Oral immunization with a novel attenuated Salmonella Gallinarum encoding infectious bronchitis virus spike protein induces protective immune responses against fowl typhoid and infectious bronchitis in chickens

Abstract Fowl typhoid (FT), a septicemic disease caused by Salmonella Gallinarum (SG), and infectious bronchitis (IB) are two economically important avian diseases that affect poultry industry worldwide. Herein, we exploited a live attenuated SG mutant, JOL967, to deliver spike (S) protein 1 of IB virus (V) to elicit protective immunity against both FT and IB in chickens. The codon optimized S1 nucleotide sequence was cloned in-frame into a prokaryotic constitutive expression vector, pJHL65. Subsequently, empty pJHL65 or recombinant pJHL65-S1 plasmid was electroporated into JOL967 and the resultant clones were designated as JOL2068 and JOL2077, respectively. Our results demonstrated that the chickens vaccinated once orally with JOL2077 elicited significantly (p < 0.05) higher IBV-specific humoral and cell-mediated immunity compared to JOL2068 and PBS control groups. Consequently, on challenge with the virulent IBV strain at 28th day post-vaccination, JOL2077 vaccinated birds displayed significantly (p < 0.05) lower inflammatory lesions in virus-targeted tissues compared to control groups. Furthermore, 33.3% (2 of 6) of birds vaccinated with JOL2077 vaccine had shown virus recovery from tracheal tissues compared to 100% (6 of 6) recovery obtained in both the control groups. Against wild-type SG lethal challenge, both JOL2077 and JOL2068 vaccinated groups exhibited only 10% mortality compared to 80% mortality observed in PBS control group. In conclusion, we show that JOL2077 can induce efficient IBV- and carrier-specific protective immunity and can act as a bivalent vaccine against FT and IB. Further studies are warranted to investigate the potential of JOL2077 vaccine in broiler and young layer birds

Potent O-antigen-deficient (rough) mutants of Salmonella Typhimurium secreting Lawsonia intracellularis antigens enhance immunogenicity and provide single-immunization protection against proliferative enteropathy and salmonellosis in a murine model

Abstract The obligate intracellular pathogen Lawsonia intracellularis (LI), the etiological agent of proliferative enteropathy (PE), poses a substantial economic loss in the swine industry worldwide. In this study, we genetically engineered an O-antigen-deficient (rough) Salmonella strain secreting four selected immunogenic LI antigens, namely OptA, OptB, LfliC, and Lhly. The genes encoding these antigens were individually inserted in the expression vector plasmid pJHL65, and the resultant plasmids were transformed into the ∆asd ∆lon ∆cpxR ∆rfaL Salmonella Typhimurium (ST) strain JOL1800. The individual expression of the selected LI antigens in JOL1800 was validated by an immunoblotting assay. We observed significant (P < 0.05) induction of systemic IgG and mucosal IgA responses against each LI antigen or Salmonella outer membrane protein in mice immunized once orally with a mixture of four JOL1800-derived strains. Further, mRNA of IL-4 and IFN-γ were highly upregulated in splenic T cells re-stimulated in vitro with individual purified antigens. Subsequently, immunized mice showed significant protection against challenge with 106.9 TCID50 LI or 2 × 109 CFU of a virulent ST strain. At day 8 post-challenge, no mice in the immunized groups showed the presence of LI-specific genomic DNA (gDNA) in stool samples, while 50% of non-immunized mice were positive for LI-specific gDNA. Further, all the immunized mice survived the virulent ST challenge, compared to a 20% mortality rate observed in the control mice. Collectively, the constructed rough ST-based LI vaccine candidate efficiently elicited LI and ST-specific humoral and cell-mediated immunity and conferred proper dual protection against PE and salmonellosis

Extreme Learning Machines [Trends & Controversies]

This special issue includes eight original works that detail the further developments of ELMs in theories, applications, and hardware implementation. In "Representational Learning with ELMs for Big Data," Liyanaarachchi Lekamalage Chamara Kasun, Hongming Zhou, Guang-Bin Huang, and Chi Man Vong propose using the ELM as an auto-encoder for learning feature representations using singular values. In "A Secure and Practical Mechanism for Outsourcing ELMs in Cloud Computing," Jiarun Lin, Jianping Yin, Zhiping Cai, Qiang Liu, Kuan Li, and Victor C.M. Leung propose a method for handling large data applications by outsourcing to the cloud that would dramatically reduce ELM training time. In "ELM-Guided Memetic Computation for Vehicle Routing," Liang Feng, Yew-Soon Ong, and Meng-Hiot Lim consider the ELM as an engine for automating the encapsulation of knowledge memes from past problem-solving experiences. In "ELMVIS: A Nonlinear Visualization Technique Using Random Permutations and ELMs," Anton Akusok, Amaury Lendasse, Rui Nian, and Yoan Miche propose an ELM method for data visualization based on random permutations to map original data and their corresponding visualization points. In "Combining ELMs with Random Projections," Paolo Gastaldo, Rodolfo Zunino, Erik Cambria, and Sergio Decherchi analyze the relationships between ELM feature-mapping schemas and the paradigm of random projections. In "Reduced ELMs for Causal Relation Extraction from Unstructured Text," Xuefeng Yang and Kezhi Mao propose combining ELMs with neuron selection to optimize the neural network architecture and improve the ELM ensemble's computational efficiency. In "A System for Signature Verification Based on Horizontal and Vertical Components in Hand Gestures," Beom-Seok Oh, Jehyoung Jeon, Kar-Ann Toh, Andrew Beng Jin Teoh, and Jaihie Kim propose a novel paradigm for hand signature biometry- for touchless applications without the need for handheld devices. Finally, in "An Adaptive and Iterative Online Sequential ELM-Based Multi-Degree-of-Freedom Gesture Recognition System," Hanchao Yu, Yiqiang Chen, Junfa Liu, and Guang-Bin Huang propose an online sequential ELM-based efficient gesture recognition algorithm for touchless human-machine interaction