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Ms. Julianne Vilela
Lancaster University, Lancaster, UK

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Research Keywords & Expertise

0 Bioinformatics
0 Genetics
0 Virology
0 Vaccine development
0 Molecular Biology and Genetic engineering

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Short Biography

Julianne is currently pursuing her PhD in Biomedical and Life Sciences specialising in Virology at Lancaster University, United Kingdom. Her doctoral work aims at reducing the effect of viral pathogens on poultry development by creating new and improved vaccines. Her previous research focuses on understanding the genomes of plants and animals, as well as elucidating the molecular mechanisms of virus pathogenesis between organisms. Throughout her career as a researcher, she was part of many active R&D projects funded by the Philippine government and private institutions.

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Review
Published: 24 November 2020 in Frontiers in Cellular and Infection Microbiology
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Clustered regularly interspaced short palindromic repeats associated protein nuclease 9 (CRISPR-Cas9) technology offers novel approaches to precisely, cost-effectively, and user-friendly edit genomes for a wide array of applications and across multiple disciplines. This methodology can be leveraged to underpin host-virus interactions, elucidate viral gene functions, and to develop recombinant vaccines. The successful utilization of CRISPR/Cas9 in editing viral genomes has paved the way of developing novel and multiplex viral vectored poultry vaccines. Furthermore, CRISPR/Cas9 can be exploited to rectify major limitations of conventional approaches including reversion to virulent form, recombination with field viruses and transgene, and genome instability. This review provides comprehensive analysis of the potential of CRISPR/Cas9 genome editing technique in understanding avian virus-host interactions and developing novel poultry vaccines. Finally, we discuss the simplest and practical aspects of genome editing approaches in generating multivalent recombinant poultry vaccines that conform simultaneous protection against major avian diseases.

ACS Style

Julianne Vilela; Mohammed A. Rohaim; Muhammad Munir. Application of CRISPR/Cas9 in Understanding Avian Viruses and Developing Poultry Vaccines. Frontiers in Cellular and Infection Microbiology 2020, 10, 1 .

AMA Style

Julianne Vilela, Mohammed A. Rohaim, Muhammad Munir. Application of CRISPR/Cas9 in Understanding Avian Viruses and Developing Poultry Vaccines. Frontiers in Cellular and Infection Microbiology. 2020; 10 ():1.

Chicago/Turabian Style

Julianne Vilela; Mohammed A. Rohaim; Muhammad Munir. 2020. "Application of CRISPR/Cas9 in Understanding Avian Viruses and Developing Poultry Vaccines." Frontiers in Cellular and Infection Microbiology 10, no. : 1.

Journal article
Published: 01 September 2020 in Viruses
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Until vaccines and effective therapeutics become available, the practical solution to transit safely out of the current coronavirus disease 19 (CoVID-19) lockdown may include the implementation of an effective testing, tracing and tracking system. However, this requires a reliable and clinically validated diagnostic platform for the sensitive and specific identification of SARS-CoV-2. Here, we report on the development of a de novo, high-resolution and comparative genomics guided reverse-transcribed loop-mediated isothermal amplification (LAMP) assay. To further enhance the assay performance and to remove any subjectivity associated with operator interpretation of results, we engineered a novel hand-held smart diagnostic device. The robust diagnostic device was further furnished with automated image acquisition and processing algorithms and the collated data was processed through artificial intelligence (AI) pipelines to further reduce the assay run time and the subjectivity of the colorimetric LAMP detection. This advanced AI algorithm-implemented LAMP (ai-LAMP) assay, targeting the RNA-dependent RNA polymerase gene, showed high analytical sensitivity and specificity for SARS-CoV-2. A total of ~200 coronavirus disease (CoVID-19)-suspected NHS patient samples were tested using the platform and it was shown to be reliable, highly specific and significantly more sensitive than the current gold standard qRT-PCR. Therefore, this system could provide an efficient and cost-effective platform to detect SARS-CoV-2 in resource-limited laboratories.

