted Acrs rapidly expands, few online resources have been developed to deal with this wealth of information. To overcome this shortcoming, we developed AcrHub, an integrative database to provide an all-in-one solution for investigating, predicting and mapping Acr proteins. AcrHub catalogs 339 non-redundant experimentally validated Acrs and over 70 000 predicted Acrs extracted from genome sequence data from a diverse range of prokaryotic organisms and their viruses. It integrates state-of-the-art predictors to predict potential Acrs, and incorporates three analytical modules: similarity analysis, phylogenetic analysis and homology network analysis, to analyze their relationships with known Acrs. By interconnecting all modules as a platform, AcrHub presents enriched and in-depth analysis of known and potential Acrs and therefore provides new and exciting insights into the future of Acr discovery and validation. AcrHub is freely available at http://pacrispr.erc.monash.edu/AcrHub/.

AcrDB: a database of anti-CRISPR operons in prokaryotes and viruses.

CRISPR-Cas is an anti-viral mechanism of prokaryotes that has been widely adopted for genome editing. To make CRISPR-Cas genome editing more controllable and safer to use, anti-CRISPR proteins have been recently exploited to prevent excessive/prolonged Cas nuclease cleavage. Anti-CRISPR (Acr) proteins are encoded by (pro)phages/(pro)viruses, and have the ability to inhibit their host's CRISPR-Cas systems. We have built an online database AcrDB (http://bcb.unl.edu/AcrDB) by scanning ∼19 000 genomes of prokaryotes and viruses with AcrFinder, a recently developed Acr-Aca (Acr-associated regulator) operon prediction program. Proteins in Acr-Aca operons were further processed by two machine learning-based programs (AcRanker and PaCRISPR) to obtain numerical scores/ranks. Compared to other anti-CRISPR databases, AcrDB has the following unique features: (i) It is a genome-scale database with the largest collection of data (39 799 Acr-Aca operons containing Aca or Acr homologs); (ii) It offers a user-friendly web interface with various functions for browsing, graphically viewing, searching, and batch downloading Acr-Aca operons; (iii) It focuses on the genomic context of Acr and Aca candidates instead of individual Acr protein family and (iv) It collects data with three independent programs each having a unique data mining algorithm for cross validation. AcrDB will be a valuable resource to the anti-CRISPR research community.

BastionHub: a universal platform for integrating and analyzing substrates secreted by Gram-negative bacteria.

Gram-negative bacteria utilize secretion systems to export substrates into their surrounding environment or directly into neighboring cells. These substrates are proteins that function to promote bacterial survival: by facilitating nutrient collection, disabling competitor species or, for pathogens, to disable host defenses. Following a rapid development of computational techniques, a growing number of substrates have been discovered and subsequently validated by wet lab experiments. To date, several online databases have been developed to catalogue these substrates but they have limited user options for in-depth analysis, and typically focus on a single type of secreted substrate. We therefore developed a universal platform, BastionHub, that incorporates extensive functional modules to facilitate substrate analysis and integrates the five major Gram-negative secreted substrate types (i.e. from types I-IV and VI secretion systems). To our knowledge, BastionHub is not only the most comprehensive online database available, it is also the first to incorporate substrates secreted by type I or type II secretion systems. By providing the most up-to-date details of secreted substrates and state-of-the-art prediction and visualized relationship analysis tools, BastionHub will be an important platform that can assist biologists in uncovering novel substrates and formulating new hypotheses. BastionHub is freely available at http://bastionhub.erc.monash.edu/.

Tall tails: cryo-electron microscopy of phage tail DNA ejection conduits.

The majority of phages, viruses that infect prokaryotes, inject their genomic material into their host through a tubular assembly known as a tail. Despite the genomic diversity of tailed phages, only three morphological archetypes have been described: contractile tails of Myoviridae-like phages; short non-contractile tails of Podoviridae-like phages; and long and flexible non-contractile tails of Siphoviridae-like phages. While early cryo-electron microscopy (cryo-EM) work elucidated the organisation of the syringe-like injection mechanism of contractile tails, the intrinsic flexibility of the long non-contractile tails prevented high-resolution structural determination. In 2020, four cryo-EM structures of Siphoviridae-like tail tubes were solved and revealed common themes and divergences. The central tube is structurally conserved and homologous to the hexameric rings of the tail tube protein (TTP) also found in contractile tails, bacterial pyocins, and type VI secretion systems. The interior surface of the tube presents analogous motifs of negatively charged amino acids proposed to facilitate ratcheting of the DNA during genome ejection. The lack of a conformational change upon genome ejection implicates the tape measure protein in triggering genome release. A distinctive feature of Siphoviridae-like tails is their flexibility. This results from loose inter-ring connections that can asymmetrically stretch on one side to allow bending and flexing of the tube without breaking. The outer surface of the tube differs greatly and may be smooth or rugged due to additional Ig-like domains in TTP. Some of these variable domains may contribute to adsorption of the phage to prokaryotic and eukaryotic cell surfaces affecting tropism and virulence.

PaCRISPR: a server for predicting and visualizing anti-CRISPR proteins.

