Peripheral nerve demyelination and neuropathic pain

Chronic neuropathic pain, characterised by allodynia (perception of innocuous stimuli as painful) and hyperalgesia (facilitated responses to painftil stimuli), is poorly understood and is usually resistant to classical analgesics. Such abnormal pain phenomena can be associated with human demyelinating conditions such as Charcot-Marie-Tooth disease. Using mouse models of peripheral nerve demyelination, we have provided evidence for the consequent establishment of neuropathic pain and investigated possible underlying mechanisms. The first model investigated was the Prx-mi\\ mouse. The murine periaxin gene (Prx) is expressed in Schwann cells and encodes L- and S-periaxin, two abundant PDZ-domain proteins thought to have a role in stabilisation of myelin in the peripheral nervous system (PNS). Prx-null mice show progressive demyelination in peripheral nerves and electrophysiological investigations indicate the presence of spontaneous action potential discharge; abnormal activity thought to be critical for the development of persistent pain states. Consistent with the time course of demyelination, Rrx-null mice display an increased behavioural reflex sensitivity to cutaneous mechanical and noxious thermal stimulation. To further investigate the link between demyelination of peripheral nerves and neuropathic pain, we have also characterised a novel model of focal peripheral nerve demyelinating neuropathy. Focal demyelination of the sciatic or saphenous nerve was induced with lysolecithin (lysophosphatidylcholine) and resulted in an increased behavioural reflex sensitivity to both thermal and mechanical tests, peaking at 9-14 days following treatment. Nerve morphology was investigated using light and electron microscopy, which revealed 30- 40% demyelination of the treated nerve, (without lysolecithin-treated axon loss) coinciding with peak behavioural changes. Changes in the excitability of saphenous nerves were revealed, with spontaneous action potential discharge of 2-3 impulses per second present at peak behavioural change. No associated change in peripheral activation thresholds or conduction velocity was observedIn both models, immunohistochemical investigations revealed no cell loss in the dorsal root ganglia (DRG) and no evidence for axonal damage. Similar methods revealed changes in the expression of neuropeptide Y, and the sodium channels Nav1.3 and Nav1.8 in DRG neurones. Such changes may account for increased nerve excitability and are known to occur in other models of nerve injury. However, these changes in the demyelinating models occur in a more restricted manner, specifically in the cells of formerly myelinated fibres. Intrathecal injections of the selective NMDA receptor antagonist, [R]-CPP, indicated that NMDA receptor-dependent changes are crucial for the development of a neuropathic pain like state following peripheral nerve demyelination. Intrathecal administration of pharmacological agents indicated a role for the transcription factor NFkB in the production of the behavioural reflex sensitivity of lysolecithin-treated mice, as well as identifying the endogenous cannabinoid system as an effective inhibitory regulator and potential analgesic target.This study describes the first mouse models of peripheral nerve demyelination designed for the study of neuropathic pain and reveals phenotypic changes in DRG, which may contribute to the development of a neuropathic pain-like state. Therefore, these models may be useful for the evaluation of novel therapeutic targets for the treatment of demyelination-associated neuropathic pain

Pathogenesis of HIV-associated sensory neuropathy: evidence from in vivo and in vitro experimental models

HIV-associated sensory neuropathy (HIV-SN) is a frequent neurological complication of HIV infection and its treatment with some antiretroviral drugs. We review the pathogenesis of the viral- and drug-induced causes of the neuropathy, and its primary symptom, pain, based on evidence from in vivo and in vitro models of HIV-SN. Viral coat proteins mediate nerve fibre damage and hypernociception through direct and indirect mechanisms. Direct interactions between viral proteins and nerve fibres dominate axonal pathology, while somal pathology is dominated by indirect mechanisms that occur secondary to virus-mediated activation of glia and macrophage infiltration into the dorsal root ganglia. The treatment-induced neuropathy and resulting hypernociception arise primarily from drug-induced mitochondrial dysfunction, but the sequence of events initiated by the mitochondrial dysfunction that leads to the nerve fibre damage and dysfunction are still unclear. Overall, the models that have been developed to study the pathogenesis of HIV-SN, and hypernociception associated with the neuropathy, are reasonable models and have provided useful insights into the pathogenesis of HIV-SN. As new models are developed they may ultimately lead to identification of therapeutic targets for the prevention or treatment of this common neurological complication of HIV infection

sChemNET: a deep learning framework for predicting small molecules targeting microRNA function

