Targeting phenylalanine assemblies as a prospective disease-modifying therapy for phenylketonuria.

Phenylketonuria is characterized by the accumulation of phenylalanine, resulting in severe cognitive and neurological disorders if not treated by a remarkably strict diet. There are two approved drugs today, yet both provide only a partial solution. We have previously demonstrated the formation of amyloid-like toxic assemblies by aggregation of phenylalanine, suggesting a new therapeutic target to be further pursued. Moreover, we showed that compounds that halt the formation of these assemblies also prevent their resulting toxicity. Here, we performed high-throughput screening, searching for compounds with inhibitory effects on phenylalanine aggregation. Morin hydrate, one of the most promising hits revealed during the screen, was chosen to be tested in vivo using a phenylketonuria mouse model. Morin hydrate significantly improved cognitive and motor function with a reduction in the number of phenylalanine brain deposits. Moreover, while phenylalanine levels remained high, we observed a recovery in dopaminergic, adrenergic, and neuronal markers. To conclude, the ability of Morin hydrate to halt phenylalanine aggregation without reducing phenylalanine levels implies the toxic role of the phenylalanine assemblies in phenylketonuria and opens new avenues for disease-modifying treatment.

Integration of transcription regulation and functional genomic data reveals lncRNA SNHG6's role in hematopoietic differentiation and leukemia.

BACKGROUND: Long non-coding RNAs (lncRNAs) are pivotal players in cellular processes, and their unique cell-type specific expression patterns render them attractive biomarkers and therapeutic targets. Yet, the functional roles of most lncRNAs remain enigmatic. To address the need to identify new druggable lncRNAs, we developed a comprehensive approach integrating transcription factor binding data with other genetic features to generate a machine learning model, which we have called INFLAMeR (Identifying Novel Functional LncRNAs with Advanced Machine Learning Resources). METHODS: INFLAMeR was trained on high-throughput CRISPR interference (CRISPRi) screens across seven cell lines, and the algorithm was based on 71 genetic features. To validate the predictions, we selected candidate lncRNAs in the human K562 leukemia cell line and determined the impact of their knockdown (KD) on cell proliferation and chemotherapeutic drug response. We further performed transcriptomic analysis for candidate genes. Based on these findings, we assessed the lncRNA small nucleolar RNA host gene 6 (SNHG6) for its role in myeloid differentiation. Finally, we established a mouse K562 leukemia xenograft model to determine whether SNHG6 KD attenuates tumor growth in vivo. RESULTS: The INFLAMeR model successfully reconstituted CRISPRi screening data and predicted functional lncRNAs that were previously overlooked. Intensive cell-based and transcriptomic validation of nearly fifty genes in K562 revealed cell type-specific functionality for 85% of the predicted lncRNAs. In this respect, our cell-based and transcriptomic analyses predicted a role for SNHG6 in hematopoiesis and leukemia. Consistent with its predicted role in hematopoietic differentiation, SNHG6 transcription is regulated by hematopoiesis-associated transcription factors. SNHG6 KD reduced the proliferation of leukemia cells and sensitized them to differentiation. Treatment of K562 leukemic cells with hemin and PMA, respectively, demonstrated that SNHG6 inhibits red blood cell differentiation but strongly promotes megakaryocyte differentiation. Using a xenograft mouse model, we demonstrate that SNHG6 KD attenuated tumor growth in vivo. CONCLUSIONS: Our approach not only improved the identification and characterization of functional lncRNAs through genomic approaches in a cell type-specific manner, but also identified new lncRNAs with roles in hematopoiesis and leukemia. Such approaches can be readily applied to identify novel targets for precision medicine.