Search

The cross-talk between T cells and astrocytes occurring under physiological and, even more, neuroinflammatory conditions may profoundly impact the generation of adaptive immune responses in the nervous tissue. In this study, we used a standardized in vitro co-culture assay to investigate the immunomodulatory properties of astrocytes differing for age, sex, and species. Mouse neonatal astrocytes enhanced T cell vitality but suppressed T lymphocyte proliferation in response to mitogenic stimuli or myelin antigens, regardless of the Th1, Th2 or Th17 T cell phenotype. Studies comparing glia cells from adult and neonatal animals showed that adult astrocytes were more efficient in inhibiting T lymphocyte activation than neonatal astrocytes, regardless of their sex. Differently from primary cultures, mouse and human astrocytes derived from reprogrammed fibroblasts did not interfere with T cell proliferation. Overall, we describe a standardized astrocyte-T cell interaction in vitro assay and demonstrate that primary astrocytes and iAstrocytes may differ in modulating T cell function. [ABSTRACT FROM AUTHOR]

Demyelination is a key pathogenic feature of multiple sclerosis (MS). Here, we evaluated the astrocyte contribution to myelin loss and focused on the neurotrophin receptor TrkB, whose up-regulation on the astrocyte finely demarcated chronic demyelinated areas in MS and was paralleled by neurotrophin loss. Mice lacking astrocyte TrkB were resistant to demyelination induced by autoimmune or toxic insults, demonstrating that TrkB signaling in astrocytes fostered oligodendrocyte damage. In vitro and ex vivo approaches highlighted that astrocyte TrkB supported scar formation and glia proliferation even in the absence of neurotrophin binding, indicating TrkB transactivation in response to inflammatory or toxic mediators. Notably, our neuropathological studies demonstrated copper dysregulation in MS and model lesions and TrkB-dependent expression of copper transporter (CTR1) on glia cells during neuroinflammation. In vitro experiments evidenced that TrkB was critical for the generation of glial intracellular calcium flux and CTR1 up-regulation induced by stimuli distinct from neurotrophins. These events led to copper uptake and release by the astrocyte, and in turn resulted in oligodendrocyte loss. Collectively, these data demonstrate a pathogenic demyelination mechanism via the astrocyte release of copper and open up the possibility of restoring copper homeostasis in the white matter as a therapeutic target in MS. [ABSTRACT FROM AUTHOR]

Hepatocyte nuclear factor 4 α (HNF4α), a transcription factor (TF) essential for embryonic development, has been recently shown to regulate the expression of inflammatory genes. To characterize HNF4a function in immunity, we measured the effect of HNF4α antagonists on immune cell responses in vitro and in vivo. HNF4α blockade reduced immune activation in vitro and disease severity in the experimental model of multiple sclerosis (MS). Network biology studies of human immune transcriptomes unraveled HNF4α together with SP1 and c-myc as master TF regulating differential expression at all MS stages. TF expression was boosted by immune cell activation, regulated by environmental MS risk factors and higher in MS immune cells compared to controls. Administration of compounds targeting TF expression or function demonstrated non-synergic, interdependent transcriptional control of CNS autoimmunity in vitro and in vivo. Collectively, we identified a coregulatory transcriptional network sustaining neuroinflammation and representing an attractive therapeutic target for MS and other inflammatory disorders. • HNF4α supports monocyte and T cell function. • HNF4α, SP1 and c-myc are shared transcription factor network hubs among MS stages. • Hub levels are higher in MS immune cells and regulated by environmental risk factors. • Hubs exert non-synergic, interdependent control of CNS autoimmunity in vivo. [ABSTRACT FROM AUTHOR]

