Biology subjects

Lopez-Cortes, A.

Publications and source records attributed to Lopez-Cortes, A..

12 recordsLinked to original sources

bioRxiv2024

NeoPhAR: Next-generation Phage Therapy for Antimicrobial Resistance

Growing antimicrobial resistance (AMR) infections are among the top contemporary concerns in public healthcare systems which can place considerable burdens on healthcare systems. Phage therapy has long been considered a viable option to help combat AMR infections, including on broad geographic scales. One bottleneck in the application of phage therapy is the accurate matching of host-specific phages to bacterial strains, which is traditionally done using molecular techniques. Here we present an open-access deep learning-based model that shows incredible potential to accurately predict phages for therapeutic use. Our goal is to attenuate the matching process so that specific phages, or a cocktail of phages can be prepared for patients with a high probability of therapeutic success, helping to democratize phage therapy treatments even in low to middle-income countries where genomic resources can be limited, costly, or time prohibitive. We feel this is an important first step towards incorporating and applying bioinformatic practices in promising fields such as phage therapy.

bioinformatics↗

bioRxiv2024

Mpox Vaccine Design Through Immunoinformatics and Computational Epitope Prediction

The Mpox virus (Monkeypox virus) poses significant public health risks due to its potential for severe outbreaks in humans. This study presents an innovative vaccine design using bioinformatics to identify epitopes that activate helper T cells (HTLs) via the human leukocyte antigen class II (HLA-II) complex. Starting with 50,040 vaccine candidates, 14 epitopes with the highest HLA-II affinity were selected based on antigenicity, allergenicity, toxicity, stability, and homology. These epitopes were integrated into a multi-epitope vaccine with spacers and adjuvants to enhance the immune response. A 3D model was developed, confirming structural stability and optimal epitope exposure through molecular dynamics simulations. The results indicate that the vaccine can induce robust immune responses, suggesting its potential effectiveness against the Mpox virus. Additionally, population coverage analysis supports its promise as a significant tool for controlling Mpox epidemics and advancing global public health initiatives.

bioinformatics↗

bioRxiv2024

Highly diverse - low abundance methanogenic communities in hypersaline microbial mats of Guerrero Negro B.C.S., assessed through microcosm experiments

Methanogenic communities of hypersaline microbial mats of Guerrero Negro, Baja California Sur, Mexico, have been recognized to be dominated by methylotrophic methanogens. However, recent studies of environmental samples have evidenced the presence of hydrogenotrophic and methyl-reducing methanogenic members, although at low relative abundances. Physical and geochemical conditions that stimulate the development of these groups in hypersaline environments, remains elusive. Thus, in this study the taxonomic diversity of methanogenic archaea of two sites of Exportadora de Sal S.A was assessed by mcrA gene high throughput sequencing from microcosm experiments with different substrates (both competitive and non-competitive). Results confirmed the dominance of the order Methanosarcinales in all treatments, but an increase in the abundance of Methanomassiliiccocales was also observed, mainly in the treatment without substrate addition. Moreover, incubations supplemented with hydrogen and carbon dioxide, as well as the mixture of hydrogen, carbon dioxide and trimethylamine, managed to stimulate the richness and abundance of other than Methanosarcinales methanogenic archaea. Several OTUs that were not assigned to known methanogens resulted phylogenetically distributed into at least nine orders. Environmental samples revealed a wide diversity of methanogenic archaea of low relative abundance that had not been previously reported for this environment, suggesting that the importance and diversity of methanogens in hypersaline ecosystems may have been overlooked. This work also provided insights into how different taxonomic groups responded to the evaluated incubation conditions.

microbiology↗

bioRxiv2020

Characterization Of Ancestral Origin Of Cystic Fibrosis Of Patients With New Reported Mutations In CFTR

The incidence of Cystic fibrosis (CF) and the frequency of the variants for CFTR depend on the population; furthermore, CF symptomatology is characterized by obstructive lung disease, pancreatic insufficiency among others, reliant on the individual genotype. Ecuadorian population is a mixture of Native Americans, Europeans, and Africans. That population admixture could be the reason for the new mutations reported in a previous study by Ruiz et al. (2019). A panel of 46 Ancestry Informative Markers was used to estimate the ancestral proportions of each available sample (12 samples in total). As a result, the Native American ancestry proportion was the most prevalent in almost all individuals, except for three patients from Guayaquil with the mutation [c.757G>A:p.Gly253Arg; c.1352G>T:p.Gly451Val] who had the highest European composition.

genetics↗

bioRxiv2020

TCGA Pan-Cancer genomic analysis of Alternative Lengthening of Telomeres (ALT) related genes

