Phage display – a tool for detection and prevention against pathogens. A review

In this article are reviewed the promising uses of phage display in the areas such as microbial pathogens detection of and vaccination. Phage display is a molecular technique by which foreign proteins are expressed at the surface of phage particles. Such phages thereby become vehicles for expression that not only carry within them the nucleotide sequence encoding expressed proteins, but have also the capability to replicate. Recent data acquired from genome sequencing and advances in phage biology research have aided the development of phage-derived bacterial detection and treatment strategies.

Increased expression of recombinant chitosanase by co-expression of Hac1p in the yeast Pichia pastoris.

Background: Pichia pastoris is one of the most popular eukaryotic hosts for producing heterologous proteins, while increasing secretion of target proteins is still a top priority for their application in industrial fields. Recently, the research effort to enhance protein production therein has focused on up-regulating the unfolded protein response (UPR). Objective: We evaluated the effects of activated UPR via Hac1p co-expression with the promoter AOX1 (PAOX1) or GAP (PGAP) on expression of recombinant chitosanase (rCBS) in P. pastoris. Method: The DNA sequence encoding the chitosanase was chemically synthesized and cloned into pPICZαA and the resulted pPICZαA/rCBS was transformed into P. pastoris for expressing rCBS. The P. pastoris HAC1i cDNA was chemically synthesized and cloned into pPIC3.5K to give pPIC3.5K/Hac1p. The HAC1i cDNA was cloned into pGAPZB and then inserted with HIS4 gene from pAO815 to construct the vector pGAPZB/Hac1p/HIS4. For co-expression of Hac1p, the two plasmids pPIC3.5K/Hac1p and pGAPZB/Hac1p/HIS4 were transformed into P. pastoris harboring the CBS gene. The rCBS was assessed based on chitosanase activity and analyzed by SDS-PAGE. The enhanced Kar2p was detected with western blotting to evaluate UPR. Results: Hac1p co-expression with PAOX1 enhanced rCBS secretion by 41% at 28°C. Although the level of UPR resulted from Hac1p co-expression with PAOX1 was equivalent to that with PGAP in terms of the quantity of Kar2p (a hallmark of the UPR), substitution of PGAP for PAOX1 further increased rCBS production by 21%. The methanol-utilizing phenotype of P. pastoris did not affect rCBS secretion with co-expression of Hac1p or not. Finally, Hac1p co-expression with PAOX1 or PGAP promoted rCBS secretion from 22 to 30°C and raised the optimum induction temperature. Conclusion: The study indicated that Hac1p co-expression with PAOX1 or PGAP is an effective strategy to trigger UPR of P. pastoris and a feasible means for improving production of rCBS therein.

Molecular Characterization of Novel x-Type HMW Glutenin Subunit 1B × 6.5 in Wheat

A novel high molecular weight glutenin subunit encoded by the Glu-1B locus was identified in the French genotype Bagou, which we named 1B × 6.5. This subunit differed in SDS-PAGE from well-known 1B × 6 and 1B × 7 subunits, which are also encoded at this locus. Subunit 1B × 6.5 has a theoretical molecular weight of 88,322.83 Da, which is more mobile than 1B × 6 subunit, and isoelectric point (pI) of about 8.7, which is lower than that for 1B × 6 subunit. The specific primers were designed to amplify and sequence 2476 bp of the Glu-1B locus from genotype Bagou. A high level of similarity was found between the sequence encoding 1B × 6.5 and other x-type encoding alleles of this locus.

Increasing The Grain Yield And Grain Protein Content of Common Wheat (Triticum Aestivum) By Introducing Missense Mutations In The Q Gene

Grain yield (GY) and grain protein content (GPC) are important traits for wheat breeding and production; however, they are usually negatively correlated. The Q gene is the most important domestication gene in cultivated wheat because it influences many traits, including GY and GPC. Additionally, Qc1 is an overexpressed Q allele containing a missense mutation in the microRNA172-binding site. The common wheat (Triticum aestivum) mutant S-Cp1-1, which carries Qc1, has a very high GPC and some unfavorable characteristics, including dwarfism and compact spikes, which decrease the GY. We previously suggested that missense mutations in the sequences encoding the AP2 domains of Qc1 can be exploited to enhance the agronomic performance of wheat. In this study, we characterized two new Q alleles (Qs1 and Qc1-N8). Compared with the wild-type Q allele, Qs1 contains a missense mutation in the sequence encoding the first AP2 domain, whereas Qc1-N8 has two missense mutations, one in the sequence encoding the second AP2 domain and the other in the microRNA172-binding site. The Qs1 allele did not significantly affect the GPC or other processing quality parameters, but it adversely affected the GY by decreasing the thousand kernel weight and grain number per spike. In contrast, Qc1-N8 positively affected the GPC and GY by increasing the thousand kernel weight and grain number per spike, thereby reversing the unfavorable agronomic characteristics resulting from Qc1. Thus, we generated a novel germplasm relevant for wheat breeding. Furthermore, our findings provide new information useful for enhancing cereal crops via non-transgenic approaches.

