A machine learning-based SNP-set analysis approach for identifying disease-associated susceptibility loci.

Identifying disease-associated susceptibility loci is one of the most pressing and crucial challenges in modeling complex diseases. Existing approaches to biomarker discovery are subject to several limitations including underpowered detection, neglect for variant interactions, and restrictive dependence on prior biological knowledge. Addressing these challenges necessitates more ingenious ways of approaching the "missing heritability" problem. This study aims to discover disease-associated susceptibility loci by augmenting previous genome-wide association study (GWAS) using the integration of random forest and cluster analysis. The proposed integrated framework is applied to a hepatitis B virus surface antigen (HBsAg) seroclearance GWAS data. Multiple cluster analyses were performed on (1) single nucleotide polymorphisms (SNPs) considered significant by GWAS and (2) SNPs with the highest feature importance scores obtained using random forest. The resulting SNP-sets from the cluster analyses were subsequently tested for trait-association. Three susceptibility loci possibly associated with HBsAg seroclearance were identified: (1) SNP rs2399971, (2) gene LINC00578, and (3) locus 11p15. SNP rs2399971 is a biomarker reported in the literature to be significantly associated with HBsAg seroclearance in patients who had received antiviral treatment. The latter two loci are linked with diseases influenced by the presence of hepatitis B virus infection. These findings demonstrate the potential of the proposed integrated framework in identifying disease-associated susceptibility loci. With further validation, results herein could aid in better understanding complex disease etiologies and provide inputs for a more advanced disease risk assessment for patients.

Genome sequence for the blue-flowered Andean shrub Iochroma cyaneum reveals extensive discordance across the berry clade of Solanaceae.

The tomato (Solanum lycopersicum L.) family, Solanaceae, is a model clade for a wide range of applied and basic research questions. Currently, reference-quality genomes are available for over 30 species from seven genera, and these include numerous crops as well as wild species [e.g., Jaltomata sinuosa (Miers) Mione and Nicotiana attenuata Torr. ex S. Watson]. Here we present the genome of the showy-flowered Andean shrub Iochroma cyaneum (Lindl.) M. L. Green, a woody lineage from the tomatillo (Physalis philadelphica Lam.) subfamily Physalideae. The assembled size of the genome (2.7 Gb) is more similar in size to pepper (Capsicum annuum L.) (2.6 Gb) than to other sequenced diploid members of the berry clade of Solanaceae [e.g., potato (Solanum tuberosum L.), tomato, and Jaltomata]. Our assembly recovers 92% of the conserved orthologous set, suggesting a nearly complete genome for this species. Most of the genomic content is repetitive (69%), with Gypsy elements alone accounting for 52% of the genome. Despite the large amount of repetitive content, most of the 12 I. cyaneum chromosomes are highly syntenic with tomato. Bayesian concordance analysis provides strong support for the berry clade, including I. cyaneum, but reveals extensive discordance along the backbone, with placement of chili pepper and Jaltomata being highly variable across gene trees. The I. cyaneum genome contributes to a growing wealth of genomic resources in Solanaceae and underscores the need for expanded sampling of diverse berry genomes to dissect major morphological transitions.

Avian Orthoavulavirus Type-1 as Vaccine Vector against Respiratory Viral Pathogens in Animal and Human.

Avian orthoavulaviruses type-1 (AOaV-1) have recently transitioned from animal vaccine vector to a bona fide vaccine delivery vehicle in human. Owing to induction of robust innate and adaptive immune responses in mucus membranes in both birds and mammals, AOaVs offer an attractive vaccine against respiratory pathogens. The unique features of AOaVs include over 50 years of safety profile, stable expression of foreign genes, high infectivity rates in avian and mammalian hosts, broad host spectrum, limited possibility of recombination and lack of pre-existing immunity in humans. Additionally, AOaVs vectors allow the production of economical and high quantities of vaccine antigen in chicken embryonated eggs and several GMP-grade mammalian cell lines. In this review, we describe the biology of AOaVs and define protocols to manipulate AOaVs genomes in effectively designing vaccine vectors. We highlighted the potential and established portfolio of AOaV-based vaccines for multiple respiratory and non-respiratory viruses of veterinary and medical importance. We comment on the limitations of AOaV-based vaccines and propose mitigations strategies. The exploitation of AOaVs vectors is expanding at an exciting pace; thus, we have limited the scope to their use as vaccines against viral pathogens in both animals and humans.

Application of CRISPR/Cas9 in Understanding Avian Viruses and Developing Poultry Vaccines.

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.

Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (AI-LAMP) for Rapid Detection of SARS-CoV-2.

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.

Machine learning approach to single nucleotide polymorphism-based asthma prediction.

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.