Gene co-expression network analysis of Trypanosoma brucei in tsetse fly vector

Weighted gene co-expression network analysis reveals immune evasion related genes in Echinococcus granulosus sensu stricto

Cystic echinococcosis (CE) is a zoonotic disease caused by the tapeworm Echinococcus granulosus sensu lato (s.l). In the intermediate host, this disease is characterized by the growth of cysts in viscera such as liver and lungs, inside of which the parasite develops to the next infective stage known as protoscoleces. There are records that the infected viscera affect the development and morphology of E. granulosus s.l. protoscolex in hosts such as buffalo or humans. However, the molecular mechanisms that drive these differences remains unknown. Weighted gene co-expression network analysis (WGCNA) using a set of RNAseq data obtained from E. granulosus sensu stricto (s.s.) protoscoleces found in liver and lung cysts reveals 34 modules in protoscoleces of liver origin, of which 12 have differential co-expression from protoscoleces of lung origin. Three of these twelve modules contain hub genes related to immune evasion: tegument antigen, tegumental protein, ubiquitin hydrolase isozyme L3, COP9 signalosome complex subunit 3, tetraspanin CD9 antigen, and the methyl-CpG-binding protein Mbd2. Also, two of the twelve modules contain only hypothetical proteins with unknown orthology, which means that there are a group of unknown function proteins co-expressed inside the protoscolex of liver CE cyst origin. This is the first evidence of gene expression differences in protoscoleces from CE cysts found in different viscera, with co-expression networks that are exclusive to protoscoleces from liver CE cyst samples. This should be considered in the control strategies of CE, as intermediate hosts can harbor CE cysts in liver, lungs, or both organs simultaneously.

Prioritization of Trypanosoma brucei editosome protein interactions interfaces at residue resolution through proteome-scale network analysis

BACKGROUND

Trypanosoma brucei is the causative agent for trypanosomiasis in humans and livestock, which presents a growing challenge due to drug resistance. While identifying novel drug targets is vital, the process is delayed due to a lack of functional information on many of the pathogen's proteins. Accordingly, this paper presents a computational framework for prioritizing drug targets within the editosome, a vital molecular machinery responsible for mitochondrial RNA processing in T. brucei. Importantly, this framework may eliminate the need for prior gene or protein characterization, potentially accelerating drug discovery efforts.

RESULTS

By integrating protein-protein interaction (PPI) network analysis, PPI structural modeling, and residue interaction network (RIN) analysis, we quantitatively ranked and identified top hub editosome proteins, their key interaction interfaces, and hotspot residues. Our findings were cross-validated and further prioritized by incorporating them into gene set analysis and differential expression analysis of existing quantitative proteomics data across various life stages of T. brucei. In doing so, we highlighted PPIs such as KREL2-KREPA1, RESC2-RESC1, RESC12A-RESC13, and RESC10-RESC6 as top candidates for further investigation. This includes examining their interfaces and hotspot residues, which could guide drug candidate selection and functional studies.

CONCLUSION

RNA editing offers promise for target-based drug discovery, particularly with proteins and interfaces that play central roles in the pathogen's life cycle. This study introduces an integrative drug target identification workflow combining information from the PPI network, PPI 3D structure, and reside-level information of their interface which can be applicable to diverse pathogens. In the case of T. brucei, via this pipeline, the present study suggested potential drug targets with residue-resolution from RNA editing machinery. However, experimental validation is needed to fully realize its potential in advancing urgently needed antiparasitic drug development.

Gene co-expression network identifies critical genes, pathways and regulatory motifs mediating the progression of rift valley fever in Bostaurus

Rift Valley Fever (RVF) is a mosquito-borne viral disease caused by the Rift Valley Fever Virus. The disease is a zoonosis that largely affects domestic animals, including sheep, goats, and cattle, resulting in severe morbidity and mortality marked by massive storm abortions. To halt human and livestock deaths due to RVF, the development of efficacious vaccines and therapeutics is a compelling and urgent priority. We sought to identify potential key modules (gene clusters), hub genes, and regulatory motifs involved in the pathogenesis of RVF in Bos taurus that are amenable to inhibition. We analyzed 39 Bos taurus RNA-Seq samples using the weighted gene co-expression network analysis (WGCNA) R package and uncovered significantly enriched modules containing genes with potential pivotal roles in RVF progression. Moreover, regulatory motif analysis conducted using the Multiple Expectation Maximization for Motif Elicitation (MEME) suite identified motifs that probably modulate vital biological processes. Gene ontology terms associated with identified motifs were inferred using the GoMo human database. The gene co-expression network constructed in WGCNA using 5000 genes contained seven (7) modules, out of which four were significantly enriched for terms associated with response to viruses, response to interferon-alpha, innate immune response, and viral defense. Additionally, several biological pathways implicated in developmental processes, anatomical structure development, and multicellular organism development were identified. Regulatory motifs analysis identified short, repeated motifs whose function(s) may be amenable to disruption by novel therapeutics. Predicted functions of identified motifs include tissue development, embryonic organ development, and organ morphogenesis. We have identified several hub genes in enriched co-expressed gene modules and regulatory motifs potentially involved in the pathogenesis of RVF in B. taurus that are likely viable targets for disruption by novel therapeutics.