ACS Style

Mohammed Rohaim; Emily Clayton; Irem Sahin; Julianne Vilela; Manar Khalifa; Mohammad Al-Natour; Mahmoud Bayoumi; Aurore Poirier; Manoharanehru Branavan; Mukunthan Tharmakulasingam; Nouman Chaudhry; Ravinder Sodi; Amy Brown; Peter Burkhart; Wendy Hacking; Judy Botham; Joe Boyce; Hayley Wilkinson; Craig Williams; Jayde Whittingham-Dowd; Elisabeth Shaw; Matt Hodges; Lisa Butler; Michelle Bates; Roberto La Ragione; Wamadeva Balachandran; Anil Fernando; Muhammad Munir. Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (ai-LAMP) for Rapid Detection of SARS-CoV-2. Viruses 2020, 12, 972 .

AMA Style

Mohammed Rohaim, Emily Clayton, Irem Sahin, Julianne Vilela, Manar Khalifa, Mohammad Al-Natour, Mahmoud Bayoumi, Aurore Poirier, Manoharanehru Branavan, Mukunthan Tharmakulasingam, Nouman Chaudhry, Ravinder Sodi, Amy Brown, Peter Burkhart, Wendy Hacking, Judy Botham, Joe Boyce, Hayley Wilkinson, Craig Williams, Jayde Whittingham-Dowd, Elisabeth Shaw, Matt Hodges, Lisa Butler, Michelle Bates, Roberto La Ragione, Wamadeva Balachandran, Anil Fernando, Muhammad Munir. Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (ai-LAMP) for Rapid Detection of SARS-CoV-2. Viruses. 2020; 12 (9):972.

Chicago/Turabian Style

Mohammed Rohaim; Emily Clayton; Irem Sahin; Julianne Vilela; Manar Khalifa; Mohammad Al-Natour; Mahmoud Bayoumi; Aurore Poirier; Manoharanehru Branavan; Mukunthan Tharmakulasingam; Nouman Chaudhry; Ravinder Sodi; Amy Brown; Peter Burkhart; Wendy Hacking; Judy Botham; Joe Boyce; Hayley Wilkinson; Craig Williams; Jayde Whittingham-Dowd; Elisabeth Shaw; Matt Hodges; Lisa Butler; Michelle Bates; Roberto La Ragione; Wamadeva Balachandran; Anil Fernando; Muhammad Munir. 2020. "Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (ai-LAMP) for Rapid Detection of SARS-CoV-2." Viruses 12, no. 9: 972.

Other
Published: 10 July 2020
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Until vaccines and effective therapeutics become available, the practical way to transit safely out of the current lockdown may include the implementation of an effective testing, tracing and tracking system. However, this requires a reliable and clinically validated diagnostic platform for the sensitive and specific identification of SARS-CoV-2. Here, we report on the development of a de novo, high-resolution and comparative genomics guided reverse-transcribed loop-mediated isothermal amplification (LAMP) assay. To further enhance the assay performance and to remove any subjectivity associated with operator interpretation of result, we engineered a novel hand-held smart diagnostic device. The robust diagnostic device was further furnished with automated image acquisition and processing algorithms, and the collated data was processed through artificial intelligence (AI) pipelines to further reduce the assay run time and the subjectivity of the colorimetric LAMP detection. This advanced AI algorithm-implemented LAMP (ai-LAMP) assay, targeting the RNA-dependent RNA polymerase gene, showed high analytical sensitivity and specificity for SARS-CoV-2. A total of ∼200 coronavirus disease (CoVID-19)-suspected patient samples were tested using the platform and it was shown to be reliable, highly specific and significantly more sensitive than the current gold standard qRT-PCR. The system could provide an efficient and cost-effective platform to detect SARS-CoV-2 in resource-limited laboratories.

ACS Style

Mohammed A Rohaim; Emily Clayton; Irem Sahin; Julianne Vilela; Manar E Khalifa; Mohammed Q Al-Natour; Mahmoud Bayoumi; Aurore Poirier; Manoharanehru Branavan; Mukunthan Tharmakulasingam; Nouman S Chaudhry; Ravinder Sodi; Amy Brown; Peter Burkhart; Wendy Hacking; Judy Botham; Joe Boyce; Hayley Wilkinson; Craig Williams; Michelle Bates; Roberto La Ragione; Wamadeva Balachandran; Anil Fernando; Muhammad Munir. Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (ai-LAMP) for Rapid and Reliable Detection of SARS-CoV-2. 2020, 1 .

