Multi-year analysis of the global preclinical antibacterial pipeline: trends and gaps.

Antimicrobial resistance (AMR) is a major global health threat estimated to have caused the deaths of 1.27 million people in 2019, which is more than HIV/AIDS and malaria deaths combined. AMR also has significant consequences on the global economy. If not properly addressed, AMR could immensely impact the world's economy, further increasing the poverty burden in low- and middle-income countries. To mitigate the risk of a post-antibiotic society, where the ability to effectively treat common bacterial infections is being severely threatened, it is necessary to establish a continuous supply of new and novel antibacterial medicines. However, there are gaps in the current pipeline that will prove difficult to address, given the time required to develop new agents. To understand the status of upstream antibiotic development and the challenges faced by drug developers in the early development stage, the World Health Organization has regularly assessed the preclinical and clinical antibacterial development pipeline. The review identifies potential new classes of antibiotics or novel mechanisms of action that can better address resistant bacterial strains. This proactive approach is necessary to stay ahead of evolving resistance patterns and to support the availability of effective treatment options. This review examines the trends in preclinical development and attempts to identify gaps and potential opportunities to overcome the numerous hurdles in the early stages of the antibacterial research and development space.

Metagenomic identification, purification and characterisation of the Bifidobacterium adolescentis BgaC ß-galactosidase.

Members of the human gut microbiota use glycoside hydrolase (GH) enzymes, such as ß-galactosidases, to forage on host mucin glycans and dietary fibres. A human faecal metagenomic fosmid library was constructed and functionally screened to identify novel ß-galactosidases. Out of the 16,000 clones screened, 30 ß-galactosidase-positive clones were identified. The ß-galactosidase gene found in the majority of the clones was BAD_1582 from Bifidobacterium adolescentis, subsequently named bgaC. This gene was cloned with a hexahistidine tag, expressed in Escherichia coli and His-tagged-BgaC was purified using Ni2+-NTA affinity chromatography and size filtration. The enzyme had optimal activity at pH 7.0 and 37 °C, with a wide range of pH (4-10) and temperature (0-40 °C) stability. It required a divalent metal ion co-factor; maximum activity was detected with Mg2+, while Cu2+ and Mn2+ were inhibitory. Kinetic parameters were determined using ortho-nitrophenyl-ß-D-galactopyranoside (ONPG) and lactose substrates. BgaC had a Vmax of 107 µmol/min/mg and a Km of 2.5 mM for ONPG and a Vmax of 22 µmol/min/mg and a Km of 3.7 mM for lactose. It exhibited low product inhibition by galactose with a Ki of 116 mM and high tolerance for glucose (66% activity retained in presence of 700 mM glucose). In addition, BgaC possessed transglycosylation activity to produce galactooligosaccharides (GOS) from lactose, as determined by TLC and HPLC analysis. The enzymatic characteristics of B. adolescentis BgaC make it an ideal candidate for dairy industry applications and prebiotic manufacture.Key points• Bifidobacterium adolescentis BgaC ß-galactosidase was selected from human faecal metagenome.• BgaC possesses sought-after properties for biotechnology, e.g. low product inhibition.• BgaC has transglycosylation activity producing prebiotic oligosaccharides. Graphical Abstract.

Genome-wide RNAi screen identifies human host factors crucial for influenza virus replication.

Influenza A virus, being responsible for seasonal epidemics and reoccurring pandemics, represents a worldwide threat to public health. High mutation rates facilitate the generation of viral escape mutants, rendering vaccines and drugs directed against virus-encoded targets potentially ineffective. In contrast, targeting host cell determinants temporarily dispensable for the host but crucial for virus replication could prevent viral escape. Here we report the discovery of 287 human host cell genes influencing influenza A virus replication in a genome-wide RNA interference (RNAi) screen. Using an independent assay we confirmed 168 hits (59%) inhibiting either the endemic H1N1 (119 hits) or the current pandemic swine-origin (121 hits) influenza A virus strains, with an overlap of 60%. Notably, a subset of these common hits was also essential for replication of a highly pathogenic avian H5N1 strain. In-depth analyses of several factors provided insights into their infection stage relevance. Notably, SON DNA binding protein (SON) was found to be important for normal trafficking of influenza virions to late endosomes early in infection. We also show that a small molecule inhibitor of CDC-like kinase 1 (CLK1) reduces influenza virus replication by more than two orders of magnitude, an effect connected with impaired splicing of the viral M2 messenger RNA. Furthermore, influenza-virus-infected p27(-/-) (cyclin-dependent kinase inhibitor 1B; Cdkn1b) mice accumulated significantly lower viral titres in the lung, providing in vivo evidence for the importance of this gene. Thus, our results highlight the potency of genome-wide RNAi screening for the dissection of virus-host interactions and the identification of drug targets for a broad range of influenza viruses.

