Investigation of molecular mechanisms of biofilm dispersal and attempts to create an inducible gene expression system in 'Campylobacter'

Campylobacter jejuni is the leading cause of bacterial foodborne diseases worldwide. C. jejuni and Campylobacter coli are the most predominant species of the genus that are responsible for gastrointestinal diseases in humans (Epps et al., 2013). C. jejuni 11168H strain can form biofilms, whose cells are more resistant to antimicrobials and disinfectants, perhaps due to the fact that the bacterial cells are secured in an extra cellular polymeric matrix consisting of eDNA (extracellular DNA), proteins, and polysaccharides (Brown et al., 2015b). Bacterial dispersion takes place following the biofilm maturation step. The dispersed cells demonstrate greater colonizing properties than their sessile counterparts (Guilhen et al., 2017). Therefore, studying the factors governing the dispersal process is vital as it would give a better understanding of the pathogen and may lead to the development of novel antimicrobial treatments, which in turn may help prevent future cases of campylobacteriosis. In this study, dispersal of C. jejuni 11168H biofilms was observed. The role of the cj0979 gene in biofilm formation was investigated and it was concluded that this gene alone was not responsible for the regulation of C. jejuni 11168H biofilm dispersal. Purified Cj0979 exhibited DNase activity and could degrade lambda DNA. In addition, purified Cj0979 reduced C. jejuni 11168H biofilm development, when added to the initial stages of biofilm formation. Importantly, these findings provide insights into the dispersal process of C. jejuni biofilms, which has not been reported elsewhere. A development and validation of an arabinoseinducible gene expression system was undertaken in thisresearch to aid in the study of essential genes and nuclease encoding genes involved in biofilm dispersal. As a primary step, arabinose transporter genes araE and lacYA177C were introduced into C. jejuni since the transporters required for arabinose uptake are not present within these bacteria. Arabinose is essential for promoter function. The integration constructs carrying the arabinose transporter genes, were verified by genome sequencing, confirming that they were free of deletions and pointmutations. The gene cassettes carrying the araE and modified lacY genes were successfully integrated into the C. jejuni 11168H chromosome. Despite successful introduction of araE and modified lacY genes into C. jejuni 11168H induction of gene expression from PBAD promoter could not be achieved. Possible reasons for this finding are discussed

Expression of Escherichia coli araE and modified lacY genes in Campylobacter jejuni is not sufficient for arabinose transport

Introduction. Unlike Escherichia coli, Campylobacter jejuni is unable to import a range of sugars, including arabinose, which makes common expression vectors, such as pBAD33, non-functional in these bacteria. Aim. The aim of this study was to investigate whether the E. coli transporters AraE and modified LacY (LacYA177C) would enable C. jejuni to uptake arabinose. Methodology and Results. The respective genes of E. coli were constitutively expressed in C. jejuni strain 11168H after integration into the chromosome via homologous recombination. Vectors carrying these genes also contained a reporter gene, gfp, under the control of the arabinose-inducible promoter, pBAD. These constructs were verified in E. coli by demonstrating the induction of gfp in the presence of arabinose. Integration of the genes into one of the rRNA gene clusters was verified by PCR and genome sequencing. The latter also confirmed that the inserted gene clusters contained no mutations. Expression of the gfp gene in the presence of arabinose inducer was monitored using fluorescence microscopy of colonies and fluorimetry using both whole cells and lysates. Conclusion. The results demonstrated the inability of C. jejuni to use arabinose transporters, which are fully functional in E. coli, suggesting a remarkable difference in the physiology of these bacteria

CmeABC multidrug efflux pump contributes to antibiotic resistance and promotes 'Campylobacter jejuni' survival and multiplication in 'Acanthamoeba polyphaga'

Campylobacter jejuni is a foodborne pathogen recognized as the leading cause of human bacterial gastroenteritis. The wide use of antibiotics in medicine and in animal husbandry has led to an increased incidence of antibiotic resistance in Campylobacter In addition to a role in multidrug resistance, the Campylobacter CmeABC RND-type efflux pump, which is associated with multidrug resistance (MDR), may also be involved in virulence. As a vehicle of pathogenic microorganisms, the protozoan Acanthamoeba is a good model for the investigation of bacterial survival in the environment and molecular mechanisms of pathogenicity. The interaction between C. jejuni 81-176 and A. polyphaga was investigated in this study by using a modified gentamicin protection assay. In addition, a possible role for the CmeABC MDR pump in this interaction was explored. Here we report that this MDR pump is beneficial for the intracellular survival and multiplication of C. jejuni in A. polyphaga, but is dispensable for biofilm formation and motility.Importance The endosymbiotic relationship between amoebae and microbial pathogens may contribute to persistence and spreading of the latter in the environment, which has significant implications to human health. In this study we found that Campylobacter jejuni was able to survive and multiply inside Acanthamoeba. polyphaga Since these microorganisms can co-exist in the same environment (e.g. in poultry farms), the latter may increase the risk of infection with Campylobacter Our data suggests that, in addition to its role in antibiotic resistance, the CmeABC MDR efflux pump also plays a role in bacterial survival within amoebae. Furthermore, we demonstrated a synergistic effect of the CmeABC MDR efflux pump and TetO on bacterial resistance to tetracycline. Due to its role both in antibiotic resistance and virulence of C. jejuni, the CmeABC MDR efflux pump could be considered as a good target for the development of antibacterial drugs against this pathogen