Sphingosine 1-phosphate (S1P) is a potent bioactive sphingolipid binding to specific G protein-coupled receptors expressed in several organs. The relevance of S1P-S1P receptor axis in the pathophysiology of immune and nervous systems has encouraged the development of S1P receptor modulators for the treatment of neurological, autoimmune and/or inflammatory disorders. Currently, four S1P receptor modulators are approved drugs for multiple sclerosis (MS), an inflammatory disorder of the central nervous system. As main pharmacologic effect, these treatments induce lymphopenia due to the loss of responsiveness to S1P gradients guiding lymphocyte egress from lymphoid organs into the bloodstream. Recent data point to immunological effects of the S1P modulators beyond the inhibition of lymphocyte trafficking. Further, these drugs may cross the blood-brain barrier and directly target CNS resident cells expressing S1P receptors. Here we review the role of S1P signalling in neuroimmunology at the light of the evidences generated from the study of the mechanism of action of S1P receptor modulators in MS and integrate this information with findings derived from neuroinflammatory animal models and in vitro observations. These insights can direct the application of therapeutic approaches targeting S1P receptors in other disease areas. [ABSTRACT FROM AUTHOR]

Whether Clonal Hematopoiesis (CH) represents a risk factor for severity of the COVID-19 disease remains a controversial issue. We report the first high- sensitivity analysis of CH in COVID-19 patients (threshold of detection at 0.5% vs 1 or 2% in previous studies). We analyzed 24 patients admitted to ICU for COVID-19 (COV-ICU) and 19 controls, including healthy subjects and asymptomatic SARS-CoV2-positive individuals. Despite the significantly higher numbers of CH mutations identified (80% mutations with <2% variant allele frequency, VAF), we did not find significant differences between COV-ICU patients and controls in the prevalence of CH or in the numbers, VAF or functional categories of the mutated genes, suggesting that CH is not overrepresented in patients with COVID-19. However, when considering potential drivers CH mutations (CH-PD), COV-ICU patients showed higher clonal complexity, in terms of both mutation numbers and VAF, and enrichment of variants reported in myeloid neoplasms. However, we did not score an impact of increased CH-PD on patient survival or clinical parameters associated with inflammation. These data suggest that COVID-19 influence the clonal composition of the peripheral blood and call for further investigations addressing the potential long-term clinical impact of CH on people experiencing severe COVID-19. We acknowledge that it will indispensable to perform further studies on larger patient cohorts in order to validate and generalize our conclusions. Moreover, we performed CH analysis at a single time point. It will be necessary to consider longitudinal approaches with long periods of follow-up in order to assess if the COVID-19 disease could have an impact on the evolution of CH and long-term consequences in patients that experienced severe COVID-19. [ABSTRACT FROM AUTHOR]

Long-read sequencing allows analyses of single nucleic-acid molecules and produces sequences in the order of tens to hundreds kilobases. Its application to whole-genome analyses allows identification of complex genomic structural-variants (SVs) with unprecedented resolution. SV identification, however, requires complex computational methods, based on either read-depth or intra- and inter-alignment signatures approaches, which are limited by size or type of SVs. Moreover, most currently available tools only detect germline variants, thus requiring separate computation of sample pairs for comparative analyses. To overcome these limits, we developed a novel tool (Germline And SOmatic structuraL varIants detectioN and gEnotyping; GASOLINE) that groups SV signatures using a sophisticated clustering procedure based on a modified reciprocal overlap criterion, and is designed to identify germline SVs, from single samples, and somatic SVs from paired test and control samples. GASOLINE is a collection of Perl, R and Fortran codes, it analyzes aligned data in BAM format and produces VCF files with statistically significant somatic SVs. Germline or somatic analysis of 30 × sequencing coverage experiments requires 4–5 h with 20 threads. GASOLINE outperformed currently available methods in the detection of both germline and somatic SVs in synthetic and real long-reads datasets. Notably, when applied on a pair of metastatic melanoma and matched-normal sample, GASOLINE identified five genuine somatic SVs that were missed using five different sequencing technologies and state-of-the art SV calling approaches. Thus, GASOLINE identifies germline and somatic SVs with unprecedented accuracy and resolution, outperforming currently available state-of-the-art WGS long-reads computational methods. [ABSTRACT FROM AUTHOR]