Telomere maintenance mechanisms (TMM) are used by cancer cells to avoid apoptosis, 85-90% reactivate telomerase, while 10-15% use the alternative lengthening of telomeres (ALT). Due to anti-telomerase-based treatments, some tumors have the ability to switch from a telomerase-dependent mechanism to ALT, in fact, the co-existence between telomerase and the ALT pathway have been observed in a variety of cancer types. Despite different elements in the ALT pathway have been uncovered, the molecular mechanism and other factors are still poorly understood, which difficult the detection and treatment of ALT-positive cells, which are known to present poor prognosis. Therefore, with the aim to identify potential molecular markers to be used in the study of ALT, we combined simplistic in silico approaches in 411 telomere maintenance (TM) genes which have been previously validated or predicted to be involved in the ALT pathway. In consequence, we conducted a genomic analysis of these genes in 31 Pan-Cancer Atlas studies (n=9,282) from The Cancer Genome Atlas in the cBioPortal and found 325,936 genomic alterations, being mRNA high and low the top alterations with 65.8% and 10.7% respectively. Moreover, we analyzed the highest frequency means of genomics alterations, identified and proposed 20 genes, which are highly mutated and up and down regulated in the cancer studies and could be used for future analysis in the study of ALT. Finally, we made a protein-protein interaction network and enrichment analysis to obtain an insight into the main pathways these genes are involved. We could observe their role in main processes related to the ALT mechanism like homologous recombination, homology directed repair (HDR), HDR through homologous recombination and telomere maintenance and organization.. Overall, due to the lack of understanding of the molecular mechanisms and detection of ALT-positive cancers, we identified and proposed more molecular targets that can be used for expression analysis and additional ex vivo assays to validate them as new potential therapeutic markers in the study of the ALT mechanism.

cancer biology↗

bioRxiv2020

Metastatic signaling of hypoxia-related genes across TCGA Pan-Cancer types

Many primary-tumor subregions have low levels of molecular oxygen, termed hypoxia. Hypoxic tumors are at elevated risk for local failure and distant metastasis. Metastatic disease is the leading cause of cancer-related deaths and involves critical interactions between tumor cells and the microenvironment. Here we focused on elucidating the molecular hallmarks of tumor hypoxia that remains poorly defined. To fill this gap, we analyzed the genomic alterations and hypoxia score of 233 hypoxia-related genes of 6,343 individuals across 17 TCGA Pan-Cancer types. In addition, we analyzed a protein-protein interactome (PPi) network and the shortest paths from hypoxic proteins to metastasis. As results, mRNA high alteration was prevalent in all cancer types. Genomic alterations and hypoxia score presented a highest frequency in tumor stage 4 and positive metastasis status in all cancer types. The most significant signaling pathways were HIF-1, ErbB, PI3K-Akt, FoxO, mTOR, Ras and VEGF. The PPi network revealed a strong association among hypoxic proteins, cancer driver proteins and metastasis driver proteins. The analysis of shortest paths revealed 99 ways to spread metastasis signaling from hypoxic proteins. Additionally, we proposed 62 hypoxic genes strongly associated with metastasis and 27 of them with high amount of genomic alterations. Overall, tumor hypoxia may drive aggressive molecular features across cancer types. Hence, we identified potential biomarkers and therapeutic targets regulated by hypoxia that could be incorporated into strategies aimed at improving novel drug development and treating metastasis.

cancer biology↗

bioRxiv2020

In silico analyses reveal new putative Breast Cancer RNA-binding proteins

Breast cancer (BC) is the leading cause of cancer-associated death among women worldwide. Despite treatment efforts, advanced BC with distant organ metastases is considered incurable. A better understanding of BC molecular processes is therefore of great interest to identify new therapeutic targets. Although large-scale efforts, such as The Cancer Genome Atlas (TCGA), have completely redefined cancer drug development, diagnosis, and treatment, additional key aspects of tumor biology remain to be discovered. In that respect, post-transcriptional regulation of tumorigenesis represents an understudied aspect of cancer research. As key regulators of this process, RNA-binding proteins (RBPs) are emerging as critical modulators of tumorigenesis but only few have defined roles in BC. To unravel new putative BC RBPs, we have performed in silico analyses of all human RBPs in three major cancer databases (TCGA-Breast Invasive Carcinoma, the Human Protein Atlas, and the Cancer Dependency Map project) along with complementary bioinformatics resources (STRING protein-protein interactions and the Network of Cancer Genes 6.0). Thus, we have identified six putative BC progressors (MRPL13, SCAMP3, CDC5L, DARS2, PUF60, and PLEC), and five BC suppressors RBPs (SUPT6H, MEX3C, UPF1, CNOT1, and TNKS1BP1). These proteins have never been studied in BC but show similar cancer-associated features than well-known BC proteins. Further research should focus on the mechanisms by which these proteins promote or suppress breast tumorigenesis, holding the promise of new therapeutic pathways along with novel drug development strategies.

