Lack of imbalance between the master regulators TTF1/NKX2-1 and ΔNp63/p40 implies adverse prognosis in non-small cell lung cancer.

The transcription factors TTF1/NKX2-1 and ΔNp63/p40 are the counterposed molecular markers associated with the main Non-Small Cell Lung Cancer subtypes: TTF1 for adenocarcinoma, p40 for squamous cell carcinoma. Although they generally display a mutually exclusive expression, some exceptions exist simultaneously lacking or (very rarely) expressing both markers, either pattern being associated to poor prognosis. Hence, we quantitatively analyzed the relationship between their coordinated activity and prognosis. By analyzing the respective downstream transcriptional programs of the two genes, we defined a simple quantitative index summarizing the amount of mutual exclusivity between their activities, called Mean Absolute Activity (MAA). Systematic analysis of the MAA index in a dataset of 1018 NSCLC samples replicated on a validation dataset of 275 showed that the loss of imbalance between TTF-1 and p40 corresponds to a steady, progressive reduction in both overall and recurrence-free survival. Coherently, samples correspondent to more balanced activities were enriched for pathways related to increased malignancy and invasiveness. Importantly, multivariate analysis showed that the prognostic significance of the proposed index MAA is independent of other clinical variables including stage, sex, age and smoke exposure. These results hold irrespectively of tumor morphology across NSCLC subtypes, providing a unifying description of different expression patterns.

Utility of KI-67 as a prognostic biomarker in pulmonary neuroendocrine neoplasms: a systematic review and meta-analysis.

OBJECTIVES: Ki-67, a marker of cellular proliferation, is associated with prognosis across a wide range of tumours, including gastroenteropancreatic neuroendocrine neoplasms (NENs), lymphoma, urothelial tumours and breast carcinomas. Its omission from the classification system of pulmonary NENs is controversial. This systematic review sought to assess whether Ki-67 is a prognostic biomarker in lung NENs and, if feasible, proceed to a meta-analysis. RESEARCH DESIGN AND METHODS: Medline (Ovid), Embase, Scopus and the Cochrane library were searched for studies published prior to 28 February 2019 and investigating the role of Ki-67 in lung NENs. Eligible studies were those that included more than 20 patients and provided details of survival outcomes, namely, HRs with CIs according to Ki-67 percentage. Studies not available as a full text or without an English manuscript were excluded. This study was prospectively registered with PROSPERO. RESULTS: Of 11 814 records identified, seven studies met the inclusion criteria. These retrospective studies provided data for 1268 patients (693 TC, 281 AC, 94 large cell neuroendocrine carcinomas and 190 small cell lung carcinomas) and a meta-analysis was carried out to estimate a pooled effect. Random effects analyses demonstrated an association between a high Ki-67 index and poorer overall survival (HR of 2.02, 95% CI 1.16 to 3.52) and recurrence-free survival (HR 1.42; 95% CI 1.01 to 2.00). CONCLUSION: This meta-analysis provides evidence that high Ki-67 labelling indices are associated with poor clinical outcomes for patients diagnosed with pulmonary NENs. This study is subject to inherent limitations, but it does provide valuable insights regarding the use of the biomarker Ki-67, in a rare tumour. PROSPERO REGISTRATION NUMBER: CRD42018093389.

MEDTEC Students against Coronavirus: Investigating the Role of Hemostatic Genes in the Predisposition to COVID-19 Severity.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the etiologic agent of the coronavirus disease 2019 (COVID-19) pandemic. Besides virus intrinsic characteristics, the host genetic makeup is predicted to account for the extreme clinical heterogeneity of the disease, which is characterized, among other manifestations, by a derangement of hemostasis associated with thromboembolic events. To date, large-scale studies confirmed that genetic predisposition plays a role in COVID-19 severity, pinpointing several susceptibility genes, often characterized by immunologic functions. With these premises, we performed an association study of common variants in 32 hemostatic genes with COVID-19 severity. We investigated 49,845 single-nucleotide polymorphism in a cohort of 332 Italian severe COVID-19 patients and 1668 controls from the general population. The study was conducted engaging a class of students attending the second year of the MEDTEC school (a six-year program, held in collaboration between Humanitas University and the Politecnico of Milan, allowing students to gain an MD in Medicine and a Bachelor's Degree in Biomedical Engineering). Thanks to their willingness to participate in the fight against the pandemic, we evidenced several suggestive hits (p < 0.001), involving the PROC, MTHFR, MTR, ADAMTS13, and THBS2 genes (top signal in PROC: chr2:127192625:G:A, OR = 2.23, 95%CI = 1.50-3.34, p = 8.77 × 10-5). The top signals in PROC, MTHFR, MTR, ADAMTS13 were instrumental for the construction of a polygenic risk score, whose distribution was significantly different between cases and controls (p = 1.62 × 10-8 for difference in median levels). Finally, a meta-analysis performed using data from the Regeneron database confirmed the contribution of the MTHFR variant chr1:11753033:G:A to the predisposition to severe COVID-19 (pooled OR = 1.21, 95%CI = 1.09-1.33, p = 4.34 × 10-14 in the weighted analysis).

