Biology subjects

van Opijnen, T.

Publications and source records attributed to van Opijnen, T..

29 records · Page 2Linked to original sources

bioRxiv2020

Essential gene analysis in Acinetobacter baumannii by high-density transposon mutagenesis and CRISPR interference

Acinetobacter baumannii is a poorly understood bacterium capable of life-threatening infections in hospitals. Few antibiotics remain effective against this highly resistant pathogen. Developing rationally-designed antimicrobials that can target A. baumannii requires improved knowledge of the proteins that carry out essential processes allowing growth of the organism. Unfortunately, studying essential genes has been challenging using traditional techniques, which usually require time-consuming recombination-based genetic manipulations. Here, we performed saturating mutagenesis with dual transposon systems to identify essential genes in A. baumannii and we developed a CRISPR-interference (CRISPRi) system for facile analysis of these genes. We show that the CRISPRi system enables efficient transcriptional silencing in A. baumannii. Using these tools, we confirmed the essentiality of the novel cell division protein AdvA and discovered a previously uncharacterized AraC-family transcription factor (ACX60_RS03245) that is necessary for growth. In addition, we show that capsule biosynthesis is a conditionally essential process, with mutations in late-acting steps causing toxicity in strain ATCC 17978 that can be bypassed by blocking early-acting steps or activating the BfmRS stress response. These results open new avenues for analysis of essential pathways in A. baumannii. ImportanceNew approaches are urgently needed to control A. baumannii, one of the most drug resistant pathogens known. To facilitate the development of novel targets that allow inhibition of the pathogen, we performed a large-scale identification of genes whose products the bacterium needs for growth. We also developed a CRISPR-based gene knockdown tool that operates efficiently in A. baumannii, allowing rapid analysis of these essential genes. We used these methods to define multiple processes vital to the bacterium, including a previously uncharacterized gene-regulatory factor and export of a protective polymeric capsule. These tools will enhance our ability to investigate processes critical for the essential biology of this challenging hospital-acquired pathogen.

microbiology↗

bioRxiv2020

Detection of a multi-disease biomarker in Salivawith Graphene Field Effect Transistors

Human carbonic anhydrase 1 (CA1) has been suggested as a biomarker for identification of several diseases including cancers, pancreatitis, diabetes, and Sjogrens syndrome. However, the lack of a rapid, cheap, accurate, and easy-to-use quantification technique has prevented widespread utilization of CA1 for practical clinical applications. To this end, we present a label-free electronic biosensor for detection of CA1 utilizing highly sensitive graphene field effect transistors (G-FETs) as a transducer and specific RNA aptamers as a probe. The binding of CA1 with aptamers resulted in a positive shift in Dirac voltage VD of the G-FETs, the magnitude of which depended on target concentration. These aptameric G-FET biosensors showed the binding affinity (KD) of [~] 2.3 ng/ml (70 pM), which is four orders lower than that reported using a gel shift assay. This lower value of KD enabled us to achieve a detection range (10 pg/ml - 100 ng/ml) which is well in line with the clinically relevant range. These highly sensitive devices allowed us to further prove their clinical relevance by successfully detecting the presence of CA1 in human saliva samples. Utilization of this label-free biosensor could facilitate the early stage identification of various diseases associated with changes in concentration of CAs.

biophysics↗

bioRxiv2020

Topologically correct synthetic reconstruction of pathogen social behavior found in deep tissue sites

Within deep tissue sites, extracellular bacterial pathogens often replicate in clusters that are surrounded by immune cells. Disease is modulated by interbacterial interactions as well as bacterial-host cell interactions resulting in microbial growth, phagocytic attack and secretion of host antimicrobial factors. To overcome the limited ability to manipulate these infection sites, we established a system for Yersinia pseudotuberculosis (Yptb) growth in microfluidics-driven microdroplets that regenerates microbial social behavior in tissues. Chemical generation of nitric oxide (NO) in the absence of immune cells was sufficient to reconstruct microbial social behavior, as witnessed by expression of the NO-inactivating protein Hmp on the extreme periphery of microcolonies, mimicking spatial regulation in tissues. Similarly, activated macrophages that expressed inducible NO synthase (iNOS) drove peripheral expression of Hmp, allowing regeneration of social behavior observed in tissues. These results argue that topologically correct microbial tissue growth and associated social behavior can be reconstructed in culture.

microbiology↗

bioRxiv2020

Boundary-Forest Clustering: Large-Scale Consensus Clustering of Biological Sequences