Anti-CRISPRs are widespread amongst bacteriophage and promote bacteriophage infection by inactivating the bacterial host's CRISPR-Cas defence system. Identifying and characterizing anti-CRISPR proteins opens an avenue to explore and control CRISPR-Cas machineries for the development of new CRISPR-Cas based biotechnological and therapeutic tools. Past studies have identified anti-CRISPRs in several model phage genomes, but a challenge exists to comprehensively screen for anti-CRISPRs accurately and efficiently from genome and metagenome sequence data. Here, we have developed an ensemble learning based predictor, PaCRISPR, to accurately identify anti-CRISPRs from protein datasets derived from genome and metagenome sequencing projects. PaCRISPR employs different types of feature recognition united within an ensemble framework. Extensive cross-validation and independent tests show that PaCRISPR achieves a significantly more accurate performance compared with homology-based baseline predictors and an existing toolkit. The performance of PaCRISPR was further validated in discovering anti-CRISPRs that were not part of the training for PaCRISPR, but which were recently demonstrated to function as anti-CRISPRs for phage infections. Data visualization on anti-CRISPR relationships, highlighting sequence similarity and phylogenetic considerations, is part of the output from the PaCRISPR toolkit, which is freely available at http://pacrispr.erc.monash.edu/.

Protease-associated import systems are widespread in Gram-negative bacteria.

Bacteria have evolved sophisticated uptake machineries in order to obtain the nutrients required for growth. Gram-negative plant pathogens of the genus Pectobacterium obtain iron from the protein ferredoxin, which is produced by their plant hosts. This iron-piracy is mediated by the ferredoxin uptake system (Fus), a gene cluster encoding proteins that transport ferredoxin into the bacterial cell and process it proteolytically. In this work we show that gene clusters related to the Fus are widespread in bacterial species. Through structural and biochemical characterisation of the distantly related Fus homologues YddB and PqqL from Escherichia coli, we show that these proteins are analogous to components of the Fus from Pectobacterium. The membrane protein YddB shares common structural features with the outer membrane ferredoxin transporter FusA, including a large extracellular substrate binding site. PqqL is an active protease with an analogous periplasmic localisation and iron-dependent expression to the ferredoxin processing protease FusC. Structural analysis demonstrates that PqqL and FusC share specific features that distinguish them from other members of the M16 protease family. Taken together, these data provide evidence that protease associated import systems analogous to the Fus are widespread in Gram-negative bacteria.

Systematic analysis and prediction of type IV secreted effector proteins by machine learning approaches.

In the course of infecting their hosts, pathogenic bacteria secrete numerous effectors, namely, bacterial proteins that pervert host cell biology. Many Gram-negative bacteria, including context-dependent human pathogens, use a type IV secretion system (T4SS) to translocate effectors directly into the cytosol of host cells. Various type IV secreted effectors (T4SEs) have been experimentally validated to play crucial roles in virulence by manipulating host cell gene expression and other processes. Consequently, the identification of novel effector proteins is an important step in increasing our understanding of host-pathogen interactions and bacterial pathogenesis. Here, we train and compare six machine learning models, namely, Naïve Bayes (NB), K-nearest neighbor (KNN), logistic regression (LR), random forest (RF), support vector machines (SVMs) and multilayer perceptron (MLP), for the identification of T4SEs using 10 types of selected features and 5-fold cross-validation. Our study shows that: (1) including different but complementary features generally enhance the predictive performance of T4SEs; (2) ensemble models, obtained by integrating individual single-feature models, exhibit a significantly improved predictive performance and (3) the 'majority voting strategy' led to a more stable and accurate classification performance when applied to predicting an ensemble learning model with distinct single features. We further developed a new method to effectively predict T4SEs, Bastion4 (Bacterial secretion effector predictor for T4SS), and we show our ensemble classifier clearly outperforms two recent prediction tools. In summary, we developed a state-of-the-art T4SE predictor by conducting a comprehensive performance evaluation of different machine learning algorithms along with a detailed analysis of single- and multi-feature selections.

Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods.

The roles of proteolytic cleavage have been intensively investigated and discussed during the past two decades. This irreversible chemical process has been frequently reported to influence a number of crucial biological processes (BPs), such as cell cycle, protein regulation and inflammation. A number of advanced studies have been published aiming at deciphering the mechanisms of proteolytic cleavage. Given its significance and the large number of functionally enriched substrates targeted by specific proteases, many computational approaches have been established for accurate prediction of protease-specific substrates and their cleavage sites. Consequently, there is an urgent need to systematically assess the state-of-the-art computational approaches for protease-specific cleavage site prediction to further advance the existing methodologies and to improve the prediction performance. With this goal in mind, in this article, we carefully evaluated a total of 19 computational methods (including 8 scoring function-based methods and 11 machine learning-based methods) in terms of their underlying algorithm, calculated features, performance evaluation and software usability. Then, extensive independent tests were performed to assess the robustness and scalability of the reviewed methods using our carefully prepared independent test data sets with 3641 cleavage sites (specific to 10 proteases). The comparative experimental results demonstrate that PROSPERous is the most accurate generic method for predicting eight protease-specific cleavage sites, while GPS-CCD and LabCaS outperformed other predictors for calpain-specific cleavage sites. Based on our review, we then outlined some potential ways to improve the prediction performance and ease the computational burden by applying ensemble learning, deep learning, positive unlabeled learning and parallel and distributed computing techniques. We anticipate that our study will serve as a practical and useful guide for interested readers to further advance next-generation bioinformatics tools for protease-specific cleavage site prediction.

