Biology subjects

Guardia, G. D. A.

Publications and source records attributed to Guardia, G. D. A..

3 recordsLinked to original sources

bioRxiv2022

Acute myeloid leukemia expresses a specific group of olfactory receptors

Acute myeloid leukemia (AML) is the most common form of acute leukemia in adults. Although new drugs for specific molecular subsets of AML have recently emerged, the 5-year overall survival rate is still approximately 25%. The treatment options for AML have remained stagnant for decades, and novel molecular targets for this disease are needed. Olfactory receptors (ORs) are seven transmembrane G-protein coupled receptors preferentially expressed in sensory neurons, in which they play a critical role in recognizing odorant molecules. Recent studies have revealed ectopic expression and putative function of ORs in nonolfactory tissues and pathologies, including AML. Here, we comprehensively investigated OR expression in 151 AML samples, 51 healthy tissues (approximately 11,200 samples), and 15 other cancer types (6,400 samples). Our analyses identified a group of 19 ORs with a distinct and major expression pattern in AML. The expression of these ORs was experimentally validated in an independent set of AML samples and cell lines. We also identified an OR signature with prognostic value for AML patients. Finally, we identified cancer-related genes that were coexpressed with the ORs in the AML samples. In summary, we conducted a high-throughput computational study to identify ORs that can be used as novel biomarkers for the diagnosis of AML and as potential drug targets. The same approach may be used to investigate OR expression in other types of cancer.

cancer biology↗

bioRxiv2021

Molecular dynamics analysis of fast-spreading severe acute respiratory syndrome coronavirus 2 variants and their effects in the interaction with human angiotensin-converting enzyme 2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is evolving with mutations in the Spike protein, especially in the receptor-binding domain (RBD). The failure of public health measures to contain the spread of the disease in many countries has given rise to novel viral variants with increased transmissibility. However, key questions about how quickly the variants can spread and whether they can cause a more severe disease remain unclear. Herein, we performed a structural investigation using molecular dynamics simulations and determined dissociation constant (KD) values using surface plasmon resonance (SPR) assays of three fastspreading SARS-CoV-2 variants, Alpha, Beta and Gamma ones, as well as genetic factors in the host cells that may be related to the viral infection. Our results suggest that the SARS-CoV-2 variants facilitate their entry into the host cell by moderately increased binding affinities to the human ACE2 receptor, different torsions in hACE2 mediated by RBD variants, and an increased Spike exposure time to proteolytic enzymes. We also found that other host cell aspects, such as gene and isoform expression of key genes for the infection (ACE2, FURIN and TMPRSS2), may have few contributions to the SARS-CoV-2 variants infectivity. In summary, we concluded that a combination of viral and host cell factors allows SARS-CoV-2 variants to increase their abilities to spread faster than wild-type. O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

molecular biology↗

bioRxiv2020

Reboot: a straightforward approach to identify genes and splicing isoforms associated with cancer patient prognosis

Nowadays, the massive amount of data generated by modern sequencing technologies provides an unprecedented opportunity to find genes associated with cancer patient prognosis, connecting basic and translational research. However, treating high dimensionality of gene expression data and integrating it with clinical variables are major challenges to carry out these analyses. Here, we present Reboot, an original and efficient algorithm to find genes and splicing isoforms associated with cancer patient survival, disease progression, or other clinical endpoints. Reboot innovates by using a multivariate strategy with penalized Cox regression (LASSO method) combined with a bootstrap approach, in addition to statistical tests for supporting the findings, which are automatically plotted. Applying Reboot on data from 154 glioblastoma patients, we identified a three-gene signature (IKBIP, OSMR, PODNL1) whose increased derived risk score was significantly associated with worse patients prognosis, even in conjunction with other well-established clinical parameters. Similarly, Reboot was able to find a seven-splicing isoforms signature (CENPF-201; MLKL-202; NUP54-201; MCF2L-201; TFDP1-207; BBS1-206; HTT-202) related to worse overall survival in 177 pancreatic adenocarcinoma patients with elevated risk scores after uni- and multivariate analyses. In summary, Reboot is an efficient, intuitive, and straightforward way for finding genes or splicing isoforms (transcripts) signatures relevant to patient prognosis, which can democratize this kind of analysis and shed light on still under-investigated sets of cancer-related genes. Reboot effectively runs on either servers or personal computers and it is freely available at github.com/galantelab/reboot.

bioinformatics↗