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Neuroinflammation, as defined by the presence of classically activated microglia, is thought to play a key role in numerous neurodegenerative disorders such as Alzheimer’s disease. While modulating neuroinflammation could prove beneficial against neurodegeneration, identifying its most relevant biological processes and pharmacological targets remains highly challenging. In the present study, we combined text-mining, functional enrichment and protein-level functional interaction analyses to 1) identify the proteins significantly associated to neuroinflammation in Alzheimer’s disease over the scientific literature, 2) distinguish the key proteins most likely to control the neuroinflammatory processes significantly associated to Alzheimer's disease, 3) identify their regulatory microRNAs among those dysregulated in Alzheimer's disease and 4) assess their pharmacological targetability. 94 proteins were found to be significantly associated to neuroinflammation in Alzheimer’s disease over the scientific literature and IL4, IL10 and IL13 signaling as well as TLR-mediated MyD88- and TRAF6-dependent responses were their most significantly enriched biological processes. IL10, TLR4, IL6, AKT1, CRP, IL4, CXCL8, TNF-alpha, ITGAM, CCL2 and NOS3 were identified as the most potent regulators of the functional interaction network formed by these immune processes. These key proteins were indexed to be regulated by 63 microRNAs dysregulated in Alzheimer's disease, 13 long non-coding RNAs and targetable by 55 small molecules and 8 protein-based therapeutics. In conclusion, our study identifies eleven key proteins with the highest ability to control neuroinflammatory processes significantly associated to Alzheimer’s disease, as well as pharmacological compounds with single or pleiotropic actions acting on them. As such, it may facilitate the prioritization of diagnostic and target-engagement biomarkers as well as the development of effective therapeutic strategies against neuroinflammation in Alzheimer’s disease.

BackgroundEndometriosis is defined by implantation and invasive growth of endometrial tissue in extra-uterine locations causing heterogeneous symptoms, and a unique clinical picture for each patient. Understanding the complex biological mechanisms underlying these symptoms and the protein networks involved may be useful for early diagnosis and identification of pharmacological targets.MethodsIn the present study, we combined three approaches (i) a text-mining analysis to perform a systematic search of proteins over existing literature, (ii) a functional enrichment analysis to identify the biological pathways in which proteins are most involved, and (iii) a protein–protein interaction (PPI) network to identify which proteins modulate the most strongly the symptomatology of endometriosis.ResultsTwo hundred seventy-eight proteins associated with endometriosis symptomatology in the scientific literature were extracted. Thirty-five proteins were selected according to degree and betweenness scores criteria. The most enriched biological pathways associated with these symptoms were (i) Interleukin-4 and Interleukin-13 signaling (p = 1.11 x 10-16), (ii) Signaling by Interleukins (p = 1.11 x 10-16), (iii) Cytokine signaling in Immune system (p = 1.11 x 10-16), and (iv) Interleukin-10 signaling (p = 5.66 x 10-15).ConclusionOur study identified some key proteins with the ability to modulate endometriosis symptomatology. Our findings indicate that both pro- and anti-inflammatory biological pathways may play important roles in the symptomatology of endometriosis. This approach represents a genuine systemic method that may complement traditional experimental studies. The current data can be used to identify promising biomarkers for early diagnosis and potential therapeutic targets.

Objective Endometriosis is a complex full-body inflammation disease with an average time to diagnosis of 7–10 years. Social networks give opportunity to patient to openly discuss about their condition, share experiences, and seek advice. Thus, data from social media may provide insightful data about patient's experience. This study aimed at applying a text-mining approach to online social networks in order to identify early signs associated with endometriosis. Methods An automated exploration technique of online forums was performed to extract posts. After a cleaning step of the built corpus, we retrieved all symptoms evoked by women, and connected them to the MedDRA dictionary. Then, temporal markers allowed targeting only the earliest symptoms. The latter were those evoked near a marker of precocity. A co-occurrence approach was further applied to better account for the context of evocations. Results Results were visualised using the graph-oriented database Neo4j. We collected 7148 discussions threads and 78,905 posts from 10 French forums. We extracted 41 groups of contextualised symptoms, including 20 groups of early symptoms associated with endometriosis. Among these groups of early symptoms, 13 were found to portray already known signs of endometriosis. The remaining 7 clusters of early symptoms were limb oedema, muscle pain, neuralgia, haematuria, vaginal itching, altered general condition (i.e. dizziness, fatigue, nausea) and hot flush. Conclusion We pointed out some additional symptoms of endometriosis qualified as early symptoms, which can serve as a screening tool for prevention and/or treatment purpose. The present findings offer an opportunity for further exploration of early biological processes triggering this disease.

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