Characterization and genome-informatic analysis of a novel lytic mendocina phage vB_PmeS_STP12 suitable for phage therapy pseudomonas or biocontrol.

BACKGROUND: A novel lytic bacteriophage (phage) was isolated with Pseudomonas mendocina strain STP12 (P. mendocina) from the untreated site of Sewage Treatment Plant of Lovely Professional University, India. P. mendocina is a Gram-negative, rod-shaped, aerobic bacterium belonging to the family Pseudomonadaceae and has been reported in fifteen (15) cases of economically important diseases worldwide. METHODS AND RESULTS: Here, a novel phage specifically infecting and killing P. mendocina strain STP12 was isolated from sewage sample using enrichment, spot test and double agar overlay (DAOL) method and was designated as vB_PmeS_STP12. The phage vB-PmeS-STP12 was viable at wide range of pH and temperature ranging from 4 to10 and - 20 to 70 °C respectively. Host range and efficiency of plating (EOP) analysis indicated that phage vB-PmeS-STP12 was capable of infecting and killing P. mendocina strain STP6 with EOP of 0.34. Phage vB_PmeS_STP12 was found to have a significant bacterial reduction (p < 0.005) at all the doses administered, particularly at optimal MOI of 1 PFU/CFU, compared to the control. Morphological analysis using high resolution transmission electron microscopy (HR-TEM) revealed an icosahedral capsid of ~ 55 nm in diameter on average with a short, non-contractile tail. The genome of vB_PmeS_STP12 is a linear, dsDNA containing 36,212 bp in size with a GC content of 58.87% harbouring 46 open reading frames (ORFs). The 46 predicted ORFs encode proteins with functional information categorized as lysis, replication, packaging, regulation, assembly, infection, immune, and hypothetical. However, the genome of vB_PmeS_STP12 appeared to be devoid of tRNAs, integrase gene, toxins genes, virulence factors, antimicrobial resistance genes (ARGs) and CRISPR arrays. The blast analysis with phylogeny revealed that vB_PmeS_STP12 is genetically similar to Pseudomonas phage PMBT14, Pseudomonas phage Almagne and Serratia phage Serbin with a highest identity of 74.00%, 74.93% and 59.48% respectively. CONCLUSIONS: Taken together, characterization, morphological analysis and genome-informatics indicated that vB_PmeS_STP12 is podovirus morphotype belonging to the class Caudoviticetes, family Zobellviridae which appeared to be devoid of integrase gene, ARGs, CRISPR arrays, virulence factors and toxins genes, exhibiting stability and infectivity at wide range of pH (4 to10) and temperature (-20 to 70 °C), thereby making vB_PmeS_STP12 suitable for phage therapy or biocontrol. Based on the bibliometric analysis and data availability with respect to sequences deposited in GenBank, this is the first report of a phage infecting Pseudomonas mendocina.

Bacteriophage genome engineering for phage therapy to combat bacterial antimicrobial resistance as an alternative to antibiotics.

Bacteriophages (phages) are viruses that mainly infect bacteria and are ubiquitously distributed in nature, especially to their host. Phage engineering involves nucleic acids manipulation of phage genome for antimicrobial activity directed against pathogens through the applications of molecular biology techniques such as synthetic biology methods, homologous recombination, CRISPY-BRED and CRISPY-BRIP recombineering, rebooting phage-based engineering, and targeted nucleases including CRISPR/Cas9, zinc-finger nucleases (ZFNs) and transcription activator-like effector nucleases (TALENs). Management of bacteria is widely achieved using antibiotics whose mechanism of action has been shown to target both the genetic dogma and the metabolism of pathogens. However, the overuse of antibiotics has caused the emergence of multidrug-resistant (MDR) bacteria which account for nearly 5 million deaths as of 2019 thereby posing threats to the public health sector, particularly by 2050. Lytic phages have drawn attention as a strong alternative to antibiotics owing to the promising efficacy and safety of phage therapy in various models in vivo and human studies. Therefore, harnessing phage genome engineering methods, particularly CRISPR/Cas9 to overcome the limitations such as phage narrow host range, phage resistance or any potential eukaryotic immune response for phage-based enzymes/proteins therapy may designate phage therapy as a strong alternative to antibiotics for combatting bacterial antimicrobial resistance (AMR). Here, the current trends and progress in phage genome engineering techniques and phage therapy are reviewed.

Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset.

COVID-19 or 2019-nCoV is no longer pandemic but rather endemic, with more than 651,247 people around world having lost their lives after contracting the disease. Currently, there is no specific treatment or cure for COVID-19, and thus living with the disease and its symptoms is inevitable. This reality has placed a massive burden on limited healthcare systems worldwide especially in the developing nations. Although neither an effective, clinically proven antiviral agents' strategy nor an approved vaccine exist to eradicate the COVID-19 pandemic, there are alternatives that may reduce the huge burden on not only limited healthcare systems but also the economic sector; the most promising include harnessing non-clinical techniques such as machine learning, data mining, deep learning and other artificial intelligence. These alternatives would facilitate diagnosis and prognosis for 2019-nCoV pandemic patients. Supervised machine learning models for COVID-19 infection were developed in this work with learning algorithms which include logistic regression, decision tree, support vector machine, naive Bayes, and artificial neutral network using epidemiology labeled dataset for positive and negative COVID-19 cases of Mexico. The correlation coefficient analysis between various dependent and independent features was carried out to determine a strength relationship between each dependent feature and independent feature of the dataset prior to developing the models. The 80% of the training dataset were used for training the models while the remaining 20% were used for testing the models. The result of the performance evaluation of the models showed that decision tree model has the highest accuracy of 94.99% while the Support Vector Machine Model has the highest sensitivity of 93.34% and Naïve Bayes Model has the highest specificity of 94.30%.

Predictive Data Mining Models for Novel Coronavirus (COVID-19) Infected Patients' Recovery.

Novel coronavirus (COVID-19 or 2019-nCoV) pandemic has neither clinically proven vaccine nor drugs; however, its patients are recovering with the aid of antibiotic medications, anti-viral drugs, and chloroquine as well as vitamin C supplementation. It is now evident that the world needs a speedy and quicker solution to contain and tackle the further spread of COVID-19 across the world with the aid of non-clinical approaches such as data mining approaches, augmented intelligence and other artificial intelligence techniques so as to mitigate the huge burden on the healthcare system while providing the best possible means for patients' diagnosis and prognosis of the 2019-nCoV pandemic effectively. In this study, data mining models were developed for the prediction of COVID-19 infected patients' recovery using epidemiological dataset of COVID-19 patients of South Korea. The decision tree, support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor algorithms were applied directly on the dataset using python programming language to develop the models. The model predicted a minimum and maximum number of days for COVID-19 patients to recover from the virus, the age group of patients who are of high risk not to recover from the COVID-19 pandemic, those who are likely to recover and those who might be likely to recover quickly from COVID-19 pandemic. The results of the present study have shown that the model developed with decision tree data mining algorithm is more efficient to predict the possibility of recovery of the infected patients from COVID-19 pandemic with the overall accuracy of 99.85% which stands to be the best model developed among the models developed with other algorithms including support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor.

Predictive Supervised Machine Learning Models for Diabetes Mellitus.

Diabetes mellitus (DM) is one of the deadliest diseases in the world, especially in developed nations. In recent years, it has become rampant in the developing nations such as Nigeria, posing more threats to individuals in the latter than those in the former. More than 415 million people were reported to suffer from DM worldwide as of 2015, with type 2 of the disease accounting for approximately 90% of the cases. The number of people with DM is expected to rise to 592 million by the year 2035. Therefore, DM is one of the growing public health concerns in Nigeria. In this study, the diagnostic dataset of DM type 2 was collected from the Murtala Mohammed Specialist Hospital, Kano, and used to develop predictive supervised machine learning models based on logistic regression, support vector machine, K-nearest neighbor, random forest, naive Bayes and gradient booting algorithms. The random forest predictive learning-based model appeared to be one of the best developed models with 88.76% in terms of accuracy; however, in terms of receiver operating characteristic curve, random forest and gradient booting predictive learning-based models were found to be the best predictive learning models with 86.28% predictive ability, respectively.