The mRNA content of plasma extracellular vesicles provides a window into the brain during cerebral malaria disease progression

The mRNA content of plasma extracellular vesicles provides a window into the brain during cerebral malaria disease progression | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The mRNA content of plasma extracellular vesicles provides a window into the brain during cerebral malaria disease progression Abdirahman Abdi, Kioko Mwikali, Shaban Mwangi, Alena Pance, Lynette Ochola-Oyier, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3375373/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The impact of cerebral malaria on the transcriptional profiles of cerebral tissue is difficult to study using non-invasive approaches. We isolated plasma extracellular vesicles (EVs) from patients with cerebral malaria and community controls and sequenced their RNA content. Deconvolution of the tissue origins of the EV-RNA revealed that EVs from cerebral malaria patients are predominantly enriched in transcripts of brain origin. Next, we used manifold learning on the EV-RNAseq data to determine pseudotime against the community control samples as the baseline reference. We found that neuronal transcripts in plasma EVs decreased as pseudotime progressed, while transcripts of glial, endothelial, and immune cell origins increased over pseudotime. Pseudotime was associated with clinicopathological parameters of disease severity, including retinopathy, metabolic acidosis, respiratory rate, anaemia, malnutrition, depth of unconsciousness and death. Plasma EVs further provided evidence of platelet activation, TNF signalling, neurotrophin signalling, long-term potentiation and glutamatergic signalling during late disease stages of cerebral malaria. The transcriptional responses of cerebral tissue in cerebral malaria can be studied non-invasively using EVs circulating in peripheral blood. Biological sciences/Molecular biology/Transcriptomics Health sciences/Diseases/Infectious diseases/Malaria Biological sciences/Neuroscience/Molecular neuroscience Figures Figure 1 Figure 2 Figure 3 Introduction Cerebral malaria is an encephalopathy caused by Plasmodium falciparum infection 1,2 . Cerebral malaria was originally defined clinically as a coma in the presence of peripheral blood parasitaemia and not directly attributable to other causes such as hypoglycaemia, convulsion and meningitis 3 . Sequestration of parasitised erythrocytes in the brain microvascular system is a key mechanism leading to neurological impairment and the driver of disease severity in cerebral malaria 2 . However, the cerebral tissues can only be studied directly during postmortem evaluation 4 . This limitation has motivated the search for non-invasive ways of accurately diagnosing cerebral malaria. The retina comprises brain-like tissues and shows parasite sequestration and pathology comparable to that in canonical brain tissues 5–7 , but unlike the brain, the retina can be directly visualised; thus, sequestration-associated retinal pathology can be visualised clinically via non-invasive ophthalmological techniques 8 . This approach revealed a retinal pathology, termed "retinopathy", that can be used as a surrogate marker for parasite sequestration in the brain 9–12 . Patients with retinopathy-positive cerebral malaria (CM-R + ) are then considered the "true" cases of P. falciparum -induced cerebral pathology 9,13 . In contrast, those with retinopathy-negative cerebral malaria (CM-R − ) are suspected of having either encephalopathy caused by other aetiologies with incidental P. falciparum infection 9,13 , or coma secondary to systemic disturbance (e.g. metabolic consequences of malaria such as hypoglycaemia). Retinopathy was found to be highly sensitive (87–100%) and specific (75–87%) for cerebral malaria caused by P. falciparum 14 , but it provides no information on the molecular mechanisms of cerebral malaria pathogenesis. We propose an alternative non-invasive approach through extracellular vesicles (EVs) circulating in the blood. EVs are nanosized molecules secreted by all cells into biological fluids and are surrounded by a limiting phospholipid membrane 15 . They contain biological cargo such as RNA, lipids, and proteins reflecting the metabolic and physiological status of the parent cells or tissues. EVs can also cross tissue-blood barriers and circulate in biofluids without diluting their contents. This makes EVs attractive non-invasive tools for studying the pathology of diseases affecting inaccessible tissues, such as cerebral tissue 15 , in contrast to circulating immune cells that provide information limited to the immune compartment 16,17 . The temporal molecular alterations that occur as cerebral malaria progresses cannot be obtained through time series data from cerebral malaria patients because of the ethical imperative to begin treatment, and patients often present at hospitals at varying stages of disease progression after an unknown period of pre-admission illness. Researchers in other fields have used pseudotime or trajectory inference methods such as manifold learning to construct pseudo-temporal models of disease progression using gene expression data from cross-sectional samples 18,19 . Pseudotime inference algorithms work under the premise that each sample represents a snapshot of a disease stage and that the notional sampling time varies among the patients 20 . Here, we apply pseudotime analysis to construct a cerebral malaria disease progression model based on plasma EV transcriptomes from cross-sectional samples obtained from CM-R + and CM-R − patients using samples from healthy community controls as a baseline reference group. Results Solid tissue atlas of plasma-derived EVs in cerebral malaria. The study included 76 children admitted with cerebral malaria at Kilifi County Hospital (KCH) as previously described 21,22 (clinical parameters provided in Suppl data 1 ) and 8 community controls (CC) without P. falciparum infection. We sequenced the RNA content of plasma EVs from all individuals and applied support vector regression 23,24 to deconvolute the composition of solid tissues and brain cells in our EV-RNAseq data (Fig. 1 a-d; Suppl Fig. 1a ). We found that 32.2% of the plasma EV-RNA isolated from cerebral malaria patients originated from genes highly expressed in solid tissues (Fig. 1 a), and 67.8% were from genes highly expressed in whole blood cells (Fig. 1 a). Within the solid tissue fraction, the brain predominated (37.7%), followed by peripheral nerves (14.4%) and the small intestines (7.7%), (Fig. 1 b; Suppl Fig. 1a ). Additionally, the absolute proportion of RNA from transcripts expressed by the brain and peripheral nerves was relatively higher in CM-R + and CM-R − compared to CC (Fig. 1 c). Within the brain fraction, RNA from genes highly expressed in brain-endothelial cells (32%), microglia (27.6%), and neurons (26.2%) dominated the plasma EV-transcriptomes, while genes expressed in other brain cell types contributed less than 10% (Fig. 1 d). To validate the EV deconvolution approach, we downloaded and deconvoluted plasma EV-RNAseq data (GSE100207) generated from hepatocellular carcinoma (HCC) patients 25 . We observed that tissue-derived plasma EV-RNA in HCC patients originated predominantly from the liver (31.9%) and the adipose tissue (23.5%) ( Suppl Fig. 1b-c ), thus validating the EV-origin deconvolution analysis. Plasma EV-RNA resolves the heterogeneity among cerebral malaria patients and identifies retinopathy as a late-stage disease phenotype. We explored whether plasma EV transcriptomes reflected the heterogeneity of clinically defined cerebral malaria patients