Expression of E. coli araE and mutated lacY genes in Campylobacter jejuni is not sufficient for arabinose transport.

Aims The aim of this study was to investigate possible effects of araE and mutated lacY genes on arabinose uptake ability of C. jejuni. Methods and results This study was conducted in an attempt to develop an arabinose-inducible gene expression system for campylobacter bacteria, which are naturally refractory to transport of this sugar. The araE and lacYA177C genes known to be involved in arabinose transport in E. coli, were constitutively expressed in Campylobacter jejuni strain 11168H after integration into the chromosome via homologous recombination (1). Vectors carrying these genes also contained a reporter gene gfp under the control of arabinose-inducible promoter pBAD. These constructs were verified in E. coli by demonstrating induction of gfp in the presence of arabinose. Integration of the genes into one of the rRNA gene clusters was verified by PCR and genome sequencing. The latter also confirmed that the inserted gene clusters contained no mutations. Expression of gfp gene in the presence of arabinose inducer was monitored using fluorescence microscopy of colonies and fluorimetry using both whole cells and lysates. Low, but statistically significant difference compared to uninduced control was only detected in 11168H/lacYA177C strain. Conclusions The results of this study revealed inability of araE and lacY derivatives of C. jejuni to transport arabinose at the same level as that in E. coli indicating a remarkable difference in the biology of these bacteria Significance of study The current study will help in understanding of C. jejuni requirements for arabinose transport, and will assist in future development of inducible gene expression systems for these bacteria

Mobile-based oral cancer classification for point-of-care screening.

SignificanceOral cancer is among the most common cancers globally, especially in low- and middle-income countries. Early detection is the most effective way to reduce the mortality rate. Deep learning-based cancer image classification models usually need to be hosted on a computing server. However, internet connection is unreliable for screening in low-resource settings.AimTo develop a mobile-based dual-mode image classification method and customized Android application for point-of-care oral cancer detection.ApproachThe dataset used in our study was captured among 5025 patients with our customized dual-modality mobile oral screening devices. We trained an efficient network MobileNet with focal loss and converted the model into TensorFlow Lite format. The finalized lite format model is ∼16.3  MB and ideal for smartphone platform operation. We have developed an Android smartphone application in an easy-to-use format that implements the mobile-based dual-modality image classification approach to distinguish oral potentially malignant and malignant images from normal/benign images.ResultsWe investigated the accuracy and running speed on a cost-effective smartphone computing platform. It takes ∼300  ms to process one image pair with the Moto G5 Android smartphone. We tested the proposed method on a standalone dataset and achieved 81% accuracy for distinguishing normal/benign lesions from clinically suspicious lesions, using a gold standard of clinical impression based on the review of images by oral specialists.ConclusionsOur study demonstrates the effectiveness of a mobile-based approach for oral cancer screening in low-resource settings

Exploring uncertainty measures in convolutional neural network for semantic segmentation of oral cancer images

SignificanceOral cancer is one of the most prevalent cancers, especially in middle- and low-income countries such as India. Automatic segmentation of oral cancer images can improve the diagnostic workflow, which is a significant task in oral cancer image analysis. Despite the remarkable success of deep-learning networks in medical segmentation, they rarely provide uncertainty quantification for their output.AimWe aim to estimate uncertainty in a deep-learning approach to semantic segmentation of oral cancer images and to improve the accuracy and reliability of predictions.ApproachThis work introduced a UNet-based Bayesian deep-learning (BDL) model to segment potentially malignant and malignant lesion areas in the oral cavity. The model can quantify uncertainty in predictions. We also developed an efficient model that increased the inference speed, which is almost six times smaller and two times faster (inference speed) than the original UNet. The dataset in this study was collected using our customized screening platform and was annotated by oral oncology specialists.ResultsThe proposed approach achieved good segmentation performance as well as good uncertainty estimation performance. In the experiments, we observed an improvement in pixel accuracy and mean intersection over union by removing uncertain pixels. This result reflects that the model provided less accurate predictions in uncertain areas that may need more attention and further inspection. The experiments also showed that with some performance compromises, the efficient model reduced computation time and model size, which expands the potential for implementation on portable devices used in resource-limited settings.ConclusionsOur study demonstrates the UNet-based BDL model not only can perform potentially malignant and malignant oral lesion segmentation, but also can provide informative pixel-level uncertainty estimation. With this extra uncertainty information, the accuracy and reliability of the model’s prediction can be improved