MicroRNAs (miRNAs) have been implicated in human disorders, from cancers to infectious diseases. Targeting miRNAs or their target genes with small molecules offers opportunities to modulate dysregulated cellular processes linked to diseases. Yet, predicting small molecules associated with miRNAs remains challenging due to the small size of small molecule-miRNA datasets. Herein, we develop a generalized deep learning framework, sChemNET, for predicting small molecules affecting miRNA bioactivity based on chemical structure and sequence information. sChemNET overcomes the limitation of sparse chemical information by an objective function that allows the neural network to learn chemical space from a large body of chemical structures yet unknown to affect miRNAs. We experimentally validated small molecules predicted to act on miR-451 or its targets and tested their role in erythrocyte maturation during zebrafish embryogenesis. We also tested small molecules targeting the miR-181 network and other miRNAs using in-vitro and in-vivo experiments. We demonstrate that our machine-learning framework can predict bioactive small molecules targeting miRNAs or their targets in humans and other mammalian organisms.This article is published as Galeano, D., Imrat, Haltom, J. et al. sChemNET: a deep learning framework for predicting small molecules targeting microRNA function. Nat Commun 15, 9149 (2024). https://doi.org/10.1038/s41467-024-49813-w

Tissue-specific immunopathology in fatal COVID-19

Funding: Inflammation in COVID-19: Exploration of Critical Aspects of Pathogenesis (ICECAP) receives funding and support from the Chief Scientist Office (RapidResearch in COVID-19 programme [RARC-19] funding call, “Inflammation in Covid-19: Exploration of Critical Aspects of Pathogenesis; COV/EDI/20/10” to D.A.D., C.D.L., C.D.R., J.K.B., and D.J.H.), LifeArc (through the University of Edinburgh STOPCOVID funding award to K.D., D.A.D., and C.D.L.), UK Research and Innovation (UKRI) (Coronavirus Disease [COVID-19] Rapid Response Initiative; MR/V028790/1 to C.D.L., D.A.D., and J.A.H.), and Medical Research Scotland (CVG-1722-2020 to D.A.D., C.D.L., C.D.R., J.K.B., and D.J.H.). C.D.L. is funded by a Wellcome Trust Clinical Career Development Fellowship(206566/Z/17/Z). J.K.B. and C.D.R. are supported by the Medical Research Council (grant MC_PC_19059) as part of the International Severe AcuteRespiratory Infection Consortium Coronavirus Clinical Characterisation Consortium (ISARIC-4C). D.J.H., I.H.U., and M.E. are supported by the Industrial Centre for Artificial Intelligence Research in Digital Diagnostics. S.P. is supported by Kidney Research UK, and G.T. is supported by the Melville Trust for the Cure and Care of Cancer. Identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and sequencing work was supported by theU.S. Food and Drug Administration grant HHSF223201510104C (“Ebola Virus Disease: correlates of protection, determinants of outcome and clinicalmanagement”; amended to incorporate urgent COVID-19 studies) and contract 75F40120C00085 (“Characterization of severe coronavirus infection inhumans and model systems for medical countermeasure development and evaluation”; awarded to J.A.H.). J.A.H. is also funded by the Centre of Excellence in Infectious Diseases Research and the Alder Hey Charity. R.P.-R. is directly supported by the Medical Research Council Discovery Medicine North Doctoral Training Partnership. The group of J.A.H. is supported by the National Institute for Health Research Health Protection Research Unit in Emerging and Zoonotic Infections at the University of Liverpool in partnership with Public Health England and in collaboration with Liverpool School of Tropical Medicine and the University of Oxford.Rationale: In life-threatening Covid-19, corticosteroids reduce mortality, suggesting that immune responses have a causal role in death. Whether this deleterious inflammation is primarily a direct reaction to the presence of SARS-CoV-2 or an independent immunopathologic process is unknown. Objectives: To determine SARS-CoV-2 organotropism and organ-specific inflammatory responses, and the relationships between viral presence, inflammation, and organ injury. Methods: Tissue was acquired from eleven detailed post-mortem examinations. SARS-CoV-2 organotropism was mapped by multiplex PCR and sequencing, with cellular resolution achieved by in situ viral spike protein detection. Histological evidence of inflammation was quantified from 37 anatomical sites, and the pulmonary immune response characterized by multiplex immunofluorescence. Measurements and main results: Multiple aberrant immune responses in fatal Covid-19 were found, principally involving the lung and reticuloendothelial system, and these were not clearly topologically associated with the virus. Inflammation and organ dysfunction did not map to the tissue and cellular distribution of SARS-CoV-2 RNA and protein, both between and within tissues. An arteritis was identified in the lung, which was further characterised as a monocyte/myeloid-rich vasculitis, and occurred along with an influx of macrophage/monocyte-lineage cells into the pulmonary parenchyma. In addition, stereotyped abnormal reticulo-endothelial responses, including excessive reactive plasmacytosis and iron-laden macrophages, were present and dissociated from viral presence in lymphoid tissues. Conclusions: Tissue-specific immunopathology occurs in Covid-19, implicating a significant component of immune-mediated, virus-independent immunopathology as a primary mechanism in severe disease. Our data highlight novel immunopathological mechanisms, and validate ongoing and future efforts to therapeutically target aberrant macrophage and plasma cell responses as well as promoting pathogen tolerance in Covid-19.Publisher PDFPeer reviewe