Coexpression of ΔNp63/p40 and TTF1 Within Most of the Same Individual Cells Identifies Life-Threatening NSCLC Featuring Squamous and Glandular Biphenotypic Differentiation: Clinicopathologic Correlations.

Introduction: Double occurrence of TTF1 and ΔNp63/p40 (henceforth, p40) within the same individual cells is exceedingly rare in lung cancer. Little is known on their biological and clinical implications. Methods: Two index cases immunoreactive for both p40 and TTF1 and nine tumors selected from The Cancer Genome Atlas (TCGA) according to the mRNA levels of the two relevant genes entered the study. Results: The two index cases were peripherally located, poorly differentiated, and behaviorally unfavorable carcinomas, which shared widespread p40 and TTF1 decoration within the same individual tumor cells. They also retained SMARCA2 and SMARCA4 expression, while variably stained for p53, cytokeratin 5, and programmed death-ligand 1. A subset of basal cells p40+/TTF1+ could be found in normal distal airways. Biphenotypic glandular and squamous differentiation was unveiled by electron microscopy, along with EGFR, RAD51B, CCND3, or NF1 mutations and IGF1R, MYC, CCND1, or CDK2 copy number variations on next-generation sequencing analysis. The nine tumors from TCGA (0.88% of 1018 tumors) shared the same poor prognosis, clinical presentation, and challenging histology and had activated pathways of enhanced angiogenesis and epithelial-mesenchymal transition. Mutation and copy number variation profiles did not differ from the other TCGA tumors. Conclusions: Double p40+/TTF1+ lung carcinomas are aggressive and likely underrecognized non-small cell carcinomas, whose origin could reside in double-positive distal airway stem-like basal cells through either de novo-basal-like or differentiating cell mechanisms according to a model of epithelial renewal.

Automated Analysis of Proliferating Cells Spatial Organisation Predicts Prognosis in Lung Neuroendocrine Neoplasms.

Lung neuroendocrine neoplasms (lung NENs) are categorised by morphology, defining a classification sometimes unable to reflect ultimate clinical outcome. Subjectivity and poor reproducibility characterise diagnosis and prognosis assessment of all NENs. Here, we propose a machine learning framework for tumour prognosis assessment based on a quantitative, automated and repeatable evaluation of the spatial distribution of cells immunohistochemically positive for the proliferation marker Ki-67, performed on the entire extent of high-resolution whole slide images. Combining features from the fields of graph theory, fractality analysis, stochastic geometry and information theory, we describe the topology of replicating cells and predict prognosis in a histology-independent way. We demonstrate how our approach outperforms the well-recognised prognostic role of Ki-67 Labelling Index on a multi-centre dataset comprising the most controversial lung NENs. Moreover, we show that our system identifies arrangement patterns in the cells positive for Ki-67 that appear independently of tumour subtyping. Strikingly, the subset of these features whose presence is also independent of the value of the Labelling Index and the density of Ki-67-positive cells prove to be especially relevant in discerning prognostic classes. These findings disclose a possible path for the future of grading and classification of NENs.

Morphologic and molecular classification of lung neuroendocrine neoplasms.

