Co-expression network with protein–protein interaction and transcription regulation in malaria parasite Plasmodium falciparum

Enhancing Gene Co-Expression Network Inference for the Malaria Parasite Plasmodium falciparum

BACKGROUND

Malaria results in more than 550,000 deaths each year due to drug resistance in the most lethal Plasmodium (P.) species P. falciparum. A full P. falciparum genome was published in 2002, yet 44.6% of its genes have unknown functions. Improving the functional annotation of genes is important for identifying drug targets and understanding the evolution of drug resistance.

RESULTS

Genes function by interacting with one another. So, analyzing gene co-expression networks can enhance functional annotations and prioritize genes for wet lab validation. Earlier efforts to build gene co-expression networks in P. falciparum have been limited to a single network inference method or gaining biological understanding for only a single gene and its interacting partners. Here, we explore multiple inference methods and aim to systematically predict functional annotations for all P. falciparum genes. We evaluate each inferred network based on how well it predicts existing gene-Gene Ontology (GO) term annotations using network clustering and leave-one-out crossvalidation. We assess overlaps of the different networks' edges (gene co-expression relationships), as well as predicted functional knowledge. The networks' edges are overall complementary: 47-85% of all edges are unique to each network. In terms of the accuracy of predicting gene functional annotations, all networks yielded relatively high precision (as high as 87% for the network inferred using mutual information), but the highest recall reached was below 15%. All networks having low recall means that none of them capture a large amount of all existing gene-GO term annotations. In fact, their annotation predictions are highly complementary, with the largest pairwise overlap of only 27%. We provide ranked lists of inferred gene-gene interactions and predicted gene-GO term annotations for future use and wet lab validation by the malaria community.

CONCLUSIONS

The different networks seem to capture different aspects of the P. falciparum biology in terms of both inferred interactions and predicted gene functional annotations. Thus, relying on a single network inference method should be avoided when possible.

SUPPLEMENTARY DATA

Attached.

Composition and stage dynamics of mitochondrial complexes in Plasmodium falciparum

Our current understanding of mitochondrial functioning is largely restricted to traditional model organisms, which only represent a fraction of eukaryotic diversity. The unusual mitochondrion of malaria parasites is a validated drug target but remains poorly understood. Here, we apply complexome profiling to map the inventory of protein complexes across the pathogenic asexual blood stages and the transmissible gametocyte stages of Plasmodium falciparum. We identify remarkably divergent composition and clade-specific additions of all respiratory chain complexes. Furthermore, we show that respiratory chain complex components and linked metabolic pathways are up to 40-fold more prevalent in gametocytes, while glycolytic enzymes are substantially reduced. Underlining this functional switch, we find that cristae are exclusively present in gametocytes. Leveraging these divergent properties and stage dynamics for drug development presents an attractive opportunity to discover novel classes of antimalarials and increase our repertoire of gametocytocidal drugs.

Clinical expression and antigenic profiles of a Plasmodium vivax vaccine candidate: merozoite surface protein 7 (PvMSP-7)

Background

Vivax malaria is the predominant form of malaria outside Africa, affecting about 14 million people worldwide, with about 2.5 billion people exposed. Development of a Plasmodium vivax vaccine is a priority, and merozoite surface protein 7 (MSP-7) has been proposed as a plausible candidate. The P. vivax genome contains 12 MSP-7 genes, which contribute to erythrocyte invasion during blood-stage infection. Previous analysis of MSP-7 sequence diversity suggested that not all paralogs are functionally equivalent. To explore MSP-7 functional diversity, and to identify the best vaccine candidate within the family, MSP-7 expression and antigenicity during bloodstream infections were examined directly from clinical isolates.

Methods

Merozoite surface protein 7 gene expression was profiled using RNA-seq data from blood samples isolated from ten human patients with vivax malaria. Differential expression analysis and co-expression cluster analysis were used to relate PvMSP-7 expression to genetic markers of life cycle stage. Plasma from vivax malaria patients was also assayed using a custom peptide microarray to measure antibody responses against the coding regions of 12 MSP-7 paralogs.

Results

Ten patients presented diverse transcriptional profiles that comprised four patient groups. Two MSP-7 paralogs, 7A and 7F, were expressed abundantly in all patients, while other MSP-7 genes were uniformly rare (e.g. 7J). MSP-7H and 7I were significantly more abundant in patient group 4 only, (two patients having experienced longer patency), and were co-expressed with a schizont-stage marker, while negatively associated with liver-stage and gametocyte-stage markers. Screening infections with a PvMSP-7 peptide array identified 13 linear B-cell epitopes in five MSP-7 paralogs that were recognized by plasma from all patients.

Conclusions

These results show that MSP-7 family members vary in expression profile during blood infections; MSP-7A and 7F are expressed throughout the intraerythrocytic development cycle, while expression of other paralogs is focused on the schizont. This may reflect developmental regulation, and potentially functional differentiation, within the gene family. The frequency of B-cell epitopes among paralogs also varies, with MSP-7A and 7L consistently the most immunogenic. Thus, MSP-7 paralogs cannot be assumed to have equal potential as vaccines. This analysis of clinical infections indicates that the most abundant and immunogenic paralog is MSP-7A.

Electronic supplementary material

The online version of this article (10.1186/s12936-019-2826-7) contains supplementary material, which is available to authorized users.

Metabolic gene NR4A1 as a potential therapeutic target for non-smoking female non-small cell lung cancer patients

BACKGROUND

Although cigarette smoking is considered one of the key risk factors for lung cancer, 15% of male patients and 53% of female patients with lung cancer are non-smokers. Metabolic changes are critical features of cancer. Therapeutic target identification from a metabolic perspective in non-small cell lung cancer (NSCLC) tissue of female non-smokers has long been ignored.