Aberrant DNA methylation at CpG dinucleotides is a cancer hallmark that is associated with the emergence of resistance to anti cancer treatment, though molecular mechanisms and biological significance remain elusive. Genome scale methylation maps by currently used methods are based on chemical modification of DNA and are best suited for analyses of methylation at CpG rich regions (CpG islands). We report the first high coverage whole-genome map in cancer using the long read nanopore technology, which allows simultaneous DNA-sequence and -methylation analyses on native DNA. We analyzed clonal epigenomic/genomic evolution in Acute Myeloid Leukemias (AMLs) at diagnosis and relapse, after chemotherapy. Long read sequencing coupled to a novel computational method allowed definition of differential methylation at unprecedented resolution, and showed that the relapse methylome is characterized by hypermethylation at both CpG islands and sparse CpGs regions. Most differentially methylated genes, however, were not differentially expressed nor enriched for chemoresistance genes. A small fraction of under-expressed and hyper-methylated genes at sparse CpGs, in the gene body, was significantly enriched in transcription factors (TFs). Remarkably, these few TFs supported large gene-regulatory networks including 50% of all differentially expressed genes in the relapsed AMLs and highly-enriched in chemoresistance genes. Notably, hypermethylated regions at sparse CpGs were poorly conserved in the relapsed AMLs, under-represented at their genomic positions and showed higher methylation entropy, as compared to CpG islands. Analyses of available datasets confirmed TF binding to their target genes and conservation of the same gene-regulatory networks in large patient cohorts. Relapsed AMLs carried few patient specific structural variants and DNA mutations, apparently not involved in drug resistance. Thus, drug resistance in AMLs can be mainly ascribed to the selection of random epigenetic alterations at sparse CpGs of a few transcription factors, which then induce reprogramming of the relapsing phenotype, independently of clonal genomic evolution. DNA methylation patterns from Acute Myeloid Leukemia patients highlight sparse CpG regions as drivers for chemoresistance. [ABSTRACT FROM AUTHOR]

The coexistence of the need to improve economic conditions and the conscious use of environmental resources plays a central role in today's sustainable development challenge. In this study, a novel integrated framework to evaluate the impact of new technological interventions is presented and an application to smallholder coffee farms and their supply chains in Kenya is proposed. This methodology is able to combine multiple information through the joint use of three approaches: supply chain analysis, input-output analysis, and energy system modeling. Application to the context of the Kenyan coffee sector enables framework validation: shading management measures, the introduction of eco-pulpers, and the exploitation of coffee waste biomass for power generation were compared within a holistic high-level perspective. The implementation of shading practices, carried out with fruit trees, shows the most relevant effects from the economic point of view, providing farmers with an additional source of income and generating $903 of work for every million of local currency (about $9k) invested in this solution. The same investment would save up to 1.46 M m3 of water per year with the eco-pulpers technology. Investing the same amount in coffee-biomass power plants would displace a small portion of production from heavy-duty oil and avoid importing a portion of fertilizer, saving up to 11 tons of CO2 and around $4k per year. The results suggest the optimal allocation of a $100m budget, which can be affected by adding additional constraints on minimum environmental or social targets in line with sustainable development goals. [ABSTRACT FROM AUTHOR]

Oligodendrocytes are the primary target of demyelinating disorders, and progressive neurodegenerative changes may evolve in the CNS. DNA damage and oxidative stress are considered key pathogenic events, but the underlying molecular mechanisms remain unclear. Moreover, animal models do not fully recapitulate human diseases, complicating the path to effective treatments. Here we report that mice with cell-autonomous deletion of the nuclear COP9 signalosome component CSN5 (JAB1) in oligodendrocytes develop DNA damage and defective DNA repair in myelinating glial cells. Interestingly, oligodendrocytes lacking JAB1 expression underwent a senescence-like phenotype that fostered chronic inflammation and oxidative stress. These mutants developed progressive CNS demyelination, microglia inflammation, and neurodegeneration, with severe motor deficits and premature death. Notably, blocking microglia inflammation did not prevent neurodegeneration, whereas the deletion of p21CIP1 but not p16INK4a pathway ameliorated the disease. We suggest that senescence is key to sustaining neurodegeneration in demyelinating disorders and may be considered a potential therapeutic target. [ABSTRACT FROM AUTHOR]

