Decoding the role of the lncRNA HOTAIRM1 in human motor neurons

The mammalian genome produces thousands of long non-coding RNAs (lncRNAs), which have been demonstrated to be fundamental in the control of many biological processes. These molecules play a crucial role in the multilayered regulation of physiological and disease-related gene expression programs, having significant implications in shaping central nervous system (CNS) complexity. Neuronal differentiation is a timely and spatially regulated process, relying on precisely orchestrated gene expression control. The coordinated activity of transcription factors and non-coding RNAs (ncRNAs), organized in intricate regulatory networks, drives cell fate specification ensuring correct and specific neuronal functions. We previously described,1 at both the molecular and functional level, the lncRNA nHOTAIRM1 as a neuronal-enriched transcript, which is upregulated during in vitro neuronal differentiation and highly expressed in post-mitotic motor neurons (MNs). We demonstrated that the nuclear nHOTAIRM1, even if much less abundant than its cytoplasmic counterpart, it is involved in the achievement of correct neuronal differentiation timing as an epigenetic regulator of NEUROG2 expression.1 Remarkably, among all human brain tissues, nHOTAIRM1 is specifically expressed in the spinal cord. Consistently, we found that nHOTAIRM1 accumulates in MN-enriched ventral spinal cord lineages differentiated from human induced pluripotent stem cells (iPSCs).1 All this evidence prompted us to further investigate the role of the highly expressed nHOTAIRM1 specifically on MN generation and/or function, to ultimately determine whether its deregulation affects MN differentiation and activity. To experimentally address these questions, here we applied a genome editing-based loss-of-function approach to a model system that efficiently recapitulates spinal MN differentiation, and we identified key nHOTAIRM1 target genes implicated in MN maturation, morphology and activity. Our findings allowed us to conclude that nHOTAIRM1 directs multiple crucial aspects of MN physiology, from their development to the acquisition of appropriate morphological features and motor function

RNA Deregulation in Amyotrophic Lateral Sclerosis: The Noncoding Perspective

RNA metabolism is central to cellular physiopathology. Almost all the molecular pathways underpinning biological processes are affected by the events governing the RNA life cycle, ranging from transcription to degradation. The deregulation of these processes contributes to the onset and progression of human diseases. In recent decades, considerable efforts have been devoted to the characterization of noncoding RNAs (ncRNAs) and to the study of their role in the homeostasis of the nervous system (NS), where they are highly enriched. Acting as major regulators of gene expression, ncRNAs orchestrate all the steps of the differentiation programs, participate in the mechanisms underlying neural functions, and are crucially implicated in the development of neuronal pathologies, among which are neurodegenerative diseases. This review aims to explore the link between ncRNA dysregulation and amyotrophic lateral sclerosis (ALS), the most frequent motoneuron (MN) disorder in adults. Notably, defective RNA metabolism is known to be largely associated with this pathology, which is often regarded as an RNA disease. We also discuss the potential role that these transcripts may play as diagnostic biomarkers and therapeutic targets

Emergenza Covid-19. Nuove tecnologie e nuove forme di socialità

Questo libro raccoglie un ciclo di seminari organizzati all'inizio della pandemia, fra aprile e maggio 2020, presso l'Università degli studi Teramo. Era allora — ed è ancora, nel momento in cui chiudo questa raccolta — l'occasione per osservare il cambiamento, e le risorse messe in campo per affrontarlo: tecnologico-strumentali, ma anche concettuali, sociali ed umane.Come abbiamo risposto alla crisi? Cosa sta succedendo nelle nostre vite? Ma soprattutto: quali problemi ci si stanno ponendo? E quali risorse abbiamo — o non abbiamo — a disposizione per rispondervi? Il volume raccoglie gli interventi di Maurizio Napolitano sul civic hacking, di Paolo Subioli sull'uso consapevole delle tecnologie, di Gianluigi Tiddia sul turismo, di Antonella Tollis sulla comunicazione istituazionale, e di Pina Manente sul data journalism

HOTAIRM1 regulates neuronal differentiation by controlling NEUROGENIN 2 and the downstream neurogenic cascade

Neuronal differentiation is a timely and spatially regulated process orchestrated by a synergy between transcription factors and noncoding RNAs. Here we demonstrate that the long noncoding RNA HOTAIRM1 epigenetically controls the expression of the proneural transcription factor NEUROGENIN 2, that is key to neuronal fate commitment and critical for brain development. We also show that HOTAIRM1 activity impacts on NEUROGENIN 2 downstream regulatory cascade, thus contributing to the achievement of proper neuronal differentiation timing. Finally, we identify the RNA-binding proteins HNRNPK and FUS as regulators of HOTAIRM1 biogenesis and metabolism

