Hefin Rhys

Background: Phenotypic studies have identified distinct patterns of autistic characteristics in genetic syndromes associated with intellectual disability (ID), leading to diagnostic uncertainty and compromised access to autism-related support. Previous research has tended to include small samples and diverse measures, which limits the generalisability of findings. In this study, we generated detailed profiles of autistic characteristics in a large sample of > 1500 individuals with rare genetic syndromes. Methods: Profiles of autistic characteristics based on the Social Communication Questionnaire (SCQ) scores were generated for thirteen genetic syndrome groups (Angelman n = 154, Cri du Chat n = 75, Cornelia de Lange n = 199, fragile X n = 297, Prader–Willi n = 278, Lowe n = 89, Smith–Magenis n = 54, Down n = 135, Sotos n = 40, Rubinstein–Taybi n = 102, 1p36 deletion n = 41, tuberous sclerosis complex n = 83 and Phelan–McDermid n = 35 syndromes). It was hypothesised that each syndrome group would evidence a degree of specificity in autistic characteristics. To test this hypothesis, a classification algorithm via support vector machine (SVM) learning was applied to scores from over 1500 individuals diagnosed with one of the thirteen genetic syndromes and autistic individuals who did not have a known genetic syndrome (ASD; n = 254). Self-help skills were included as an additional predictor. Results: Genetic syndromes were associated with different but overlapping autism-related profiles, indicated by the substantial accuracy of the entire, multiclass SVM model (55% correctly classified individuals). Syndrome groups such as Angelman, fragile X, Prader–Willi, Rubinstein–Taybi and Cornelia de Lange showed greater phenotypic specificity than groups such as Cri du Chat, Lowe, Smith–Magenis, tuberous sclerosis complex, Sotos and Phelan-McDermid. The inclusion of the ASD reference group and self-help skills did not change the model accuracy. Limitations: The key limitations of our study include a cross-sectional design, reliance on a screening tool which focuses primarily on social communication skills and imbalanced sample size across syndrome groups. Conclusions: These findings replicate and extend previous work, demonstrating syndrome-specific profiles of autistic characteristics in people with genetic syndromes compared to autistic individuals without a genetic syndrome. This work calls for greater precision of assessment of autistic characteristics in individuals with genetic syndromes associated with ID.

The heterogeneous nature of human CD34⁺ hematopoietic stem cells (HSCs) has hampered our understanding of the cellular and molecular trajectories that HSCs navigate during lineage commitment. Using various platforms including single cell RNA-sequencing and extensive xenotransplantation, we have uncovered an uncharacterized human CD34⁺ HSC population. These CD34⁺EPCR⁺(CD38/CD45RA)⁻ (simply as EPCR⁺) HSCs have a high repopulating and self-renewal abilities, reaching a stem cell frequency of ~1 in 3 cells, the highest described to date. Their unique transcriptomic wiring in which many gene modules associated with differentiated cell lineages confers their multilineage lineage output both in vivo and in vitro. At the single cell level, EPCR⁺ HSCs are the most transcriptomically and functionally homogenous human HSC population defined to date and can also be easily identified in post-natal tissues. Therefore, this EPCR⁺ population not only offers a high human HSC resolution but also a well-structured human hematopoietic hierarchical organization at the most primitive level.

The adaptive cellular response to low oxygen tensions is mediated by the hypoxia‐inducible factors (HIFs), a family of heterodimeric transcription factors composed of HIF‐α and HIF‐β subunits. Prolonged HIF expression is a key contributor to cellular transformation, tumorigenesis and metastasis. As such, HIF degradation under hypoxic conditions is an essential homeostatic and tumour‐suppressive mechanism. LIMD1 complexes with PHD2 and VHL in physiological oxygen levels (normoxia) to facilitate proteasomal degradation of the HIF‐α subunit. Here, we identify LIMD1 as a HIF‐1 target gene, which mediates a previously uncharacterised, negative regulatory feedback mechanism for hypoxic HIF‐α degradation by modulating PHD2‐LIMD1‐VHL complex formation. Hypoxic induction of LIMD1 expression results in increased HIF‐α protein degradation, inhibiting HIF‐1 target gene expression, tumour growth and vascularisation. Furthermore, we report that copy number variation at the LIMD1 locus occurs in 47.1% of lung adenocarcinoma patients, correlates with enhanced expression of a HIF target gene signature and is a negative prognostic indicator. Taken together, our data open a new field of research into the aetiology, diagnosis and prognosis of LIMD1‐negative lung cancers. Synopsis This study identifies the tumour suppressor LIMD1 as a facilitator of an adaptive cellular response to hypoxia. Under hypoxic conditions, LIMD1 expression is induced, facilitating degradation of HIF, limiting HIF transcriptional activity and mitigating its pro‐tumorigenic effects. The LIMD1 promoter contains a hypoxia responsive element (HRE) and its expression is induced under hypoxic conditions driving increased HIF degradation via the PHD/VHL axis. HIF protein stability and transcriptional activity are reduced under chronic hypoxic conditions by LIMD1. Aberration of the LIMD1‐dependent hypoxic negative feedback loop causes larger and more vascularised tumours to grow in vivo. LIMD1 is lost in 47% of adenocarcinoma driving a hypoxic gene signature that serves as a negative prognostic indicator.