Network and systems biology: essential steps in virtualising drug discovery and development.

The biological processes that keep us healthy or cause disease, as well as the mechanisms of action of possible drugs are inherently complex. In the face of this complexity, attempts at discovering new drugs to treat diseases have alternated between trial-and-error (typically on experimental systems) and grand simplification, usually based on much too little information. We now have the chance to combine these strategies through establishment of 'virtual patient' models, centred on a detailed molecular characterisation of thousands or even, in the future, millions of patients. In doing so, we lay the foundations for truly personalised therapy, as well as a far-reaching virtualisation of drug discovery and development in oncology and other areas of medicine.

Effects of selective digestive decontamination (SDD) on the gut resistome.

OBJECTIVES: Selective digestive decontamination (SDD) is an infection prevention measure for critically ill patients in intensive care units (ICUs) that aims to eradicate opportunistic pathogens from the oropharynx and intestines, while sparing the anaerobic flora, by the application of non-absorbable antibiotics. Selection for antibiotic-resistant bacteria is still a major concern for SDD. We therefore studied the impact of SDD on the reservoir of antibiotic resistance genes (i.e. the resistome) by culture-independent approaches. METHODS: We evaluated the impact of SDD on the gut microbiota and resistome in a single ICU patient during and after an ICU stay by several metagenomic approaches. We also determined by quantitative PCR the relative abundance of two common aminoglycoside resistance genes in longitudinally collected samples from 12 additional ICU patients who received SDD. RESULTS: The patient microbiota was highly dynamic during the hospital stay. The abundance of antibiotic resistance genes more than doubled during SDD use, mainly due to a 6.7-fold increase in aminoglycoside resistance genes, in particular aph(2″)-Ib and an aadE-like gene. We show that aph(2″)-Ib is harboured by anaerobic gut commensals and is associated with mobile genetic elements. In longitudinal samples of 12 ICU patients, the dynamics of these two genes ranged from a ∼10(4) fold increase to a ∼10(-10) fold decrease in relative abundance during SDD. CONCLUSIONS: ICU hospitalization and the simultaneous application of SDD has large, but highly individualized, effects on the gut resistome of ICU patients. Selection for transferable antibiotic resistance genes in anaerobic commensal bacteria could impact the risk of transfer of antibiotic resistance genes to opportunistic pathogens.

Activation of NF-κB by Neisseria gonorrhoeae is associated with microcolony formation and type IV pilus retraction.

The early stage of infection with Neisseria gonorrhoeae (Ngo), the causative agent of gonorrhoea, is marked by type IV pilus (Tfp)-mediated attachment and the formation of bacterial microcolonies on epithelial cells. Retraction of the Ngo Tfp generates substantial force on its substrate which can elicit host cell signalling. Here, we observed that this retraction force could also activate nuclear factor (NF)-κB, the central signalling cascade of innate immunity. Using a p65-GFP-expressing epithelial cell line, we show that piliated Ngo induce asynchronous NF-κB activation in infected cells, which is temporally associated with the formation of gonococcal microcolonies. A mutant lacking PilT, an ATPase necessary for Tfp retraction, induced markedly reduced NF-κB activation. This was accompanied by decreased NF-κB target gene transcription and cytokine release. The impaired ability of the pilT mutant to activate NF-κB was compensated by applying mechanical shear stress to the infected host cells, indicating that the mechanical forces generated by retractile pili are involved in the retraction-dependent activation of NF-κB elicited by gonococcal microcolonies. Thus, our work provides evidence for an intriguing relationship between microcolony growth, pilus retraction and host cell signalling, with likely implications with regard to the course of symptomatic versus asymptomatic gonococcal infections.