Bacterial species with large sequence diversity enable studies focused on comparative genomics, population genetics and pan-genome evolution. In such analyses it is key to determine whether sequences (e.g. genes) from different strains, are the same or different. This is often achieved by clustering orthologous genes based on sequence similarity. Importantly, one limitation of existing pan-genome clustering methods is that they do not assign a confidence score to the identified clusters. Given that clustering ground truth is unavailable when working with pan-genomes, the absence of confidence scores makes performance evaluation on real data an open challenge. Moreover, most pan-genome clustering solutions do not accommodate cluster augmentation, which is the addition of new sequences to an already clustered set of sequences. Finally, the pan-genome size of many organisms prevents direct application of powerful clustering techniques that do not scale to large datasets. Here, we present Boundary-Forest Clustering (BFClust), a method that addresses these challenges in three main steps: 1) The approximate-nearest-neighbor retrieval method Boundary-Forest is used as a representative selection step; 2) Downstream clustering of the representatives is performed using Markov Clustering (MCL); 3) Consensus clustering is applied across the Boundary-Forest, improving clustering accuracy and enabling confidence score calculation. First, MCL is favorably benchmarked against 6 powerful clustering methods. To explore the strengths of the entire BFClust approach, it is applied to 4 different datasets of the bacterial pathogen Streptococcus pneumoniae, and compared against 4 other pan-genome clustering tools. Unlike existing approaches, BFClust is fast, accurate, robust to noise and allows augmentation. Moreover, BFClust uniquely identifies low-confidence clusters in each dataset, which can negatively impact downstream analyses and interpretation of pan-genomes. Being the first tool that outputs confidence scores both when clustering de novo, and during cluster augmentation, BFClust offers a way of automatically evaluating and eliminating ambiguity in pan-genomes. Author SummaryClustering of biological sequences is a critical step in studying bacterial species with large sequence diversity. Existing clustering approaches group sequences together based on similarity. However, these approaches do not offer a way of evaluating the confidence of their output. This makes it impossible to determine whether the clustering output reflect biologically relevant clusters. Most existing methods also do not allow cluster augmentation, which is the quick incorporation and clustering of newly available sequences with an already clustered set. We present Boundary-Forest Clustering (BFClust) as a method that can generate cluster confidence scores, as well as allow cluster augmentation. In addition to having these additional key functionalities and being scalable to large dataset sizes, BFClust matches and outperforms state-of-the-art software in terms of accuracy, robustness to noise and speed. We show on 4 Streptococcus pneumoniae datasets that the confidence scores uniquely generated by BFClust can indeed be used to identify ambiguous sequence clusters. These scores thereby serve as a quality control step before further analysis on the clustering output commences. BFClust is currently the only biological sequence clustering tool that allows augmentation and outputs confidence scores, which should benefit most pan-genome studies.

bioinformatics↗

bioRxiv2019

Rapid, selective and single cell electrical detection of antibiotic resistant bacteria

The rapid increase in antibiotic resistant pathogenic bacteria has become a global threat, which besides the development of new drugs, requires rapid, cheap, scalable, and accurate diagnostics. Label free biosensors relying on electrochemical, mechanical, and mass based detection of whole bacterial cells have attempted to meet these requirements. However, the trade-off between selectivity and sensitivity of such sensors remains a key challenge. In particular, point-of-care diagnostics that are able to reduce and/or prevent unneeded antibiotic prescriptions require highly specific probes with sensitive and accurate transducers that can be miniaturized and multiplexed, and that are easy to operate and cheap. Towards achieving this goal, we present a number of advances in the use of graphene field effect transistors (G-FET) including the first use of peptide probes to electrically detect antibiotic resistant bacteria in a highly specific manner. In addition, we dramatically reduce the needed concentration for detection by employing dielectrophoresis for the first time in a G-FET, allowing us to monitor changes in the Dirac point due to individual bacterial cells. Specifically, we realized rapid binding of bacterial cells to a G-FET by electrical field guiding to the device to realize an overall 3 order of magnitude decrease in cell-concentration enabling a single-cell detection limit, and 9-fold reduction in needed time to 5 minutes. Utilizing our new biosensor and procedures, we demonstrate the first selective, electrical detection of the pathogenic bacterial species Staphylococcus aureus and antibiotic resistant Acinetobacter baumannii on a single platform.

microbiology↗

bioRxiv2019

Entropy of a bacterial stress response is a generalizable predictor for fitness and antibiotic sensitivity.