RESULTS

Based on microarray data retrieved from Affymetrix expression arrays E-GEOD-19804, we found that the downregulated genes in non-smoking female NSCLC patients tended to participate in protein/amino acid and lipid metabolism, while upregulated genes were more involved in protein/amino acid and carbohydrate metabolism. Combining nutrient metabolic co-expression, protein-protein interaction network construction and overall survival assessment, we identified NR4A1 and TIE1 as potential therapeutic targets for NSCLC in female non-smokers. To accelerate the drug development for non-smoking female NSCLC patients, we identified nilotinib as a potential agonist targeting NR4A1 encoded protein by molecular docking and molecular dynamic stimulation. We also show that nilotinib inhibited proliferation and induced senescence of cells in non-smoking female NSCLC patients in vitro.

CONCLUSIONS

These results not only uncover nutrient metabolic characteristics in non-smoking female NSCLC patients, but also provide a new paradigm for identifying new targets and drugs for novel therapy for such patients.

Novel insights into global translational regulation through Pumilio family RNA-binding protein Puf3p revealed by ribosomal profiling

RNA binding proteins (RBPs) can regulate the stability, localization, and translation of their target mRNAs. Among them, Puf3p is a well-known Pumilio family RBP whose biology has been intensively studied. Nevertheless, the impact of Puf3p on the translational regulation of its downstream genes still remains to be investigated at the genome-wide level. In this study, we combined ribosome profiling and RNA-Seq in budding yeast (Saccharomyces cerevisiae) to investigate Puf3p's functions in translational regulation. Comparison of translational efficiency (TE) between wild-type and puf3Δ strains demonstrates extensive translational modulation in the absence of Puf3p (over 27% genes are affected at the genome level). Besides confirming its known role in regulating mitochondrial metabolism, our data demonstrate that Puf3p serves as a key post-transcriptional regulator of downstream RBPs by regulating their translational efficiencies, indicating a network of interactions among RBPs at the post-transcriptional level. Furthermore, Puf3p switches the balance of translational flux between mitochondrial and cytosolic ribosome biogenesis to adapt to changes in cellular metabolism. In summary, our results indicate that TE can be utilized as an informative index to interrogate the mechanism underlying RBP functions, and provide novel insights into Puf3p's mode-of-action.

Network-based analysis of differentially expressed genes in cerebrospinal fluid (CSF) and blood reveals new candidate genes for multiple sclerosis

BACKGROUND

The involvement of multiple genes and missing heritability, which are dominant in complex diseases such as multiple sclerosis (MS), entail using network biology to better elucidate their molecular basis and genetic factors. We therefore aimed to integrate interactome (protein-protein interaction (PPI)) and transcriptomes data to construct and analyze PPI networks for MS disease.

METHODS

Gene expression profiles in paired cerebrospinal fluid (CSF) and peripheral blood mononuclear cells (PBMCs) samples from MS patients, sampled in relapse or remission and controls, were analyzed. Differentially expressed genes which determined only in CSF (MS vs. control) and PBMCs (relapse vs. remission) separately integrated with PPI data to construct the Query-Query PPI (QQPPI) networks. The networks were further analyzed to investigate more central genes, functional modules and complexes involved in MS progression.

RESULTS

The networks were analyzed and high centrality genes were identified. Exploration of functional modules and complexes showed that the majority of high centrality genes incorporated in biological pathways driving MS pathogenesis. Proteasome and spliceosome were also noticeable in enriched pathways in PBMCs (relapse vs. remission) which were identified by both modularity and clique analyses. Finally, STK4, RB1, CDKN1A, CDK1, RAC1, EZH2, SDCBP genes in CSF (MS vs. control) and CDC37, MAP3K3, MYC genes in PBMCs (relapse vs. remission) were identified as potential candidate genes for MS, which were the more central genes involved in biological pathways.

DISCUSSION

This study showed that network-based analysis could explicate the complex interplay between biological processes underlying MS. Furthermore, an experimental validation of candidate genes can lead to identification of potential therapeutic targets.

Testis-Specific Y-Centric Protein-Protein Interaction Network Provides Clues to the Etiology of Severe Spermatogenic Failure

Pinpointing causal genes for spermatogenic failure (SpF) on the Y chromosome has been an ever daunting challenge with setbacks during the past decade. Since complex diseases result from the interaction of multiple genes and also display considerable missing heritability, network analysis is more likely to explicate an etiological molecular basis. We therefore took a network medicine approach by integrating interactome (protein-protein interaction (PPI)) and transcriptome data to reconstruct a Y-centric SpF network. Two sets of seed genes (Y genes and SpF-implicated genes (SIGs)) were used for network reconstruction. Since no PPI was observed among Y genes, we identified their common immediate interactors. Interestingly, 81% (N = 175) of these interactors not only interacted directly with SIGs, but also they were enriched for differentially expressed genes (89.6%; N = 43). The SpF network, formed mainly by the dys-regulated interactors and the two seed gene sets, comprised three modules enriched for ribosomal proteins and nuclear receptors for sex hormones. Ribosomal proteins generally showed significant dys-regulation with RPL39L, thought to be expressed at the onset of spermatogenesis, strongly down-regulated. This network is the first global PPI network pertaining to severe SpF and if experimentally validated on independent data sets can lead to more accurate diagnosis and potential fertility recovery of patients.