HOTAIRM1 regulates neuronal differentiation by controlling NEUROGENIN 2 and the downstream neurogenic cascade

Neuronal differentiation is a timely and spatially regulated process, relying on precisely orchestrated gene expression control. The sequential activation/repression of genes driving cell fate specification is achieved by complex regulatory networks, where transcription factors and noncoding RNAs work in a coordinated manner. Here, we provide the molecular, functional and mechanistic characterization of the long noncoding RNA HOTAIRM1 (HOXA Transcript Antisense RNA, Myeloid-Specific 1) as a new player in neuronal differentiation. At the molecular level, we describe HOTAIRM1 neuronal isoform and identify the RNA-binding proteins HNRNPK and FUS as regulators of its biogenesis and metabolism. Functionally, we discover that HOTAIRM1 controls the expression of the proneural transcription factor NEUROGENIN 2, that is key to neuronal fate commitment and critical for brain development. We demonstrate that, during neuronal differentiation, nuclear HOTAIRM1 controls the transitory expression of NEUROGENIN 2. Mechanistically, HOTAIRM1 acts as an epigenetic regulator that, recruiting the repressive complex PRC2, contributes to limit the time-window of NEUROGENIN 2 expression. Remarkably, HOTAIRM1 also controls NEUROGENIN 2 downstream regulatory cascade contributing to the achievement of proper neuronal differentiation timing

Dataset Figure_5: Whi5-GFP intensity versus size and time in a synchronous G1 population

Whi5-GFP intensity as a function of time for the different FOV of elutriated cells. This dataset contains: 1. Raw TIF_images_NADH – These are autofluorescence images taken at each time point for the purposes of calculating cell size. Time points were every 10 minutes except at 30 minutes which had to be discarded due to poor focus. 2. Raw TIF_images_WHI5 – These raw image files correspond to images of Whi5-GFP excited at 1000 nm fpr all 11 time points of different FOV obtained at 3 different z positions (1-3) (0, -0.5 um, + 0.5 um). Whi5-GFP intensity values for each nucleus were taken for the z-position that gave the highest intensity for each nucleus. 3. NADHDATA_with_time plot.xlsx is the analysis of the raw images of auto-fluorescence exciting at 750 nm. The only relevant information for the Figure is in Colume C sheet 1. It is the cell area in total pixels. 4. Whi5Data_BestFocusPlanes_All_FOV.xlsx is the analysis of the Whi5-GFP images for Whi5-GFP intensity vs time and size for each time point (which corresponds to a different FOV)

Dataset Figure_6: Cln3 levels pulse prior to Start

Data and Matlab script 1. FLmean_Cln3_GLU: CSV file containing mean GFP concentration from Cln3-P2A-GFP for single daughter cells grown in glucose. Each cell is followed from birth until a few minutes after bud appearance. Data is organized by columns (one column corresponds to one cell). The first column is the vector of measurement time points (in minutes). 2. Vol_Cln3_GLU: CSV file containing cell volume for each of the daughter cells described above. Data is organized by columns (one column corresponds to one cell). The first column is the vector of measurement time points (in minutes). 3. bud_times: Excel file containing the time of bud appearance for each of the daughter cells described above 4. GP_FLtot_Cln3_GLU_example: Matlab script used to perform Gaussian process regression (see description in Methods) on the total GFP fluorescence for each of the daughter cells described above. The script produces single-cell Cln3 abundance data used to generate Fig. 5c of the main text and Extended Data Figure 4H. NOTE: in order to run, the Matlab script requires the installation of the GPML Matlab toolbox (freely available at http://www.gaussianprocess.org/gpml/code/matlab/doc/)

Dataset Figure_4: Nuclear Whi5-GFP intensity versus time from repeated imaging of the same individual cells

Whi5-GFP intensity as a function of time for the same FOVs. Only small daughter cells in the asynchronous population at time point 0 were quantified as a function of time. The folder 'Image Files' contains raw image files for Whi5-GFP for FOV1,3,4,5 & 6 at each of the 6 time points (0, 20, 40, 60, 80, 100 min). The folder 'Excel_analysis_output' contains the output files for all five FOV (1,3,4,5,6) for 1. fovX_t0 or time 100_whi5_nadh_*.xlsx – at time 0 and time 100 minutes. These files correspond to the analysis of the background auto-fluorescence excited at 750 nm used to calculate size. The only relevant information from these analyses is the Cyto size (fL) column in tab 3 of each file 2. fovX_tX_whi5_*.xlsx Analysis of all time points and all FOV for Whi5-GFP intensities