National Mass Drug Administration Costs for Lymphatic Filariasis Elimination

Lymphatic filariasis (LF), commonly known as elephantiasis, is a profoundly disfiguring parasitic disease caused by thread-like nematode worms. This disease can often be disabling, thus reducing the potential productivity of the affected individuals. The WHO places the number of people at risk in 83 countries at 1.307 billion. This study was undertaken in seven countries—Burkina Faso, Ghana, Egypt, Tanzania, the Philippines, the Dominican Republic, and Haiti—using a common protocol to determine the costs of mass drug administration (MDA) programs to interrupt transmission of infection with LF, because there is lack of sufficient information about the costs of these programs. The results demonstrate that LF MDA is affordable and relatively inexpensive when compared to other public health programs. In the context of initiatives for integrating programs for the control and elimination of neglected tropical diseases, this study adds specifically to the relatively scarce body of information about the costs of MDA programs for LF. It also adds to the general knowledge about the application of methods that can be used to estimate the costs and cost-effectiveness of an integrated approach

The RNA Polymerase Dictates ORF1 Requirement and Timing of LINE and SINE Retrotransposition

Mobile elements comprise close to one half of the mass of the human genome. Only LINE-1 (L1), an autonomous non-Long Terminal Repeat (LTR) retrotransposon, and its non-autonomous partners—such as the retropseudogenes, SVA, and the SINE, Alu—are currently active human retroelements. Experimental evidence shows that Alu retrotransposition depends on L1 ORF2 protein, which has led to the presumption that LINEs and SINEs share the same basic insertional mechanism. Our data demonstrate clear differences in the time required to generate insertions between marked Alu and L1 elements. In our tissue culture system, the process of L1 insertion requires close to 48 hours. In contrast to the RNA pol II-driven L1, we find that pol III transcribed elements (Alu, the rodent SINE B2, and the 7SL, U6 and hY sequences) can generate inserts within 24 hours or less. Our analyses demonstrate that the observed retrotransposition timing does not dictate insertion rate and is independent of the type of reporter cassette utilized. The additional time requirement by L1 cannot be directly attributed to differences in transcription, transcript length, splicing processes, ORF2 protein production, or the ability of functional ORF2p to reach the nucleus. However, the insertion rate of a marked Alu transcript drastically drops when driven by an RNA pol II promoter (CMV) and the retrotransposition timing parallels that of L1. Furthermore, the “pol II Alu transcript” behaves like the processed pseudogenes in our retrotransposition assay, requiring supplementation with L1 ORF1p in addition to ORF2p. We postulate that the observed differences in retrotransposition kinetics of these elements are dictated by the type of RNA polymerase generating the transcript. We present a model that highlights the critical differences of LINE and SINE transcripts that likely define their retrotransposition timing

Prevalence of cognitive impairment in individuals aged over 65 in an urban area: DERIVA study

