The phenotype of MEGF8-related Carpenter syndrome (CRPT2) is refined through the identification of eight new patients.

Carpenter syndrome (CRPTS) is a rare autosomal recessive condition caused by biallelic variants in genes that encode negative regulators of hedgehog signalling (RAB23 [CRPT1] or, more rarely, MEGF8 [CRPT2]), and is characterised by craniosynostosis, polysyndactyly, and other congenital abnormalities. We describe a further six families comprising eight individuals with MEGF8-associated CRPT2, increasing the total number of reported cases to fifteen, and refine the phenotype of CRPT2 compared to CRPT1. The core features of craniosynostosis, polysyndactyly and (in males) cryptorchidism are almost universal in both CRPT1 and CRPT2. However, laterality defects are present in nearly half of those with MEGF8-associated CRPT2, but are rare in RAB23-associated CRPT1. Craniosynostosis in CRPT2 commonly involves a single midline suture in comparison to the multi-suture craniosynostosis characteristic of CRPT1. No patient to date has carried two MEGF8 gene alterations that are both predicted to lead to complete loss-of-function, suggesting that a variable degree of residual MEGF8 activity may be essential for viability and potentially contributing to variable phenotypic severity. These data refine the phenotypic spectrum of CRPT2 in comparison to CRPT1 and more than double the number of likely pathogenic MEGF8 variants in this rare disorder.

Meta-analysis of single-cell and single-nucleus transcriptomics reveals kidney cell type consensus signatures.

While the amount of studies involving single-cell or single-nucleus RNA-sequencing technologies grows exponentially within the biomedical research area, the kidney field requires reference transcriptomic signatures to allocate each cluster its matching cell type. The present meta-analysis of 39 previously published datasets, from 7 independent studies, involving healthy human adult kidney samples, offers a set of 24 distinct consensus kidney cell type signatures. The use of these signatures may help to assure the reliability of cell type identification in future studies involving single-cell and single-nucleus transcriptomics while improving the reproducibility in cell type allocation.

A non-coding variant in the Kozak sequence of RARS2 strongly decreases protein levels and causes pontocerebellar hypoplasia.

Bi-allelic variants in the mitochondrial arginyl-transfer RNA synthetase (RARS2) gene have been involved in early-onset encephalopathies classified as pontocerebellar hypoplasia (PCH) type 6 and in epileptic encephalopathy. A variant (NM_020320.3:c.-2A > G) in the promoter and 5'UTR of the RARS2 gene has been previously identified in a family with PCH. Only a mild impact of this variant on the mRNA level has been detected. As RARS2 is non-dosage-sensitive, this observation is not conclusive in regard of the pathogenicity of the variant.We report and describe here a new patient with the same variant in the RARS2 gene, at the homozygous state. This patient presents with a clinical phenotype consistent with PCH6 although in the absence of lactic acidosis. In agreement with the previous study, we measured RARS2 mRNA levels in patient's fibroblasts and detected a partially preserved gene expression compared to control. Importantly, this variant is located in the Kozak sequence that controls translation initiation. Therefore, we investigated the impact on protein translation using a bioinformatic approach and western blotting. We show here that this variant, additionally to its effect on the transcription, also disrupts the consensus Kozak sequence, and has a major impact on RARS2 protein translation. Through the identification of this additional case and the characterization of the molecular consequences, we clarified the involvement of this Kozak variant in PCH and on protein synthesis. This work also points to the current limitation in the pathogenicity prediction of variants located in the translation initiation region.

Investigating genotype-to-phenotype correlation in CHARGE syndrome by deep phenotyping and multiparametric clustering.

CHARGE syndrome, due to CHD7 pathogenic variations, is an autosomal dominant disorder characterized by a large spectrum of severity. Despite the great number of variations reported, no clear genotype-to-phenotype correlation has been reported. Unsupervised machine learning and clustering was undertaken using a retrospective cohort of 42 patients, after deep radiologic and clinical phenotyping, to establish genotype-phenotype correlation for CHD7-related CHARGE syndrome. It resulted in three clusters showing phenotypes of different severities. While no clear genotype-phenotype correlation appeared within the first two clusters, a single patient was outlying the cohort data (cluster 3) with the most atypical phenotype and the most distal frameshift variant in the gene. We added two other patients with similar distal pathogenic variants and observed a tendency toward mild and/or atypical phenotypes. We hypothesized that this finding could potentially be related to escaping nonsense mediated RNA decay, but found no evidence of such decay in vivo for any of the CHD7 pathogenic variation tested. This indicates that this milder phenotype may rather result from the production of a protein retaining all functional domains.