Neuroendocrine neoplasms (NENs) of the lung encompass neuroendocrine tumors (NETs) composed of typical (TC) and atypical (AC) carcinoids and full-fledged carcinomas (NECs) inclusive of large cell neuroendocrine carcinoma (LCNEC) and small cell carcinoma (SCLC). NETs and NECs are thought to represent distinct and separate lesions with neither molecular overlap nor common developmental continuum. Two perspectives were addressed regarding the morphologic and molecular classification of lung NENs: (i) a supervised approach by browsing the traditional classification, the relevant gene alterations, and their clinical implications; and (ii) an unsupervised approach, by reappraising neoplasms according to risk factors and natural history of disease to construct an interpretation model relied on biological data. We herein emphasize lights and shadows of the current classification of lung NENs and provide an alternative outlook on these tumors focused on what we currently know about the biological determinants and the natural history of disease.

Clinical implications of lung neuroendocrine neoplasm classification.

INTRODUCTION: Neuroendocrine neoplasms of the lung (Lung NENs) encompass NE tumors (NETs), which are in turn split into typical and atypical carcinoids, and NE carcinomas (NECs), which group together small-cell carcinoma and large-cell NE carcinoma. This classification is the current basis for orienting the daily practice of these patients, with diagnostic, prognostic, and predictive inferences. AREAS COVERED: The clinical implications of lung NEN classification are addressed according to three converging perspectives, which were dissected through an extensive literature overview: (1) how to put intratumor heterogeneity into the context of the current classification; (2) how to contextualize immunohistochemistry markers to improve diagnosis, prognosis, and therapy prediction; and (3) how to use immuno-oncology strategies for life-threatening NECs, which still account for 90% or more of lung NENs. EXPERT OPINION: We provide practical insights to account for intratumor heterogeneity, practice the choice of immunohistochemistry markers, and emphasize once again the added value of immuno-oncology in the setting of personalized medicine of lung NENs.

All-Trans Retinoic Acid Stimulates Viral Mimicry, Interferon Responses and Antigen Presentation in Breast-Cancer Cells.

All-trans retinoic acid (ATRA), a recognized differentiating agent, has significant potential in the personalized/stratified treatment of breast cancer. The present study reports on the molecular mechanisms underlying the anti-tumor activity of ATRA in breast cancer. The work is based on transcriptomic experiments performed on ATRA-treated breast cancer cell-lines, short-term tissue cultures of patient-derived mammary-tumors and a xenograft model. ATRA upregulates gene networks involved in interferon-responses, immune-modulation and antigen-presentation in retinoid-sensitive cells and tumors characterized by poor immunogenicity. ATRA-dependent upregulation of these gene networks is caused by a viral mimicry process, involving the activation of endogenous retroviruses. ATRA induces a non-canonical type of viral mimicry, which results in increased expression of the IRF1 (Interferon Responsive Factor 1) transcription factor and the DTX3L (Deltex-E3-Ubiquitin-Ligase-3L) downstream effector. Functional knockdown studies indicate that IRF1 and DTX3L are part of a negative feedback loop controlling ATRA-dependent growth inhibition of breast cancer cells. The study is of relevance from a clinical/therapeutic perspective. In fact, ATRA stimulates processes controlling the sensitivity to immuno-modulatory drugs, such as immune-checkpoint-inhibitors. This suggests that ATRA and immunotherapeutic agents represent rational combinations for the personalized treatment of breast cancer. Remarkably, ATRA-sensitivity seems to be relatively high in immune-cold mammary tumors, which are generally resistant to immunotherapy.

ImaGene: a convolutional neural network to quantify natural selection from genomic data.

BACKGROUND: The genetic bases of many complex phenotypes are still largely unknown, mostly due to the polygenic nature of the traits and the small effect of each associated mutation. An alternative approach to classic association studies to determining such genetic bases is an evolutionary framework. As sites targeted by natural selection are likely to harbor important functionalities for the carrier, the identification of selection signatures in the genome has the potential to unveil the genetic mechanisms underpinning human phenotypes. Popular methods of detecting such signals rely on compressing genomic information into summary statistics, resulting in the loss of information. Furthermore, few methods are able to quantify the strength of selection. Here we explored the use of deep learning in evolutionary biology and implemented a program, called ImaGene, to apply convolutional neural networks on population genomic data for the detection and quantification of natural selection. RESULTS: ImaGene enables genomic information from multiple individuals to be represented as abstract images. Each image is created by stacking aligned genomic data and encoding distinct alleles into separate colors. To detect and quantify signatures of positive selection, ImaGene implements a convolutional neural network which is trained using simulations. We show how the method implemented in ImaGene can be affected by data manipulation and learning strategies. In particular, we show how sorting images by row and column leads to accurate predictions. We also demonstrate how the misspecification of the correct demographic model for producing training data can influence the quantification of positive selection. We finally illustrate an approach to estimate the selection coefficient, a continuous variable, using multiclass classification techniques. CONCLUSIONS: While the use of deep learning in evolutionary genomics is in its infancy, here we demonstrated its potential to detect informative patterns from large-scale genomic data. We implemented methods to process genomic data for deep learning in a user-friendly program called ImaGene. The joint inference of the evolutionary history of mutations and their functional impact will facilitate mapping studies and provide novel insights into the molecular mechanisms associated with human phenotypes.