PeNGaRoo, a combined gradient boosting and ensemble learning framework for predicting non-classical secreted proteins.

MOTIVATION: Gram-positive bacteria have developed secretion systems to transport proteins across their cell wall, a process that plays an important role during host infection. These secretion mechanisms have also been harnessed for therapeutic purposes in many biotechnology applications. Accordingly, the identification of features that select a protein for efficient secretion from these microorganisms has become an important task. Among all the secreted proteins, 'non-classical' secreted proteins are difficult to identify as they lack discernable signal peptide sequences and can make use of diverse secretion pathways. Currently, several computational methods have been developed to facilitate the discovery of such non-classical secreted proteins; however, the existing methods are based on either simulated or limited experimental datasets. In addition, they often employ basic features to train the models in a simple and coarse-grained manner. The availability of more experimentally validated datasets, advanced feature engineering techniques and novel machine learning approaches creates new opportunities for the development of improved predictors of 'non-classical' secreted proteins from sequence data. RESULTS: In this work, we first constructed a high-quality dataset of experimentally verified 'non-classical' secreted proteins, which we then used to create benchmark datasets. Using these benchmark datasets, we comprehensively analyzed a wide range of features and assessed their individual performance. Subsequently, we developed a two-layer Light Gradient Boosting Machine (LightGBM) ensemble model that integrates several single feature-based models into an overall prediction framework. At this stage, LightGBM, a gradient boosting machine, was used as a machine learning approach and the necessary parameter optimization was performed by a particle swarm optimization strategy. All single feature-based LightGBM models were then integrated into a unified ensemble model to further improve the predictive performance. Consequently, the final ensemble model achieved a superior performance with an accuracy of 0.900, an F-value of 0.903, Matthew's correlation coefficient of 0.803 and an area under the curve value of 0.963, and outperforming previous state-of-the-art predictors on the independent test. Based on our proposed optimal ensemble model, we further developed an accessible online predictor, PeNGaRoo, to serve users' demands. We believe this online web server, together with our proposed methodology, will expedite the discovery of non-classically secreted effector proteins in Gram-positive bacteria and further inspire the development of next-generation predictors. AVAILABILITY AND IMPLEMENTATION: http://pengaroo.erc.monash.edu/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

FusC, a member of the M16 protease family acquired by bacteria for iron piracy against plants.

Iron is essential for life. Accessing iron from the environment can be a limiting factor that determines success in a given environmental niche. For bacteria, access of chelated iron from the environment is often mediated by TonB-dependent transporters (TBDTs), which are ß-barrel proteins that form sophisticated channels in the outer membrane. Reports of iron-bearing proteins being used as a source of iron indicate specific protein import reactions across the bacterial outer membrane. The molecular mechanism by which a folded protein can be imported in this way had remained mysterious, as did the evolutionary process that could lead to such a protein import pathway. How does the bacterium evolve the specificity factors that would be required to select and import a protein encoded on another organism's genome? We describe here a model whereby the plant iron-bearing protein ferredoxin can be imported across the outer membrane of the plant pathogen Pectobacterium by means of a Brownian ratchet mechanism, thereby liberating iron into the bacterium to enable its growth in plant tissues. This import pathway is facilitated by FusC, a member of the same protein family as the mitochondrial processing peptidase (MPP). The Brownian ratchet depends on binding sites discovered in crystal structures of FusC that engage a linear segment of the plant protein ferredoxin. Sequence relationships suggest that the bacterial gene encoding FusC has previously unappreciated homologues in plants and that the protein import mechanism employed by the bacterium is an evolutionary echo of the protein import pathway in plant mitochondria and plastids.

Filamentous phages: masters of a microbial sharing economy.

Bacteriophage ("bacteria eaters") or phage is the collective term for viruses that infect bacteria. While most phages are pathogens that kill their bacterial hosts, the filamentous phages of the sub-class Inoviridae live in cooperative relationships with their bacterial hosts, akin to the principal behaviours found in the modern-day sharing economy: peer-to-peer support, to offset any burden. Filamentous phages impose very little burden on bacteria and offset this by providing service to help build better biofilms, or provision of toxins and other factors that increase virulence, or modified behaviours that provide novel motile activity to their bacterial hosts. Past, present and future biotechnology applications have been built on this phage-host cooperativity, including DNA sequencing technology, tools for genetic engineering and molecular analysis of gene expression and protein production, and phage-display technologies for screening protein-ligand and protein-protein interactions. With the explosion of genome and metagenome sequencing surveys around the world, we are coming to realize that our knowledge of filamentous phage diversity remains at a tip-of-the-iceberg stage, promising that new biology and biotechnology are soon to come.