Genomic analysis of host gene responses to cerebral Plasmodium falciparum malaria

Abstract

Introduction A vaccine for malaria is urgently required but no vaccine has yet shown satisfactory protective efficacy especially for Plasmodium falciparum. P. falciparum infection can progress to cerebral malaria (CM), a neurological syndrome with exceedingly high mortality. Designing effective P. falciparum vaccines require more understanding of the protective immune response while the host immune response to CM and the mechanisms are still elusive. Here, we aim to identify host gene responses to CM and host gene networks associated with CM pathogenesis. Methods An innovative genomic analysis strategy, the weighted gene coexpression network analysis (WGCNA) combined with differential gene expression analysis, was used in this study. Data for analysis contain 93 whole blood samples, derived from two previous public transcriptome datasets. Results This approach led to the identification of numerous differentially expressed human transcripts and dozens of coexpression gene modules. We further identified nine key genes, including MBP, SAMSN1, PSMF1, SLC39A8, EIF3B, SMPDL3A, FABP5, SPSB3, and SHARPIN, of which the last four genes were first identified to be related to CM in the present study. Conclusion The results provided a comprehensive characterization of host gene expression profiles in CM and offered some new insight into malaria vaccine design. These identified key genes could be potential targets or immune modulators for novel therapeutic interventions of CM.

Transcriptome Analysis of Plasmodium falciparum Isolates From Benin Reveals Specific Gene Expression Associated With Cerebral Malaria

“…Although recent studies have focused on host transcriptome signatures associated with CM [ 33–35 ], characterization of the gene expression profile of P falciparum isolated from CM needs further improvement. In this context, we analyzed the gene expression of 15 CM and 15 UM P falciparum isolates from a Beninese pediatric population, to better understand the parasite factors associated with CM.…”

Suppression of SAMSN1 contributes to neuroprotection in neonatal rats suffering from hypoxic–ischemic encephalopathy injury

“…Another study has found that platelet gene expression profiling can detect the early signal of impending acute myocardial infarction (AMI) before the occurrence of infarction, and SAMSN1 is one of the DEGs in platelet gene expression profiling 18 ; meanwhile, SAMSN1 is reported as a gene associated with coronary atherosclerosis and B‐cell activation 19 . In the nervous system, SAMSN1 was related to cerebral malaria by using genomic analysis of host gene responses to cerebral Plasmodium falciparum malaria 20 . Moreover, in Alzheimer's disease, using GWAS, SAMSN1 was found to be below the genomewide significance threshold, and it was significantly upregulated when exposed to amyloid β‐protein 21 .…”

“… 19 In the nervous system, SAMSN1 was related to cerebral malaria by using genomic analysis of host gene responses to cerebral Plasmodium falciparum malaria. 20 Moreover, in Alzheimer's disease, using GWAS, SAMSN1 was found to be below the genomewide significance threshold, and it was significantly upregulated when exposed to amyloid β‐protein. 21 Meanwhile, we found that SAMSN1 was significantly increased post‐HI injury, as well as increased in SY5Y cells and cortical fetal human cortical neurons subjected to OGD, while silencing SAMSN1 could improve cell viability and promote cell growth.…”

Diagnosis of cerebral malaria: Tools to reduce Plasmodium falciparum associated mortality

“…Remarkably, among children with CM, those who died from malaria complications generally had higher concentrations of CXCL10 and IFN-g than those who recovered (Cabantous et al, 2020). Li et al identified MBP, SAMSN1, PSMF1, SLC39A8, EIF3B, SMPDL3A, FABP5, SPSB3, and SHARPIN genes, which were associated with CM, and suggested that these genes may be good potential targets or immune modulators for novel therapeutic interventions of CM (Li et al, 2021). In addition, newly developed technologies like Olink can further help to identify protein signatures utilizing minimal sample volume with unrivalled specificity and sensitivity, as recently shown for active tuberculosis (Mousavian et al, 2022).…”