Differential protein analysis of serum exosomes post-intravenous immunoglobulin therapy in patients with Kawasaki disease.

BACKGROUND: Kawasaki disease, which is characterised by systemic vasculitides accompanied by acute fever, is regularly treated by intravenous immunoglobulin to avoid lesion formation in the coronary artery; however, the mechanism of intravenous immunoglobulin therapy is unclear. Hence, we aimed to analyse the global expression profile of serum exosomal proteins before and after administering intravenous immunoglobulin. METHODS: Two-dimensional electrophoresis coupled with mass spectrometry analysis was used to identify the differentially expressed proteome of serum exosomes in patients with Kawasaki disease before and after intravenous immunoglobulin therapy. RESULTS: Our analysis revealed 69 differential protein spots in the Kawasaki disease group with changes larger than 1.5-fold and 59 differential ones in patients after intravenous immunoglobulin therapy compared with the control group. Gene ontology analysis revealed that the acute-phase response disappeared, the functions of the complement system and innate immune response were enhanced, and the antibacterial humoral response pathway of corticosteroids and cardioprotection emerged after administration of intravenous immunoglobulin. Further, we showed that complement C3 and apolipoprotein A-IV levels increased before and decreased after intravenous immunoglobulin therapy and that the insulin-like growth factor-binding protein complex acid labile subunit displayed reverse alteration before and after intravenous immunoglobulin therapy. These observations might be potential indicators of intravenous immunoglobulin function. CONCLUSIONS: Our results show the differential proteomic profile of serum exosomes of patients with Kawasaki disease before and after intravenous immunoglobulin therapy, such as complement C3, apolipoprotein A-IV, and insulin-like growth factor-binding protein complex acid labile subunit. These results may be useful in the identification of markers for monitoring intravenous immunoglobulin therapy in patients with Kawasaki disease.

FANSe2: a robust and cost-efficient alignment tool for quantitative next-generation sequencing applications.

Correct and bias-free interpretation of the deep sequencing data is inevitably dependent on the complete mapping of all mappable reads to the reference sequence, especially for quantitative RNA-seq applications. Seed-based algorithms are generally slow but robust, while Burrows-Wheeler Transform (BWT) based algorithms are fast but less robust. To have both advantages, we developed an algorithm FANSe2 with iterative mapping strategy based on the statistics of real-world sequencing error distribution to substantially accelerate the mapping without compromising the accuracy. Its sensitivity and accuracy are higher than the BWT-based algorithms in the tests using both prokaryotic and eukaryotic sequencing datasets. The gene identification results of FANSe2 is experimentally validated, while the previous algorithms have false positives and false negatives. FANSe2 showed remarkably better consistency to the microarray than most other algorithms in terms of gene expression quantifications. We implemented a scalable and almost maintenance-free parallelization method that can utilize the computational power of multiple office computers, a novel feature not present in any other mainstream algorithm. With three normal office computers, we demonstrated that FANSe2 mapped an RNA-seq dataset generated from an entire Illunima HiSeq 2000 flowcell (8 lanes, 608 M reads) to masked human genome within 4.1 hours with higher sensitivity than Bowtie/Bowtie2. FANSe2 thus provides robust accuracy, full indel sensitivity, fast speed, versatile compatibility and economical computational utilization, making it a useful and practical tool for deep sequencing applications. FANSe2 is freely available at http://bioinformatics.jnu.edu.cn/software/fanse2/.