Transcriptomic approaches for identifying potential transmission blocking vaccine candidates in Plasmodium falciparum: a review of current knowledge and future directions.

Utilizing transcriptomics, promising methods for identifying unique genes associated with Plasmodium gametocyte development offer a potential avenue for novel candidate targets in transmission blocking vaccine development. In this review, we identified 40 publicly available transcriptomic datasets related to parasite factors linked with sexual stage transmission, from which we analyzed two RNA-Seq datasets to identify potential genes crucial for the transmission of P. falciparum from humans to mosquito vectors. Differential expression analysis revealed 3500 (2489 upregulated and 1011 downregulated) common genes differentially expressed throughout sexual stage development of P. falciparum occurring in both humans (gametocyte stage II, V) and mosquitoes (ookinete). Among which 1283 (914 upregulated and 369 downregulated) and 826 (719 upregulated and 107 downregulated) genes were specific to female and male gametocytes, respectively. Also, 830 potential transition associated genes were identified that may be involved in the adaptation and survival of the parasite in between human and mosquito stages. Additionally, we reviewed the functional aspects of important genes highly expressed throughout the sexual stage pathway and evaluated their suitability as vaccine candidates. The review provides researchers with insight into the importance of publicly available transcriptomic datasets for identifying critical and novel gametocyte markers that may aid in the development of rational transmission blocking strategies. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-023-03752-3.

Integrated multiomics analysis of chromosome 19 miRNA cluster in bladder cancer.

With 46 microRNAs (miRNAs) embedded tandemly over a distance of ~100 kb, chromosome 19 microRNA cluster (C19MC) is the largest miRNA cluster in the human genome. The C19MC is transcribed from a long noncoding genomic region and is usually expressed simultaneously at a higher level. Hence, we performed an integrative multiomics data analysis to examine C19MC regulation, expression patterns, and their impact on bladder cancer (BCa). We found that 43 members of C19MC were highly expressed in BCa. However, its co-localization with recurrent copy number variation (CNV) gain was not statistically significant to implicate its upregulation. It has been reported that C19MC expression is regulated by a well-established CpG island situated 17.6 kb upstream of the transcription start site, but we found that CpG probes at this island were hypomethylated, which was not statistically significant in the BCa cohort. In addition, the promoter region of C19MC is strongly regulated by a group of seven transcription factors (NR2F6, SREBF1, TBP, GATA3, GABPB1, ETV4, and ZNF444) and five chromatin modifiers (SMC3, KDMA1, EZH2, RAD21, and CHD7). Interestingly, these 12 genes were found to be overexpressed in BCa patients. Further, C19MC targeted 42 tumor suppressor (TS) genes that were downregulated, of which 15 were significantly correlated with patient survival. Our findings suggest that transcription factors and chromatin modifiers at the promoter region may regulate C19MC overexpression. The upregulated C19MC members, transcription regulators, and TS genes can be further exploited as potential diagnostic and prognostic indicators as well as for therapeutic management of BCa.

Exome sequencing of choreoacanthocytosis reveals novel mutations in VPS13A and co-mutation in modifier gene(s).

Choreoacanthocytosis, one of the forms of neuroacanthocytosis, is caused by mutations in vacuolar protein sorting-associated protein A (VPS13A), and is often misdiagnosed with other form of neuroacanthocytosis with discrete genetic defects. The phenotypic variations among the patients with VPS13A mutations significantly obfuscates the understanding of the disease and treatment strategies. In this study, two unrelated cases were identified, exhibiting the core phenotype of neuroacanthocytosis but with considerable clinical heterogeneity. Case 1 presented with an additional Parkinsonism phenotype, whereas seizures were evident in case 2. To decipher the genetic basis, whole exome sequencing followed by validation with Sanger sequencing was performed. A known homozygous pathogenic nonsense mutation (c.799C > T; p.R267X) in exon 11 of the VPS13A gene was identified in case 1 that resulted in a truncated protein. A novel missense mutation (c.9263T > G; p.M3088R) in exon 69 of VPS13A identified in case 2 was predicted as pathogenic. In silico analysis of the p.M3088R mutation at the C-terminus of VPS13A suggests a loss of interaction with TOMM40 and may disrupt mitochondrial localization. We also observed an increase in mitochondrial DNA copy numbers in case 2. Mutation analysis revealed benign heterozygous variants in interacting partners of VPS13A such as VAPA in case 1. Our study confirmed the cases as ChAc and identified the novel homozygous variant of VPS13A (c.9263T > G; p.M3088R) within the mutation spectrum of VPS13A-associated ChAc. Furthermore, mutations in VPS13A and co-mutations in its potential interacting partner(s) might contribute to the diverse clinical manifestations of ChAc, which requires further study.