Severe hematopoietic stem cell inflammation compromises chronic granulomatous disease gene therapy.

X-linked chronic granulomatous disease (CGD) is associated with defective phagocytosis, life-threatening infections, and inflammatory complications. We performed a clinical trial of lentivirus-based gene therapy in four patients (NCT02757911). Two patients show stable engraftment and clinical benefits, whereas the other two have progressively lost gene-corrected cells. Single-cell transcriptomic analysis reveals a significantly lower frequency of hematopoietic stem cells (HSCs) in CGD patients, especially in the two patients with defective engraftment. These two present a profound change in HSC status, a high interferon score, and elevated myeloid progenitor frequency. We use elastic-net logistic regression to identify a set of 51 interferon genes and transcription factors that predict the failure of HSC engraftment. In one patient, an aberrant HSC state with elevated CEBPß expression drives HSC exhaustion, as demonstrated by low repopulation in a xenotransplantation model. Targeted treatments to protect HSCs, coupled to targeted gene expression screening, might improve clinical outcomes in CGD.

Recessive PRDM13 mutations cause fatal perinatal brainstem dysfunction with cerebellar hypoplasia and disrupt Purkinje cell differentiation.

Pontocerebellar hypoplasias (PCHs) are congenital disorders characterized by hypoplasia or early atrophy of the cerebellum and brainstem, leading to a very limited motor and cognitive development. Although over 20 genes have been shown to be mutated in PCHs, a large proportion of affected individuals remains undiagnosed. We describe four families with children presenting with severe neonatal brainstem dysfunction and pronounced deficits in cognitive and motor development associated with four different bi-allelic mutations in PRDM13, including homozygous truncating variants in the most severely affected individuals. Brain MRI and fetopathological examination revealed a PCH-like phenotype, associated with major hypoplasia of inferior olive nuclei and dysplasia of the dentate nucleus. Notably, histopathological examinations highlighted a sparse and disorganized Purkinje cell layer in the cerebellum. PRDM13 encodes a transcriptional repressor known to be critical for neuronal subtypes specification in the mouse retina and spinal cord but had not been implicated, so far, in hindbrain development. snRNA-seq data mining and in situ hybridization in humans show that PRDM13 is expressed at early stages in the progenitors of the cerebellar ventricular zone, which gives rise to cerebellar GABAergic neurons, including Purkinje cells. We also show that loss of function of prdm13 in zebrafish leads to a reduction in Purkinje cells numbers and a complete absence of the inferior olive nuclei. Altogether our data identified bi-allelic mutations in PRDM13 as causing a olivopontocerebellar hypoplasia syndrome and suggest that early deregulations of the transcriptional control of neuronal fate specification could contribute to a significant number of cases.

Integrative genetic and immune cell analysis of plasma proteins in healthy donors identifies novel associations involving primary immune deficiency genes.

BACKGROUND: Blood plasma proteins play an important role in immune defense against pathogens, including cytokine signaling, the complement system, and the acute-phase response. Recent large-scale studies have reported genetic (i.e., protein quantitative trait loci, pQTLs) and non-genetic factors, such as age and sex, as major determinants to inter-individual variability in immune response variation. However, the contribution of blood-cell composition to plasma protein heterogeneity has not been fully characterized and may act as a mediating factor in association studies. METHODS: Here, we evaluated plasma protein levels from 400 unrelated healthy individuals of western European ancestry, who were stratified by sex and two decades of life (20-29 and 60-69 years), from the Milieu Intérieur cohort. We quantified 229 proteins by Luminex in a clinically certified laboratory and their levels of variation were analyzed together with 5.2 million single-nucleotide polymorphisms. With respect to non-genetic variables, we included 254 lifestyle and biochemical factors, as well as counts of seven circulating immune cell populations measured by hemogram and standardized flow cytometry. RESULTS: Collectively, we found 152 significant associations involving 49 proteins and 20 non-genetic variables. Consistent with previous studies, age and sex showed a global, pervasive impact on plasma protein heterogeneity, while body mass index and other health status variables were among the non-genetic factors with the highest number of associations. After controlling for these covariates, we identified 100 and 12 pQTLs acting in cis and trans, respectively, collectively associated with 87 plasma proteins and including 19 novel genetic associations. Genetic factors explained the largest fraction of the variability of plasma protein levels, as compared to non-genetic factors. In addition, blood-cell fractions, including leukocytes, lymphocytes, monocytes, neutrophils, eosinophils, basophils, and platelets, had a larger contribution to inter-individual variability than age and sex and appeared as confounders of specific genetic associations. Finally, we identified new genetic associations with plasma protein levels of five monogenic Mendelian disease genes including two primary immunodeficiency genes (Ficolin-3 and FAS). CONCLUSIONS: Our study identified novel genetic and non-genetic factors associated to plasma protein levels which may inform health status and disease management.