Evidence of independent introductions of RHDV2 strains in Poland based on the genome analysis of viral isolates from 2016–2018

The aim of this study was the molecular epidemiology of independently introduced RHDV2 strains in Poland. The nucleotide sequences of RHDV2 diagnosed in domestic rabbits in 2018 in the voivodeships of Swietokrzyskie (strain PIN), Malopolskie (strain LIB) and Mazowieckie (strain WAK), and RHDVa from 2015 (strain F77-3) recognized in wild rabbits in Kujawsko-Pomorskie voivodeship were compared to the genome sequences of the first native RHDV2 strains from 2016–2017. The reference sequences available in public databases, the representative for a classical RHDV (G1-G5 genogroups), RHDVa (G6), non-pathogenic caliciviruses (RCV, GI.3 and GI.4) as well as original and recombinant RHDV2 isolates were included for this analysis. Nucleotide sequence similarity among the most distanced RHDV2 strains isolated in Poland in 2018 was from 92.3% to 98.2% in the genome sequence encoding ORF1, ORF2 and 3’UTR, between 94.8–98.7% in the VP60 gene and between 91.3-98.1% in non-structural proteins (NSP) region. The diversity between three RHDV2 and RHDVa from 2015 was up to 16.3% in the VP60 region. Similarities are shown for the VP60 tree within the RHDV2 group, however, the nucleotide analysis of NSP region revealed the differences between older and new native RHDV2 strains. The Polish RHDV2 isolates from 2016-2017 clustered together with RHDV G1/RHDV2 recombinants, first identified in the Iberian Peninsula in 2012, while all strains from 2018 are close to the original RHDV2. The F77-3 strain clustered to well supported RHDVa (G6) genetic group, together with other Polish and European RHDVa isolates. Based on the results of phylogenetic characterization of RHDV2 strains detected in Poland between 2016–2018 and the chronology of their emergence it can be concluded that RHDV2 strains of 2018 and RHDV2 strains of 2016–2017 were introduced independently thus confirming their different origin and simultaneous pathway of spreading.

Haemophilus influenzae Meningitis Direct Diagnosis by Metagenomic Next-Generation Sequencing: A Case Report

Current routine real-time PCR methods used for the point-of-care diagnosis of infectious meningitis do not allow for one-shot genotyping of the pathogen, as in the case of deadly Haemophilus influenzae meningitis. Real-time PCR diagnosed H. influenzae meningitis in a 22-year-old male patient, during his hospitalisation following a more than six-metre fall. Using an Oxford Nanopore Technologies real-time sequencing run in parallel to real-time PCR, we detected the H. influenzae genome directly from the cerebrospinal fluid sample in six hours. Furthermore, BLAST analysis of the sequence encoding for a partial DUF417 domain-containing protein diagnosed a non-b serotype, non-typeable H.influenzae belonging to lineage H. influenzae 22.1-21. The Oxford Nanopore metagenomic next-generation sequencing approach could be considered for the point-of-care diagnosis of infectious meningitis, by direct identification of pathogenic genomes and their genotypes/serotypes.

Performance improvement for a 2D convolutional neural network by using SSC encoding on protein–protein interaction tasks