Multisite analysis of high-grade serous epithelial ovarian cancers identifies genomic regions of focal and recurrent copy number alteration in 3q26.2 and 8q24.3.

High-grade serous epithelial ovarian cancer (HGS-EOC) is a systemic disease, with marked intra and interpatient tumor heterogeneity. The issue of spatial and temporal heterogeneity has long been overlooked, hampering the possibility to identify those genomic alterations that persist, before and after therapy, in the genome of all tumor cells across the different anatomical districts. This knowledge is the first step to clarify those molecular determinants that characterize the tumor biology of HGS-EOC and their route toward malignancy. In our study, -omics data were generated from 79 snap frozen matched tumor biopsies, withdrawn before and after chemotherapy from 24 HGS-EOC patients, gathered together from independent cohorts. The landscape of somatic copy number alterations depicts a more homogenous and stable genomic portrait than the single nucleotide variant profile. Genomic identification of significant targets in cancer analysis identified two focal and minimal common regions (FMCRs) of amplification in the cytoband 3q26.2 (region α, 193 kb long) and 8q24.3 (region ß, 495 kb long). Analysis in two external databases confirmed regions α and ß are features of HGS-EOC. The MECOM gene is located in region α, and 15 genes are in region ß. No functional data are yet available for the genes in the ß region. In conclusion, we have identified for the first time two FMCRs of amplification in HGS-EOC, opening up a potential biological role in its etiopathogenesis.

Recent advances in the molecular landscape of lung neuroendocrine tumors.

INTRODUCTION: Neuroendocrine tumors of the lung (Lung-NETs) make up a heterogenous family of neoplasms showing neuroendocrine differentiation and encompass carcinoids and neuroendocrine carcinomas. On molecular grounds, they considered two completely distinct and separate tumor groups with no overlap of molecular alterations nor common developmental mechanisms. Areas covered: Two perspectives were evaluated based on an extensive review and rethinking of literature: (1) the current classification as an instrument to obtaining clinical and molecular insights into the context of Lung-NETs; and (2) an alternative and innovative interpretation of these tumors, proposing a tripartite separation into early aggressive primary high-grade neuroendocrine tumors (HGNET), differentiating or secondary HGNET, and indolent NET. Expert opinion: We herein provide an alternative outlook on Lung-NETs, which is a paradigm shift to current pathogenesis models and expands the understanding of these tumors.

A retinoic acid-dependent stroma-leukemia crosstalk promotes chronic lymphocytic leukemia progression.

In chronic lymphocytic leukemia (CLL), the non-hematopoietic stromal microenvironment plays a critical role in promoting tumor cell recruitment, activation, survival, and expansion. However, the nature of the stromal cells and molecular pathways involved remain largely unknown. Here, we demonstrate that leukemic B lymphocytes induce the activation of retinoid acid synthesis and signaling in the microenvironment. Inhibition of RA-signaling in stromal cells causes deregulation of genes associated with adhesion, tissue organization and chemokine secretion including the B-cell chemokine CXCL13. Notably, reducing retinoic acid precursors from the diet or inhibiting RA-signaling through retinoid-antagonist therapy prolong survival by preventing dissemination of leukemia cells into lymphoid tissues. Furthermore, mouse and human leukemia cells could be distinguished from normal B-cells by their increased expression of Rarγ2 and RXRα, respectively. These findings establish a role for retinoids in murine CLL pathogenesis, and provide new therapeutic strategies to target the microenvironment and to control disease progression.

PML promotes metastasis of triple-negative breast cancer through transcriptional regulation of HIF1A target genes.

