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Abstract Background Neurodevelopmental disorders (NDDs) are heterogeneous conditions due to alterations of a variety of molecular mechanisms and cell dysfunctions. SETD5 haploinsufficiency leads to NDDs due to chromatin defects. Epigenetic basis of NDDs has been reported in an increasing number of cases while mitochondrial dysfunctions are more common within NDD patients than in the general population. Methods We investigated in vitro neural stem cells as well as the brain of the Setd5 haploinsufficiency mouse model interrogating its transcriptome, analyzing mitochondrial structure, biochemical composition, and dynamics, as well as mitochondrial functionality. Results Mitochondrial impairment is facilitated by transcriptional aberrations originated by the decrease of the SETD5 enzyme. Low levels of SETD5 resulted in fragmented mitochondria, reduced mitochondrial membrane potential, and ATP production both in neural precursors and neurons. Mitochondria were also mislocalized in mutant neurons, with reduced organelles within neurites and synapses. Limitations We found several defects in the mitochondrial compartment; however, we can only speculate about their position in the hierarchy of the pathological mechanisms at the basis of the disease. Conclusions Our study explores the interplay between chromatin regulation and mitochondria functions as a possible important aspect of SETD5-associated NDD pathophysiology. Our data, if confirmed in patient context, suggest that the mitochondrial activity and dynamics may represent new therapeutic targets for disorders associated with the loss of SETD5.

Abstract Within the chromatin, distal elements interact with promoters to regulate specific transcriptional programs. Histone acetylation, interfering with the net charges of the nucleosomes, is a key player in this regulation. Here, we report that the oncoprotein SET is a critical determinant for the levels of histone acetylation within enhancers. We disclose that a condition in which SET is accumulated, the severe Schinzel-Giedion Syndrome (SGS), is characterized by a failure in the usage of the distal regulatory regions typically employed during fate commitment. This is accompanied by the usage of alternative enhancers leading to a massive rewiring of the distal control of the gene transcription. This represents a (mal)adaptive mechanism that, on one side, allows to achieve a certain degree of differentiation, while on the other affects the fine and corrected maturation of the cells. Thus, we propose the differential in cis-regulation as a contributing factor to the pathological basis of SGS and possibly other the SET-related disorders in humans.

ABSTRACTRelative soil water content (RSWC) is widely used to characterize the impact of water stress (WS) on vegetation. In bi-layer ecosystems, such as olive groves, this impact must be primarily estimated for the tree component, which, having greater rooting depth, responds more slowly to WS than understory grass. This complicates the application of methods for RSWC prediction, which must be properly adapted to consider the deeper soil layer influential on olive trees. The current study investigates the modification of a recently proposed RSWC simulation method based on a combination of meteorological and satellite-derived normalized difference vegetation index (NDVI) data. The application of the method to an olive grove in Central Italy requires the estimation of both weather and NDVI contributions affecting solely olive trees, which is carried out through the use of appropriate data processing techniques. The RSWC estimates obtained reasonably reproduce the ground RSWC observations referred to the 1 m soil layer, which are representative of the WS affecting olive trees (r2 = 0.795, RMSE = 0.15 and MBE = −0.09). The limits and prospects of this method are finally discussed with particular reference to the possible integration of the RSWC estimates within more complex ecosystem models.

Abstract Pancreatic cancer (PC) has a very low survival rate mainly due to late diagnosis and refractoriness to therapies. The latter also cause adverse effects negatively affecting the patients’ quality of life, often requiring dose reduction or discontinuation of scheduled treatments, compromising the chances of cure. We explored the effects of a specific probiotic blend on PC mice xenografted with KRAS wild-type or KRASG12D mutated cell lines alone or together with gemcitabine+nab-paclitaxel treatment to then assess tumor volume and clinical pathological variables. Beside a semi-quantitative histopathological evaluation of murine tumor and large intestine samples, histochemical and immunohistochemical analyses were carried out to evaluate collagen deposition, proliferation index Ki67, immunological microenvironment tumor-associated, DNA damage markers and also mucin production. Blood cellular and biochemical parameters and serum metabolomics were further analyzed. 16S sequencing was performed to analyze the composition of fecal microbiota. Gemcitabine+nab-paclitaxel treatment impaired gut microbial profile in KRAS wild-type and KRASG12D mice. Counteracting gemcitabine+nab-paclitaxel- induced dysbiosis through the administration of probiotics ameliorated chemotherapy side effects and decreased cancer-associated stromatogenesis. Milder intestinal damage and improved blood count were also observed upon probiotics treatment as well as a positive effect on fecal microbiota, yielding an increase in species richness and in short chain fatty acids producing- bacteria. Mice’ serum metabolomic profiles revealed significant drops in many amino acids upon probiotics administration in KRAS wild-type mice while in animals transplanted with PANC-1 KRASG12D mutated all treated groups showed a sharp decline in serum levels of bile acids with respect to control mice. These results suggest that counteracting gemcitabine+nab-paclitaxel-induced dysbiosis ameliorates chemotherapy side effects by restoring a favorable microbiota composition. Relieving adverse effects of the chemotherapy through microbiota manipulation could be a desirable strategy in order to improve pancreatic cancer patients’ quality of life and to increase the chance of cure.

