Background: Trypanosoma bruceispecies are motile protozoan parasites that are cyclically transmitted by tsetse fly (genusGlossina) causing human sleeping sickness and nagana in livestock in sub-Saharan Africa. African trypanosomes display digenetic life cycle stages in the tsetse fly vector and in their mammalian host. Experimental work on insect-stage trypanosomes is challenging because of the difficulty in setting up successfulin vitrocultures. Therefore, there is limited knowledge on the trypanosome biology during its development in the tsetse fly. Consequently, this limits the development of new strategies for blocking parasite transmission in the tsetse fly.Methods: In this study, RNA-Seq data of insect-stage trypanosomes were used to construct aT. bruceigene co-expression network using the weighted gene co-expression analysis (WGCNA) method. The study identified significant enriched modules for genes that play key roles during the parasite’s development in tsetse fly. Furthermore, potential 3′ untranslated region (UTR) regulatory elements for genes that clustered in the same module were identified using the Finding Informative Regulatory Elements (FIRE) tool.Results: A fraction of gene modules (12 out of 27 modules) in the constructed network were found to be enriched in functional roles associated with the cell division, protein biosynthesis, mitochondrion, and cell surface. Additionally, 12 hub genes encoding proteins such as RNA-binding protein 6 (RBP6), arginine kinase 1 (AK1),bruceialanine-rich protein (BARP), among others, were identified for the 12 significantly enriched gene modules. In addition, the potential regulatory elements located in the 3′ untranslated regions of genes within the same module were predicted.Conclusions: The constructed gene co-expression network provides a useful resource for network-based data mining to identify candidate genes for functional studies. This will enhance understanding of the molecular mechanisms that underlie important biological processes during parasite’s development in tsetse fly. Ultimately, these findings will be key in the identification of potential molecular targets for disease control.Graphical

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.

BackgroundTrypanosoma brucei species are motile protozoan parasites that are cyclically transmitted by tsetse fly (genus Glossina) causing human sleeping sickness and nagana in livestock in sub-Saharan Africa. African trypanosomes display digenetic life cycle stages in the tsetse fly vector and in their mammalian host. Experimental work on insect-stage trypanosomes is challenging due to the difficulty in setting up successful in vitro cultures. Therefore, there is limited knowledge on the trypanosome biology during its development in the tsetse fly. Consequently, this limits the development of new strategies for blocking parasite transmission in the tsetse fly. MethodsIn this study, RNA-Seq data of insect-stage trypanosomes were used to construct a T. brucei gene co-expression network using weighted gene co-expression analysis (WGCNA) method. The study identified significant enriched modules for genes that play key roles during the parasite’s development in tsetse fly. Further, potential 3’ untranslated region (UTR) regulatory elements for genes that clustered in the same module were identified using Finding Informative Regulatory Elements (FIRE) tool.ResultsA fraction of gene modules (12 out of 27 modules) in the constructed network were found to be enriched in functional roles associated with cell division, protein biosynthesis, mitochondrion, and cell surface. Additionally, 12 hub genes encoding proteins such as RNA-binding protein 6 (RBP6), Arginine kinase 1 (AK1), brucei alanine rich protein (BARP), among others, were identified for the 12 significantly enriched gene modules. In addition, the potential regulatory elements located in the 3’ untranslated regions of genes within the same module were predicted. ConclusionsThe constructed gene co-expression network provides a useful resource for network-based data mining to identify candidate genes for functional studies. This will enhance understanding of the molecular mechanisms that underlie important biological processes during parasite’s development in tsetse fly. Ultimately, these findings will be key in the identification of potential molecular targets for disease control.