and whether they could be used to resolve disease progression at the molecular level. Applying manifold learning to the EV transcriptomes obtained from the cross-sectionally sampled patients, we defined the molecular disease stage of the samples - often called pseudotime or trajectory 18 (Fig. 2 a). The samples from malaria patients were ordered based on their similarity in EV-RNA abundance using the community control (CC) samples as the baseline reference, and this order was used to infer the molecular disease pseudotime (Fig. 2 a). Samples with later pseudotime were primarily seen among the CM-R + patients while earlier pseudotime samples primarily originated from CM-R − and CC (Fig. 2 a-b). We adopted a statistical approach to compare disease pseudotime between CM-R + , CM-R − , and CC and found that disease progression was significantly (p-value < 0.001) more advanced in CM-R + compared to CM-R − (Fig. 2 c). Using receiver operating characteristic (ROC) analysis, we benchmarked pseudotime against retinopathy and found retinopathy was 100% sensitive and about 78% specific for late-stage cerebral malaria (Fig. 2 d). Next, we applied linear regression to assess whether disease pseudotime was associated with clinicopathological parameters provided in Suppl data 1 . Later pseudotime was positively associated with retinopathy, metabolic acidosis, in-hospital death, and respiration rate, and negatively associated with mid-upper arm circumference (MUAC; a surrogate for nutritional status), haemoglobin (Hb) and Blantyre Coma Scores (BCS) (Fig. 2 e). Surprisingly, measures of parasitaemia (peripheral parasitaemia and Pf HRP2) were not associated with disease pseudotime, suggesting that cerebral disease progression was not simply a correlate of parasite burden (Fig. 2 e). These observations suggest that pseudotime, as calculated here, is an accurate proxy for disease progression. Alteration of plasma EV transcriptomes as a function of pseudotime. We fitted harmonic regression models to the gene profiles and found that 70% of the total EV transcripts (7438/10150) were significantly altered as a function of pseudotime (nominal p-value < 0.05) ( Suppl data 2 ). We constructed a phaseogram of disease progression using the significantly altered transcripts, and gene clustering analysis over pseudotime identified four non-overlapping gene clusters, which we named c1 to c4 (Fig. 3 a; Suppl data 2) . We used Fisher's exact test to analyse the gene overlap between the clusters and reference lists of published cell-type-specific markers 26,27 . Early disease-stage clusters (c1 and c2), which decreased with disease progression, were enriched for neuronal gene sets, while the late ones (c3 and c4), which increased with disease progression, were enriched for glial (astrocytes, and microglia) gene sets (Fig. 3 b-c; Suppl data 3; Suppl data 4 ). We exemplify the above observations using smoothed curves of five neuronal markers, including serotonin receptor (HTR5A), glial-derived neurotrophic factor receptor alpha 2 (GFRA2), and ganglioside-induced differentiation-associated protein 1 (GDAP1) and five glial cell markers, the most notable being markers of reactive gliosis, neurocan (NCAN) 28 and glial fibrillary acidic protein (GFAP) 29 , and the astrocytic water channel aquaporin 4 (AQP4) 30 (Fig. 3 d). Transcripts belonging to immune cells, notably neutrophils and erythropoiesis (erythroblasts) were enriched in late disease-stage clusters (c3 and c4) (Fig. 3 b; Suppl data 4 ). These results insinuate that cerebral malaria proceeds along a smooth transcriptional cascade of declining neuronal transcripts and a progressive increase in glial and immune cell transcripts, which can be studied via EVs circulating in peripheral blood. Lastly, we performed enrichment analysis using the KEGG gene sets 31 to determine whether transcripts enriched in late pseudotime clusters (c3 and c4) belong to biological pathways that could be associated with cerebral malaria pathogenesis. We found that cluster 3 genes were linked to neural functions (long-term potentiation, glutamatergic synapse and neurotrophin signalling) and vascular processes (TNF signalling, VEGF signalling, platelet activation and the complement cascade), while cluster 4 was enriched for genes implicated in age-related disorders such as Parkinson's disease, amyotrophic lateral sclerosis, Huntington's disease and Alzheimer's diseases (Fig. 3 e; Suppl data 5 ). When we performed enrichment analysis using the Wikipathway genesets 32 , we noted that "neuroinflammation and glutamatergic signalling" and "VEGFA-VEGFR2 signalling" were associated with late pseudotime clusters (Fig. 3 f; Suppl data 6 ). Taken together, our data proves that it is feasible to non-invasively study the pathological processes that drive infectious encephalopathies such as cerebral malaria by analysing the biological contents of circulating EVs. Discussion Cerebral malaria is a complication of P. falciparum characterised by impaired consciousness, among other neurological complications 1,33 . However, despite extensive research, the pathological process by which malaria parasites cause cerebral malaria 34,35 is poorly defined. In this study, we hypothesised that the RNA content of circulating extracellular vesicles (EVs) could be used to study neuropathological processes during cerebral malaria, specifically the transcriptional profiles of cerebral tissue. We show that the RNA content of circulating EVs reflects biological processes that occur as cerebral malaria progresses and could be used as a non-invasive means to study disease mechanisms and identify diagnostic biomarkers. Our results showed that after blood, brain cells predominated as the source of circulating EV-RNAs in cerebral malaria patients. Our analysis of pseudotime revealed that retinopathy is a late-stage disease marker. This implies that CM-R − might be a less severe form of cerebral malaria that can progress to CM-R + , which is consistent with other recent data 36 and challenges the dogma that CM-R − represents encephalopathies of other aetiologies besides P. falciparum 9,13 . Consistently, pseudotime was also positively associated with other malaria clinicopathological parameters of severity, such as in-hospital death, metabolic acidosis, raised respiratory rate, anaemia, and depth of coma, reinforcing the hypothesis that pseudotime, as a latent variable calculated from the EV-RNAseq data, represents cerebral malaria progression. We observed declining neuronal transcript levels during the late-disease stage, which coincides with increased glial (astroglia and microglia) transcripts. The increase in glial transcripts likely indicates progressive activation of astrocytes and microglia, as observed previously in experimental 37–39 and human cerebral malaria 40–42 . Astrocytes form part of the neurovascular unit (NVU), interact with neurons and the vascular system 43 , and thus respond to neuronal and vascular stress signals. The astroglia cell response is usually marked by increased expression of protein constituents of astrocyte intermediate filaments, including GFAP and NCAN 28,29 . We observed that the corresponding RNA from GFAP and NCAN in plasma EVs from cerebral malaria patients increased with disease progression. Although our data showing neuronal decline and increased gliosis is consistent with the trend observed in neurodegenerative disease progression 44 , neurological impairment in cerebral malaria is usually reversible, except in the minority with severe disease, suggesting that gliosis in cerebral malaria indicates an early response to vascular injury or neuronal hypofunction such as synapse loss 45 and