Gene coexpression network during ontogeny in the yellow fever mosquito, Aedes aegypti

BACKGROUND

The behaviors and ontogeny of Aedes aegypti are closely related to the spread of diseases caused by dengue (DENV), chikungunya (CHIKV), Zika (ZIKV), and yellow fever (YFV) viruses. During the life cycle, Ae. aegypti undergoes drastic morphological, metabolic, and functional changes triggered by gene regulation and other molecular mechanisms. Some essential regulatory factors that regulate insect ontogeny have been revealed in other species, but their roles are still poorly investigated in the mosquito.

RESULTS

Our study identified 6 gene modules and their intramodular hub genes that were highly associated with the ontogeny of Ae. aegypti in the constructed network. Those modules were found to be enriched in functional roles related to cuticle development, ATP generation, digestion, immunity, pupation control, lectins, and spermatogenesis. Additionally, digestion-related pathways were activated in the larvae and adult females but suppressed in the pupae. The integrated protein‒protein network also identified cilium-related genes. In addition, we verified that the 6 intramodular hub genes encoding proteins such as EcKinase regulating larval molt were only expressed in the larval stage. Quantitative RT‒PCR of the intramodular hub genes gave similar results as the RNA-Seq expression profile, and most hub genes were ontogeny-specifically expressed.

CONCLUSIONS

The constructed gene coexpression network provides a useful resource for network-based data mining to identify candidate genes for functional studies. Ultimately, these findings will be key in identifying potential molecular targets for disease control.

RNA-seq reveals that overexpression of TcUBP1 switches the gene expression pattern towards that of the infective form of Trypanosoma cruzi

Trypanosomes regulate gene expression mainly by using post-transcriptional mechanisms. Key factors responsible for carrying out this regulation are RNA-binding proteins (RBPs), affecting subcellular localization, translation, and/or transcript stability. Trypanosoma cruzi U-rich RBP 1 (TcUBP1) is a small protein that modulates the expression of several surface glycoproteins of the trypomastigote infective stage of the parasite. Its mRNA targets are known but the impact of its overexpression at the transcriptome level in the insect-dwelling epimastigote cells has not yet been investigated. Thus, in the present study, by using a tetracycline-inducible system, we generated a population of TcUBP1-overexpressing parasites and analyzed its effect by RNA-seq methodology. This allowed us to identify 793 up- and 371 down-regulated genes with respect to the wild-type control sample. Among the up-regulated genes, it was possible to identify members coding for the TcS superfamily, MASP, MUCI/II, and protein kinases, whereas among the down-regulated transcripts, we found mainly genes coding for ribosomal, mitochondrial, and synthetic pathway proteins. RNA-seq comparison with two previously published datasets revealed that the expression profile of this TcUBP1-overexpressing replicative epimastigote form resembles the transition to the infective metacyclic trypomastigote stage. We identified novel cis-regulatory elements in the 3'-untranslated region of the affected transcripts and confirmed that UBP1m -a signature TcUBP1 binding element previously characterized in our lab- is enriched in the list of stabilized genes. We can conclude that the overall effect of TcUBP1 overexpression on the epimastigote transcriptome is mainly the stabilization of mRNAs coding for proteins that are important for parasite infection.

In silico-driven analysis of the Glossina morsitans morsitans antennae transcriptome in response to repellent or attractant compounds

BACKGROUND

High-throughput sequencing generates large volumes of biological data that must be interpreted to make meaningful inference on the biological function. Problems arise due to the large number of characteristics p (dimensions) that describe each record [n] in the database. Feature selection using a subset of variables extracted from the large datasets is one of the approaches towards solving this problem.

METHODOLOGY

In this study we analyzed the transcriptome of Glossina morsitans morsitans (Tsetsefly) antennae after exposure to either a repellant (δ-nonalactone) or an attractant (ε-nonalactone). We identified 308 genes that were upregulated or downregulated due to exposure to a repellant (δ-nonalactone) or an attractant (ε-nonalactone) respectively. Weighted gene coexpression network analysis was used to cluster the genes into 12 modules and filter unconnected genes. Discretized and association rule mining was used to find association between genes thereby predicting the putative function of unannotated genes.

RESULTS AND DISCUSSION

Among the significantly expressed chemosensory genes (FDR < 0.05) in response to Ɛ-nonalactone were gustatory receptors (GrIA and Gr28b), ionotrophic receptors (Ir41a and Ir75a), odorant binding proteins (Obp99b, Obp99d, Obp59a and Obp28a) and the odorant receptor (Or67d). Several non-chemosensory genes with no assigned function in the NCBI database were co-expressed with the chemosensory genes. Exposure to a repellent (δ-nonalactone) did not show any significant change between the treatment and control samples. We generated a coexpression network with 276 edges and 130 nodes. Genes CAH3, Ahcy, Ir64a, Or67c, Ir8a and Or67a had node degree values above 11 and therefore could be regarded as the top hub genes in the network. Association rule mining showed a relation between various genes based on their appearance in the same itemsets as consequent and antecedent.