Genes implicated in bacterial stress responses have been used to construct models that infer the growth outcome of a bacterium in the presence of antibiotics with the objective to develop novel diagnostic methods in the clinic. Current models are trained on data specific to a species or type of stress, making them potentially limited in their application. It is unclear if a generalizable response-signature exists that can predict bacterial fitness independent of strain, species or type of stress. Here we present a substantial RNA-Seq and experimental evolution dataset for 9 strains and species, under multiple antibiotic and non-antibiotic stress conditions. We show that gene panel-based models can accurately predict antibiotic mechanism of action, as well as the fitness outcome of Streptococcus pneumoniae in the presence of antibiotics or under nutrient depletion. However, these models quickly become species-specific as gene homology is limited. Instead, we define a new concept, transcriptomic entropy, which we use to quantify the amount of transcriptional disruption that occurs in a bacterium when responding to the environment. With entropy at the center, we train a suite of predictive (machine learning) models enabling generalizable fitness and antibiotic sensitivity predictions. These entropy-based models that predict bacterial fitness are validated for 7 Gram-positive and -negative species under antibiotic and non-antibiotic conditions indicating that transcriptomic entropy can be used as a generalizable stress signature. Moreover, rather than being a binary indicator of fitness, an entropy-based model was developed and validated to predict the minimum inhibitory concentration of an antibiotic. Lastly, we show that the inclusion of a varied-set of multi-omics features of a bacterial stress response further enhances fitness predictions by reducing ambiguity. By demonstrating the feasibility of generalizable predictions of bacterial fitness, this work establishes the fundamentals for potentially new approaches in infectious disease diagnostics, including antibiotic susceptibility testing.\n\nSignificance statementAccurate predictions of bacterial fitness outcome could potentially have clinical diagnostic value, such as predicting optimum antibiotic choice and dosage for treating infectious diseases. Existing models of fitness predictions rely mainly on gene panel approaches, which may be species- and stress-specific due to a lack of gene and response conservation. In order to overcome this limitation, we generated a substantial experimental dataset and identified entropy as a universal stress response signature that quantifies the level of transcriptional disruption that is indicative of fitness outcome under a stressful condition. We present and validate for Gram-positive and negative species a suite of entropy-based models that enable accurate predictions of fitness outcome and the level of antibiotic sensitivity in a species and stress-type independent manner.

systems biology↗

bioRxiv2019

Dynamic pneumococcal genetic adaptations support bacterial growth and inflammation during coinfection with influenza

Streptococcus pneumoniae (pneumococcus) is one of the primary bacterial pathogens that complicates influenza virus infections. These bacterial coinfections increase influenza-associated morbidity and mortality through a number of immunological and viral-mediated mechanisms, but the specific bacterial genes that contribute to post-influenza pathogenicity are not known. Here, we used genome-wide transposon mutagenesis (Tn-Seq) to reveal bacterial genes that confer improved fitness in influenza-infected hosts. The majority of the 32 identified genes are involved in bacterial metabolism, including nucleotide biosynthesis, amino acid biosynthesis, protein translation, and membrane transport. We generated single-gene deletion (SGD) mutants of five identified genes: SPD1414, SPD2047 (cbiO1), SPD0058 (purD), SPD1098, and SPD0822 (proB), to investigate their effect on in vivo fitness, disease severity, and host immune responses. Growth of SGD mutants was slightly attenuated in vitro and in vivo, but each still grew to high titers in the lungs of mock- and influenza-infected hosts. Despite high bacterial loads, mortality was significantly reduced or delayed with all SGD mutants. Time-dependent reductions in pulmonary neutrophils, inflammatory macrophages, and select proinflammatory cytokines and chemokines were also observed. Immunohistochemical staining further revealed that neutrophil phenotype and distribution was altered in the lungs of influenza-SGD coinfected animals. These studies demonstrate a critical role for specific bacterial genes and for bacterial metabolism in driving virulence and modulating immune function during influenza-associated bacterial pneumonia.

microbiology↗

bioRxiv2018

The landscape of intrinsic and evolved fluoroquinolone resistance in Acinetobacter baumannii includes suppression of drug-induced prophage replication

The emergence of fluoroquinolone resistance in nosocomial pathogens has restricted the clinical efficacy of this antibiotic class. In Acinetobacter baumannii, the majority of clinical isolates now show high-level resistance due to mutations in gyrA (DNA gyrase) and parC (Topo IV). To investigate the molecular basis for fluoroquinolone resistance, an exhaustive mutation analysis was performed in both drug sensitive and resistant strains to identify loci that alter the sensitivity of the organism to ciprofloxacin. To this end, parallel fitness tests of over 60,000 unique insertion mutations were performed in strains with various alleles in genes encoding the drug targets. The spectrum of mutations that altered drug sensitivity was found to be similar in the drug sensitive and double mutant gyrAparC background having resistance alleles in both genes. In contrast, introduction of a single gyrA resistance allele, resulting in preferential poisoning of Topo IV by ciprofloxacin, led to extreme alterations in the insertion mutation fitness landscape. The distinguishing feature of preferential Topo IV poisoning was induction of DNA synthesis in the region of two endogenous prophages, which appeared to occur in situ. Induction of the selective DNA synthesis in the gyrA background was also linked to enhanced activation of SOS response and heightened transcription of prophage genes relative to that observed in either the WT or gyrAparC double mutants. Therefore, the accumulation of mutations that result in the stepwise evolution of high ciprofloxacin resistance is tightly connected to suppression of hyperactivation of the SOS response and endogenous prophage DNA synthesis.\n\nImportanceFluoroquinolones have been extremely successful antibiotics. Their clinical efficacy derives from the ability to target multiple bacterial enzymes critical to DNA replication, the topoisomerases DNA gyrase and Topo IV. Unfortunately, mutations lowering drug affinity for both enzymes are now widespread, rendering these drugs ineffective for many pathogens. To undermine this form of resistance, we sought to understand how bacteria with target alterations differentially cope with fluoroquinolone exposures. We studied this problem in the nosocomial pathogen A. baumannii, which causes resistant, life-threating infections. Employing genome-wide approaches, we uncovered numerous pathways that could be exploited to lower fluoroquinolone resistance independently of target alteration. Remarkably, fluoroquinolone targeting of Topo IV in specific mutants caused dramatic prophage hyperinduction, a response that was muted in strains with DNA gyrase as the primary target. This work demonstrates that resistance evolution via target modification can profoundly modulate the antibiotic stress response, revealing potential resistance-associated liabilities.