not overt neuronal death, except in extremely severe cases 34 . Taking our findings together, we propose the following: that reduced microcirculatory flow resulting from parasite sequestration in the brain 46,47 results suboptimal brain perfusion 48–50 and neuronal hypofunction (evidenced by falling neuronal transcript levels), which is associated with progressive increase in glial cell activity 43 (evidenced by a progressive increase in glial transcripts), and other vascular- (VEGFA-VEGFR2 signalling 51–57 , platelet activation and coagulation 58 ), and neuronal- (neurotrophin signalling 59 , long-term potentiation 60 and glutamatergic signaling 61,62 ) related adaptive processes during late-stages of cerebral malaria, which may turn maladaptive and pathological 60,63 . In conclusion, we show that the contents of circulating EVs can be used to study the brain in patients with cerebral malaria. We demonstrate that the molecular sequence of neurovascular events in cerebral malaria is accessible antemortem via EVs, despite the inaccessibility of neuronal tissue to direct sampling. This will allow a more complete study of the pathogenesis of the illness, identification of biomarkers to predict disease progression and design of therapeutic interventions. Methods Samples and design The EV-RNAseq data was generated from 76 archived plasma samples from children with cerebral malaria who had been assessed for retinopathy and eight community-control adults without P. falciparum infection. Ethical approval of the study was provided by the Scientific Ethics Review Unit (SERU) of the Kenya Medical Research Institute (KEMRI) under the protocol KEMRI/SERU/3149. Written consents were provided by the parents or guardians of the children who donated the plasma samples. The subset of patients included in this study represented the whole cohort, as the proportion of CM-R + 30/76 (39%) and CM-R − 46/76 (61%) were largely similar to those documented in the original studies 21,22 . The sample size of the study was pragmatically determined based on available samples, clinical data, and resources. Isolation of EVs from plasma and RNA extraction Plasma was diluted in 13.5 ml of PBS and passed through a 0.22 µM filter. The filtrate was transferred into new ultracentrifuge tubes and spun at 150000 x g for 2 h at 4℃. The supernatant was discarded while the pellet was resuspended in 300 µL of PBS and treated with RNase A at 37 ℃ to digest non-vesicular RNA. After 15 minutes, the mixture was transferred to 13.5 ml ultracentrifuge tubes (Beckman). The tubes were filled using PBS and ultracentrifuged at 150000 x g for 2 h at 4 ℃. The final pellets were digested using 250 µl of RNA lysis buffer (Bioline) and kept at -80 ℃ until when required. RNA was isolated using the Isolate II RNA Min Kit (Bioline), following the manufacturer's instructions. Bead-assisted flow cytometry using antibodies to EV markers CD63 and CD9 was used to validate the EV isolation protocol. Library preparation from plasma EV-RNA The dUTP protocol developed by Chappell and others 64 was used to prepare the cDNA libraries for sequencing. Briefly, total EV-RNA is used to generate the first strand. Before second strand synthesis, the samples were cleaned using RNAcleanXP beads to remove traces of dNTPs. During the synthesis of the second strand, dTTP was replaced with dUTP. Double-stranded cDNA was then enzymatically shredded and ligated to NEXTflex adapters. The cDNA was treated with uracil glycosylase, which digests dUTPs to make the libraries strand-specific and amplified in 15 cycles to increase yield. Sequencing was done in two batches: 1 using the Hiseq 4000 genome analyser at the Wellcome Sanger Institute (WSI), UK and 2 using the NextSeq 500 genome analyser at the International Livestock Research Institute (ILRI), Kenya. Normalisation of RNAseq data RNAseq fastq files were quality-checked, and transcript read estimates were obtained by aligning the data to the human transcriptome using Kallisto 65 . The count data were normalised by gene length and sequencing depth, converted to counts per million (CPM) units, and used as input for all downstream analyses. Deconvolution of EV-RNA data Support vector regression was used to estimate the RNA fractions of solid tissue and brain cell-specific RNA. The solid tissue signature matrix used is publically available 24 , while the brain cell signature matrix was constructed from the Darmanis brain cell data 66 . The blood-tissue matrix was generated by determining marker genes between solid tissues and blood using the Human Protein Atlas tissue RNAseq data 67 . The Seurat tool was used to select the markers to find the tissue-specific markers. Estimation of cerebral malaria progression pseudotime We used deep learning to estimate pseudotimes of the EV transcriptome samples. A pseudotime trajectory was inferred from the CPM data using PhenoPath 68 and fine-tuned using Slingshot 69 . PhenoPath is a tool that employs Bayesian statistics to model the latent expression of each sample. Harmonic regression models were fitted to determine the EV-RNAs altered as a function of the inferred disease pseudotime, and a nominal p-value < 0.05 was used as the cut-off for significance. Linear regression and ROC were used to compare pseudotime to retinopathy and other clinicopathological parameters, while Spearman's rank correlation was applied to determine association of brain cell EV-RNA fractions with disease progression pseduotime. The phaseogram of disease progression was constructed using ComplexHeatmap and subdivided into four clusters using kmeans 70 . The overlap between the four clusters and a published reference list of brain cell-specific markers 26,27 was tested using Fisher's exact test. KEGG and Wikipathway analysis were also performed using Fisher's exact test. Declarations Data availability The EV-RNAseq is available from Gene Expression Omnibus (GEO) under the accession number GSE242856 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE242856). The following secure token can be used to review our data: gdodqoacptkbpcv. The HCC data was obtained from the GEO repository under the accession number GSE100207. The Darmanis single-cell RNAseq data is available from GEO under the accession number GSE67835. The solid tissue signature matrix was downloaded from https://github.com/HuangLab-Fudan/EV-origin/blob/master/Matrix_tissue.csv. The HPA tissue RNAseq data was downloaded from https://www.proteinatlas.org/download/normal_tissue.tsv.zip. Code availability No new software was generated in this study. Acknowledgements This work was supported by Wellcome Trust Awards: 209289/Z/17/Z (to AIA), 222323/Z/21/Z and 206194/Z/17/Z (to JCR) and 203077/Z/16/Z (a core Award to KEMRI-Wellcome Trust Research Programme). MK was supported by the Initiative to Develop African Research Leaders (IDeAL), part of DELTAS Africa Initiative [DEL-15-003]. For Open Access purposes, the author has applied a CC-BY public copyright licence to any author-accepted manuscript version arising from this submission. The funder had no role in study design, data collection and analysis, the decision to publish, or the writing of the manuscript. Author Contributions Conceptualisation: M.K, S.M, A.P, P.B, J.C.R, A.I.A Methodology: M.K, S.M, P.B, J.C.R, A.I.A Investigation: M.K, S.M, A.P, I.L.O, S.K, C.N, P.B, J.C.R, A.I.A Visualisation: M.K, A.I.A Funding acquisition: P.B, J.C.R, A.I.A Project administration: P.B, J.C.R, A.I.A Supervision: A.P, P.B, J.C.R, A.I.A Writing – original draft: M.K, A.P, P.B, J.C.R, A.I.A Writing – review & editing: M.K, S.M, A.P, I.L.O, S.K, C.N, P.B, J.C.R, A.I.A Competing Interests The authors declare that they have no competing interests. References Marsh, K. et al. Indicators of life-threatening malaria in African children. N Engl J Med 332 , 1399-1404 (1995). White, N. J. & Ho, M. The pathophysiology of malaria. Adv Parasitol 31 , 83-173 (1992). https://doi.org:10.1016/s0065-308x(08)60021-4 Newton, C. R., Taylor, T. E. & Whitten, R. O. 