[ENG]Background: Few data are available on the prevalence of cognitive impairment (CI) in Spain, and the existing information shows important variations depending on the geographical setting and the methodology employed. The aim of this study was to determine the prevalence of CI in individuals aged over 65 in an urban area, and to analyze its associated risk factors. Methods: Design: A descriptive, cross-sectional, home questionnaire-based study; Setting: Populational, urban setting. Participants: The reference population comprised over-65s living in the city of Salamanca (Spain) in 2009. Randomized sampling stratified according to health district was carried out, and a total of 480 people were selected. In all, 327 patients were interviewed (68.10%), with a mean age of 76.35 years (SD: 7.33). Women accounted for 64.5% of the total. Measurements: A home health questionnaire was used to obtain the following data: age, sex, educational level, family structure, morbidity and functionality. All participants completed a neuropsychological test battery. The prevalence data were compared with those of the European population, with direct adjustment for age and sex. Diagnoses were divided into three general categories: normal cognitive function, cognitive impairment- no dementia (CIND), and dementia. Results: The prevalence of CI among these over-65s was 19% (14.7% CIND and 4.3% dementia). The age-and sexadjusted global prevalence of CI was 14.9%. CI increased with age (p < 0.001) and decreased with increasing educational level (p < 0.001). Significant risk factors were found with the multivariate analyses: age (OR = 1.08, 95% CI: 1.03-1.12), anxiety-depression (OR = 3.47, 95%CI: 1.61-7.51) and diabetes (OR = 2.07, 95%CI: 1.02-4.18). In turn, years of education was found to be a protective factor (OR = 0.79, 95%CI: 0.70-0.90). Although CI was more frequent among women and in people living without a partner, these characteristics were not significantly associated with CI risk. Conclusions: The observed raw prevalence of CI was 19% (14.9% after adjusting for age and sex). Older age and the presence of diabetes and anxiety-depression increased the risk of CI, while higher educational level reduced the risk

Network analysis of sea turtle movements and connectivity: A tool for conservation prioritization

