Biology subjects

Farres, J.

Publications and source records attributed to Farres, J..

6 recordsLinked to original sources

bioRxiv2026

A covalent irreversible inhibitor binds in two mutually exclusive conformations to the active-site cysteine residue of human aldehyde dehydrogenase 1A3

Aldehyde dehydrogenases (ALDH) are enzymes that catalyze the NAD(P)+-dependent oxidation of aldehydes into carboxylic acids, playing roles in detoxification, biosynthesis, and regulatory functions. Dysfunction of ALDH is associated with serious conditions such as alcohol intolerance, cancer, cardiovascular problems, and neurological disorders. In humans, ALDH1A1 and ALDH1A3 isoforms act as retinaldehyde dehydrogenases and are overexpressed in various cancers, where high levels are associated with increased tumor malignancy, cancer stem cell traits, and therapeutic resistance. ALDH1A3 is recognized as a promising target for anticancer therapies, with several inhibitors, mainly reversible, developed to specifically target it or the enzyme family. Since ALDH enzymes can also display esterase activity, we used this property to develop an in vitro assay specifically targeting the esterase function of ALDH1A3. A highly conserved active-site cysteine in ALDH1A3 is located at the bottom of two converging channels, which define the substrate- and cofactor-binding pockets. To target this catalytic cysteine, we screened a library of 3,200 cysteine-focused covalent fragments. This led to the identification of Z3405279217 (Z34), an acrylamide-based covalent compound that inhibits both ALDH1A1 and ALDH1A3 at sub-micromolar levels. Biochemical and biophysical tests confirmed that Z34 acts as a time-dependent, covalent, and irreversible binder to the active-site cysteine. In this work, we determined the Cryo-EM structure of the ALDH1A3-Z34 complex at 2.26 [A] resolution, confirming the covalent attachment to the catalytic cysteine of Z34. Notably, two mutually exclusive covalent binding modes were observed: one occupying the substrate-binding pocket and the other the cofactor-binding region. Z34 displayed unexpected binding modes within the active site and holds promise as a lead compound for future drug development. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=184 HEIGHT=200 SRC="FIGDIR/small/738401v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1d9ebd2org.highwire.dtl.DTLVardef@96470org.highwire.dtl.DTLVardef@a110c7org.highwire.dtl.DTLVardef@548a93_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2026

A sensitive fluorometric assay to detect aldo-keto reductase and carbonyl reductase activity based on a naphthaldehyde derivative

We have developed a fluorometric assay for detecting reductase activity in biological samples through 4-methoxy-1-naphthalenemethanol (MONOL-41) formation. The enzyme carbonyl reductase 1 (CBR1) and four members of the aldo-keto reductase (AKR) 1 family (AKR1A1, AKR1B1, AKR1B10, AKR1C3) were evaluated for their ability to reduce 4-methoxy-1-naphthaldehyde (MONAL-41). AKR1B1 and CBR1 followed Michaelis-Menten kinetics, whereas AKR1B10, AKR1A1, and AKR1C3 showed substrate inhibition above 10 {micro}M (70 {micro}M for AKR1C3). Among the tested enzymes, AKR1B10 displayed the highest catalytic efficiency in the absence of substrate inhibition. The MONOL-41 assay was compared with the standard NADPH-based method, showing improved sensitivity, robustness, and lower detection limits (0.77 {micro}g/mL vs. 1.49 {micro}g/mL). These results confirm its suitability for monitoring AKR1B10 activity. The assay was then applied to A549 cell extracts, which express multiple reductases. Activity decreased at substrate concentrations above 10 {micro}M, suggesting a predominant role of AKR1B10. Inhibition studies using tolrestat and high MONAL-41 concentrations indicated a limited contribution of CBR1 ([~]7-8%). Considering both catalytic efficiency and expression levels, AKR1B10 appears to be the main contributor to reductase activity in this model. In A549 living cells, MONAL-41 showed no cytotoxicity up to 50 {micro}M and enabled real-time monitoring due to its membrane permeability. However, oxidation by aldehyde dehydrogenases can generate MONOIC-41, which has similar spectral properties but a lower quantum yield, potentially affecting signal interpretation. Overall, this assay represents a sensitive and cost-effective tool for detecting reductase activity and screening inhibitors.

biochemistry↗

bioRxiv2024

ALDH1 subtype-specific inhibitor targets key cell populations in triple negative breast cancer

Aggressive breast cancer subtypes, particularly triple-negative breast cancer (TNBC), lack effective targeted therapies, requiring novel approaches. This study focuses on the aldehyde dehydrogenase 1A (ALDH1A) subfamily, comprising ALDH1A1, ALDH1A2, and ALDH1A3, and their roles in tumor biology and the tumor microenvironment. Comprehensive transcriptomic and single-cell analyses revealed subtype- and cell-specific expression patterns of ALDH1A isoforms, with ALDH1A3 predominantly expressed in epithelial cancer cells of basal-like tumors, while ALDH1A1 and ALDH1A2 were expressed in stromal and immune-associated subpopulations. Guided by these findings, we developed ABD0171, a selective ALDH1A3 inhibitor that demonstrated potent isoform-specific activity. ABD0171 effectively disrupted key pathways in TNBC cells in vitro, including IL6/JAK/STAT3, tPA and Src/FAK, and exhibited superior selectivity, robust antitumor and antimetastatic effects, and a favorable safety profile in vivo. These results establish ALDH1A3 as a promising therapeutic target and validate ABD0171 as a candidate to address current challenges in aggressive breast cancers.