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Bioinformatics 32 , 2847-2849 (2016). https://doi.org:10.1093/bioinformatics/btw313 Supplementary Files Supplementary Data Files are not available with this version Additional Declarations There is NO Competing Interest. Supplementary Files BrainEVMSSupplementaryfiguresubmitted.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3375373","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":236243644,"identity":"95b4dd49-f484-4796-a363-82dd2d56a248","order_by":0,"name":"Abdirahman Abdi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYBACNmYeBomEigQgk7EBSFjIENTCz94D1HIGrkWCh6AWyZ4zDBKMbQkwPhFaDG7kHrzxcF6anO7sww3MPBUSPPzt3YkPGCru2TXg1JKXbJG4LcfY7FwiUMsZCR6JM2c3GzCcKU7GrSXHTCJxW0XitjOM7b9z2yR4DCRyt4GcmozLYfb33wC1zKmoB2ppYM79B9ay/Qc+LRBbGnISzMBaGiC2MAC12OHWAvRLwrE0Q7Atf45B/AIK9gTcWnIP3vxRkyxvdob9AeOMGhs5/vbejR8+VCTY49KCAwCtSGwgUQ8wYEjWMQpGwSgYBcMVAAAGxVjibKQ66gAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-7989-2125","institution":"KEMRI Wellcome Trust Research Programme","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Abdirahman","middleName":"","lastName":"Abdi","suffix":""},{"id":236243645,"identity":"d9d42789-9e88-4961-b629-dacbca03c8f0","order_by":1,"name":"Kioko Mwikali","email":"","orcid":"","institution":"KEMRI Wellcome Trust Research Programme","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kioko","middleName":"","lastName":"Mwikali","suffix":""},{"id":236243646,"identity":"74e0beaf-97e4-455e-a2f3-46ea0f58a0fa","order_by":2,"name":"Shaban Mwangi","email":"","orcid":"","institution":"KEMRI Wellcome Trust Research Programme","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shaban","middleName":"","lastName":"Mwangi","suffix":""},{"id":236243647,"identity":"9775a289-4901-49e5-9741-da50654ebc9a","order_by":3,"name":"Alena Pance","email":"","orcid":"https://orcid.org/0000-0002-9017-2644","institution":"University of Hertfordshire","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Alena","middleName":"","lastName":"Pance","suffix":""},{"id":236243648,"identity":"0c3994b6-c37b-43bf-bf2e-c2f0c3e3721c","order_by":4,"name":"Lynette Ochola-Oyier","email":"","orcid":"","institution":"KEMRI-Wellcome Trust Research Programme","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lynette","middleName":"","lastName":"Ochola-Oyier","suffix":""},{"id":236243649,"identity":"276930cd-8165-428f-b5f3-292b3258901a","order_by":5,"name":"Symon Kariuki","email":"","orcid":"","institution":"[email protected]","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Symon","middleName":"","lastName":"Kariuki","suffix":""},{"id":236243650,"identity":"71c5ba23-303a-42c9-8206-26e8ffb83b71","order_by":6,"name":"Charles Newton","email":"","orcid":"","institution":"[email protected]","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Charles","middleName":"","lastName":"Newton","suffix":""},{"id":236243651,"identity":"ddd72ea4-4f21-4be1-8495-43eb0e918c2c","order_by":7,"name":"Philip Bejon","email":"","orcid":"","institution":"KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research - Coast","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Philip","middleName":"","lastName":"Bejon","suffix":""},{"id":236243652,"identity":"1919c005-4fd7-4307-a126-63169e0d6444","order_by":8,"name":"Julian Rayner","email":"","orcid":"https://orcid.org/0000-0002-9835-1014","institution":"University of Cambridge","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Julian","middleName":"","lastName":"Rayner","suffix":""}],"badges":[],"createdAt":"2023-09-21 09:21:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3375373/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3375373/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":43833991,"identity":"8ab559e8-f066-4d60-b630-71b3e329c23f","added_by":"auto","created_at":"2023-09-28 14:21:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":318212,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBrain-derived RNAs are enriched in circulating EVs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea \u003c/strong\u003eThe relative comparison of blood and solid tissue RNA fractions in plasma EVs from cerebral malaria patients. \u003cstrong\u003eb\u003c/strong\u003e The relative distributions of solid tissue fractions of circulating EVs in cerebral malaria. \u003cstrong\u003ec\u003c/strong\u003e The estimated absolute proportion of RNA expressed by brain and nerves is higher in retinopathy positive (CM-R\u003csup\u003e+\u003c/sup\u003e) and negative (CM-R\u003csup\u003e-\u003c/sup\u003e) cerebral malaria compared to community controls (CC). \u003cstrong\u003ed\u003c/strong\u003e Brain cell relative fractions estimated from the plasma EV-RNA data.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3375373/v1/712689be835a068126df5647.png"},{"id":43833992,"identity":"177ca555-04e4-4211-9030-61326f6c3813","added_by":"auto","created_at":"2023-09-28 14:21:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":328074,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eManifold learning infers disease stage from plasma-EV transcriptomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea \u003c/strong\u003eEV-RNA samples coloured by disease progression pseudotime. The scatter plot shows that cerebral malaria evolves in a single trajectory \u003cstrong\u003eb\u003c/strong\u003e EV-RNA samples coloured by retinopathy status depicting that late stage pseudotime are enriched for the CM-R+ sample set. \u003cstrong\u003ec\u003c/strong\u003e Boxplots comparing disease pseudotime between CC, CM-R- and CM-R+. Inferred disease stage is significantly more advanced in CM-R+ than CM-R-. \u003cstrong\u003ed\u003c/strong\u003e A Forest plot showing results of linear regression comparing retinopathy and clinical parameters. Red shows positive correlations, black non-significant correlations, and blue negative correlations. The estimated pseudotime is concordant with known clinical parameters. \u003cstrong\u003ee\u003c/strong\u003e ROC curve showing that retinopathy is 100% sensitive and 78% specific that cerebral malaria has progressed to late stage pseudotime.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3375373/v1/f33ff5d375e0785f15f73854.png"},{"id":43833993,"identity":"7b3afac7-1d3b-4ac5-bd59-7a5a3b0df9ad","added_by":"auto","created_at":"2023-09-28 14:21:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":423096,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBrain-derived plasma EV-RNA varies over pseudotime\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea \u003c/strong\u003ePhaseogram showing variation of plasma EV-RNA as a function of disease progression pseudotime. The transcripts are clustered along pseudotime revealing four clusters, c1 to c4. \u003cstrong\u003eb \u003c/strong\u003eEnrichment analysis using PanglaoDB cell markers shows that early pseudotime clusters (c1 and c2) are enriched for neuronal markers while late pseudotime clusters are enriched for glial (oligodendrocyte, astrocyte and microglia) and immune cells. \u003cstrong\u003ec \u003c/strong\u003eEnrichment analysis using Darmanis brain cell markers also shows that cerebral malaria is characterized by neuronal loss and increased glial gene expression. \u003cstrong\u003ed\u003c/strong\u003e Representative EV-RNA profiles of neuronal and glial cell markers. \u003cstrong\u003ee \u003c/strong\u003eKEGG enrichment analysis results showing that cluster 3 is enriched for transcripts belonging to vascular processes and neural functions while cluster 4 is enriched for transcripts linked to pathways of neurodegeneration. \u003cstrong\u003ef \u003c/strong\u003eWikipathway enrichment analysis results showing that genes involved in VEGFA-VEGFR2 signalling and neuroinflammation and glutamatergic signalling belonged to late-stage clusters\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3375373/v1/4a9ba8827b0a9032dabb459b.png"},{"id":43835550,"identity":"972792f3-a735-4967-b3c6-7f081b83e0d4","added_by":"auto","created_at":"2023-09-28 14:37:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1438038,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3375373/v1/dbfc99f2-e64a-424e-a589-eb4916cee3be.pdf"},{"id":43834834,"identity":"b94cc068-fa27-4f90-914a-2aa7bcdfe59b","added_by":"auto","created_at":"2023-09-28 14:29:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":207244,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"BrainEVMSSupplementaryfiguresubmitted.