Adaptive immune receptor features related to breast cancer tissue in Kenyan patients: high immunoglobulin gene expression and high levels of gamma-delta T-cells.

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.

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.

Building awareness and capacity of bioinformatics and open science skills in Kenya: a sensitize, train, hack, and collaborate model.

We have applied the sensitize-train-hack-community model to build awareness of and capacity in bioinformatics in Kenya. Open science is the practice of science openly and collaboratively, with tools, techniques, and data freely shared to facilitate reuse and collaboration. Open science is not a mandatory curriculum course in schools, whereas bioinformatics is relatively new in some African regions. Open science tools can significantly enhance bioinformatics, leading to increased reproducibility. However, open science and bioinformatics skills, especially blended, are still lacking among students and researchers in resource-constrained regions. We note the need to be aware of the power of open science among the bioinformatics community and a clear strategy to learn bioinformatics and open science skills for use in research. Using the OpenScienceKE framework-Sensitize, Train, Hack, Collaborate/Community-the BOSS (Bioinformatics and Open Science Skills) virtual events built awareness and empowered researchers with the skills and tools in open science and bioinformatics. Sensitization was achieved through a symposium, training through a workshop and train-the-trainer program, hack through mini-projects, community through conferences, and continuous meet-ups. In this paper, we discuss how we applied the framework during the BOSS events and highlight lessons learnt in planning and executing the events and their impact on the outcome of each phase. We evaluate the impact of the events through anonymous surveys. We show that sensitizing and empowering researchers with the skills works best when the participants apply the skills to real-world problems: project-based learning. Furthermore, we have demonstrated how to implement virtual events in resource-constrained settings by providing Internet and equipment support to participants, thus improving accessibility and diversity.

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

BACKGROUND: Trypanosoma 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 because of 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. METHODS: In this study, RNA-Seq data of insect-stage trypanosomes were used to construct a T. brucei gene 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), 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. 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.

Open Science in Kenya: Where Are We?

According to the United Nations Educational, Scientific, and Cultural Organization (UNESCO), Open Science is the movement to make scientific research and data accessible to all. It has great potential for advancing science. At its core, it includes (but is not limited to) open access, open data, and open research. Some of the associated advantages are promoting collaboration, sharing and reproducibility in research, and preventing the reinvention of the wheel, thus saving resources. As research becomes more globalized and its output grows exponentially, especially in data, the need for open scientific research practices is more evident - the future of modern science. This has resulted in a concerted global interest in open science uptake. Even so, barriers still exist. The formal training curriculum in most, if not all, universities in Kenya does not equip students with the knowledge and tools to subsequently practice open science in their research. Therefore, to work openly and collaboratively, there is a need for awareness and training in the use of open science tools. These have been neglected, especially in most developing countries, and remain barriers to the cause. Moreover, there is scanty research on the state of affairs regarding the practice and/or adoption of open science. Thus, we developed, through the OpenScienceKE framework, a model to narrow the gap. A sensitize-train-hack-collaborate model was applied in Nairobi, the economic and administrative capital of Kenya. Using the model, we sensitized through seminars, trained on the use of tools through workshops, applied the skills learned in training through hackathons to collaboratively answer the question on the state of open science in Kenya. While the former parts of the model had 20-50 participants, the latter part mainly involved participants with a bioinformatics background, leveraging their advanced computational skills. This model resulted in an open resource that researchers can use to publish as open access cost-effectively. Moreover, we observed a growing interest in open science practices in Kenya through literature search and data mining and that lack of awareness and skills may still hinder the adoption and practice of open science. Furthermore, at the time of the analyses, we surprisingly found that out of the 20,069 papers downloaded from BioRXiv, only 18 had Kenyan authors, a majority of which are international (16) collaborations. This may suggest poor uptake of the use of preprints among Kenyan researchers. The findings in this study highlight the state of open science in Kenya and challenges facing its adoption and practice while bringing forth possible areas for primary consideration in the campaign toward open science. It also proposes a model (sensitize-train-hack-collaborate model) that may be adopted by researchers, funders and other proponents of open science to address some of the challenges faced in promoting its adoption in Kenya.