cancer biology↗

bioRxiv2024

Integration of expression datasets to identify biomarkers for accurate Gleason scoring in Prostate Cancer

Prostate cancer is a significant global health issue with considerable mortality rates, emphasising the urgent need for advanced treatment options and improved diagnostic methods. Current diagnostic standards for prostate cancer, including PSA testing and digital rectal examination, often produce false positives, resulting in unnecessary biopsies for patients. This limitation highlights the critical requirement to incorporate more precise biomarkers to enhance diagnostic accuracy and reduce unnecessary procedures. This study aims to investigate biomarker candidates that can effectively determine prostate cancer aggressiveness. By integrating diverse prostate tissue expression datasets and employing machine-learning techniques, this approach seeks to refine diagnostics and provide insights into the molecular underpinnings of the disease, potentially transforming early detection and patient management strategies. Our proposed biomarkers achieve a minimum precision of 0.80, addressing the false positives limitations associated with classical prostate cancer biomarkers. Moreover, the ROC-AUC profiles of most of the candidates proposed in this study align with those exhibited by other innovative biomarkers recently proposed (ROC-AUC [≥] 0.70). We believe these biomarkers are promising candidates for further in vivo and in vitro investigation.

cancer biology↗

bioRxiv2022

The usage of transcriptomics datasets as sources of Real-World Data for clinical trialling

BackgroundRandomised Clinical Trials (RCT) reflect results within their specific controlled settings, necessitating further studies to understand outcomes across all possible scenarios. The usage of Real-World Data (RWD) has been recently considered to be a viable alternative to overcome these issues and complement clinical conclusions. Molecular profiles of patients captured by high-throughput measures reflect their medical conditions. When this information is linked to clinical and demographical information, nuances in transcriptomics data can uncover subtle variations in disease pathways among distinct patient groups. This work focuses on the construction of a patient repository database with molecular and clinical information resulting from the integration of publicly available transcriptomics datasets. ResultsPatient data were integrated into the patient repository by using a novel post-processing technique allowing for the usage of samples originating from different/multiple Gene Expression Omnibus (GEO) datasets. Our post-processing technique, which we have named MicroArray Cross-plAtfoRm pOst-prOcessiNg (MACAROON), aims to standardise and integrate transcriptomics data (considering batch effects and possible processing-originated artefacts). This process was able to better reproduce the down streaming biological conclusions in a 45% improvement compared to other methods available. Furthermore, RWD was mined from GEO samples metadata and a clinical and demographical characterisation of the database was obtained. RWD mining was done through a manually curated synonym dictionary allowing for the correct assignment (95.33% median accuracy; 80.14% average) of medical conditions. ConclusionsOur strategy produced a repository, which includes molecular, clinical and demographical RWD by integrating multiple public datasets. The exploration of these data facilitates the discovery of clinical outcomes and molecular pathways specific to predetermined patient populations.

bioinformatics↗

bioRxiv2020

CuBlock: A cross-platform normalization method for gene-expression microarrays

MotivationCross-(multi)platform normalization of gene-expression microarray data remains an unresolved issue. Despite the existence of several algorithms, they are either constrained by the need to normalize all samples of all platforms together, compromising scalability and reuse, by adherence to the platforms of a specific provider, or simply by poor performance. In addition, many of the methods presented in the literature have not been specifically tested against multi-platform data and/or other methods applicable in this context. Thus, we set out to develop a normalization algorithm appropriate for gene-expression studies based on multiple, potentially large microarray sets collected along multiple platforms and at different times, applicable in systematic studies aimed at extracting knowledge from the wealth of microarray data available in public repositories; for example, for the extraction of Real-World Data to complement data from Randomized Controlled Trials. Our main focus or criterion for performance was on the capacity of the algorithm to properly separate samples from different biological groups. ResultsWe present CuBlock, an algorithm addressing this objective, together with a strategy to validate cross-platform normalization methods. To validate the algorithm and benchmark it against existing methods, we used two distinct data sets, one specifically generated for testing and standardization purposes and one from an actual experimental study. Using these data sets, we benchmarked CuBlock against ComBat (Johnson et al., 2007), YuGene (Le Cao et al., 2014), DBNorm (Meng et al., 2017), Shambhala (Borisov et al., 2019) and a simple log2 transform as reference. We note that many other popular normalization methods are not applicable in this context. CuBlock was the only algorithm in this group that could always and clearly differentiate the underlying biological groups after mixing the data, from up to six different platforms in this study. AvailabilityCuBlock can be downloaded from https://www.mathworks.com/matlabcentral/fileexchange/77882-cublock Contactxouse@anaxomics.com, xavier.daura@uab.cat Supplementary informationSupplementary data are available at bioRxiv online.

bioinformatics↗