The top significant Gene Ontology (GO) categories of the differentially expressed genes (DEGs) in three ontologies: BP, biological process; MF, molecular function; and CC, cellular component.

The top significant Gene Ontology (GO) categories of the differentially expressed genes (DEGs) in three ontologies: BP, biological process; MF, molecular function; and CC, cellular component.</p

Gene expression comparison between infected and healthy controls.

Volcano plot displaying combined effect size (x-axis) and negative log10 of the false discovery rate value (y-axis). The significant up and down-regulated genes are plotted as red dots.</p

Weighted gene co-expression network analysis (WGCNA) of differentially expressed genes (DEGs).

Hierarchical cluster tree representing seven modules of co-expressed genes. The gene dendrogram was constructed by clustering dissimilarity using consensus Topological Overlap. The color row indicates the corresponding module colors. Each colored row represents a module color-coded to highlight a group of genes with strong interconnections.</p

The pathway enrichment analysis of up and down-regulated genes between infected and healthy controls.

The circles size and color mean the gene ratio and the adjusted p-value, respectively. The top pathways are shown.</p

The number of differentially expressed genes (DEGs) in different transcription factor families.

The number of up- or down-regulated are shown for each transcription factor family.</p

Schematic overview of the strategy for understanding aspects of response of immune system cells to <i>Leishmania</i> infection.

Schematic overview of the strategy for understanding aspects of response of immune system cells to Leishmania infection.</p

Venn diagram for overlap visualization between meta-analysis results and feature genes identified from the feature selection models.

Venn diagram for overlap visualization between meta-analysis results and feature genes identified from the feature selection models.</p

The xlsx file, including the A to M Tables.

BackgroundLeishmaniasis is a parasitic disease caused by the Leishmania protozoan affecting millions of people worldwide, especially in tropical and subtropical regions. The immune response involves the activation of various cells to eliminate the infection. Understanding the complex interplay between Leishmania and the host immune system is crucial for developing effective treatments against this disease.MethodsThis study collected extensive transcriptomic data from macrophages, dendritic, and NK cells exposed to Leishmania spp. Our objective was to determine the Leishmania-responsive genes in immune system cells by applying meta-analysis and feature selection algorithms, followed by co-expression analysis.ResultsAs a result of meta-analysis, we discovered 703 differentially expressed genes (DEGs), primarily associated with the immune system and cellular metabolic processes. In addition, we have substantiated the significance of transcription factor families, such as bZIP and C2H2 ZF, in response to Leishmania infection. Furthermore, the feature selection techniques revealed the potential of two genes, namely G0S2 and CXCL8, as biomarkers and therapeutic targets for Leishmania infection. Lastly, our co-expression analysis has unveiled seven hub genes, including PFKFB3, DIAPH1, BSG, BIRC3, GOT2, EIF3H, and ATF3, chiefly related to signaling pathways.ConclusionsThese findings provide valuable insights into the molecular mechanisms underlying the response of immune system cells to Leishmania infection and offer novel potential targets for the therapeutic goals.</div

Noninvasive and Continuous Monitoring of On-Chip Stem Cell Osteogenesis Using a Reusable Electrochemical Immunobiosensor

Noninvasive monitoring of biofabricated tissues during the biomanufacturing process is needed to obtain reproducible, healthy, and functional tissues. Measuring the levels of biomarkers secreted from tissues is a promising strategy to understand the status of tissues during biofabrication. Continuous and real-time information from cultivated tissues enables users to achieve scalable manufacturing. Label-free biosensors are promising candidates for detecting cell secretomes since they can be noninvasive and do not require labor-intensive processes such as cell lysing. Moreover, most conventional monitoring techniques are single-use, conducted at the end of the fabrication process, and, challengingly, are not permissive to in-line and continual detection. To address these challenges, we developed a noninvasive and continual monitoring platform to evaluate the status of cells during the biofabrication process, with a particular focus on monitoring the transient processes that stem cells go through during in vitro differentiation over extended periods. We designed and evaluated a reusable electrochemical immunosensor with the capacity for detecting trace amounts of secreted osteogenic markers, such as osteopontin (OPN). The sensor has a low limit of detection (LOD), high sensitivity, and outstanding selectivity in complex biological media. We used this OPN immunosensor to continuously monitor on-chip osteogenesis of human mesenchymal stem cells (hMSCs) cultured 2D and 3D hydrogel constructs inside a microfluidic bioreactor for more than a month and were able to observe changing levels of OPN secretion during culture. The proposed platform can potentially be adopted for monitoring a variety of biological applications and further developed into a fully automated system for applications in advanced cellular biomanufacturing