AMA Style

Mohammed A Rohaim, Emily Clayton, Irem Sahin, Julianne Vilela, Manar E Khalifa, Mohammed Q Al-Natour, Mahmoud Bayoumi, Aurore Poirier, Manoharanehru Branavan, Mukunthan Tharmakulasingam, Nouman S Chaudhry, Ravinder Sodi, Amy Brown, Peter Burkhart, Wendy Hacking, Judy Botham, Joe Boyce, Hayley Wilkinson, Craig Williams, Michelle Bates, Roberto La Ragione, Wamadeva Balachandran, Anil Fernando, Muhammad Munir. Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (ai-LAMP) for Rapid and Reliable Detection of SARS-CoV-2. . 2020; ():1.

Chicago/Turabian Style

Mohammed A Rohaim; Emily Clayton; Irem Sahin; Julianne Vilela; Manar E Khalifa; Mohammed Q Al-Natour; Mahmoud Bayoumi; Aurore Poirier; Manoharanehru Branavan; Mukunthan Tharmakulasingam; Nouman S Chaudhry; Ravinder Sodi; Amy Brown; Peter Burkhart; Wendy Hacking; Judy Botham; Joe Boyce; Hayley Wilkinson; Craig Williams; Michelle Bates; Roberto La Ragione; Wamadeva Balachandran; Anil Fernando; Muhammad Munir. 2020. "Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (ai-LAMP) for Rapid and Reliable Detection of SARS-CoV-2." , no. : 1.

Research article
Published: 04 December 2019 in PLoS ONE
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Machine learning (ML) is poised as a transformational approach uniquely positioned to discover the hidden biological interactions for better prediction and diagnosis of complex diseases. In this work, we integrated ML-based models for feature selection and classification to quantify the risk of individual susceptibility to asthma using single nucleotide polymorphism (SNP). Random forest (RF) and recursive feature elimination (RFE) algorithm were implemented to identify the SNPs with high implication to asthma. K-nearest neighbor (kNN) and support vector machine (SVM) algorithms were trained to classify the identified SNPs whether associated with non-asthmatic or asthmatic samples. Feature selection step showed that RF outperformed RFE and the feature importance score derived from RF was consistently high for a subset of SNPs, indicating the robustness of RF in selecting relevant features associated with asthma. Model comparison showed that the integration of RF-SVM obtained the highest model performance with an accuracy, precision, and sensitivity of 62.5%, 65.3%, and 69%, respectively, when compared to the baseline, RF-kNN, and an external MeanDiff-kNN models. Furthermore, results show that the occurrence of asthma can be predicted with an Area under the Curve (AUC) of 0.62 and 0.64 for RF-SVM and RF-kNN models, respectively. This study demonstrates the integration of ML models to augment traditional methods in predicting genetic predisposition to multifactorial diseases such as asthma.

ACS Style

Joverlyn Gaudillo; Jae Joseph Russell Rodriguez; Allen Nazareno; Lei Rigi Baltazar; Julianne Vilela; Rommel Bulalacao; Mario Domingo; Jason Albia. Machine learning approach to single nucleotide polymorphism-based asthma prediction. PLoS ONE 2019, 14, e0225574 .

AMA Style

Joverlyn Gaudillo, Jae Joseph Russell Rodriguez, Allen Nazareno, Lei Rigi Baltazar, Julianne Vilela, Rommel Bulalacao, Mario Domingo, Jason Albia. Machine learning approach to single nucleotide polymorphism-based asthma prediction. PLoS ONE. 2019; 14 (12):e0225574.

Chicago/Turabian Style

Joverlyn Gaudillo; Jae Joseph Russell Rodriguez; Allen Nazareno; Lei Rigi Baltazar; Julianne Vilela; Rommel Bulalacao; Mario Domingo; Jason Albia. 2019. "Machine learning approach to single nucleotide polymorphism-based asthma prediction." PLoS ONE 14, no. 12: e0225574.