Vaccination is a key control measure of COVID-19 by preventing severe effects of disease outcomes, reducing hospitalization rates and death, and increasing immunity. However, vaccination can affect the evolution and adaptation of SARS-CoV-2, largely through vaccine-induced immune pressure. Here we investigated intrahost recombination and single nucleotide variations (iSNVs) on the SARS-CoV-2 genome in non-vaccinated and vaccinated sequences from the Kenyan population to profile intrahost viral genetic evolution and adaptations driven by vaccine-induced immune pressure. We identified recombination hotspots in the S, N, and ORF1a/b genes and showed the genetic evolution landscape of SARS-CoV-2 by comparing within-wave and inter-wave recombination events from the beginning of the pandemic (June 2020) to (December 2022) in Kenya. We further reveal differential expression of recombinant RNA species between vaccinated and non-vaccinated individuals and perform an in-depth analysis of iSNVs to identify and characterize the functional properties of non-synonymous mutations found in ORF-1 a/b, S, and N genes. Lastly, we detected a minority variant in non-vaccinated patients in Kenya, with an immune escape mutation S255F of the spike gene and showed differential recombinant RNA species. Overall, this work identified unique in vivo mutations and intrahost recombination patterns in SARS-CoV-2 which could have significant implications for virus evolution, virulence, and immune escape.

Background: Wastewater and Environmental Surveillance (WES) has become a useful public health tool as an early-warning system revealing emergence/re-emergence of pathogenic diseases and spread of antimicrobial resistance (AMR). We characterized the taxonomic composition, relative abundance, and antibiotic resistance genes (ARGs) of wastewater-identified bacterial pathogens across socioeconomically and epidemiologically diverse sewerage catchments in Nairobi, Kenya—a key East African urban city representing a low- and middle-income country (LMIC). Results: Metagenomic analysis of Nairobi’s wastewater revealed distinct bacterial and antimicrobial resistance (AMR) profiles. Campylobacteraceae (52.5% ± 17.9%) and Bacteroidaceae (19.8% ± 9.9%) dominated the communities. While Arcobacter cryaerophilus was ubiquitous, Bacteroides fragilis and A. suis abundances varied by neighborhood socioeconomic status. We detected critical clinical pathogens—including Escherichia coli, Vibrio cholerae, Mycobacterium tuberculosis, and the ESKAPE species—alongside 207 distinct ARGs conferring resistance to 11 antibiotic classes. Both taxonomic and ARG compositions showed high spatial heterogeneity, with maximum variation in low-income areas. Temporal analysis captured shifting pathogen dynamics, and metagenomic abundances for Vibrio cholerae and Klebsiella pneumoniae were validated via qPCR. Conclusions: Our findings underscore the utility of WES as a scalable, non-invasive public health tool. By capturing community-level pathogen composition and AMR dynamics, WES bypasses the limitations of clinical diagnostic access, providing a vital early-warning system for underserved urban populations. To maximize its public health utility, environmental genomic signals must serve as actionable triggers for coordinated One Health responses, including targeted clinical diagnostics, localized antimicrobial stewardship reviews, proactive risk communication, and prioritized sanitation infrastructure upgrades.

Background Characterization of the breast cancer (BC) immune response may provide information for a point of intervention, such as application of immunotherapeutic treatments. In this study, we sought to recover and characterize the adaptive immune receptor (IR) recombination reads from genomics files representing Kenyan patients, to better understand the immune response specifically related to those patients. Methods We used a previously applied algorithm and software to obtain productive IR recombination reads from cancer and adjacent normal tissue samples representing 22 Kenyan BC patients. Results From both the RNAseq and exome files, there were significantly more T-cell receptor (TCR) recombination reads recovered from tumor samples compared to marginal tissue samples. Also, the immunoglobulin (IG) genes were expressed at a much higher level than the TCR genes (p-value = 0.0183) in the tumor samples. And, the tumor IG CDR3s consistently represented more positively charged amino acid R-groups, in comparison to the marginal tissue, IG CDR3s. Conclusion For Kenyan patients, a high level of IG expression, representing specific CDR3 chemistries, was associated with BC. These results lay the foundation for studies that could support specific immunotherapeutic interventions for Kenyan BC patients.