Wolfram syndrome 1 (WS1) is a rare genetic disorder caused by mutations in the WFS1 gene leading to a wide spectrum of clinical dysfunctions, among which blindness, diabetes, and neurological deficits are the most prominent. WFS1 encodes for the endoplasmic reticulum (ER) resident transmembrane protein wolframin with multiple functions in ER processes. However, the WFS1-dependent etiopathology in retinal cells is unknown. Herein, we showed that Wfs1 mutant mice developed early retinal electrophysiological impairments followed by marked visual loss. Interestingly, axons and myelin disruption in the optic nerve preceded the degeneration of the retinal ganglion cell bodies in the retina. Transcriptomics at pre-degenerative stage revealed the STAT3-dependent activation of proinflammatory glial markers with reduction of the homeostatic and pro-survival factors glutamine synthetase and BDNF. Furthermore, label-free comparative proteomics identified a significant reduction of the monocarboxylate transport isoform 1 (MCT1) and its partner basigin that are highly enriched on retinal glia and myelin-forming oligodendrocytes in optic nerve together with wolframin. Loss of MCT1 caused a failure in lactate transfer from glial to neuronal cell bodies and axons leading to a chronic hypometabolic state. Thus, this bioenergetic impairment is occurring concurrently both within the axonal regions and cell bodies of the retinal ganglion cells, selectively endangering their survival while impacting less on other retinal cells. This metabolic dysfunction occurs months before the frank RGC degeneration suggesting an extended time-window for intervening with new therapeutic strategies focused on boosting retinal and optic nerve bioenergetics in WS1.

Whether microglial activation constitutes a primary pathological event in synucleinopathies is not known. Here, the authors generated a mouse model over-expressing α-synuclein in microglial cells and found these mice developed progressive dopaminergic neuron degeneration and microglia developed a reactive, pro-inflammatory state with phagocytic exhaustion.

Schinzel-Giedion syndrome (SGS) is a fatal developmental syndrome characterized by severe intellectual and physical deficits due, at least in part, to early neurodegeneration. Here the authors introduce a human SGS model that displays disease-relevant phenotypes to demonstrate that neuronal death in SGS originates from developmental alterations mainly in safeguarding cell identity and homeostasis.

Future climate change is expected to significantly alter the growth of vegetation in grassland systems, in terms of length of the growing season, forage production, and climate-altering gas emissions. The main objective of this work was, therefore, to simulate the future impacts of foreseen climate change in the context of two pastoral systems in the central Italian Apennines and test different adaptation strategies to cope with these changes. The PaSim simulation model was, therefore, used for this purpose. After calibration by comparison with observed data of aboveground biomass (AGB) and leaf area index (LAI), simulations were able to produce various future outputs, such as length of growing season, AGB, and greenhouse gas (GHG) emissions, for two time windows (i.e., 2011–2040 and 2041–2070) using 14 global climate models (GCMs) for the generation of future climate data, according to RCP (Representative Concentration Pathways) 4.5 and 8.5 scenarios under business-as-usual management (BaU). As a result of increasing temperatures, the fertilizing effect of CO2, and a similar trend in water content between present and future, simulations showed a lengthening of the season (i.e., mean increase: +8.5 and 14 days under RCP4.5 and RCP8.5, respectively, for the period 2011–2040, +19 and 31.5 days under RCP4.5 and RCP8.5, respectively, for the period 2041–2070) and a rise in forage production (i.e., mean biomass peak increase of the two test sites under BaU: +53.7% and 62.75% for RCP4.5. and RCP8.5, respectively, in the 2011–2040 period, +115.3% and 176.9% in RCP4.5 and RCP8.5 in 2041–2070, respectively,). Subsequently, three different alternative management strategies were tested: a 20% rise in animal stocking rate (+20 GI), a 15% increase in grazing length (+15 GL), and a combination of these two management factors (+20 GI × 15 GL). Simulation results on alternative management strategies suggest that the favorable conditions for forage production could support the increase in animal stocking rate and grazing length of alternative management strategies (i.e., +20 GI, +15 GL, +20 GI × 15 GL). Under future projections, net ecosystem exchange (NEE) and nitrogen oxide (N2O) emissions decreased, whereas methane (CH4) rose. The simulated GHG future changes varied in magnitude according to the different adaptation strategies tested. The development and assessment of adaptation strategies for extensive pastures of the Central Apennines provide a basis for appropriate agricultural policy and optimal land management in response to the ongoing climate change.