Russia's invasion of Ukraine fuelled an energy crisis, which considerably impacted Europe given its heavy reliance on Russian natural gas imports. This study uses an ensemble of four global integrated assessment models, which are further soft-linked to two sectoral models, and explores the synergies and trade-offs among three approaches to living without Russian gas in Europe: (a) replacing with other gas imports, (b) boosting domestic energy production, and (c) reducing demand and accelerating energy efficiency. We find that substituting Russian gas from other trade partners would miss an opportunity to accelerate decarbonisation in end-use sectors while risking further fossil-fuel lock-ins, despite featuring the lowest gas price spikes and potentially reducing heating costs for end-users in the near term. Boosting domestic, primarily renewable, energy production on the other hand would instead require considerable investments, potentially burdening consumers. Energy demand reductions, however, could offer considerable space for further emissions cuts at the lowest power-sector investment costs; nonetheless, an energy efficiency-driven strategy would also risk relocation of energy-intensive industries, an aspect of increasing relevance to EU policymakers. • Three 'corner' strategies to replacing Russian natural gas in the EU are explored. • EU climate targets can still be reached in all three strategies, but costs increase. • Promoting energy efficiency or domestic renewables can boost end-use decarbonisation. • An 'energy efficiency first' response may save millions € in annual electricity costs. • Southern Europe is the most vulnerable to high electricity prices across strategies. [ABSTRACT FROM AUTHOR]

In state-of-the-art energy systems modelling, reservoir hydropower is represented as any other thermal power plant: energy production is constrained by the plant's installed capacity and a capacity factor calibrated on the energy produced in previous years. Natural water resource variability across different temporal scales and the subsequent filtering effect of water storage mass balances are not accounted for, leading to biased optimal power dispatch strategies. In this work, we aim at introducing a novelty in the field by advancing the representation of reservoir hydropower generation in energy systems modelling by explicitly including the most relevant hydrological constraints, such as time-dependent water availability, hydraulic head, evaporation losses, and cascade releases. This advanced characterization is implemented in an open-source energy modelling framework. The improved model is then demonstrated on the Zambezi River Basin in the South Africa Power Pool. The basin has an estimated hydropower potential of 20,000 megawatts (MW) of which about 5,000 MW has been already developed. Results show a better alignment of electricity production with observed data, with a reduction of estimated hydropower production up to 35% with respect to the baseline Calliope implementation. These improvements are useful to support hydropower management and planning capacity expansion in countries richly endowed with water resource or that are already strongly relying on hydropower for electricity production. [ABSTRACT FROM AUTHOR]

The mass-scale integration of electric vehicles into the power system is a key pillar of the European energy transition agenda. Yet, the extent to which such integration would represent a burden for the power system of each member country is still an unanswered question. This is mainly due to a lack of accurate and context-specific representations of aggregate mobility and charging patterns for large electric vehicle fleets. Here, we develop and validate against empirical data an open-source model that simulates such patterns at high (1-min) temporal resolution, based on easy-to-gather, openly accessible data. We hence apply the model – which we name RAMP-mobility – to 28 European countries, showing for the first time the existence of marked differences in mobility and charging patterns across those, due to a combination of weather and socio-economic factors. We hence quantify the impact that fully-electric car fleets would have on the demand to be met by each country's power system: an uncontrolled deployment of electric vehicles would increase peak demand in the range 35–51%, whilst a plausible share of adoption of smart charging strategies could limit the increase to 30–41%. On the contrary, plausible technology (battery density) and infrastructure (charging power) developments would not provide substantial benefits. Efforts for electric vehicles integration should hence primarily focus on mechanisms to support smart vehicle-to-grid interaction. The approach is applicable generally beyond Europe and can provide policy makers with quantitatively reliable insights about electric vehicles impact on the power system. • We generate validated mobility and charging patterns for electric vehicles in Europe. • Hourly patterns vary substantially across countries due to weather and user behaviour. • Electric vehicles may increase peak power systems demand by 36–51%. • Plausible shares of smart-charging vehicles may limit such an increase to 30–41%. • Smart-charging substantially outperforms battery and infrastructure developments. [ABSTRACT FROM AUTHOR]

