Biology subjects

Lamichhane, A.

Publications and source records attributed to Lamichhane, A..

4 recordsLinked to original sources

bioRxiv2026

Discovery and Characterization of Interleukin-4-Specific Affibodies for Affinity-Controlled Protein Release and Macrophage Polarization

Interleukin-4 (IL-4) is a key immunoregulatory cytokine that promotes type 2 inflammation, drives macrophage polarization toward an anti-inflammatory M2 phenotype, and supports tissue repair. However, clinical translation of IL-4 therapies to modulate the immune response is limited by the need for precise control over its delivery to avoid immune dysregulation. Here, we report an affinity-based strategy to modulate IL-4 delivery and bioactivity using engineered affibody proteins. A yeast surface display library was screened via magnetic- and fluorescence-activated cell sorting to identify two IL-4-specific affibodies with moderate binding affinities (dissociation constants, KD = 459 and 141 nM). Circular dichroism confirmed expected alpha-helical folding, and biolayer interferometry characterized the kinetics of IL-4 binding. Structural modeling using AlphaFold3 and RosettaDock and molecular dynamics simulations using GROMACS predicted distinct binding sites for each IL-4-specific affibody on the IL-4 protein and suggested potential interference with receptor complex formation. Bioactivity studies using murine bone marrow-derived macrophages demonstrated that IL-4 complexed with affibodies maintained Ym1 gene expression but significantly reduced Ym1 protein levels, indicating partial inhibition of IL-4 signaling. To enable controlled cytokine delivery via affinity interactions, affibodies were conjugated to polyethylene glycol maleimide (PEG-mal) hydrogels, which were loaded with IL-4. Affibody-conjugated hydrogels achieved high IL-4 loading efficiency (>90%) and exhibited sustained release over 7 days. Increasing affibody-to-IL-4 ratios significantly reduced both the rate and total amount of cytokine release. Overall, this work establishes IL-4-specific affibodies as versatile tools for tuning cytokine presentation and modulating bioactivity and provides a promising approach for regulating inflammatory responses and advancing cytokine-based therapies with improved temporal control. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=163 SRC="FIGDIR/small/723637v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@146d4c1org.highwire.dtl.DTLVardef@139dcfeorg.highwire.dtl.DTLVardef@1dbe8f3org.highwire.dtl.DTLVardef@9ac211_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2026

Oncogene-Mechanics Axis: KRAS G12C Confers Agility Enabling Malignant Mechano-responses to Peristalsis in Colorectal Cancer

Oncogene activity and mechanical forced individually and collective drive colorectal cancer, yet the integration of these signals is unknown. We used a patented peristalsis bioreactor to determine how oncogenic KRAS G12C mutations alter the cellular response to colonic peristalsis. Although both ehalthy intestinal cells and KRAS G12C cells sensed peristalsis via ERK phosphorylation, their mechano-responses diverged significantly. Peristalsis triggered a 9-fold enrichment of LGR5+ cancer stem cells in KRAS G12C cancer cells, an effect absent in healthy controls. Using Brillouin microscopy, we discovered that KRAS G12C induced a more agile and deformable mechano-phenotype by lowering intracellular viscosity, a state further amplified by peristalsis. This agility allowed KRAS G12C cancer cells to leverage, rather than resist peristalsis, resulting in LGR5 enrichment and malignant progression. Pharmacologic inhibition of KRAS G12C reverse the mechano-phenotype, while introducing KRAS G12C into healthy cells recapitulated it. Our findings identify a novel KRAS oncogene-mechanics axis, suggesting that targeting the cell's mechanical state could be a powerful complement to emerging KRAS-directed therapies.

bioengineering↗

bioRxiv2025

The impact of patient biology on racial disparities in breast cancer outcome

Hormone receptor positive (HR+) breast cancer is the most common subtype of breast cancer diagnosed globally. Despite effective targeted therapies, HR+ breast cancer remains a leading cause of cancer-related death in women. Long-standing epidemiological research identifies significantly worse outcomes for Black women diagnosed with HR+ breast cancer relative to White women. While structural factors such as access to healthcare and education level contribute to this outcome disparity, it persists even in analyses where these factors are controlled. In-depth analyses of the somatic molecular biology that may underlie these outcome disparities are hampered by a lack of datasets that represent Black patient populations. Here, we generate a HR+ breast cancer patient transcriptomic dataset that overrepresents Black women and controls for access to healthcare and education level. We find that signatures relating to the tumor microenvironment, i.e. collagen deposition and prognostically unfavorable T-cell landscapes are enriched in HR+ tumors from Black women. Importantly, we find, using experimental model systems in vitro and in vivo, that race-aligned collagen deposition patterns are at least partially attributable to tumor cell-intrinsic signaling and critical for Black breast cancer metastasis. We also find that unfavorable T-cell signatures in HR+ tumors from Black women, which have previously been attributed to race and ancestry, are more strongly poverty-aligned. Using multiple independent datasets, we identify STAT4 as a potential master regulator of this poverty-associated tumor immune signature. Together, these findings provide new evidence that somatic molecular biology of breast cancer patients can be modified by multiple structural factors such as self-identified race and poverty burden to promote poor patient outcomes. Integrating an understanding of structural factors into molecular cancer research is critical for implementing truly personalized, and maximally effective, oncology systems.

cancer biology↗

bioRxiv2024

Natural Language Processing-like Deep Learning Aided in Identification and Validation of Thiosulfinate Tolerance Clusters in Diverse Bacteria

Allicin tolerance (alt) clusters in phytopathogenic bacteria, which provide resistance to thiosulfinates like allicin, are challenging to find using conventional approaches due to their varied architecture and the paradox of being vertically maintained within genera despite likely being horizontally transferred. This results in significant sequential diversity that further complicates their identification. Natural language processing (NLP) - like techniques, such as those used in DeepBGC, offers a promising solution by treating gene clusters like a language, allowing for identifying and collecting gene clusters based on patterns and relationships within the sequences. We curated and validated alt-like clusters in Pantoea ananatis 97-1R (PA), Burkholderia gladioli pv. gladioli FDAARGOS 389 (BG), and Pseudomonas syringae pv. tomato DC3000 -(PTO). Leveraging sequences from the RefSeq bacterial database, we conducted comparative analyses of gene synteny, gene/protein sequences, protein structures, and predicted protein interactions. This approach enabled the discovery of several novel alt-like clusters previously undetectable by other methods, which were further validated experimentally. Our work highlights the effectiveness of NLP-like techniques for identifying underrepresented gene clusters and expands our understanding of the diversity and utility of alt-like clusters in diverse bacterial genera. This work demonstrates the potential of these techniques to simplify the identification process and enhance the applicability of biological data in real-world scenarios. Significance StatementThiosulfinates, like allicin, are potent antifeedants and antimicrobials produced by Allium species and pose a challenge for phytopathogenic bacteria. Phytopathogenic bacteria have been shown to utilize an allicin tolerance (alt) gene cluster to circumvent this host response, leading to economically significant yield losses. Due to the complexity of mining these clusters, we applied techniques akin to natural language processing to analyze Pfam domains and gene proximity. This approach led to the identification of novel alt-like gene clusters, showcasing the potential of artificial intelligence to reveal elusive and underrepresented genetic clusters and enhance our understanding of their diversity and role across various bacterial genera.

pathology↗