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3375373/v1/bb858a5a17f3acd90272b888.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"The mRNA content of plasma extracellular vesicles provides a window into the brain during cerebral malaria disease progression","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCerebral malaria is an encephalopathy caused by \u003cem\u003ePlasmodium falciparum\u003c/em\u003e infection \u003csup\u003e1,2\u003c/sup\u003e. Cerebral malaria was originally defined clinically as a coma in the presence of peripheral blood parasitaemia and not directly attributable to other causes such as hypoglycaemia, convulsion and meningitis \u003csup\u003e3\u003c/sup\u003e. Sequestration of parasitised erythrocytes in the brain microvascular system is a key mechanism leading to neurological impairment and the driver of disease severity in cerebral malaria \u003csup\u003e2\u003c/sup\u003e. However, the cerebral tissues can only be studied directly during postmortem evaluation \u003csup\u003e4\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis limitation has motivated the search for non-invasive ways of accurately diagnosing cerebral malaria. The retina comprises brain-like tissues and shows parasite sequestration and pathology comparable to that in canonical brain tissues \u003csup\u003e5\u0026ndash;7\u003c/sup\u003e, but unlike the brain, the retina can be directly visualised; thus, sequestration-associated retinal pathology can be visualised clinically via non-invasive ophthalmological techniques \u003csup\u003e8\u003c/sup\u003e. This approach revealed a retinal pathology, termed \"retinopathy\", that can be used as a surrogate marker for parasite sequestration in the brain \u003csup\u003e9\u0026ndash;12\u003c/sup\u003e. Patients with retinopathy-positive cerebral malaria (CM-R\u003csup\u003e+\u003c/sup\u003e) are then considered the \"true\" cases of \u003cem\u003eP. falciparum\u003c/em\u003e-induced cerebral pathology \u003csup\u003e9,13\u003c/sup\u003e. In contrast, those with retinopathy-negative cerebral malaria (CM-R\u003csup\u003e\u0026minus;\u003c/sup\u003e) are suspected of having either encephalopathy caused by other aetiologies with incidental \u003cem\u003eP. falciparum\u003c/em\u003e infection \u003csup\u003e\u003cem\u003e9,13\u003c/em\u003e\u003c/sup\u003e, or coma secondary to systemic disturbance (e.g. metabolic consequences of malaria such as hypoglycaemia). Retinopathy was found to be highly sensitive (87\u0026ndash;100%) and specific (75\u0026ndash;87%) for cerebral malaria caused by \u003cem\u003eP. falciparum\u003c/em\u003e \u003csup\u003e14\u003c/sup\u003e, but it provides no information on the molecular mechanisms of cerebral malaria pathogenesis.\u003c/p\u003e \u003cp\u003eWe propose an alternative non-invasive approach through extracellular vesicles (EVs) circulating in the blood. EVs are nanosized molecules secreted by all cells into biological fluids and are surrounded by a limiting phospholipid membrane \u003csup\u003e15\u003c/sup\u003e. They contain biological cargo such as RNA, lipids, and proteins reflecting the metabolic and physiological status of the parent cells or tissues. EVs can also cross tissue-blood barriers and circulate in biofluids without diluting their contents. This makes EVs attractive non-invasive tools for studying the pathology of diseases affecting inaccessible tissues, such as cerebral tissue \u003csup\u003e15\u003c/sup\u003e, in contrast to circulating immune cells that provide information limited to the immune compartment \u003csup\u003e16,17\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe temporal molecular alterations that occur as cerebral malaria progresses cannot be obtained through time series data from cerebral malaria patients because of the ethical imperative to begin treatment, and patients often present at hospitals at varying stages of disease progression after an unknown period of pre-admission illness. Researchers in other fields have used pseudotime or trajectory inference methods such as manifold learning to construct pseudo-temporal models of disease progression using gene expression data from cross-sectional samples \u003csup\u003e18,19\u003c/sup\u003e. Pseudotime inference algorithms work under the premise that each sample represents a snapshot of a disease stage and that the notional sampling time varies among the patients \u003csup\u003e20\u003c/sup\u003e. Here, we apply pseudotime analysis to construct a cerebral malaria disease progression model based on plasma EV transcriptomes from cross-sectional samples obtained from CM-R\u003csup\u003e+\u003c/sup\u003e and CM-R\u003csup\u003e\u0026minus;\u003c/sup\u003e patients using samples from healthy community controls as a baseline reference group.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eSolid tissue atlas of plasma-derived EVs in cerebral malaria.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe study included 76 children admitted with cerebral malaria at Kilifi County Hospital (KCH) as previously described \u003csup\u003e21,22\u003c/sup\u003e (clinical parameters provided in \u003cb\u003eSuppl data 1\u003c/b\u003e) and 8 community controls (CC) without \u003cem\u003eP. falciparum\u003c/em\u003e infection. We sequenced the RNA content of plasma EVs from all individuals and applied support vector regression \u003csup\u003e23,24\u003c/sup\u003e to deconvolute the composition of solid tissues and brain cells in our EV-RNAseq data (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea-d; \u003cb\u003eSuppl Fig.\u0026nbsp;1a\u003c/b\u003e). We found that 32.2% of the plasma EV-RNA isolated from cerebral malaria patients originated from genes highly expressed in solid tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), and 67.8% were from genes highly expressed in whole blood cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Within the solid tissue fraction, the brain predominated (37.7%), followed by peripheral nerves (14.4%) and the small intestines (7.7%), (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb; \u003cb\u003eSuppl Fig.