Interpretable and Reliable Oral Cancer Classifier with Attention Mechanism and Expert Knowledge Embedding via Attention Map

Convolutional neural networks have demonstrated excellent performance in oral cancer detection and classification. However, the end-to-end learning strategy makes CNNs hard to interpret, and it can be challenging to fully understand the decision-making procedure. Additionally, reliability is also a significant challenge for CNN based approaches. In this study, we proposed a neural network called the attention branch network (ABN), which combines the visual explanation and attention mechanisms to improve the recognition performance and interpret the decision-making simultaneously. We also embedded expert knowledge into the network by having human experts manually edit the attention maps for the attention mechanism. Our experiments have shown that ABN performs better than the original baseline network. By introducing the Squeeze-and-Excitation (SE) blocks to the network, the cross-validation accuracy increased further. Furthermore, we observed that some previously misclassified cases were correctly recognized after updating by manually editing the attention maps. The cross-validation accuracy increased from 0.846 to 0.875 with the ABN (Resnet18 as baseline), 0.877 with SE-ABN, and 0.903 after embedding expert knowledge. The proposed method provides an accurate, interpretable, and reliable oral cancer computer-aided diagnosis system through visual explanation, attention mechanisms, and expert knowledge embedding

Interpretable deep learning approach for oral cancer classification using guided attention inference network.

SignificanceConvolutional neural networks (CNNs) show the potential for automated classification of different cancer lesions. However, their lack of interpretability and explainability makes CNNs less than understandable. Furthermore, CNNs may incorrectly concentrate on other areas surrounding the salient object, rather than the network's attention focusing directly on the object to be recognized, as the network has no incentive to focus solely on the correct subjects to be detected. This inhibits the reliability of CNNs, especially for biomedical applications.AimDevelop a deep learning training approach that could provide understandability to its predictions and directly guide the network to concentrate its attention and accurately delineate cancerous regions of the image.ApproachWe utilized Selvaraju et al.'s gradient-weighted class activation mapping to inject interpretability and explainability into CNNs. We adopted a two-stage training process with data augmentation techniques and Li et al.'s guided attention inference network (GAIN) to train images captured using our customized mobile oral screening devices. The GAIN architecture consists of three streams of network training: classification stream, attention mining stream, and bounding box stream. By adopting the GAIN training architecture, we jointly optimized the classification and segmentation accuracy of our CNN by treating these attention maps as reliable priors to develop attention maps with more complete and accurate segmentation.ResultsThe network's attention map will help us to actively understand what the network is focusing on and looking at during its decision-making process. The results also show that the proposed method could guide the trained neural network to highlight and focus its attention on the correct lesion areas in the images when making a decision, rather than focusing its attention on relevant yet incorrect regions.ConclusionsWe demonstrate the effectiveness of our approach for more interpretable and reliable oral potentially malignant lesion and malignant lesion classification

Field validation of deep learning based Point-of-Care device for early detection of oral malignant and potentially malignant disorders

Early detection of oral cancer in low-resource settings necessitates a Point-of-Care screening tool that empowers Frontline-Health-Workers (FHW). This study was conducted to validate the accuracy of Convolutional-Neural-Network (CNN) enabled m(mobile)-Health device deployed with FHWs for delineation of suspicious oral lesions (malignant/potentially-malignant disorders). The effectiveness of the device was tested in tertiary-care hospitals and low-resource settings in India. The subjects were screened independently, either by FHWs alone or along with specialists. All the subjects were also remotely evaluated by oral cancer specialist/s. The program screened 5025 subjects (Images: 32,128) with 95% (n = 4728) having telediagnosis. Among the 16% (n = 752) assessed by onsite specialists, 20% (n = 102) underwent biopsy. Simple and complex CNN were integrated into the mobile phone and cloud respectively. The onsite specialist diagnosis showed a high sensitivity (94%), when compared to histology, while telediagnosis showed high accuracy in comparison with onsite specialists (sensitivity: 95%; specificity: 84%). FHWs, however, when compared with telediagnosis, identified suspicious lesions with less sensitivity (60%). Phone integrated, CNN (MobileNet) accurately delineated lesions (n = 1416; sensitivity: 82%) and Cloud-based CNN (VGG19) had higher accuracy (sensitivity: 87%) with tele-diagnosis as reference standard. The results of the study suggest that an automated mHealth-enabled, dual-image system is a useful triaging tool and empowers FHWs for oral cancer screening in low-resource settings