microbiology↗

bioRxiv2018

droplet-Tn-Seq combines microfluidics with Tn-Seq identifying complex single-cell phenotypes

While Tn-Seq is a powerful tool to determine genome-wide bacterial fitness in high-throughput, culturing transposon-mutant libraries in pools can mask community or other complex single-cell phenotypes. droplet-Tn-seq solves that problem by microfluidics facilitated encapsulation of individual transposon mutants into liquid-in-oil droplets, thereby enabling isolated growth, free from the influence of the population. Importantly, all advantages of Tn-Seq are conserved, while reducing costs and greatly extending its applicability.

microbiology↗

bioRxiv2018

Forecasting bacterial survival-success and adaptive evolution through multi-omics stress response-mapping, network analyses and machine learning

Whether a bacterial pathogen establishes an infection and/or evolves antibiotic resistance depends on successful survival while experiencing stress from for instance the host immune system and/or antibiotics. Predictions on bacterial survival and adaptive outcomes could thus have great prognostic value. However, it is unknown what information is required to enable such predictions. By developing a novel network-based analysis method, a bacterium's phenotypic and transcriptional response can be objectively quantified in temporal 3D-feature space. The resulting trajectories can be interpreted as a degree of coordination, where a focused and coordinated response predicts bacterial survival-success, and a random uncoordinated response predicts survival-failure. These predictions extend to both antibiotic resistance and in vivo infection conditions and are applicable to both Gram-positive and Gram-negative bacteria. Moreover, through experimental evolution we show that the degree of coordination is an adaptive outcome - an uncoordinated response evolves into a coordinated response when a bacterium adapts to its environment. Most surprisingly, it turns out that phenotypic and transcriptional response data, network features and genome plasticity data can be used to train a machine learning model that is able to predict which genes in the genome will adapt under nutrient or antibiotic selection. Importantly, this suggests that deterministic factors help drive adaptation and that evolution is, at least partially, predictable. This work demonstrates that with the right information predictions on bacterial short-term survival and long-term adaptive outcomes are feasible, which underscores that personalized infectious disease diagnostics and treatments are possible, and should be developed.

microbiology↗

bioRxiv2018

The Transcriptional landscape of Streptococcus pneumoniae reveals a complex operon architecture and abundant riboregulation critical for growth and virulence

Efficient and highly organized transcription initiation and termination is fundamental to an organisms ability to survive, proliferate, and quickly respond to its environment. Over the last decade, our simplistic outlook of bacterial transcriptional regulation and architecture has evolved to include stimulus-responsive regulation by untranslated RNA and the formation of alternate transcriptional units. In this study, we map the transcriptional landscape of the bacterial pathogen Streptococcus pneumoniae by applying a combination of high-throughput RNA-sequencing techniques. Our study reveals a complex transcriptome wherein environment-respondent alternate transcriptional units are observed within operons stemming from internal transcription start sites (TSS) and transcription terminators (TTS) suggesting that more fine-tuning of regulation occurs than previously thought. Additionally, we identify many putative cis-regulatory RNA elements and riboswitches within 5-untranslated regions (5-UTR) of genes. By integrating TSSs and TTSs with independently collected RNA-Seq datasets from a variety of conditions, we establish the response of these regulators to changes in growth conditions and validate several of them. Furthermore, to determine the importance of ribo-regulation by 5-UTR elements for in vivo virulence, we show that the pyrR regulatory element is essential for survival, successful colonization and infection in mice suggesting that such RNA elements are potential drug targets. Importantly, we show that our approach of combining high-throughput sequencing with in vivo experiments can reconstruct a global understanding of regulation, but also pave the way for discovery of compounds that target (ribo-) regulators to mitigate virulence and antibiotic resistance.

microbiology↗