Proposal of a population wide genome-based testing for Covid-19.

Our lives (and deaths) have by now been dominated for two years by COVID-19, a pandemic that has caused hundreds of millions of disease cases, millions of deaths, trillions in economic costs, and major restrictions on our freedom. Here we suggest a novel tool for controlling the COVID-19 pandemic. The key element is a method for a population-scale PCR-based testing, applied on a systematic and repeated basis. For this we have developed a low cost, highly sensitive virus-genome-based test. Using Germany as an example, we demonstrate by using a mathematical model, how useful this strategy could have been in controlling the pandemic. We show using real-world examples how this might be implemented on a mass scale and discuss the feasibility of this approach.

Prevalence of Propionibacterium acnes in diseased prostates and its inflammatory and transforming activity on prostate epithelial cells.

Prostate cancer (PCa) is the second leading cause of male cancer deaths in the Western world. Mounting evidence has revealed that chronic inflammation can be an important initiating factor of PCa. Recent work has detected the anaerobic Gram-positive bacterium Propionibacterium acnes in cancerous prostates, but with wide-ranging detection rates. Here, using in situ immunofluorescence (ISIF), P. acnes was found in 58 out of 71 (81.7%) tested cancerous prostate tissue samples, but was absent from healthy prostate tissues (20 samples) and other cancerous tissue biopsies (59 mamma carcinoma samples). Live P. acnes bacteria were isolated from cancerous prostates and cocultured with the prostate epithelial cell line RWPE1. Microarray analysis showed that the host cell responded to P. acnes with a strong multifaceted inflammatory response. Active secretion of cytokines and chemokines, such as IL-6 and IL-8, from infected cells was confirmed. The host cell response was likely mediated by the transcriptional factors NF-κB and STAT3, which were both activated upon P. acnes infection. The P. acnes-induced host cell response also included the activation of the COX2-prostaglandin, and the plasminogen-matrix metalloproteinase pathways. Long-term exposure to P. acnes altered cell proliferation, and enabled anchorage-independent growth of infected epithelial cells, thus initiating cellular transformation. Our results suggest that P. acnes infection could be a contributing factor to the initiation or progression of PCa.

High-throughput and single-cell imaging of NF-kappaB oscillations using monoclonal cell lines.

BACKGROUND: The nuclear factor-kappaB (NF-kappaB) family of transcription factors plays a role in a wide range of cellular processes including the immune response and cellular growth. In addition, deregulation of the NF-kappaB system has been associated with a number of disease states, including cancer. Therefore, insight into the regulation of NF-kappaB activation has crucial medical relevance, holding promise for novel drug target discovery. Transcription of NF-kappaB-induced genes is regulated by differential dynamics of single NF-kappaB subunits, but only a few methods are currently being applied to study dynamics. In particular, while oscillations of NF-kappaB activation have been observed in response to the cytokine tumor necrosis factor alpha (TNFalpha), little is known about the occurrence of oscillations in response to bacterial infections. RESULTS: To quantitatively assess NF-kappaB dynamics we generated human and murine monoclonal cell lines that stably express the NF-kappaB subunit p65 fused to GFP. Furthermore, a high-throughput assay based on automated microscopy coupled to image analysis to quantify p65-nuclear translocation was established. Using this assay, we demonstrate a stimulus- and cell line-specific temporal control of p65 translocation, revealing, for the first time, oscillations of p65 translocation in response to bacterial infection. Oscillations were detected at the single-cell level using real-time microscopy as well as at the population level using high-throughput image analysis. In addition, mathematical modeling of NF-kappaB dynamics during bacterial infections predicted masking of oscillations on the population level in asynchronous activations, which was experimentally confirmed. CONCLUSIONS: Taken together, this simple and cost effective assay constitutes an integrated approach to infer the dynamics of NF-kappaB kinetics in single cells and cell populations. Using a single system, novel factors modulating NF-kappaB can be identified and analyzed, providing new possibilities for a wide range of applications from therapeutic discovery and understanding of disease to host-pathogen interactions.