Background The interactions of proteins are determined by their sequences and affect the regulation of the cell cycle, signal transduction and metabolism, which is of extraordinary significance to modern proteomics research. Despite advances in experimental technology, it is still expensive, laborious, and time-consuming to determine protein–protein interactions (PPIs), and there is a strong demand for effective bioinformatics approaches to identify potential PPIs. Considering the large amount of PPI data, a high-performance processor can be utilized to enhance the capability of the deep learning method and directly predict protein sequences. Results We propose the Sequence-Statistics-Content protein sequence encoding format (SSC) based on information extraction from the original sequence for further performance improvement of the convolutional neural network. The original protein sequences are encoded in the three-channel format by introducing statistical information (the second channel) and bigram encoding information (the third channel), which can increase the unique sequence features to enhance the performance of the deep learning model. On predicting protein–protein interaction tasks, the results using the 2D convolutional neural network (2D CNN) with the SSC encoding method are better than those of the 1D CNN with one hot encoding. The independent validation of new interactions from the HIPPIE database (version 2.1 published on July 18, 2017) and the validation of directly predicted results by applying a molecular docking tool indicate the effectiveness of the proposed protein encoding improvement in the CNN model. Conclusion The proposed protein sequence encoding method is efficient at improving the capability of the CNN model on protein sequence-related tasks and may also be effective at enhancing the capability of other machine learning or deep learning methods. Prediction accuracy and molecular docking validation showed considerable improvement compared to the existing hot encoding method, indicating that the SSC encoding method may be useful for analyzing protein sequence-related tasks. The source code of the proposed methods is freely available for academic research at https://github.com/wangy496/SSC-format/.

Accurate deep learning off-target prediction with novel sgRNA-DNA sequence encoding in CRISPR-Cas9 gene editing

Motivation Off-target predictions are crucial in gene editing research. Recently, significant progress has been made in the field of prediction of off-target mutations, particularly with CRISPR-Cas9 data, thanks to the use of deep learning. CRISPR-Cas9 is a gene editing technique which allows manipulation of DNA fragments. The sgRNA-DNA (single guide RNA-DNA) sequence encoding for deep neural networks, however, has a strong impact on the prediction accuracy. We propose a novel encoding of sgRNA-DNA sequences that aggregates sequence data with no loss of information. Results In our experiments, we compare the proposed sgRNA-DNA sequence encoding applied in a deep learning prediction framework with state-of-the-art encoding and prediction methods. We demonstrate the superior accuracy of our approach in a simulation study involving Feedforward Neural Networks (FNNs), Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) as well as the traditional Random Forest (RF), Naive Bayes (NB) and Logistic Regression (LR) classifiers.We highlight the quality of our results by building several FNNs, CNNs and RNNs with various layer depths and performing predictions on two popular CRISPOR and GUIDE-seq gene editing data sets. In all our experiments, the new encoding led to more accurate off-target prediction results, providing an improvement of the area under the Receiver Operating Characteristic (ROC) curve up to 35%. Availability The code and data used in this study are available at: https://github.com/dagrate/dl-offtarget

HMD-ARG: hierarchical multi-task deep learning for annotating antibiotic resistance genes

Background The spread of antibiotic resistance has become one of the most urgent threats to global health, which is estimated to cause 700,000 deaths each year globally. Its surrogates, antibiotic resistance genes (ARGs), are highly transmittable between food, water, animal, and human to mitigate the efficacy of antibiotics. Accurately identifying ARGs is thus an indispensable step to understanding the ecology, and transmission of ARGs between environmental and human-associated reservoirs. Unfortunately, the previous computational methods for identifying ARGs are mostly based on sequence alignment, which cannot identify novel ARGs, and their applications are limited by currently incomplete knowledge about ARGs. Results Here, we propose an end-to-end Hierarchical Multi-task Deep learning framework for ARG annotation (HMD-ARG). Taking raw sequence encoding as input, HMD-ARG can identify, without querying against existing sequence databases, multiple ARG properties simultaneously, including if the input protein sequence is an ARG, and if so, what antibiotic family it is resistant to, what resistant mechanism the ARG takes, and if the ARG is an intrinsic one or acquired one. In addition, if the predicted antibiotic family is beta-lactamase, HMD-ARG further predicts the subclass of beta-lactamase that the ARG is resistant to. Comprehensive experiments, including cross-fold validation, third-party dataset validation in human gut microbiota, wet-experimental functional validation, and structural investigation of predicted conserved sites, demonstrate not only the superior performance of our method over the state-of-art methods, but also the effectiveness and robustness of the proposed method. Conclusions We propose a hierarchical multi-task method, HMD-ARG, which is based on deep learning and can provide detailed annotations of ARGs from three important aspects: resistant antibiotic class, resistant mechanism, and gene mobility. We believe that HMD-ARG can serve as a powerful tool to identify antibiotic resistance genes and, therefore mitigate their global threat. Our method and the constructed database are available at http://www.cbrc.kaust.edu.sa/HMDARG/.