\u0026nbsp;1a\u003c/b\u003e). Additionally, the absolute proportion of RNA from transcripts expressed by the brain and peripheral nerves was relatively higher in CM-R\u003csup\u003e+\u003c/sup\u003e and CM-R\u003csup\u003e\u0026minus;\u003c/sup\u003e compared to CC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Within the brain fraction, RNA from genes highly expressed in brain-endothelial cells (32%), microglia (27.6%), and neurons (26.2%) dominated the plasma EV-transcriptomes, while genes expressed in other brain cell types contributed less than 10% (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). To validate the EV deconvolution approach, we downloaded and deconvoluted plasma EV-RNAseq data (GSE100207) generated from hepatocellular carcinoma (HCC) patients \u003csup\u003e25\u003c/sup\u003e. We observed that tissue-derived plasma EV-RNA in HCC patients originated predominantly from the liver (31.9%) and the adipose tissue (23.5%) (\u003cb\u003eSuppl Fig.\u0026nbsp;1b-c\u003c/b\u003e), thus validating the EV-origin deconvolution analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePlasma EV-RNA resolves the heterogeneity among cerebral malaria patients and identifies retinopathy as a late-stage disease phenotype.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe explored whether plasma EV transcriptomes reflected the heterogeneity of clinically defined cerebral malaria patients and whether they could be used to resolve disease progression at the molecular level. Applying manifold learning to the EV transcriptomes obtained from the cross-sectionally sampled patients, we defined the molecular disease stage of the samples - often called pseudotime or trajectory \u003csup\u003e18\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The samples from malaria patients were ordered based on their similarity in EV-RNA abundance using the community control (CC) samples as the baseline reference, and this order was used to infer the molecular disease pseudotime (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Samples with later pseudotime were primarily seen among the CM-R\u003csup\u003e+\u003c/sup\u003e patients while earlier pseudotime samples primarily originated from CM-R\u003csup\u003e\u0026minus;\u003c/sup\u003e and CC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-b). We adopted a statistical approach to compare disease pseudotime between CM-R\u003csup\u003e+\u003c/sup\u003e, CM-R\u003csup\u003e\u0026minus;\u003c/sup\u003e, and CC and found that disease progression was significantly (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) more advanced in CM-R\u003csup\u003e+\u003c/sup\u003e compared to CM-R\u003csup\u003e\u0026minus;\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Using receiver operating characteristic (ROC) analysis, we benchmarked pseudotime against retinopathy and found retinopathy was 100% sensitive and about 78% specific for late-stage cerebral malaria (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). Next, we applied linear regression to assess whether disease pseudotime was associated with clinicopathological parameters provided in \u003cb\u003eSuppl data 1\u003c/b\u003e. Later pseudotime was positively associated with retinopathy, metabolic acidosis, in-hospital death, and respiration rate, and negatively associated with mid-upper arm circumference (MUAC; a surrogate for nutritional status), haemoglobin (Hb) and Blantyre Coma Scores (BCS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). Surprisingly, measures of parasitaemia (peripheral parasitaemia and \u003cem\u003ePf\u003c/em\u003eHRP2) were not associated with disease pseudotime, suggesting that cerebral disease progression was not simply a correlate of parasite burden (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). These observations suggest that pseudotime, as calculated here, is an accurate proxy for disease progression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAlteration of plasma EV transcriptomes as a function of pseudotime.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe fitted harmonic regression models to the gene profiles and found that 70% of the total EV transcripts (7438/10150) were significantly altered as a function of pseudotime (nominal p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (\u003cb\u003eSuppl data 2\u003c/b\u003e). We constructed a phaseogram of disease progression using the significantly altered transcripts, and gene clustering analysis over pseudotime identified four non-overlapping gene clusters, which we named c1 to c4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea; \u003cb\u003eSuppl data 2)\u003c/b\u003e. We used Fisher's exact test to analyse the gene overlap between the clusters and reference lists of published cell-type-specific markers \u003csup\u003e26,27\u003c/sup\u003e. Early disease-stage clusters (c1 and c2), which decreased with disease progression, were enriched for neuronal gene sets, while the late ones (c3 and c4), which increased with disease progression, were enriched for glial (astrocytes, and microglia) gene sets (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb-c; \u003cb\u003eSuppl data 3; Suppl data 4\u003c/b\u003e). We exemplify the above observations using smoothed curves of five neuronal markers, including serotonin receptor (HTR5A), glial-derived neurotrophic factor receptor alpha 2 (GFRA2), and ganglioside-induced differentiation-associated protein 1 (GDAP1) and five glial cell markers, the most notable being markers of reactive gliosis, neurocan (NCAN) \u003csup\u003e28\u003c/sup\u003e and glial fibrillary acidic protein (GFAP) \u003csup\u003e29\u003c/sup\u003e, and the astrocytic water channel aquaporin 4 (AQP4) \u003csup\u003e30\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Transcripts belonging to immune cells, notably neutrophils and erythropoiesis (erythroblasts) were enriched in late disease-stage clusters (c3 and c4) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb; \u003cb\u003eSuppl data 4\u003c/b\u003e). These results insinuate that cerebral malaria proceeds along a smooth transcriptional cascade of declining neuronal transcripts and a progressive increase in glial and immune cell transcripts, which can be studied via EVs circulating in peripheral blood.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLastly, we performed enrichment analysis using the KEGG gene sets \u003csup\u003e31\u003c/sup\u003e to determine whether transcripts enriched in late pseudotime clusters (c3 and c4) belong to biological pathways that could be associated with cerebral malaria pathogenesis. We found that cluster 3 genes were linked to neural functions (long-term potentiation, glutamatergic synapse and neurotrophin signalling) and vascular processes (TNF signalling, VEGF signalling, platelet activation and the complement cascade), while cluster 4 was enriched for genes implicated in age-related disorders such as Parkinson's disease, amyotrophic lateral sclerosis, Huntington's disease and Alzheimer's diseases (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee; \u003cb\u003eSuppl data 5\u003c/b\u003e). When we performed enrichment analysis using the Wikipathway genesets \u003csup\u003e32\u003c/sup\u003e, we noted that \"neuroinflammation and glutamatergic signalling\" and \"VEGFA-VEGFR2 signalling\" were associated with late pseudotime clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef; \u003cb\u003eSuppl data 6\u003c/b\u003e). Taken together, our data proves that it is feasible to non-invasively study the pathological processes that drive infectious encephalopathies such as cerebral malaria by analysing the biological contents of circulating EVs.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCerebral malaria is a complication of \u003cem\u003eP. falciparum\u003c/em\u003e characterised by impaired consciousness, among other neurological complications \u003csup\u003e1,33\u003c/sup\u003e. However, despite extensive research, the pathological process by which malaria parasites cause cerebral malaria\u003csup\u003e34,35\u003c/sup\u003e is poorly defined. In this study, we hypothesised that the RNA content of circulating extracellular vesicles (EVs) could be used to study neuropathological processes during cerebral malaria, specifically the transcriptional profiles of cerebral tissue. We show that the RNA content of circulating EVs reflects biological processes that occur as cerebral malaria progresses and could be used as a non-invasive means to study disease mechanisms and identify diagnostic biomarkers.