Staphylococcus saccharolyticus: An Overlooked Human Skin Colonizer.

Coagulase-negative staphylococcal species constitute an important part of the human skin microbiota. In particular, facultative anaerobic species such as Staphylococcus epidermidis and Staphylococcus capitis can be found on the skin of virtually every human being. Here, we applied a culture-independent amplicon sequencing approach to identify staphylococcal species on the skin of healthy human individuals. While S. epidermidis and S. capitis were found as primary residents of back skin, surprisingly, the third most abundant member was Staphylococcus saccharolyticus, a relatively unstudied species. A search of skin metagenomic datasets detected sequences identical to the genome of S. saccharolyticus in diverse skin sites, including the back, forehead, and elbow pit. Although described as a slow-growing anaerobic species, a re-evaluation of its growth behavior showed that S. saccharolyticus can grow under oxic conditions, and, in particular, in a CO2-rich atmosphere. We argue here that S. saccharolyticus was largely overlooked in previous culture-dependent and -independent studies, due to its requirement for fastidious growth conditions and the lack of reference genome sequences, respectively. Future studies are needed to unravel the microbiology and host-interacting properties of S. saccharolyticus and its role as a prevalent skin colonizer.

Genomic and Ecogenomic Characterization of Proteus mirabilis Bacteriophages.

Proteus mirabilis often complicates the care of catheterized patients through the formation of crystalline biofilms which block urine flow. Bacteriophage therapy has been highlighted as a promising approach to control this problem, but relatively few phages infecting P. mirabilis have been characterized. Here we characterize five phages capable of infecting P. mirabilis, including those shown to reduce biofilm formation, and provide insights regarding the wider ecological and evolutionary relationships of these phages. Transmission electron microscopy (TEM) imaging of phages vB_PmiP_RS1pmA, vB_PmiP_RS1pmB, vB_PmiP_RS3pmA, and vB_PmiP_RS8pmA showed that all share morphologies characteristic of the Podoviridae family. The genome sequences of vB_PmiP_RS1pmA, vB_PmiP_RS1pmB, and vB_PmiP_RS3pmA showed these are species of the same phage differing only by point mutations, and are closely related to vB_PmiP_RS8pmA. Podophages characterized in this study were also found to share similarity in genome architecture and composition to other previously described P. mirabilis podophages (PM16 and PM75). In contrast, vB_PimP_RS51pmB showed morphology characteristic of the Myoviridae family, with no notable similarity to other phage genomes examined. Ecogenomic profiling of all phages revealed no association with human urinary tract viromes, but sequences similar to vB_PimP_RS51pmB were found within human gut, and human oral microbiomes. Investigation of wider host-phage evolutionary relationships through tetranucleotide profiling of phage genomes and bacterial chromosomes, indicated vB_PimP_RS51pmB has a relatively recent association with Morganella morganii and other non-Proteus members of the Morganellaceae family. Subsequent host range assays confirmed vB_PimP_RS51pmB can infect M. morganii.

The potential of nanoflow liquid chromatography-nano electrospray ionisation-mass spectrometry for global profiling the faecal metabolome.

Faeces are comprised of a wide array of metabolites arising from the circulatory system as well as the human microbiome. A global metabolite analysis (metabolomics) of faecal extracts offers the potential to uncover new compounds which may be indicative of the onset of bowel diseases such as colorectal cancer (CRC). To date, faecal metabolomics is still in its infancy and the compounds of low abundance present in faecal extracts poorly characterised. In this study, extracts of faeces from healthy subjects were profiled using a sensitive nanoflow-nanospray LC-MS platform which resulted in highly repeatable peak retention times (<2% CV) and intensities (<15% CV). Analysis of the extracts revealed wide coverage of the faecal metabolome including detection of low abundant signalling compounds such as sex steroids and eicosanoids, alongside highly abundant pharmaceuticals and tetrapyrrole metabolites. A small pilot study investigating differences in metabolomics profiles of faecal samples obtained from 7 CRC, 25 adenomatous polyp and 26 healthy groups revealed that secondary bile acids, conjugated androgens, eicosanoids, phospholipids and an unidentified haem metabolite were potential classes of metabolites that discriminated between the CRC and control sample groups. However, much larger follow up studies are needed to confirm which components of the faecal metabolome are associated with actual CRC disease rather than dietary influences. This study reveals the potential of nanospray-nanoflow LC-MS profiling of faecal samples from large scale cohort studies for uncovering the role of the faecal metabolome in colorectal disease formation.