Elucidating the molecular basis of tumor metastasis is pivotal for eradicating cancer-related mortality. Triple-negative breast cancer (TNBC) encompasses a class of aggressive tumors characterized by high rates of recurrence and metastasis, as well as poor overall survival. Here, we find that the promyelocytic leukemia protein PML exerts a prometastatic function in TNBC that can be targeted by arsenic trioxide. We found that, in TNBC patients, constitutive HIF1A activity induces high expression of PML, along with a number of HIF1A target genes that promote metastasis at multiple levels. Intriguingly, PML controls the expression of these genes by binding to their regulatory regions along with HIF1A. This mechanism is specific to TNBC cells and does not occur in other subtypes of breast cancer where PML and prometastatic HIF1A target genes are underexpressed. As a consequence, PML promotes cell migration, invasion, and metastasis in TNBC cell and mouse models. Notably, pharmacological inhibition of PML with arsenic trioxide, a PML-degrading agent used to treat promyelocytic leukemia patients, delays tumor growth, impairs TNBC metastasis, and cooperates with chemotherapy by preventing metastatic dissemination. In conclusion, we report identification of a prometastatic pathway in TNBC and suggest clinical development toward the use of arsenic trioxide for TNBC patients.

Grading lung neuroendocrine tumors: Controversies in search of a solution.

BACKGROUND: Pathological grading of tumors is a way to measure biological aggressiveness. In lung neuroendocrine tumors (NET), grading is tautologically included into the current 2015 WHO histologic classification. Little is known, however, about alternative grading systems in lung NET. METHODS: Through an extensive search of the English literature on lung NET (updated to April 2016), the following key questions were addressed: a) current concepts of grading; b) clinicians' requests for grading; c) functional parameters for grading; d) Ki-67 labeling index (LI) for grading; e) towards an effective pathology grading system. RESULTS: There is some room for inconsistency in the histologic classification of lung NET, likely due to the varying attribution of defining criteria. Innovative diffusion-weighted imaging upon magnetic resonance or molecular analysis could help separate indolent from aggressive lung NET, thus integrating a grading approach other than histology. Troubles in the clinical handling of metastatic or individual tumors when relying on morphology alone support the development of a lung-specific grading system for the more accurate prediction of prognosis and planning therapy in individual patients. To integrate the 2015 WHO classification using innovative grading based on Ki-67 LI, mitotic count and necrosis, a new proposal is emerging where three categories of lung NET are identified, namely Lu-NET G1, Lu-NET G2 and Lu-NET G3, which would allow tumors with similar behavior and therapy to be better handled according to their own biological potential. CONCLUSION: This new formulation of lung NET grading could have clinical relevance for the individual handling of patients.

Hypoxia inducible factor-1α regulates a pro-invasive phenotype in acute monocytic leukemia.

Hypoxia inducible transcription factors (HIFs) are the main regulators of adaptive responses to hypoxia and are often activated in solid tumors, but their role in leukemia is less clear. In acute myeloid leukemia (AML), in particular, controversial new findings indicate that HIF-1α can act either as an oncogene or a tumor suppressor gene, and this may depend on the stage of leukemia development and/or the AML sub-type.In this study, we find that HIF-1α promotes leukemia progression in the acute monocytic leukemia sub-type of AML through activation of an invasive phenotype. By applying a list of validated HIF-1α-target genes to different AML sub-types, we identified a HIF-1α signature that typifies acute monocytic leukemia when compared with all other AML sub-types. We validated expression of this signature in cell lines and primary cells from AML patients. Interestingly, this signature is enriched for genes that control cell motility at different levels. As a consequence, inhibiting HIF-1α impaired leukemia cell migration, chemotaxis, invasion and transendothelial migration in vitro, and this resulted in impaired bone marrow homing and leukemia progression in vivo. Our data suggest that in acute monocytic leukemia an active HIF-1α-dependent pro-invasive pathway mediates the ability of leukemic cells to migrate and invade extramedullary sites and may be targeted to reduce leukemia dissemination.

The Uromodulin Gene Locus Shows Evidence of Pathogen Adaptation through Human Evolution.