\u003c/p\u003e \u003cp\u003eOur results showed that after blood, brain cells predominated as the source of circulating EV-RNAs in cerebral malaria patients. Our analysis of pseudotime revealed that retinopathy is a late-stage disease marker. This implies that CM-R\u003csup\u003e\u0026minus;\u003c/sup\u003e might be a less severe form of cerebral malaria that can progress to CM-R\u003csup\u003e+\u003c/sup\u003e, which is consistent with other recent data\u003csup\u003e36\u003c/sup\u003e and challenges the dogma that CM-R\u003csup\u003e\u0026minus;\u003c/sup\u003e represents encephalopathies of other aetiologies besides \u003cem\u003eP. falciparum\u003c/em\u003e \u003csup\u003e9,13\u003c/sup\u003e. Consistently, pseudotime was also positively associated with other malaria clinicopathological parameters of severity, such as in-hospital death, metabolic acidosis, raised respiratory rate, anaemia, and depth of coma, reinforcing the hypothesis that pseudotime, as a latent variable calculated from the EV-RNAseq data, represents cerebral malaria progression.\u003c/p\u003e \u003cp\u003eWe observed declining neuronal transcript levels during the late-disease stage, which coincides with increased glial (astroglia and microglia) transcripts. The increase in glial transcripts likely indicates progressive activation of astrocytes and microglia, as observed previously in experimental \u003csup\u003e37\u0026ndash;39\u003c/sup\u003e and human cerebral malaria \u003csup\u003e40\u0026ndash;42\u003c/sup\u003e. Astrocytes form part of the neurovascular unit (NVU), interact with neurons and the vascular system \u003csup\u003e43\u003c/sup\u003e, and thus respond to neuronal and vascular stress signals. The astroglia cell response is usually marked by increased expression of protein constituents of astrocyte intermediate filaments, including GFAP and NCAN \u003csup\u003e28,29\u003c/sup\u003e. We observed that the corresponding RNA from GFAP and NCAN in plasma EVs from cerebral malaria patients increased with disease progression. Although our data showing neuronal decline and increased gliosis is consistent with the trend observed in neurodegenerative disease progression\u003csup\u003e44\u003c/sup\u003e, neurological impairment in cerebral malaria is usually reversible, except in the minority with severe disease, suggesting that gliosis in cerebral malaria indicates an early response to vascular injury or neuronal hypofunction such as synapse loss\u003csup\u003e45\u003c/sup\u003e and not overt neuronal death, except in extremely severe cases \u003csup\u003e34\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTaking our findings together, we propose the following: that reduced microcirculatory flow resulting from parasite sequestration in the brain\u003csup\u003e46,47\u003c/sup\u003e results suboptimal brain perfusion\u003csup\u003e48\u0026ndash;50\u003c/sup\u003e and neuronal hypofunction (evidenced by falling neuronal transcript levels), which is associated with progressive increase in glial cell activity\u003csup\u003e43\u003c/sup\u003e (evidenced by a progressive increase in glial transcripts), and other vascular- (VEGFA-VEGFR2 signalling\u003csup\u003e51\u0026ndash;57\u003c/sup\u003e, platelet activation and coagulation\u003csup\u003e58\u003c/sup\u003e), and neuronal- (neurotrophin signalling\u003csup\u003e59\u003c/sup\u003e, long-term potentiation\u003csup\u003e60\u003c/sup\u003e and glutamatergic signaling\u003csup\u003e61,62\u003c/sup\u003e) related adaptive processes during late-stages of cerebral malaria, which may turn maladaptive and pathological\u003csup\u003e60,63\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn conclusion, we show that the contents of circulating EVs can be used to study the brain in patients with cerebral malaria. We demonstrate that the molecular sequence of neurovascular events in cerebral malaria is accessible antemortem via EVs, despite the inaccessibility of neuronal tissue to direct sampling. This will allow a more complete study of the pathogenesis of the illness, identification of biomarkers to predict disease progression and design of therapeutic interventions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSamples and design\u003c/h2\u003e \u003cp\u003eThe EV-RNAseq data was generated from 76 archived plasma samples from children with cerebral malaria who had been assessed for retinopathy and eight community-control adults without \u003cem\u003eP. falciparum\u003c/em\u003e infection. Ethical approval of the study was provided by the Scientific Ethics Review Unit (SERU) of the Kenya Medical Research Institute (KEMRI) under the protocol KEMRI/SERU/3149. Written consents were provided by the parents or guardians of the children who donated the plasma samples. The subset of patients included in this study represented the whole cohort, as the proportion of CM-R\u003csup\u003e+\u003c/sup\u003e 30/76 (39%) and CM-R\u003csup\u003e\u0026minus;\u003c/sup\u003e 46/76 (61%) were largely similar to those documented in the original studies \u003csup\u003e21,22\u003c/sup\u003e. The sample size of the study was pragmatically determined based on available samples, clinical data, and resources.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eIsolation of EVs from plasma and RNA extraction\u003c/h2\u003e \u003cp\u003ePlasma was diluted in 13.5 ml of PBS and passed through a 0.22 \u0026micro;M filter. The filtrate was transferred into new ultracentrifuge tubes and spun at 150000 x g for 2 h at 4℃. The supernatant was discarded while the pellet was resuspended in 300 \u0026micro;L of PBS and treated with RNase A at 37 ℃ to digest non-vesicular RNA. After 15 minutes, the mixture was transferred to 13.5 ml ultracentrifuge tubes (Beckman). The tubes were filled using PBS and ultracentrifuged at 150000 x g for 2 h at 4 ℃. The final pellets were digested using 250 \u0026micro;l of RNA lysis buffer (Bioline) and kept at -80 ℃ until when required. RNA was isolated using the Isolate II RNA Min Kit (Bioline), following the manufacturer's instructions. Bead-assisted flow cytometry using antibodies to EV markers CD63 and CD9 was used to validate the EV isolation protocol.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eLibrary preparation from plasma EV-RNA\u003c/h2\u003e \u003cp\u003eThe dUTP protocol developed by Chappell and others \u003csup\u003e64\u003c/sup\u003e was used to prepare the cDNA libraries for sequencing. Briefly, total EV-RNA is used to generate the first strand. Before second strand synthesis, the samples were cleaned using RNAcleanXP beads to remove traces of dNTPs. During the synthesis of the second strand, dTTP was replaced with dUTP. Double-stranded cDNA was then enzymatically shredded and ligated to NEXTflex adapters. The cDNA was treated with uracil glycosylase, which digests dUTPs to make the libraries strand-specific and amplified in 15 cycles to increase yield. Sequencing was done in two batches: 1 using the Hiseq 4000 genome analyser at the Wellcome Sanger Institute (WSI), UK and 2 using the NextSeq 500 genome analyser at the International Livestock Research Institute (ILRI), Kenya.