Esaki Muthu, S. | Search bioRxiv

bioRxiv2026

Intranasal administration of Burkholderia cepacia promotes progressive acute inflammatory changes in experimental BALB/c mice

Burkholderia cepacia (B. cepacia) is an opportunistic pathogen with versatile virulence mechanisms. The pathogenesis of B.cepacia in the immunocompetent host following intranasal exposure largely remains ambiguous. Male BALB/c mice were intranasally inoculated with B. cepacia strain 20209 (1x10{square} CFU) and evaluated on days 3, 7, 14, and 21 post-infection. Histopathology of lung, liver, spleen, and kidney tissues were performed using H&E and PAS staining. Plasma cytokines were quantified using commercial multiplex assays and ELISA. Matrix metalloproteinase-2 (MMP-2) activity was assessed via gelatin zymography and metabolomic profiling by high-resolution mass spectrometry (HRMS). Histopathological analysis revealed organ-specific pathological indices such as interstitial pneumonitis, bronchitis, leukocyte infiltration, hepatic inflammation, as well as splenic hyperplasia. Similarly, MMP-2 activity revealed time-dependent modulation, reflecting dynamic proteolytic responses. Plasma and tissue IL-18 and IL-1{beta} levels demonstrated a temporal regulation, with IL-18 peaking on day 7 post-infection, while IL-1{beta} showed a biphasic expression peaking on day 3 and 14. Untargeted metabolomics revealed differential expression of lipid metabolism, and energy pathways, with higher expression of phospholipids and sphingolipids. Together, our study portrayed a physiologically relevant intranasal BALB/c model that captures both localized and systemic inflammatory responses to B. cepacia. Our findings highlight organ-specific pathologic progression and sustained inflammation providing key insights into host-pathogen interactions.

microbiology↗

bioRxiv2026

Genomic Evolution of SARS-CoV-2 Delta Variants Pre- and Post-Omicron Emergence using Alignment-free Machine Learning models

The SARS-CoV-2 Delta variant (B.1.617.2), initially classified as a variant of concern due to its enhanced transmissibility and vaccine-escape mutations, underwent further genomic changes following the emergence of the Omicron variant (B.1.1.529). This study investigates the genomic differences in Delta variant spike gene sequences collected before and after the emergence of Omicron. A total of 190 sequences were analyzed using an alignment-free approach incorporating k-mer-based feature extraction and machine learning models, including convolutional neural networks (CNN), K-means clustering, and random forest classification. The random forest model achieved 93% accuracy, with significant F1 scores, effectively distinguishing the two Delta variant groups. Comparative analysis revealed 157 persistent mutations and four vanished mutations in the post-Omicron group. Cluster analysis showed notable shifts, indicating stable yet evolving genomic patterns over time. The study demonstrates the advantage of alignment-free methods in detecting subtle sequence variations that alignment-based approaches may overlook. These findings enhance our understanding of SARS-CoV-2 evolution and provide a framework for identifying key genomic signatures relevant to public health. The methodology and insights gained offer potential applications in variant surveillance, vaccine design, and viral evolutionary studies, supporting preparedness for future SARS-CoV-2 variant emergence.

genomics↗