Rare diseases pose a global challenge, in that their collective impact on health systems is considerable, whereas their individually rare occurrence impedes research and development of efficient therapies. In consequence, patients and their families are often unable to find an expert for their affliction, let alone a cure. The tide is turning as pharmaceutical companies embrace gene therapy development and as serviceable tools for the repair of primary mutations separate the ability to create cures from underlying disease expertise. Whereas gene therapy by gene addition took decades to reach the clinic by incremental disease-specific refinements of vectors and methods, gene therapy by genome editing in its basic form merely requires certainty about the causative mutation. Suddenly we move from concept to trial in 3 years instead of 30: therapy development in the fast lane, with all the positive and negative implications of the phrase. Since their first application to eukaryotic cells in 2013, the proliferation and refinement in particular of tools based on clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein (Cas) prokaryotic RNA-guided nucleases has prompted a landslide of therapy-development studies for rare diseases. An estimated thousands of orphan diseases are up for adoption, and legislative, entrepreneurial, and research initiatives may finally conspire to find many of them a good home. Here we summarize the most significant recent achievements and remaining hurdles in the application of CRISPR/Cas technology to rare diseases and take a glimpse at the exciting road ahead.

The β-hemoglobinopathies sickle cell anemia and β-thalassemia are the focus of many gene-therapy studies. A key disease parameter is the abundance of globin chains because it indicates the level of anemia, likely toxicity of excess or aberrant globins, and therapeutic potential of induced or exogenous β-like globins. Reversed-phase high-performance liquid chromatography (HPLC) allows versatile and inexpensive globin quantification, but commonly applied protocols suffer from long run times, high sample requirements, or inability to separate murine from human β-globin chains. The latter point is problematic for in vivo studies with gene-addition vectors in murine disease models and mouse/human chimeras. This study demonstrates HPLC-based measurements of globin expression (1) after differentiation of the commonly applied human umbilical cord blood–derived erythroid progenitor-2 cell line, (2) in erythroid progeny of CD34⁺ cells for the analysis of clustered regularly interspaced short palindromic repeats/Cas9-mediated disruption of the globin regulator BCL11A, and (3) of transgenic mice holding the human β-globin locus. At run times of 8 min for separation of murine and human β-globin chains as well as of human γ-globin chains, and with routine measurement of globin-chain ratios for 12 nL of blood (tested for down to 0.75 nL) or of 300,000 in vitro differentiated cells, the methods presented here and any variant-specific adaptations thereof will greatly facilitate evaluation of novel therapy applications for β-hemoglobinopathies.

MicroRNAs (miRNAs) are small non-coding RNAs crucial for post-transcriptional and translational regulation of cellular and developmental pathways. The study of miRNAs in erythropoiesis elucidates underlying regulatory mechanisms and facilitates related diagnostic and therapy development. Here, we used DNA Nanoball (DNB) small RNA sequencing to comprehensively characterize miRNAs in human erythroid cell cultures. Based on primary human peripheral-blood-derived CD34+ (hCD34+) cells and two influential erythroid cell lines with adult and fetal hemoglobin expression patterns, HUDEP-2 and HUDEP-1, respectively, our study links differential miRNA expression to erythroid differentiation, cell type, and hemoglobin expression profile. Sequencing results validated by reverse-transcription quantitative PCR (RT-qPCR) of selected miRNAs indicate shared differentiation signatures in primary and immortalized cells, characterized by reduced overall miRNA expression and reciprocal expression increases for individual lineage-specific miRNAs in late-stage erythropoiesis. Despite the high similarity of same-stage hCD34+ and HUDEP-2 cells, differential expression of several miRNAs highlighted informative discrepancies between both cell types. Moreover, a comparison between HUDEP-2 and HUDEP-1 cells displayed changes in miRNAs, transcription factors (TFs), target genes, and pathways associated with globin switching. In resulting TF-miRNA co-regulatory networks, major therapeutically relevant regulators of globin expression were targeted by many co-expressed miRNAs, outlining intricate combinatorial miRNA regulation of globin expression in erythroid cells.

Haemoglobinopathies are common monogenic disorders with diverse clinical manifestations, partly attributed to the influence of modifier genes. Recent years have seen enormous growth in the amount of genetic data, instigating the need for ranking methods to identify candidate genes with strong modifying effects. Here, we present the first evidence-based gene ranking metric (IthaScore) for haemoglobinopathy-specific phenotypes by utilising curated data in the IthaGenes database. IthaScore successfully reflects current knowledge for well-established disease modifiers, while it can be dynamically updated with emerging evidence. Protein–protein interaction (PPI) network analysis and functional enrichment analysis were employed to identify new potential disease modifiers and to evaluate the biological profiles of selected phenotypes. The most relevant gene ontology (GO) and pathway gene annotations for (a) haemoglobin (Hb) F levels/Hb F response to hydroxyurea included urea cycle, arginine metabolism and vascular endothelial growth factor receptor (VEGFR) signalling, (b) response to iron chelators included xenobiotic metabolism and glucuronidation, and (c) stroke included cytokine signalling and inflammatory reactions. Our findings demonstrate the capacity of IthaGenes, together with dynamic gene ranking, to expand knowledge on the genetic and molecular basis of phenotypic variation in haemoglobinopathies and to identify additional candidate genes to potentially inform and improve diagnosis, prognosis and therapeutic management.