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Abstract Glucocorticoid (GC) resistance in childhood relapsed B-cell acute lymphoblastic leukemia (B-ALL) represents an important challenge. Despite decades of clinical use, the mechanisms underlying resistance remain poorly understood. Here, we report that in B-ALL, GC paradoxically induce their own resistance by activating a phospholipase C (PLC)-mediated cell survival pathway through the chemokine receptor, CXCR4. We identify PLC as aberrantly activated in GC-resistant B-ALL and its inhibition is able to induce cell death by compromising several transcriptional programs. Mechanistically, dexamethasone (Dex) provokes CXCR4 signaling, resulting in the activation of PLC-dependent Ca2+ and protein kinase C signaling pathways, which curtail anticancer activity. Treatment with a CXCR4 antagonist or a PLC inhibitor improves survival of Dex-treated NSG mice in vivo. CXCR4/PLC axis inhibition significantly reverses Dex resistance in B-ALL cell lines (in vitro and in vivo) and cells from Dex resistant ALL patients. Our study identifies how activation of the PLC signalosome in B-ALL by Dex limits the upfront efficacy of this chemotherapeutic agent.

Abstract While de novo variants (DNV) are overall at low risk of recurrence in subsequent pregnancies, a subset is at high risk due to parental mosaicism. Accurately identifying cases of parental mosaicism is therefore important for genetic counseling in clinical care. Some studies have investigated the rate of parental mosaics, but most were either limited by the sensitivity of the techniques (i.e. exome or genome sequencing), or focused on specific types of disease such as epileptic syndromes. This study aimed to determine the proportion of parental mosaicism among the DNV causing neurodevelopmental disorders (NDDs) in a series not enriched in epilepsy syndromes. We collected 189 patients with NDD-associated DNV. We applied a smMIP enrichment method and sequenced parental blood DNA samples to an average depth of 7000x. Power simulation indicated that mosaicism with an allelic fraction of 0.5% would have been detected for 87% of positions with 90% power. We observed seven parental mosaic variants (3.7% of families), of which four (2.1% of families) had an allelic fraction of less than 1%. In total, our study identifies a relatively low proportion of parental mosaicism in NDD-associated DNVs and raises the question of a biological mechanism behind the higher rates of parental mosaicism detected in other studies, particularly those focusing on epileptic syndromes.

Fabry disease (FD) is an X-linked lysosomal disease caused by an enzyme deficiency of alpha-galactosidase A (α-gal A). This deficiency leads to the accumulation of glycosphingolipids in lysosomes, resulting in a range of clinical symptoms. The complex pathogenesis of FD involves lysosomal dysfunction, altered autophagy, and mitochondrial abnormalities. Omics sciences, particularly transcriptomic analysis, comprehensively understand molecular mechanisms underlying diseases. This study focuses on genome-wide expression analysis in an FD human podocyte model to gain insights into the underlying mechanisms of podocyte dysfunction. Human control and GLA-edited podocytes were used. Gene expression data was generated using RNA-seq analysis, and differentially expressed genes were identified using DESeq2. Principal component analysis and Spearman correlation have explored gene expression trends. Functional enrichment and Reporter metabolite analyses were conducted to identify significantly affected metabolites and metabolic pathways. Differential expression analysis revealed 247 genes with altered expression levels in GLA-edited podocytes compared to control podocytes. Among these genes, 136 were underexpressed, and 111 were overexpressed in GLA-edited cells. Functional analysis of differentially expressed genes showed their involvement in various pathways related to oxidative stress, inflammation, fatty acid metabolism, collagen and extracellular matrix homeostasis, kidney injury, apoptosis, autophagy, and cellular stress response. The study provides insights into molecular mechanisms underlying Fabry podocyte dysfunction. Integrating transcriptomics data with genome-scale metabolic modeling further unveiled metabolic alterations in GLA-edited podocytes. This comprehensive approach contributes to a better understanding of Fabry disease and may lead to identifying new biomarkers and therapeutic targets for this rare lysosomal disorder.

Objective: The study aimed to address the challenge of early assessment of neonatal hypoxic-ischemic encephalopathy (HIE) severity to identify candidates for therapeutic hypothermia (TH). The objective was to develop an automated classification model for neonatal EEGs, enabling accurate HIE severity assessment 24/7., Methods: EEGs recorded within 6 h of life after perinatal anoxia were visually graded into 3 severity groups (HIE French Classification) and quantified using 6 qEEG markers measuring amplitude, continuity and frequency content. Machine learning models were developed on a dataset of 90 EEGs and validated on an independent dataset of 60 EEGs., Results: The selected model achieved an overall accuracy of 80.6% in the development phase and 80% in the validation phase. Notably, the model accurately identified 28 out of 30 children for whom TH was indicated after visual EEG analysis, with only 2 cases (moderate EEG abnormalities) not recommended for cooling., Conclusions: The combination of clinically relevant qEEG markers led to the development of an effective automated EEG classification model, particularly suited for the post-anoxic latency phase. This model successfully discriminated neonates requiring TH., Significance: The proposed model has potential as a bedside clinical decision support tool for TH., (Copyright © 2024 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Identifying genetic risk factors for highly heterogeneous disorders like epilepsy remains challenging. Here, we present the largest whole-exome sequencing study of epilepsy to date, with >54,000 human exomes, comprising 20,979 deeply phenotyped patients from multiple genetic ancestry groups with diverse epilepsy subtypes and 33,444 controls, to investigate rare variants that confer disease risk. These analyses implicate seven individual genes, three gene sets, and four copy number variants at exome-wide significance. Genes encoding ion channels show strong association with multiple epilepsy subtypes, including epileptic encephalopathies, generalized and focal epilepsies, while most other gene discoveries are subtype-specific, highlighting distinct genetic contributions to different epilepsies. Combining results from rare single nucleotide/short indel-, copy number-, and common variants, we offer an expanded view of the genetic architecture of epilepsy, with growing evidence of convergence among different genetic risk loci on the same genes. Top candidate genes are enriched for roles in synaptic transmission and neuronal excitability, particularly postnatally and in the neocortex. We also identify shared rare variant risk between epilepsy and other neurodevelopmental disorders. Our data can be accessed via an interactive browser, hopefully facilitating diagnostic efforts and accelerating the development of follow-up studies., Competing Interests: Competing Interests B.M.N is a member of the scientific advisory board at Deep Genomics and Neumora. No other authors have competing interests to declare

This study aimed to compare the diagnostic performance of visual assessment of electroencephalography (EEG) using the Grand Total EEG (GTE) score and quantitative EEG (QEEG) using spectral analysis in the context of cognitive impairment. This was a retrospective study of patients with mild cognitive impairment, with (MCI+V) or without (MCI) vascular dysfunction, and patients with dementia including Alzheimer's disease, Lewy Body Dementia and vascular dementia. The results showed that the GTE is a simple scoring system with some potential applications, but limited ability to distinguish between dementia subtypes, while spectral analysis appeared to be a powerful tool, but its clinical development requires the use of artificial intelligence tools., Competing Interests: Declaration of competing interest None., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)