As it emerges from the literature, electricity access in rural contexts is deeply intertwined with socioeconomic dynamics. However, the advent of a reliable and sufficient source of electricity is not the sole driver that might contribute to local development. Indeed, complementary activities might have a crucial role in sustaining the development of rural communities as well as the electricity access. The current research addresses the lack of counterfactual scenarios in which the impact of complementary activities on electrification projects can be investigated. The authors introduce the case study of Matembwe village, a rural community in the Njombe region of Tanzania. The data collection includes the electricity consumption, number of electricity connections, and number of income-generating activities in a timespan ranging from 1989 to 2015. The analysis is based on system dynamics. The study considers different scenarios representing the dynamics related to the following complementary actions: access to market measures, access to credit measures, and access to usable skills. On the one hand, the study reveals that the effectiveness of the considered complementary actions is limited except from the access to microcredit which fosters an increase in electricity connections by 17%. On the other hand, both access to microcredit and the starting up of a local cooperative by CEFA Onlus that reinvests its profits in the local market impact the socio-economic dimension by 69% and 22%, respectively. [ABSTRACT FROM AUTHOR]

The decarbonisation of residential heat through integration with the power system and deployment of refurbishment policies is at the core of European energy policies. Yet, heat-electricity integration may be challenged, in practice, by the large variability of heat demand across weather years. Current approaches for residential heat demand simulation fail to provide insights about the extent of such variability across many weather years and about the benefits potentially brought about by nearly zero-energy buildings. To fill this gap, this work develops an open-source space-heating demand simulation workflow that is applicable to any country's building stock. The workflow, based on a well-established lumped-parameter thermodynamic model, allows capturing sub-national weather-year variability and the mitigation effects of refurbishment. For Italy, different weather years lead to variations in heat demand up to 2 TWh/day, lasting for several days. Moreover, some weather regimes produce spatial asymmetries that may further complicate heat-electricity integration. The refurbishment of about 55% of buildings constructed before 1975 could substantially mitigate such oscillations, leading to a 31–37% reduction of yearly heat demand, primarily in colder regions. Intra-day heat demand variations, driven by user behaviour, are not substantially impacted by refurbishment, calling for the simultaneous deployment of flexible heat generating technologies. • Heat demand models fail to capture weather variability and refurbishment benefits. • This study provides an open, context-adaptable workflow that allows to do so. • For Italy, large and long-lasting demand variations occur across weather regimes. • Refurbishment can reduce absolute demand by up to 37% and mitigate oscillations. • All such effects vary spatially and must be evaluated at sub-national resolution. [ABSTRACT FROM AUTHOR]

Thanks to their modularity and their capacity to adapt to different contexts, hybrid microgrids are a promising solution to decrease greenhouse gas emissions worldwide. To properly assess their impact in different settings at country or cross-country level, microgrids must be designed for each particular situation, which leads to computationally intractable problems. To tackle this issue, a methodology is proposed to create surrogate models using machine learning techniques and a database of microgrids. The selected regression model is based on Gaussian Processes and allows to drastically decrease the computation time relative to the optimal deployment of the technology. The results indicate that the proposed methodology can accurately predict key optimization variables for the design of the microgrid system. The regression models are especially well suited to estimate the net present cost and the levelized cost of electricity (R 2 = 0.99 and 0.98). Their accuracy is lower when predicting internal system variables such as installed capacities of PV and batteries (R 2 = 0.92 and 0.86). A least-cost path towards 100% electrification coverage for the Bolivian lowlands mid-size communities is finally computed, demonstrating the usability and computational efficiency of the proposed framework. • A novel methodology to create surrogate models for energy planning is presented. • Gaussian regression is compared to multi-variable regression. • The surrogate models for Economic indicators have a high prediction accuracy. • PV and Batteries installed capacity have lower prediction accuracy. • Surrogate models are created and coupled to a rural energy planning tool. [ABSTRACT FROM AUTHOR]