Programmed death-ligand 1 (PD-L1) plays a key role in maintaining immune tolerance and also in immune evasion of cancers and pathogens. Though the identity of stimuli that induce PD-L1 in various human innate cells and their function are relatively well studied, data on the basophils remain scarce. In this study, we have identified one of the factors, such as IFN-γ, that induces PD-L1 expression in human basophils. Interestingly, we found that basophil priming by IL-3 is indispensable for IFN-γ-induced PD-L1 expression in human basophils. However, priming by other cytokines including granulocyte-macrophage colony-stimulating factor (GM-CSF) and thymic stromal lymphopoietin (TSLP) was dispensable. Analyses of a published microarray data set on IL-3-treated basophils indicated that IL-3 enhances IFNGR2, one of the chains of the IFNGR heterodimer complex, and CD274, thus providing a mechanistic insight into the role of IL-3 priming in IFN-γ-induced PD-L1 expression in human basophils.

The COVID-19 pandemic has raised the standard regarding the current vaccine development pace, as several messenger RNA (mRNA)-lipid nanoparticle (LNP) vaccines have proved their ability to induce strong immunogenicity and protective efficacy. We developed 1-methylpseudouridine-containing mRNA-LNP vaccines, expressing either the more conserved SARS-CoV-2 nucleoprotein (mRNA-N) or spike protein (mRNA-S), both based on the prototypic viral sequences. When combining both mRNA-S and mRNA-N together (mRNA-S+N), the vaccine showed high immunogenicity and broad protection against different SARS-CoV-2 variants, including wildtype, Delta, BA.1, BA.5, and BQ.1. To better understand the mechanisms behind this broad protection obtained by mRNA-S+N, we analyzed innate and adaptive immune parameters following vaccination in mice. Compared to either mRNA-S or mRNA-N alone, mice vaccinated with mRNA-S+N exhibited an increase in the innate immune response, as depicted by the higher cytokine (IL-6 and chemokine (MCP-1) levels. In addition, lymph node immunophenotyping showed the maturation and activation of dendritic cells and natural killer cells, respectively. To understand the adaptive immune response, RNA-Seq analyses of the lung and spleen samples of the vaccinated mice were performed in parallel and revealed a stronger immune gene-expression profile in the lung than that in the spleen. Compared to mRNA-S alone, mRNA-S+N vaccination elicited higher levels of expression for genes involved in multiple immune pathways, including T cells, cytokine signaling, antigen presentation, B cells, and innate immunity. Together, our studies provide immunological insights into the mechanisms of broad protection conferred by dual mRNA vaccination against SARS-CoV-2 variants.

The integrity of coral reef ecosystems worldwide rests on a fine-tuned symbiotic interaction between an invertebrate and a dinoflagellate microalga from the family Symbiodiniaceae. Recent advances in bottom-up shotgun proteomic approaches and the availability of vast amounts of genetic information about Symbiodiniaceae have provided a unique opportunity to better understand the molecular mechanisms underpinning the interactions of coral-Symbiodiniaceae. However, the resilience of this dinoflagellate cell wall, as well as the presence of polyanionic and phenolics cell wall components, requires the optimization of sample preparation techniques for successful implementation of bottom-up proteomics. Therefore, in this study we compare three different workflows—filter-aided sample preparation (FASP), single-pot solid-phase-enhanced sample preparation (SP3), and stop-and-go-extraction tips (STAGETips, ST)—to develop a high-throughput proteotyping protocol for Symbiodiniaceae algal research. We used the model isolate Symbiodinium tridacnidorum. We show that SP3 outperformed ST and FASP with regard to robustness, digestion efficiency, and contaminant removal, which led to the highest number of total (3799) and unique proteins detected from 23,593 peptides. Most of these proteins were detected with ≥2 unique peptides (73%), zero missed tryptic peptide cleavages (91%), and hydrophilic peptides (>70%). To demonstrate the functionality of this optimized SP3 sample preparation workflow, we examined the proteome of S. tridacnidorum to better understand the molecular mechanism of peridinin-chlorophyll-protein complex (PCP, light harvesting protein) accumulation under low light (LL, 30 μmol photon m⁻² s⁻¹). Cells exposed to LL for 7 days upregulated various light harvesting complex (LHCs) proteins through the mevalonate-independent pathway; proteins of this pathway were at 2- to 6-fold higher levels than the control of 120 μmol photon m⁻² s⁻¹. Potentially, LHCs which were maintained in an active phosphorylated state by serine/threonine-protein kinase were also upregulated to 10-fold over control. Collectively, our results show that the SP3 method is an efficient high-throughput proteotyping tool for Symbiodiniaceae algal research.

In Guinea, genomic surveillance has been established to generate sequences of and to identify locally circulating SARS-CoV-2 variants. This study aims to describe the distributions, genetic diversity, and origins of SARS-CoV-2 lineages circulating in Guinea during the COVID-19 pandemic. A migration analysis was performed by selecting all sequences generated in Guinea for variants of concern and interest. From March 2020 to December 2023, 1038 sequences were generated in Guinea and submitted to the Global Initiative on Sharing All Influenza Data (GISAID) database. Of these, 73.1% corresponded to SARS-CoV-2 variants of concern, which were further grouped into Omicron (69.4%), Delta (21.9%), Alpha (6.6%), and Eta (2.1%). Other variants accounted for 26.9% of the total. Among the total variants analyzed, 75 importations into Guinea from various countries worldwide were identified. Most of the importations (40%) originated from African countries, followed in significance by those from European countries (25.3%) and Asia (18.6%). A significant migratory flow was observed within Guinea. The genomic surveillance reported in this study revealed the diversity of SARS-CoV-2 variants circulating in Guinea, emphasizing the importance of large-scale sequencing analyses in understanding the dynamics of the pandemic.

Computational multiomics methods are based on machine learning methods, and are primarily used for classifying patients into subtypes, discovering novel biomarkers, drug repurposing, and advancing precision medicine. Advances in high-throughput technologies have enabled comprehensive profiling of multiple molecular layers, resulting in the emergence of multiomics approaches for a more accurate understanding of disease mechanisms, therapeutic targets, and biological heterogeneity. This review examines current applications of multiomics in oncology, ageing, and immune-mediated diseases, highlighting the strengths and challenges of integrative models in understanding disease mechanisms, identifying biomarkers, and guiding precision therapies. Integration strategies, from early to late fusion and horizontal to vertical frameworks, are also examined alongside recent advances in computational platforms and preprocessing techniques.

Breast cancer is a neoplasm resulting from the multiplication of abnormal cells in the breast tissue that initially form nodules or thickening. The survival of patients with the disease can be highly effective in cases where the diagnosis is early. However, this diagnosis requires the analysis of extensive histopathological breast tissue samples by specialists. This task is considered complex and may require the attention of the pathologist for a long period of time. Fortunately, computational methods, which include digital image processing and deep learning, can help quickly identify tumour malignancies on images from these scans and speed up diagnosis. Thus, this work proposes a methodology for the automatic diagnosis of malignant breast lesions on histopathological images based on the mutual measurement of bioinspired texture features (BiT) and deep learning features. Additionally, in this study, we also evaluated the effect of methods of colour normalisation to reduce colour variation in the images. The proposed methodology obtained a result of 92.9% accuracy with the random forest classifier. These numbers show that a combination of BiT with deep features produces better results than the techniques applied individually, and show that the proposed method can be used to compose computer-aided detection systems.