The use of very long spatial datasets from satellites has opened up numerous opportunities, including the monitoring of vegetation phenology over the course of time. Considering the importance of grassland systems and the influence of climate change on their phenology, the specific objectives of this study are: (a) to identify a methodology for a reliable estimation of grassland phenological dates from a satellite vegetation index (i.e., kernel normalized difference vegetation index, kNDVI) and (b) to quantify the changes that have occurred over the period 2001–2021 in a representative dataset of European grasslands and assess the extent of climate change impacts. In order to identify the best methodological approach for estimating the start (SOS), peak (POS) and end (EOS) of the growing season from the satellite, we compared dates extracted from the MODIS-kNDVI annual trajectories with different combinations of fitting models (FMs) and extraction methods (EM), with those extracted from the gross primary productivity (GPP) measured from eddy covariance flux towers in specific grasslands. SOS and POS were effectively identified with various FM×EM approaches, whereas satellite-EOS did not obtain sufficiently reliable estimates and was excluded from the trend analysis. The methodological indications (i.e., FM×EM selection) were then used to calculate the SOS and POS for 31 grassland sites in Europe from MODIS-kNDVI during the period 2001–2021. SOS tended towards an anticipation at the majority of sites (83.9%), with an average advance at significant sites of 0.76 days year−1. For POS, the trend was also towards advancement, although the results are less homogeneous (67.7% of sites with advancement), and with a less marked advance at significant sites (0.56 days year−1). From the analyses carried out, the SOS and POS of several sites were influenced by the winter and spring temperatures, which recorded rises during the period 2001–2021. Contrasting results were recorded for the SOS-POS duration, which did not show a clear trend towards lengthening or shortening. Considering latitude and altitude, the results highlighted that the greatest changes in terms of SOS and POS anticipation were recorded for sites at higher latitudes and lower altitudes.

Olive tree cultivation is currently a dominant agriculture activity in the Mediterranean basin, where the increasing impact of climate change coupled with the inefficient management of olive groves is negatively affecting olive oil production and quality in some marginal areas. In this context, satellite imagery may help to monitor crop growth under different environmental conditions, thus providing useful information for optimizing olive grove management and final production. However, the spatial resolution of freely-available satellite products is not yet adequate to estimate plant biophysical parameters in complex agroecosystems such as olive groves, where both olive trees and grass cover contribute to the vegetation indices (VIs) signal at pixel scale. The aim of this study is therefore to test a disentangling procedure to partition the VIs signal among the different components of the agroecosystem to use this information for the monitoring of olive growth processes during the season. Specifically, five VIs (GEMI, MCARI2, NDVI, OSAVI, MCARI2/OSAVI) as recorded by Sentinel-2 at a spatial resolution of 10 m over five olive groves in the Montalbano area (Tuscany, Central Italy), were tested as a proxy for olive tree intercepted radiation. The olive tree volume per pixel was initially used to linearly rescale the VIs signal into the relevant value for the grass cover and olive trees. The models, describing the relationship between rescaled VIs and observed fraction of Photosynthetically Active Radiation (fPAR), were fitted and then validated against independent datasets. While in the calibration phase, a greater robustness at predicting fPAR was obtained using NDVI (r = 0.96 and RRMSE = 9.86), the validation results demonstrating that GEMI and MCARI2/OSAVI provided the highest performances (GEMI: r = 0.89 and RRMSE = 21.71; MCARI2/OSAVI: r = 0.87 and RRMSE = 25.50), in contrast to MCARI2 that provided the lowest (r = 0.67 and RRMSE = 36.78). These results may be related to the VIs’ intrinsic features (e.g., lower sensitivity to atmosphere and background effects), which make some of these indices, compared to others, less sensitive to saturation effects by improving fPAR estimation (e.g., GEMI vs. NDVI). On this basis, this study evidenced the need to improve the current methodologies to reduce inter-row effects and select appropriate VIs for fPAR estimation, especially in complex agroecosystems where inter-row grass growth may affect remote sensed-derived VIs signal at an inadequate pixel resolution.