A DL-4- and TNFα-based culture system to generate high numbers of nonmodified or genetically modified immunotherapeutic human T-lymphoid progenitors.

Several obstacles to the production, expansion and genetic modification of immunotherapeutic T cells in vitro have restricted the widespread use of T-cell immunotherapy. In the context of HSCT, delayed naïve T-cell recovery contributes to poor outcomes. A novel approach to overcome the major limitations of both T-cell immunotherapy and HSCT would be to transplant human T-lymphoid progenitors (HTLPs), allowing reconstitution of a fully functional naïve T-cell pool in the patient thymus. However, it is challenging to produce HTLPs in the high numbers required to meet clinical needs. Here, we found that adding tumor necrosis factor alpha (TNFα) to a DL-4-based culture system led to the generation of a large number of nonmodified or genetically modified HTLPs possessing highly efficient in vitro and in vivo T-cell potential from either CB HSPCs or mPB HSPCs through accelerated T-cell differentiation and enhanced HTLP cell cycling and survival. This study provides a clinically suitable cell culture platform to generate high numbers of clinically potent nonmodified or genetically modified HTLPs for accelerating immune recovery after HSCT and for T-cell-based immunotherapy (including CAR T-cell therapy).

CNVxplorer: a web tool to assist clinical interpretation of CNVs in rare disease patients.

Copy Number Variants (CNVs) are an important cause of rare diseases. Array-based Comparative Genomic Hybridization tests yield a ∼12% diagnostic rate, with ∼8% of patients presenting CNVs of unknown significance. CNVs interpretation is particularly challenging on genomic regions outside of those overlapping with previously reported structural variants or disease-associated genes. Recent studies showed that a more comprehensive evaluation of CNV features, leveraging both coding and non-coding impacts, can significantly improve diagnostic rates. However, currently available CNV interpretation tools are mostly gene-centric or provide only non-interactive annotations difficult to assess in the clinical practice. Here, we present CNVxplorer, a web server suited for the functional assessment of CNVs in a clinical diagnostic setting. CNVxplorer mines a comprehensive set of clinical, genomic, and epigenomic features associated with CNVs. It provides sequence constraint metrics, impact on regulatory elements and topologically associating domains, as well as expression patterns. Analyses offered cover (a) agreement with patient phenotypes; (b) visualizations of associations among genes, regulatory elements and transcription factors; (c) enrichment on functional and pathway annotations and (d) co-occurrence of terms across PubMed publications related to the query CNVs. A flexible evaluation workflow allows dynamic re-interrogation in clinical sessions. CNVxplorer is publicly available at http://cnvxplorer.com.

Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID.

Because of the stochasticity associated with high-throughput single-cell sequencing, current methods for exploring cell-type diversity rely on clustering-based computational approaches in which heterogeneity is characterized at cell subpopulation rather than at full single-cell resolution. Here we present Cell-ID, a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell sequencing data. We applied Cell-ID to data from multiple human and mouse samples, including blood cells, pancreatic islets and airway, intestinal and olfactory epithelium, as well as to comprehensive mouse cell atlas datasets. We demonstrate that Cell-ID signatures are reproducible across different donors, tissues of origin, species and single-cell omics technologies, and can be used for automatic cell-type annotation and cell matching across datasets. Cell-ID improves biological interpretation at individual cell level, enabling discovery of previously uncharacterized rare cell types or cell states. Cell-ID is distributed as an open-source R software package.