Safety and Tolerability of Omalizumab in Children with Allergic (IgE-Mediated) Asthma: A Systematic Review and Meta-Analysis.

BACKGROUND: Omalizumab is a recombinant humanized monoclonal antibody against immunoglobulin E., which can specifically bind to IgE in blood and inhibit the release of inflammatory mediators to improve the symptoms of IgE-mediated asthma effectively. This meta-analysis was used to retrieve the studies in recent years to provide a clinical reference for the omalizumab in treating allergic asthma (AA). METHODS: The databases Ovid, Embase, Pubmed, the Cochrane Library of clinical trials, CNKI (China National Knowledge Infrastructure) (China), and Wangfang Data (China) were searched for all studies on omalizumab involvement in treating allergic childhood asthma up to January 2022. Effectiveness, rate of exacerbation within 24 weeks (and 52 weeks), and the incidence of adverse reactions and serious adverse reactions were used as the primary data analysis indicators. RESULTS: Seven eligible pieces of literature were included. Meta-analysis indicated that omalizumab could significantly improve the treatment efficacy in children with asthma [RR (Risk Ratio) = 1.24, 95% CI (Confidential Interval) (1.09, 1.41), Z = 3.30, p = 0.001], reduced the incidence of significant clinical exacerbation in children with asthma within 24 weeks [RR = 0.55, 95% CI (0.35, 0.85), Z = -2.67, p = 0.001], reduced the incidence of significant clinical exacerbation in children with asthma within 52 weeks [RR = 0.52, 95% CI (0.39, 0.71), Z = -4.2, p < 0.0001], and the incidence of total serious adverse reactions was not statistically different from placebo [RR = 1.00, 95% CI (0.98, 1.03), Z = 0.71, p = 0.479], the incidence of serious adverse reactions was significantly decreased [RR = 0.53, 95% CI (0.36, 0.77), Z = -3.35, p = 0.001]. CONCLUSIONS: In treating IgE (immunoglobulin E)-mediated asthma in children, adding oral (or subcutaneous) omalizumab to a glucocorticoid regimen can enhance the effectiveness of treatment, reduce the probability of significant exacerbation during treatment, and reduce the incidence of serious adverse reactions.

Identification and characterization of two DMD pedigrees with large inversion mutations based on a long-read sequencing pipeline.

Pathogenic large inversions are rarely reported on DMD gene due to the lack of effective detection methods. Here we report two DMD pedigrees and proposed a reliable pipeline to define large inversions in DMD patients. In the first pedigree, conventional approaches including multiplex ligation-dependent probe amplification, and whole-exome sequencing by next generation sequencing were failed to detect any pathologic variant. Then an advanced analysis pipeline which consists of RNA-seq, cDNA array capture sequencing, optical mapping, long-read sequencing was built. RNA-seq and cDNA capture sequencing showed a complete absence of transcripts of exons 3-55. Optical mapping identified a 55 Mb pericentric inversion between Xp21 and Xq21. Subsequently, long-read sequencing and Sanger sequencing determined the inversion breakpoints at 32,915,769 and 87,989,324 of the X chromosomes. In the second pedigree, long-read sequencing was directly conducted and Sanger sequencing was performed to verify the mutation. Long-read sequencing and Sanger sequencing found breakpoints at 32,581,576 and 127,797,236 on DMD gene directly. In conclusion, large inversion might be a rare but important mutation type in DMD gene. An effective pipeline was built in detecting large inversion mutations based on long-read sequencing platforms.

Tracing the origin of honey products based on metagenomics and machine learning.

The adulteration of honey is common. Recently, High Throughput Sequencing (HTS)-based metabarcoding method has been applied successfully to pollen/honey identification to determine floral composition that, in turn, can be used to identify the geographical origins of honeys. However, the lack of local references materials posed a serious challenge for HTS-based pollen identification methods. Here, we sampled 28 honey samples from various geographic origins without prior knowledge of local floral information and applied a machine learning method to determine geographical origins. The machine learning method uses a resilient backpropagation algorithm to train a neural network. The results showed that biological components in honey provided characteristic traits that enabled accurate geographic tracing for nearly all honey samples, confidently discriminating honeys to their geographic origin with >99% success rates, including those separated by as little as 39 km.