Erythrocyte miRNA-92a-3p interactions with PfEMP1 as determinants of clinical malaria.

Based on the recently added high throughput analysis data on small noncoding RNAs in modulating disease pathophysiology of malaria, we performed an integrative computational analysis for exploring the role of human-host erythrocytic microRNAs (miRNAs) and their influence on parasite survival and host homeostasis. An in silico analysis was performed on transcriptomic datasets accessed from PlasmoDB and Gene Expression Omnibus (GEO) repositories analyzed using miRanda, miRTarBase, mirDIP, and miRDB to identify the candidate miRNAs that were further subjected to network analysis using MCODE and DAVID. This was followed by immune infiltration analysis and screening for RNA degradation mechanisms. Seven erythrocytic miRNAs, miR-451a, miR-92a-3p, miR-16-5p, miR-142-3p, miR-15b-5p, miR-19b-3p, and miR-223-3p showed favourable interactions with parasite genes expressed during blood stage infection. The miR-92a-3p that targeted the virulence gene PfEMP1 showed drastic reduction during infection. Performing pathway analysis for the human-host gene targets for the miRNA identified TOB1, TOB2, CNOT4, and XRN1 genes that are associated to RNA degradation processes, with the exoribonuclease XRN1, highly enriched in the malarial samples. On evaluating the role of exoribonucleases in miRNA degradation further, the pattern of Plasmodium falciparum_XRN1 showed increased levels during infection thus suggesting a defensive role for parasite survival. This study identifies miR-92a-3p, a member of C13orf25/ miR-17-92 cluster, as a novel miRNA inhibitor of the crucial parasite genes responsible for symptomatic malaria. Evidence for a plausible link to chromosome 13q31.3 loci controlling the epigenetic disease regulation is also suggested.

CmirC: an integrated database of clustered miRNAs co-localized with copy number variations in cancer.

Genomic rearrangements and copy number variations (CNVs) are the major regulators of clustered microRNAs (miRNAs) expression. Several clustered miRNAs are harbored in and around chromosome fragile sites (CFSs) and cancer-associated genomic hotspots. Aberrant expression of such clusters can lead to oncogenic or tumor suppressor activities. Here, we developed CmirC (Clustered miRNAs co-localized with CNVs), a comprehensive database of clustered miRNAs co-localized with CNV regions. The database consists of 481 clustered miRNAs co-localized with CNVs and their expression patterns in 35 cancer types of the TCGA. The portal also provides information on CFSs, miRNA cluster candidates, genomic coordinates, target gene networks, and gene functionality. The web portal is integrated with advanced tools such as JBrowse, NCBI-BLAST, GeneSCF, visNetwork, and NetworkD3 to help the researchers in data analysis, visualization, and browsing. This portal provides a promising avenue for integrated data analytics and offers additional evidence for the complex regulation of clustered miRNAs in cancer. The web portal is freely accessible at http://slsdb.manipal.edu/cmirclust to explore clinically significant miRNAs.

Diagnostic and prognostic potential clustered miRNAs in bladder cancer.

At specific genomic loci, miRNAs are in clusters and their association with copy number variations (CNVs) may exhibit abnormal expression in several cancers. Hence, the current study aims to understand the expression of miRNA clusters residing within CNVs and the regulation of their target genes in bladder cancer. To achieve this, we used extensive bioinformatics resources and performed an integrated analysis of recurrent CNVs, clustered miRNA expression, gene expression, and drug-gene interaction datasets. The study identified nine upregulated miRNA clusters that are residing on CNV gain regions and three miRNA clusters (hsa-mir-200c/mir-141, hsa-mir-216a/mir-217, and hsa-mir-15b/mir-16-2) are correlated with patient survival. These clustered miRNAs targeted 89 genes that were downregulated in bladder cancer. Moreover, network and gene enrichment analysis displayed 10 hub genes (CCND2, ETS1, FGF2, FN1, JAK2, JUN, KDR, NOTCH1, PTEN, and ZEB1) which have significant potential for diagnosis and prognosis of bladder cancer patients. Interestingly, hsa-mir-200c/mir-141 and hsa-mir-15b/mir-16-2 cluster candidates showed significant differences in their expression in stage-specific manner during cancer progression. Downregulation of NOTCH1 by hsa-mir-200c/mir-141 may also sensitize tumors to methotrexate thus suggesting potential chemotherapeutic options for bladder cancer subjects. To overcome some computational challenges and reduce the complexity in multistep big data analysis, we developed an automated pipeline called CmiRClustFinder v1.0 (https://github.com/msls-bioinfo/CmiRClustFinder_v1.0), which can perform integrated data analysis of 35 TCGA cancer types. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-022-03225-z.