Linking pollution induced community tolerance (PICT) and microbial community structure in chronically metal polluted estuarine sediments.

We tested the ability of pollution induced community tolerance (PICT) to detect the effects of chronic metal pollution on estuarine sediment microbial communities, along a gradient spanning two orders of magnitude in metal concentrations. In tandem, we investigated the associated microbial community structure using terminal restriction fragment length polymorphism (T-RFLP). Tolerance of microbes to Cu, measured as IC50 (inhibitory concentration 50%), was strongly correlated with pore water Cu concentration (r(2)=0.842). No strong correlation existed for other metals tested, highlighting the ability of PICT to identify the pollutant causing a toxic effect. There was no correlation between microbial community structure and community tolerance to metals tested, but analysis of community structure did provide some information on reasons for observed PICT response. PICT methodology used here provided a greater strength and consistency of association with pollutant concentration compared to microbial community structure and can be recommended as a sensitive indicator of metal pollution on estuarine sediment microbial communities.

Resolution of habitat-associated ecogenomic signatures in bacteriophage genomes and application to microbial source tracking.

Just as the expansion in genome sequencing has revealed and permitted the exploitation of phylogenetic signals embedded in bacterial genomes, the application of metagenomics has begun to provide similar insights at the ecosystem level for microbial communities. However, little is known regarding this aspect of bacteriophage associated with microbial ecosystems, and if phage encode discernible habitat-associated signals diagnostic of underlying microbiomes. Here we demonstrate that individual phage can encode clear habitat-related 'ecogenomic signatures', based on relative representation of phage-encoded gene homologues in metagenomic data sets. Furthermore, we show the ecogenomic signature encoded by the gut-associated ɸB124-14 can be used to segregate metagenomes according to environmental origin, and distinguish 'contaminated' environmental metagenomes (subject to simulated in silico human faecal pollution) from uncontaminated data sets. This indicates phage-encoded ecological signals likely possess sufficient discriminatory power for use in biotechnological applications, such as development of microbial source tracking tools for monitoring water quality.

The human gut virome: form and function.

Advances in next-generation sequencing technologies and the application of metagenomic approaches have fuelled an exponential increase in our understanding of the human gut microbiome. These approaches are now also illuminating features of the diverse and abundant collection of viruses (termed the virome) subsisting with the microbial ecosystems residing within the human holobiont. Here, we focus on the current and emerging knowledge of the human gut virome, in particular on viruses infecting bacteria (bacteriophage or phage), which are a dominant component of this viral community. We summarise current insights regarding the form and function of this 'human gut phageome' and highlight promising avenues for future research. In doing so, we discuss the potential for phage to drive ecological functioning and evolutionary change within this important microbial ecosystem, their contribution to modulation of host-microbiome interactions and stability of the community as a whole, as well as the potential role of the phageome in human health and disease. We also consider the emerging concepts of a 'core healthy gut phageome' and the putative existence of 'viral enterotypes' and 'viral dysbiosis'.

Models of Models: A Translational Route for Cancer Treatment and Drug Development.

Every patient and every disease is different. Each patient therefore requires a personalized treatment approach. For technical reasons, a personalized approach is feasible for treatment strategies such as surgery, but not for drug-based therapy or drug development. The development of individual mechanistic models of the disease process in every patient offers the possibility of attaining truly personalized drug-based therapy and prevention. The concept of virtual clinical trials and the integrated use of in silico, in vitro, and in vivo models in preclinical development could lead to significant gains in efficiency and order of magnitude increases in the cost effectiveness of drug development and approval. We have developed mechanistic computational models of large-scale cellular signal transduction networks for prediction of drug effects and functional responses, based on patient-specific multi-level omics profiles. However, a major barrier to the use of such models in a clinical and developmental context is the reliability of predictions. Here we detail how the approach of using "models of models" has the potential to impact cancer treatment and drug development. We describe the iterative refinement process that leverages the flexibility of experimental systems to generate highly dimensional data, which can be used to train and validate computational model parameters and improve model predictions. In this way, highly optimized computational models with robust predictive capacity can be generated. Such models open up a number of opportunities for cancer drug treatment and development, from enhancing the design of experimental studies, reducing costs, and improving animal welfare, to increasing the translational value of results generated.