Detection of Structural Variations and Fusion Genes in Breast Cancer Samples Using Third-Generation Sequencing.

Background: Structural variations (SVs) are common genetic alterations in the human genome that could cause different phenotypes and diseases, including cancer. However, the detection of structural variations using the second-generation sequencing was limited by its short read length, which restrained our understanding of structural variations. Methods: In this study, we developed a 28-gene panel for long-read sequencing and employed it to Oxford Nanopore Technologies and Pacific Biosciences platforms. We analyzed structural variations in the 28 breast cancer-related genes through long-read genomic and transcriptomic sequencing of tumor, para-tumor, and blood samples in 19 breast cancer patients. Results: Our results showed that some somatic SVs were recurring among the selected genes, though the majority of them occurred in the non-exonic region. We found evidence supporting the existence of hotspot regions for SVs, which extended our previous understanding that they exist only for single nucleotide variations. Conclusion: In conclusion, we employed long-read genomic and transcriptomic sequencing to identify SVs from breast cancer patients and proved that this approach holds great potential in clinical application.

Comparison of the two up-to-date sequencing technologies for genome assembly: HiFi reads of Pacific Biosciences Sequel II system and ultralong reads of Oxford Nanopore.

BACKGROUND: The availability of reference genomes has revolutionized the study of biology. Multiple competing technologies have been developed to improve the quality and robustness of genome assemblies during the past decade. The 2 widely used long-read sequencing providers-Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT)-have recently updated their platforms: PacBio enables high-throughput HiFi reads with base-level resolution of >99%, and ONT generated reads as long as 2 Mb. We applied the 2 up-to-date platforms to a single rice individual and then compared the 2 assemblies to investigate the advantages and limitations of each. RESULTS: The results showed that ONT ultralong reads delivered higher contiguity, producing a total of 18 contigs of which 10 were assembled into a single chromosome compared to 394 contigs and 3 chromosome-level contigs for the PacBio assembly. The ONT ultralong reads also prevented assembly errors caused by long repetitive regions, for which we observed a total of 44 genes of false redundancies and 10 genes of false losses in the PacBio assembly, leading to over- or underestimation of the gene families in those long repetitive regions. We also noted that the PacBio HiFi reads generated assemblies with considerably fewer errors at the level of single nucleotides and small insertions and deletions than those of the ONT assembly, which generated an average 1.06 errors per kb and finally engendered 1,475 incorrect gene annotations via altered or truncated protein predictions. CONCLUSIONS: It shows that both PacBio HiFi reads and ONT ultralong reads had their own merits. Further genome reference constructions could leverage both techniques to lessen the impact of assembly errors and subsequent annotation mistakes rooted in each.

Genome-skimming provides accurate quantification for pollen mixtures.

Studies on foraging partitioning in pollinators can provide critical information to the understanding of food-web niche and pollination functions, thus aiding conservation. Metabarcoding based on PCR amplification and high-throughput sequencing has seen increasing applications in characterizing pollen loads carried by pollinators. However, amplification bias across taxa could lead to unpredictable artefacts in estimation of pollen compositions. We examined the efficacy of a genome-skimming method based on direct shotgun sequencing in quantifying mixed pollen, using mock samples (five and 14 mocks of flower and bee pollen, respectively). The results demonstrated a high level of repeatability and accuracy in identifying pollen from mixtures of varied species ratios. All pollen species were detected in all mocks, and pollen frequencies estimated from the number of sequence reads of each species were significantly correlated with pollen count proportions (linear model, R2  = 86.7%, p = 2.2e-16). For >97% of the mixed taxa, pollen proportion could be quantified by sequencing to the correct order of magnitude, even for species which constituted only 0.2% of the total pollen. In addition, DNA extracted from pollen grains equivalent to those collected from a single honeybee corbicula was sufficient for genome-skimming. We conclude that genome-skimming is a feasible approach to identifying and quantifying mixed pollen samples. By providing reliable and sensitive taxon identification and relative abundance, this method is expected to improve our understanding in studies that involve plant-pollinator interactions, such as pollen preference in corbiculate bees, pollen diet analyses and identification of landscape pollen resource use from beehives.