Common variants in the UMOD gene encoding uromodulin, associated with risk of hypertension and CKD in the general population, increase UMOD expression and urinary excretion of uromodulin, causing salt-sensitive hypertension and renal lesions. To determine the effect of selective pressure on variant frequency, we investigated the allelic frequency of the lead UMOD variant rs4293393 in 156 human populations, in eight ancient human genomes, and in primate genomes. The T allele of rs4293393, associated with CKD risk, has high frequency in most modern populations and was the one detected in primate genomes. In contrast, we identified only the derived, C allele in Denisovan and Neanderthal genomes. The distribution of the UMOD ancestral allele did not follow the ancestral susceptibility model observed for variants associated with salt-sensitive hypertension. Instead, the global frequencies of the UMOD alleles significantly correlated with pathogen diversity (bacteria, helminths) and prevalence of antibiotic-resistant urinary tract infections (UTIs). The inverse correlation found between urinary levels of uromodulin and markers of UTIs in the general population substantiates the link between UMOD variants and protection against UTIs. These data strongly suggest that the UMOD ancestral allele, driving higher urinary excretion of uromodulin, has been kept at a high frequency because of its protective effect against UTIs.

Cellular and molecular determinants of all-trans retinoic acid sensitivity in breast cancer: Luminal phenotype and RARα expression.

Forty-two cell lines recapitulating mammary carcinoma heterogeneity were profiled for all-trans retinoic acid (ATRA) sensitivity. Luminal and ER(+) (estrogen-receptor-positive) cell lines are generally sensitive to ATRA, while refractoriness/low sensitivity is associated with a Basal phenotype and HER2 positivity. Indeed, only 2 Basal cell lines (MDA-MB157 and HCC-1599) are highly sensitive to the retinoid. Sensitivity of HCC-1599 cells is confirmed in xenotransplanted mice. Short-term tissue-slice cultures of surgical samples validate the cell-line results and support the concept that a high proportion of Luminal/ER(+) carcinomas are ATRA sensitive, while triple-negative (Basal) and HER2-positive tumors tend to be retinoid resistant. Pathway-oriented analysis of the constitutive gene-expression profiles in the cell lines identifies RARα as the member of the retinoid pathway directly associated with a Luminal phenotype, estrogen positivity and ATRA sensitivity. RARα3 is the major transcript in ATRA-sensitive cells and tumors. Studies in selected cell lines with agonists/antagonists confirm that RARα is the principal mediator of ATRA responsiveness. RARα over-expression sensitizes retinoid-resistant MDA-MB453 cells to ATRA anti-proliferative action. Conversely, silencing of RARα in retinoid-sensitive SKBR3 cells abrogates ATRA responsiveness. All this is paralleled by similar effects on ATRA-dependent inhibition of cell motility, indicating that RARα may mediate also ATRA anti-metastatic effects. We define gene sets of predictive potential which are associated with ATRA sensitivity in breast cancer cell lines and validate them in short-term tissue cultures of Luminal/ER(+) and triple-negative tumors. In these last models, we determine the perturbations in the transcriptomic profiles afforded by ATRA. The study provides fundamental information for the development of retinoid-based therapeutic strategies aimed at the stratified treatment of breast cancer subtypes.

LSD1 Neurospecific Alternative Splicing Controls Neuronal Excitability in Mouse Models of Epilepsy.

Alternative splicing in the brain is dynamic and instrumental to adaptive changes in response to stimuli. Lysine-specific demethylase 1 (LSD1/KDM1A) is a ubiquitously expressed histone H3Lys4 demethylase that acts as a transcriptional co-repressor in complex with its molecular partners CoREST and HDAC1/2. In mammalian brain, alternative splicing of LSD1 mini-exon E8a gives rise to neuroLSD1, a neurospecific isoform that, upon phosphorylation, acts as a dominant-negative causing disassembly of the co-repressor complex and de-repression of target genes. Here we show that the LSD1/neuroLSD1 ratio changes in response to neuronal activation and such effect is mediated by neurospecific splicing factors NOVA1 and nSR100/SRRM4 together with a novel cis-silencer. Indeed, we found that, in response to epileptogenic stimuli, downregulation of NOVA1 reduces exon E8a splicing and expression of neuroLSD1. Using behavioral and EEG analyses we observed that neuroLSD1-specific null mice are hypoexcitable and display decreased seizure susceptibility. Conversely, in a mouse model of Rett syndrome characterized by hyperexcitability, we measured higher levels of NOVA1 protein and upregulation of neuroLSD1. In conclusion, we propose that, in the brain, correct ratio between LSD1 and neuroLSD1 contributes to excitability and, when altered, could represent a pathogenic event associated with neurological disorders involving altered E/I.