TBL1XR1 gene is associated with multiple developmental disorders presenting several neurological aspects. The relative protein is involved in the modulation of important cellular pathways and master regulators of transcriptional output, including nuclear receptor repressors, Wnt signaling, and MECP2 protein. However, TBL1XR1 mutations (including complete loss of its functions) have not been experimentally studied in a neurological context, leaving a knowledge gap in the mechanisms at the basis of the diseases. Here, we show that Tbl1xr1 knock-out mice exhibit behavioral and neuronal abnormalities. Either the absence of TBL1XR1 or its point mutations interfering with stability/regulation of NCOR complex induced decreased proliferation and increased differentiation in neural progenitors. We suggest that this developmental unbalance is due to a failure in the regulation of the MAPK cascade. Taken together, our results broaden the molecular and functional aftermath of TBL1XR1 deficiency associated with human disorders.

Current vegetation of alpine grasslands has been shaped by the combination of natural ecological factors (such as climate, soil, topography) and human activities, mainly represented by animal grazing and agricultural practices. An assessment of these factors can explain the present composition of plant communities and help to evaluate the future development of rangeland vegetation. Nowadays, the analysis of the botanical composition of grasslands is of a major importance in order to propose appropriate management plans for the sustainable exploitation of pastoral resources and their future conservation. The main purpose of this work was to assess the relevance of ecological and management factors in alpine grasslands in an area located in eastern Italy, currently used for extensive grazing, and to describe the main factors that affect the characteristics of pasture types. To this aim, about 900 ha of alpine grasslands were surveyed in Val Visdende (northern Veneto, province of Belluno, Italy) by means of 189 linear transects. Some environmental variables (altitude, slope, aspect) and factors related to management (pastoral value, animal excreta, distance from night barns) were collected for each botanical transect. Landolt indicators were calculated in order to evaluate the ecological space occupied by each type. This assessment made it possible to identify the most relevant grassland communities (namely nutrient poor, shrub encroached, nutrient rich and humid pastures) occurring in the studied area, the effectiveness of ecological indicators to describe and to differentiate vegetation groups and the effect of animal management and ecological factors in the discrimination of grassland types.

The advent of next-generation sequencing (NGS) is heavily changing both the diagnosis of human conditions and basic biological research. It is now possible to dig deep inside the genome of hundreds of thousands or even millions of people and find both common and rare genomic variants and to perform detailed phenotypic characterizations of both physiological organs and experimental models. Recent years have seen the introduction of multiple techniques using NGS to profile transcription, DNA and chromatin modifications, protein binding, etc., that are now allowing us to profile cells in bulk or even at a single-cell level. Although rare and ultra-rare diseases only affect a few people, each of these diseases represent scholarly cases from which a great deal can be learned about the pathological and physiological function of genes, pathways, and mechanisms. Therefore, for rare diseases, state-of-the-art investigations using NGS have double valence: their genomic cause (new variants) and the characterize the underlining the mechanisms associated with them (discovery of gene function) can be found. In a non-exhaustive manner, this review will outline the main usage of NGS-based techniques for the diagnosis and characterization of neurodevelopmental disorders (NDDs), under whose umbrella many rare and ultra-rare diseases fall.

The investigation of plant organisms by means of data-derived systems biology approaches based on network modeling is mainly characterized by genomic data, while the potential of proteomics is largely unexplored. This delay is mainly caused by the paucity of plant genomic/proteomic sequences and annotations which are fundamental to perform mass-spectrometry (MS) data interpretation. However, Next Generation Sequencing (NGS) techniques are contributing to filling this gap and an increasing number of studies are focusing on plant proteome profiling and protein-protein interactions (PPIs) identification. Interesting results were obtained by evaluating the topology of PPI networks in the context of organ-associated biological processes as well as plant-pathogen relationships. These examples foreshadow well the benefits that these approaches may provide to plant research. Thus, in addition to providing an overview of the main-omic technologies recently used on plant organisms, we will focus on studies that rely on concepts of module, hub and shortest path, and how they can contribute to the plant discovery processes. In this scenario, we will also consider gene co-expression networks, and some examples of integration with metabolomic data and genome-wide association studies (GWAS) to select candidate genes will be mentioned.