Biology subjects

Machado, L.

Publications and source records attributed to Machado, L..

3 recordsLinked to original sources

bioRxiv2025

Mapping Tumor Microenvironment and Treatment Response of Diffuse Midline Glioma Using Multiplexed Immunofluorescence and AI Models

BackgroundDespite its clinical promise in non-solid tumor, immunotherapy is yet to show significant clinical efficacy for brain tumors including pediatric diffuse midline glioma (DMG). This indicated the need to fully explore DMG immune tumor microenvironment (TME). MethodWhole brains (49 DMGs, 20 non-DMG, 10 non-malignant) from 79 pediatric patients were used to establish a tissue microarray (918 cores) representing primary, metastatic, and adjacent healthy sites. CellDIVE MxIF multiplex assay was used to probe for 33 immune and cell type markers. RNA sequencing (n=62 patients) defined additional immune signatures. Findings were validated using patient plasma and DMG PDX models. Our annotated single-cell atlas was used to train a spatial AI model to predict antigens from H&E staining. FindingsWe found enrichment of M1-activated microglia in primary versus adjacent healthy tissue. PD1 positive cells were significantly (p<0.01) higher in tumor compared to adjacent controls. This was validated by mRNA profiling, further indicating two distinct groups with top 35 significant (p<0.05) genes revealing synaptic signature in the metastatic cohort. We stratified the patient cohort by treatment. Imipridone cohort (n=5) showed decreased progenitor (Nestin+, Vimentin+, and SOX2+) and increased macrophages/microglia infiltration. Increased T and B cells was validated in patient plasma following imipridone therapy. Combination therapy of imipridone and immunotherapy (n=7) resulted in increased myeloid (Iba1, CD68, CD163) and lymphoid (CD3, CD8) cells. Enhanced immune engagement was validated in DMG PDX models. Machine learning resulted in a spatial AI model capable of predicting 22 antigens using H&E slides. InterpretationsDMG tumors maintain a cold immune microenvironment, which is nevertheless dynamic and responsive to therapy, indicating the need to explore combination therapies. AI-assisted antigen detection is suitable for rapid interpretation of clinical biospecimens. FundingThis work was supported by Rising Tide, SNF, LilaBean Foundation, Swifty Foundation, Swiss to Cure DIPG and Yuvaan Tiwari Foundation. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=45 SRC="FIGDIR/small/644698v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@198b382org.highwire.dtl.DTLVardef@312e0forg.highwire.dtl.DTLVardef@c71b82org.highwire.dtl.DTLVardef@1df1c35_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG

cancer biology↗

bioRxiv2024

ProteusAI: An Open-Source and User-Friendly Platform for Machine Learning-Guided Protein Design and Engineering

AO_SCPLOWBSTRACTC_SCPLOWProtein design and engineering are crucial for advancements in biotechnology, medicine, and sustainability. Machine learning (ML) models are used to design or enhance protein properties such as stability, catalytic activity, and selectivity. However, many existing ML tools require specialized expertise or lack open-source availability, limiting broader use and further development. To address this, we developed ProteusAI, a user-friendly and open-source ML platform to streamline protein engineering and design tasks. ProteusAI offers modules to support researchers in various stages of the design-build-test-learn (DBTL) cycle, including protein discovery, structure-based design, zero-shot predictions, and ML-guided directed evolution (MLDE). Our benchmarking results demonstrate ProteusAIs efficiency in improving proteins and enyzmes within a few DBTL-cycle iterations. ProteusAI democratizes access to ML-guided protein engineering and is freely available for academic and commercial use. Future work aims to expand and integrate novel methods in computational protein and enzyme design to further develop ProteusAI.

bioinformatics↗

bioRxiv2019

Directed evolution of the PcaV allosteric transcription factor to generate a biosensor for aromatic aldehydes

BackgroundGenetically encoded biosensors are useful tools for the detection of metabolites and industrially valuable molecules, and present many potential applications in biotechnology and biomedicine. However, the most common approach to develop biosensors relies on employing a limited set of naturally occurring allosteric transcript factors (aTFs). Therefore, altering the substrate specificity of aTFs towards the detection of new effectors is an important goal.\n\nResultsHere, the PcaV repressor, a member of the MarR aTF family, was used to develop a biosensor for the detection of hydroxyl-substituted benzoic acids, including protocatechuic acid (PCA). The PCA biosensor was further subjected to directed evolution to alter its substrate specificity towards vanillin and other closely related aromatic aldehydes, to generate the Van2 biosensor. Substrate recognition of Van2 was explored in vitro using a range of biochemical and biophysical analyses, and extensive in vivo genetic-phenotypic analysis was performed to determine the role of each amino acid change upon biosensor performance.\n\nConclusionsThis is the first study to report directed evolution of a member of the MarR aTF family, and demonstrates the plasticity of the PCA biosensor by altering its substrate specificity to generate a biosensor for aromatic aldehydes.

bioengineering↗