Cancer Precision Medicine: Why More Is More and DNA Is Not Enough.

Every tumour is different. They arise in patients with different genomes, from cells with different epigenetic modifications, and by random processes affecting the genome and/or epigenome of a somatic cell, allowing it to escape the usual controls on its growth. Tumours and patients therefore often respond very differently to the drugs they receive. Cancer precision medicine aims to characterise the tumour (and often also the patient) to be able to predict, with high accuracy, its response to different treatments, with options ranging from the selective characterisation of a few genomic variants considered particularly important to predict the response of the tumour to specific drugs, to deep genome analysis of both tumour and patient, combined with deep transcriptome analysis of the tumour. Here, we compare the expected results of carrying out such analyses at different levels, from different size panels to a comprehensive analysis incorporating both patient and tumour at the DNA and RNA levels. In doing so, we illustrate the additional power gained by this unusually deep analysis strategy, a potential basis for a future precision medicine first strategy in cancer drug therapy. However, this is only a step along the way of increasingly detailed molecular characterisation, which in our view will, in the future, introduce additional molecular characterisation techniques, including systematic analysis of proteins and protein modification states and different types of metabolites in the tumour, systematic analysis of circulating tumour cells and nucleic acids, the use of spatially resolved analysis techniques to address the problem of tumour heterogeneity as well as the deep analyses of the immune system of the patient to, e.g., predict the response of the patient to different types of immunotherapy. Such analyses will generate data sets of even greater complexity, requiring mechanistic modelling approaches to capture enough of the complex situation in the real patient to be able to accurately predict his/her responses to all available therapies.

The human gut virome: a multifaceted majority.

Here, we outline our current understanding of the human gut virome, in particular the phage component of this ecosystem, highlighting progress, and challenges in viral discovery in this arena. We reveal how developments in high-throughput sequencing technologies and associated data analysis methodologies are helping to illuminate this abundant 'biological dark matter.' Current evidence suggests that the human gut virome is a highly individual but temporally stable collective, dominated by phages exhibiting a temperate lifestyle. This viral community also appears to encode a surprisingly rich functional repertoire that confers a range of attributes to their bacterial hosts, ranging from bacterial virulence and pathogenesis to maintaining host-microbiome stability and community resilience. Despite the significant advances in our understanding of the gut virome in recent years, it is clear that we remain in a period of discovery and revelation, as new methods and technologies begin to provide deeper understanding of the inherent ecological characteristics of this viral ecosystem. As our understanding increases, the nature of the multi-partite interactions occurring between host and microbiome will become clearer, helping us to more rationally define the concepts and principles that will underpin approaches to using human gut virome components for medical or biotechnological applications.

Predictive Modeling of Drug Treatment in the Area of Personalized Medicine.

Despite a growing body of knowledge on the mechanisms underlying the onset and progression of cancer, treatment success rates in oncology are at best modest. Current approaches use statistical methods that fail to embrace the inherent and expansive complexity of the tumor/patient/drug interaction. Computational modeling, in particular mechanistic modeling, has the power to resolve this complexity. Using fundamental knowledge on the interactions occurring between the components of a complex biological system, large-scale in silico models with predictive capabilities can be generated. Here, we describe how mechanistic virtual patient models, based on systematic molecular characterization of patients and their diseases, have the potential to shift the theranostic paradigm for oncology, both in the fields of personalized medicine and targeted drug development. In particular, we highlight the mechanistic modeling platform ModCell™ for individualized prediction of patient responses to treatment, emphasizing modeling techniques and avenues of application.