Biology subjects

Nordin, J.

Publications and source records attributed to Nordin, J..

4 recordsLinked to original sources

bioRxiv2020

Profiling of extracellular small RNAs highlights a strong bias towards non-vesicular secretion

Extracellular environment consists of a plethora of different molecules, including extracellular miRNA that can be secreted in association with extracellular vesicles (EVs) or soluble protein complexes (non-EVs). Yet, it is generally accepted that most of the biological activity is attributed to EV-associated miRNAs. The capability of EVs to transport cargoes has attracted much interest towards developing EVs as therapeutic short RNA carriers by using endogenous loading strategies for miRNA enrichment. Here, by overexpressing miRNA and shRNA sequences of interest in source cells and using size exclusion liquid chromatography (SEC) to separate the cellular secretome into EV and non-EV fractions, we saw that strikingly, <2% of all secreted overexpressed miRNA were found in association with EVs. To see whether the prominent non-EV miRNA secretion also holds true at the basal expression level of native miRNA transcripts, both fractions were further analysed by small RNA sequencing. This revealed a global correlation of EV and non-EV miRNA abundance to that of their parent cells and showed an enrichment only for miRNAs with a relatively low cellular expression level. Further quantification showed that similarly to the transient overexpression context, an outstanding 96.2-99.9% of total secreted miRNA at its basal level was secreted to the non-EV fraction. Yet, even though EVs contain only a fraction of secreted miRNAs, these molecules were found stable at 37{degrees}C in serum-containing environment, indicating that if sufficient miRNA loading to EVs is achieved, EVs can remain miRNA delivery-competent for a prolonged period of time. This study suggests that the passive endogenous EV loading strategy can be a relatively wasteful way of loading miRNA to EVs and active miRNA loading approaches are needed for developing advanced EV miRNA therapies in the future.

molecular biology↗

bioRxiv2020

A new long-read dog assembly uncovers thousands of exons and functional elements missing in the previous reference

Here we present a new high-quality canine reference genome with gap number reduced 41-fold, from 23,836 to 585. Analysis of existing and novel data, RNA-seq, miRNA-seq and ATAC-seq, revealed a large proportion of these harboured previously hidden elements, including genes, promoters and miRNAs. Short-read dark regions were detected, and genomic regions completed, including the DLA, TCR and 366 cancer genes. 10x sequencing of 27 dogs uncovered a total of 22.1 million SNPs, Indels and larger structural variants (SVs). 1.4% overlap with protein coding genes and could provide a source of normal or aberrant phenotypic modifications.

genomics↗

bioRxiv2020

Engineering of extracellular vesicles for display of protein biotherapeutics

Extracellular vesicles (EVs) have recently emerged as a highly promising cell-free bio-therapeutics. While a range of engineering strategies have been developed to functionalize the EV surface, current approaches fail to address the limitations associated with endogenous surface display, pertaining to the heterogeneous display of commonly used EV-loading moieties among different EV subpopulations. Here we present a novel engineering platform to display multiple protein therapeutics simultaneously on the EV surface. As proof-of-concept, we screened multiple endogenous display strategies for decorating the EV surface with cytokine binding domains derived from tumor necrosis factor receptor 1 (TNFR1) and interleukin 6 signal transducer (IL6ST), which can act as decoys for the pro-inflammatory cytokines TNF and IL6, respectively. Combining synthetic biology and systematic screening of loading moieties, resulted in a three-component system which increased the display and decoy activity of TNFR1 and IL6ST, respectively. Further, this system allowed for combinatorial functionalization of two different receptors on the same EV surface. These cytokine decoy EVs significantly ameliorated disease phenotypes in three different inflammatory mouse models for systemic inflammation, neuroinflammation, and intestinal inflammation. Importantly, significantly improved in vitro and in vivo efficacy of these engineered EVs was observed when compared directly to clinically approved biologics targeting the IL6 and TNF pathways.

bioengineering↗

bioRxiv2019

SweHLA: the high confidence HLA typing bio-resource drawn from 1 000 Swedish genomes

There is a need to accurately call human leukocyte antigen (HLA) genes from existing short-read sequencing data, however there is no single solution that matches the gold standard of lab typing. Here we aimed to combine results from available software, minimising the biases of applied algorithm and HLA reference. The result is a robust HLA population resource for the published 1 000 Swedish genomes, and a framework for future HLA interrogation. HLA 2-field alleles were called using four imputation and inference methods for the classical eight genes (class I: HLA-A, -B, -C; class II: HLA-DPA1, -DPB1, -DQA1, -DQB1, -DRB1). A high confidence population set (SweHLA) was determined using an n-1 concordance rule for class I (four software) and class II (three software) alleles. Results were compared across populations and individual programs benchmarked to SweHLA. Per allele, 875 to 988 of the 1 000 samples were genotyped in SweHLA; 920 samples had at least seven loci. While a small fraction of reference alleles were common to all software (class I=1.9% and class II=4.1%), this did not affect the overall call rate. Gene-level concordance was high compared to European populations (>0.83%), with COX and PGF the dominant SweHLA haplotypes. We noted that 15/18 discordant alleles (delta allele frequency > 2) were previously reported as disease-associated. These differences could in part explain across-study genetic replication failures, reinforcing the need to use multiple software. SweHLA demonstrates a way to use existing NGS data to generate a population resource agnostic to individual HLA software biases.

genetics↗