Aim: Understanding the spatial ecology of animal movements is a critical element in conserving long-lived, highly mobile marine species. Analyzing networks developed from movements of six sea turtle species reveals marine connectivity and can help prioritize conservation efforts. Location: Global. Methods: We collated telemetry data from 1235 individuals and reviewed the literature to determine our dataset's representativeness. We used the telemetry data to develop spatial networks at different scales to examine areas, connections, and their geographic arrangement. We used graph theory metrics to compare networks across regions and species and to identify the role of important areas and connections. Results: Relevant literature and citations for data used in this study had very little overlap. Network analysis showed that sampling effort influenced network structure, and the arrangement of areas and connections for most networks was complex. However, important areas and connections identified by graph theory metrics can be different than areas of high data density. For the global network, marine regions in the Mediterranean had high closeness, while links with high betweenness among marine regions in the South Atlantic were critical for maintaining connectivity. Comparisons among species-specific networks showed that functional connectivity was related to movement ecology, resulting in networks composed of different areas and links. Main conclusions: Network analysis identified the structure and functional connectivity of the sea turtles in our sample at multiple scales. These network characteristics could help guide the coordination of management strategies for wide-ranging animals throughout their geographic extent. Most networks had complex structures that can contribute to greater robustness but may be more difficult to manage changes when compared to simpler forms. Area-based conservation measures would benefit sea turtle populations when directed toward areas with high closeness dominating network function. Promoting seascape connectivity of links with high betweenness would decrease network vulnerability.Fil: Kot, Connie Y.. University of Duke; Estados UnidosFil: Åkesson, Susanne. Lund University; SueciaFil: Alfaro Shigueto, Joanna. Universidad Cientifica del Sur; Perú. University of Exeter; Reino Unido. Pro Delphinus; PerúFil: Amorocho Llanos, Diego Fernando. Research Center for Environmental Management and Development; ColombiaFil: Antonopoulou, Marina. Emirates Wildlife Society-world Wide Fund For Nature; Emiratos Arabes UnidosFil: Balazs, George H.. Noaa Fisheries Service; Estados UnidosFil: Baverstock, Warren R.. The Aquarium and Dubai Turtle Rehabilitation Project; Emiratos Arabes UnidosFil: Blumenthal, Janice M.. Cayman Islands Government; Islas CaimánFil: Broderick, Annette C.. University of Exeter; Reino UnidoFil: Bruno, Ignacio. Instituto Nacional de Investigaciones y Desarrollo Pesquero; ArgentinaFil: Canbolat, Ali Fuat. Hacettepe Üniversitesi; Turquía. Ecological Research Society; TurquíaFil: Casale, Paolo. Università degli Studi di Pisa; ItaliaFil: Cejudo, Daniel. Universidad de Las Palmas de Gran Canaria; EspañaFil: Coyne, Michael S.. Seaturtle.org; Estados UnidosFil: Curtice, Corrie. University of Duke; Estados UnidosFil: DeLand, Sarah. University of Duke; Estados UnidosFil: DiMatteo, Andrew. CheloniData; Estados UnidosFil: Dodge, Kara. New England Aquarium; Estados UnidosFil: Dunn, Daniel C.. University of Queensland; Australia. The University of Queensland; Australia. University of Duke; Estados UnidosFil: Esteban, Nicole. Swansea University; Reino UnidoFil: Formia, Angela. Wildlife Conservation Society; Estados UnidosFil: Fuentes, Mariana M. P. B.. Florida State University; Estados UnidosFil: Fujioka, Ei. University of Duke; Estados UnidosFil: Garnier, Julie. The Zoological Society of London; Reino UnidoFil: Godfrey, Matthew H.. North Carolina Wildlife Resources Commission; Estados UnidosFil: Godley, Brendan J.. University of Exeter; Reino UnidoFil: González Carman, Victoria. Instituto National de Investigación y Desarrollo Pesquero; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Harrison, Autumn Lynn. Smithsonian Institution; Estados UnidosFil: Hart, Catherine E.. Grupo Tortuguero de las Californias A.C; México. Investigacion, Capacitacion y Soluciones Ambientales y Sociales A.C; MéxicoFil: Hawkes, Lucy A.. University of Exeter; Reino UnidoFil: Hays, Graeme C.. Deakin University; AustraliaFil: Hill, Nicholas. The Zoological Society of London; Reino UnidoFil: Hochscheid, Sandra. Stazione Zoologica Anton Dohrn; ItaliaFil: Kaska, Yakup. Dekamer—Sea Turtle Rescue Center; Turquía. Pamukkale Üniversitesi; TurquíaFil: Levy, Yaniv. University Of Haifa; Israel. Israel Nature And Parks Authority; IsraelFil: Ley Quiñónez, César P.. Instituto Politécnico Nacional; MéxicoFil: Lockhart, Gwen G.. Virginia Aquarium Marine Science Foundation; Estados Unidos. Naval Facilities Engineering Command; Estados UnidosFil: López-Mendilaharsu, Milagros. Projeto TAMAR; BrasilFil: Luschi, Paolo. Università degli Studi di Pisa; ItaliaFil: Mangel, Jeffrey C.. University of Exeter; Reino Unido. Pro Delphinus; PerúFil: Margaritoulis, Dimitris. Archelon; GreciaFil: Maxwell, Sara M.. University of Washington; Estados UnidosFil: McClellan, Catherine M.. University of Duke; Estados UnidosFil: Metcalfe, Kristian. University of Exeter; Reino UnidoFil: Mingozzi, Antonio. Università Della Calabria; ItaliaFil: Moncada, Felix G.. Centro de Investigaciones Pesqueras; CubaFil: Nichols, Wallace J.. California Academy Of Sciences; Estados Unidos. Center For The Blue Economy And International Environmental Policy Program; Estados UnidosFil: Parker, Denise M.. Noaa Fisheries Service; Estados UnidosFil: Patel, Samir H.. Coonamessett Farm Foundation; Estados Unidos. Drexel University; Estados UnidosFil: Pilcher, Nicolas J.. Marine Research Foundation; MalasiaFil: Poulin, Sarah. University of Duke; Estados UnidosFil: Read, Andrew J.. Duke University Marine Laboratory; Estados UnidosFil: Rees, ALan F.. University of Exeter; Reino Unido. Archelon; GreciaFil: Robinson, David P.. The Aquarium and Dubai Turtle Rehabilitation Project; Emiratos Arabes UnidosFil: Robinson, Nathan J.. Fundación Oceanogràfic; EspañaFil: Sandoval-Lugo, Alejandra G.. Instituto Politécnico Nacional; MéxicoFil: Schofield, Gail. Queen Mary University of London; Reino UnidoFil: Seminoff, Jeffrey A.. Noaa National Marine Fisheries Service Southwest Regional Office; Estados UnidosFil: Seney, Erin E.. University Of Central Florida; Estados UnidosFil: Snape, Robin T. E.. University of Exeter; Reino UnidoFil: Sözbilen, Dogan. Dekamer—sea Turtle Rescue Center; Turquía. Pamukkale University; TurquíaFil: Tomás, Jesús. Institut Cavanilles de Biodiversitat I Biologia Evolutiva; EspañaFil: Varo Cruz, Nuria. Universidad de Las Palmas de Gran Canaria; España. Ads Biodiversidad; España. Instituto Canario de Ciencias Marinas; EspañaFil: Wallace, Bryan P.. University of Duke; Estados Unidos. Ecolibrium, Inc.; Estados UnidosFil: Wildermann, Natalie E.. Texas A&M University; Estados UnidosFil: Witt, Matthew J.. University of Exeter; Reino UnidoFil: Zavala Norzagaray, Alan A.. Instituto politecnico nacional; MéxicoFil: Halpin, Patrick N.. University of Duke; Estados Unido