Conference paper
Published: 06 September 2019 in PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON BIOSCIENCES AND MEDICAL ENGINEERING (ICBME2019): Towards innovative research and cross-disciplinary collaborations
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Lipases (triacylglycerol acylhydrolases, E.C. 3.1.1.3) are enzymes vastly used in industrial applications. The current study aims to screen lipase-producing yeasts isolated from a tree canopy fern from the Makiling Forest Reserve (MFR), Philippines and to optimize conditions that can maximize the mass production and activity of the enzyme. From the 144 isolates, B1-7 showed the highest lipase activity in both solid (EIA 7.6) and liquid selection media (0.082 U/mL-min). Molecular identification using Internally Transcribed Spacer (ITS) primers and microscopic observation revealed that the isolate was Cryptococcus flavescens, a generally regarded as safe (GRAS) microorganism. Response Surface Method (Box-Behnken Design) showed that the maximum lipase activity (0.66 U/mL-min) and a biomass of 4 g/L were achieved at 5.0 Carbon:Nitrogen ratio, pH 6.0 and 0.5% inducer (Tween 20). Also, C:N-% inducer interaction and inducer concentration significantly affected lipase activity. After a 72h fed-batch fermentation experiment, lipase activity was ten-fold lower than the optimization results and a negative correlation (r=-0.405) between lipase activity and biomass suggested the non-dependence of lipase activity to biomass availability. Lastly, total sugar concentration remained constant implying that the organism used the degradative products of lipase as its carbon source. In conclusion, C. flavescens from MFR can be utilized for mass lipase production, but it was recommended that other parameters be examined and optimized.

ACS Style

Francisco Elegado; Charisse Leanne Legaspi; Joseph Martin Paet; Florabelle Querubin; Jarel Elgin Tolentino; Julianne Vilela; Angelito Paguio; Johnry Maloles; Jocelyn Zarate. Screening, identification and optimization of extracellular lipase production of yeast (Cryptococcus flavescens) isolated from a tree canopy fern in the Mount Makiling Forest Reserve, Philippines. PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON BIOSCIENCES AND MEDICAL ENGINEERING (ICBME2019): Towards innovative research and cross-disciplinary collaborations 2019, 2155, 020029 .

AMA Style

Francisco Elegado, Charisse Leanne Legaspi, Joseph Martin Paet, Florabelle Querubin, Jarel Elgin Tolentino, Julianne Vilela, Angelito Paguio, Johnry Maloles, Jocelyn Zarate. Screening, identification and optimization of extracellular lipase production of yeast (Cryptococcus flavescens) isolated from a tree canopy fern in the Mount Makiling Forest Reserve, Philippines. PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON BIOSCIENCES AND MEDICAL ENGINEERING (ICBME2019): Towards innovative research and cross-disciplinary collaborations. 2019; 2155 (1):020029.

Chicago/Turabian Style

Francisco Elegado; Charisse Leanne Legaspi; Joseph Martin Paet; Florabelle Querubin; Jarel Elgin Tolentino; Julianne Vilela; Angelito Paguio; Johnry Maloles; Jocelyn Zarate. 2019. "Screening, identification and optimization of extracellular lipase production of yeast (Cryptococcus flavescens) isolated from a tree canopy fern in the Mount Makiling Forest Reserve, Philippines." PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON BIOSCIENCES AND MEDICAL ENGINEERING (ICBME2019): Towards innovative research and cross-disciplinary collaborations 2155, no. 1: 020029.

Journal article
Published: 01 September 2021
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ACS Style

Julianne Vilela. SNP discovery and genetic clustering of Philippine 'Carabao' mango (Mangifera indica L. cv. 'Carabao') using genotype-by-sequencing (DArTseq). 2021, 1 .

AMA Style

Julianne Vilela. SNP discovery and genetic clustering of Philippine 'Carabao' mango (Mangifera indica L. cv. 'Carabao') using genotype-by-sequencing (DArTseq). . 2021; ():1.

Chicago/Turabian Style

Julianne Vilela. 2021. "SNP discovery and genetic clustering of Philippine 'Carabao' mango (Mangifera indica L. cv. 'Carabao') using genotype-by-sequencing (DArTseq)." , no. : 1.