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eNormalisation of RNAseq data\u003c/h2\u003e \u003cp\u003eRNAseq fastq files were quality-checked, and transcript read estimates were obtained by aligning the data to the human transcriptome using Kallisto \u003csup\u003e65\u003c/sup\u003e. The count data were normalised by gene length and sequencing depth, converted to counts per million (CPM) units, and used as input for all downstream analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDeconvolution of EV-RNA data\u003c/h2\u003e \u003cp\u003eSupport vector regression was used to estimate the RNA fractions of solid tissue and brain cell-specific RNA. The solid tissue signature matrix used is publically available \u003csup\u003e24\u003c/sup\u003e, while the brain cell signature matrix was constructed from the Darmanis brain cell data \u003csup\u003e66\u003c/sup\u003e. The blood-tissue matrix was generated by determining marker genes between solid tissues and blood using the Human Protein Atlas tissue RNAseq data \u003csup\u003e67\u003c/sup\u003e. The Seurat tool was used to select the markers to find the tissue-specific markers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eEstimation of cerebral malaria progression pseudotime\u003c/h2\u003e \u003cp\u003eWe used deep learning to estimate pseudotimes of the EV transcriptome samples. A pseudotime trajectory was inferred from the CPM data using PhenoPath \u003csup\u003e68\u003c/sup\u003e and fine-tuned using Slingshot \u003csup\u003e69\u003c/sup\u003e. PhenoPath is a tool that employs Bayesian statistics to model the latent expression of each sample. Harmonic regression models were fitted to determine the EV-RNAs altered as a function of the inferred disease pseudotime, and a nominal p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was used as the cut-off for significance. Linear regression and ROC were used to compare pseudotime to retinopathy and other clinicopathological parameters, while Spearman's rank correlation was applied to determine association of brain cell EV-RNA fractions with disease progression pseduotime. The phaseogram of disease progression was constructed using ComplexHeatmap and subdivided into four clusters using kmeans \u003csup\u003e70\u003c/sup\u003e. The overlap between the four clusters and a published reference list of brain cell-specific markers \u003csup\u003e26,27\u003c/sup\u003e was tested using Fisher's exact test. KEGG and Wikipathway analysis were also performed using Fisher's exact test.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe EV-RNAseq is available from Gene Expression Omnibus (GEO) under the accession number GSE242856 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE242856). The following secure token can be used to review our data: gdodqoacptkbpcv. The HCC data was obtained from the GEO repository under the accession number GSE100207. The Darmanis single-cell RNAseq data is available from GEO under the accession number GSE67835. The solid tissue signature matrix was downloaded from https://github.com/HuangLab-Fudan/EV-origin/blob/master/Matrix_tissue.csv. The HPA tissue RNAseq data was downloaded from https://www.proteinatlas.org/download/normal_tissue.tsv.zip.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo new software was generated in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Wellcome Trust Awards: 209289/Z/17/Z (to AIA), 222323/Z/21/Z and 206194/Z/17/Z (to JCR) and 203077/Z/16/Z (a core Award to KEMRI-Wellcome Trust Research Programme). MK was supported by the Initiative to Develop African Research Leaders (IDeAL), part of DELTAS Africa Initiative [DEL-15-003]. For Open Access purposes, the author has applied a CC-BY public copyright licence to any author-accepted manuscript version arising from this submission. The funder had no role in study design, data collection and analysis, the decision to publish, or the writing of the manuscript. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualisation: M.K, S.M, A.P, P.B, J.C.R, A.I.A\u003c/p\u003e\n\u003cp\u003eMethodology: M.K, S.M, \u0026nbsp;P.B, J.C.R, A.I.A\u003c/p\u003e\n\u003cp\u003eInvestigation: M.K, \u0026nbsp;S.M, A.P, I.L.O, S.K, C.N, P.B, J.C.R, A.I.A\u003c/p\u003e\n\u003cp\u003eVisualisation: M.K, A.I.A\u003c/p\u003e\n\u003cp\u003eFunding acquisition: P.B, J.C.R, A.I.A\u003c/p\u003e\n\u003cp\u003eProject administration: P.B, J.C.R, A.I.A\u003c/p\u003e\n\u003cp\u003eSupervision: A.P, P.B, J.C.R, A.I.A\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; original draft: M.K, A.P, P.B, J.C.R, A.I.A\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; review \u0026amp; editing: M.K, S.M, A.P, I.L.O, S.K, C.N, P.B, J.C.R, A.I.A\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMarsh, K.\u003cem\u003e et al.\u003c/em\u003e Indicators of life-threatening malaria in African children. \u003cem\u003eN Engl J Med\u003c/em\u003e \u003cstrong\u003e332\u003c/strong\u003e, 1399-1404 (1995).\u003c/li\u003e\n\u003cli\u003eWhite, N. 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Complex heatmaps reveal patterns and correlations in multidimensional genomic data. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 2847-2849 (2016). https://doi.org:10.1093/bioinformatics/btw313\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplementary Files","content":"\u003cp\u003eSupplementary Data Files are not available with this version\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3375373/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3375373/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe impact of cerebral malaria on the transcriptional profiles of cerebral tissue is difficult to study using non-invasive approaches. We isolated plasma extracellular vesicles (EVs) from patients with cerebral malaria and community controls and sequenced their RNA content. Deconvolution of the tissue origins of the EV-RNA revealed that EVs from cerebral malaria patients are predominantly enriched in transcripts of brain origin. Next, we used manifold learning on the EV-RNAseq data to determine pseudotime against the community control samples as the baseline reference. We found that neuronal transcripts in plasma EVs decreased as pseudotime progressed, while transcripts of glial, endothelial, and immune cell origins increased over pseudotime. Pseudotime was associated with clinicopathological parameters of disease severity, including retinopathy, metabolic acidosis, respiratory rate, anaemia, malnutrition, depth of unconsciousness and death. Plasma EVs further provided evidence of platelet activation, TNF signalling, neurotrophin signalling, long-term potentiation and glutamatergic signalling during late disease stages of cerebral malaria. The transcriptional responses of cerebral tissue in cerebral malaria can be studied non-invasively using EVs circulating in peripheral blood.\u003c/p\u003e","manuscriptTitle":"The mRNA content of plasma extracellular vesicles provides a window into the brain during cerebral malaria disease progression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-09-28 14:21:00","doi":"10.21203/rs.3.rs-3375373/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7526abef-9f8f-42b3-8dfa-eb589ab1dbc8","owner":[],"postedDate":"September 28th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":25017879,"name":"Biological sciences/Molecular biology/Transcriptomics"},{"id":25017880,"name":"Health sciences/Diseases/Infectious diseases/Malaria"},{"id":25017881,"name":"Biological sciences/Neuroscience/Molecular neuroscience"}],"tags":[],"updatedAt":"2023-09-28T14:21:00+00:00","versionOfRecord":[],"versionCreatedAt":"2023-09-28 14:21:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3375373","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3375373","identity":"rs-3375373","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}