cancer biology↗

bioRxiv2019

Prediction of breast cancer proteins using molecular descriptors and artificial neural networks: a focus on cancer immunotherapy proteins, metastasis driver proteins, and RNA-binding proteins

BackgroundBreast cancer (BC) is a heterogeneous disease characterized by an intricate interplay between different biological aspects such as ethnicity, genomic alterations, gene expression deregulation, hormone disruption, signaling pathway alterations and environmental determinants. Due to the complexity of BC, the prediction of proteins involved in this disease is a trending topic in drug design. MethodsThis work is proposing accurate prediction classifier for BC proteins using six sets of protein sequence descriptors and 13 machine learning methods. After using a univariate feature selection for the mix of five descriptor families, the best classifier was obtained using multilayer perceptron method (artificial neural network) and 300 features. ResultsThe performance of the model is demonstrated by the area under the receiver operating characteristics (AUROC) of 0.980 {+/-} 0.0037 and accuracy of 0.936 {+/-} 0.0056 (3-fold cross-validation). Regarding the prediction of 4504 cancer-associated proteins using this model, the best ranked cancer immunotherapy proteins related to BC were RPS27, SUPT4H1, CLPSL2, POLR2K, RPL38, AKT3, CDK3, RPS20, RASL11A and UBTD1; the best ranked metastasis driver proteins related to BC were S100A9, DDA1, TXN, PRNP, RPS27, S100A14, S100A7, MAPK1, AGR3 and NDUFA13; and the best ranked RNA-binding proteins related to BC were S100A9, TXN, RPS27L, RPS27, RPS27A, RPL38, MRPL54, PPAN, RPS20 and CSRP1. ConclusionsThis powerful model predicts several BC-related proteins which should be deeply studied to find new biomarkers and better therapeutic targets. The script and the results are available as a free repository at https://github.com/muntisa/neural-networks-for-breast-cancer-proteins.

bioinformatics↗

bioRxiv2019

Prediction of druggable proteins using machine learning and functional enrichment analysis: a focus on cancer-related proteins and RNA-binding proteins

BackgroundDruggable proteins are a trending topic in drug design. The druggable proteome can be defined as the percentage of proteins that have the capacity to bind an antibody or small molecule with adequate chemical properties and affinity. The screening and in silico modeling are critical activities for the reduction of experimental costs.\n\nMethodsThe current work proposes a unique prediction model for druggable proteins using amino acid composition descriptors of protein sequences and 13 machine learning linear and non-linear classifiers. After feature selection, the best classifier was obtained using the support vector machine method and 200 tri-amino acid composition descriptors.\n\nResultsThe high performance of the model is determined by an area under the receiver operating characteristics (AUROC) of 0.975 {+/-} 0.003 and accuracy of 0.929 {+/-} 0.006 (3-fold cross-validation). Regarding the prediction of cancer-associated proteins using this model, the best ranked druggable predicted proteins in the breast cancer protein set were CDK4, AP1S1, POLE, HMMR, RPL5, PALB2, TIMP1, RPL22, NFKB1 and TOP2A; in the cancer-driving protein set were TLL2, FAM47C, SAGE1, HTR1E, MACC1, ZFR2, VMA21, DUSP9, CTNNA3 and GABRG1; and in the RNA-binding protein set were PLA2G1B, CPEB2, NOL6, LRRC47, CTTN, CORO1A, SCAF11, KCTD12, DDX43 and TMPO.\n\nConclusionsThis powerful model predicts several druggable proteins which should be deeply studied to find better therapeutic targets and thus improve clinical trials. The scripts are freely available at https://github.com/muntisa/machine-learning-for-druggable-proteins.

bioinformatics↗

bioRxiv2019

OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine

Breast cancer (BC) is a heterogeneous disease where each OncoOmics approach needs to be fully understood as a part of a complex network. Therefore, the main objective of this study was to analyze genetic alterations, signaling pathways, protein-protein interaction networks, protein expression, dependency maps and enrichment maps in 230 previously prioritized genes by the Consensus Strategy, the Pan-Cancer Atlas, the Pharmacogenomics Knowledgebase and the Cancer Genome Interpreter, in order to reveal essential genes to accelerate the development of precision medicine in BC. The OncoOmics essential genes were rationally filtered to 144, 48 (33%) of which were hallmarks of cancer and 20 (14%) were significant in at least three OncoOmics approaches: RAC1, AKT1 CCND1, PIK3CA, ERBB2, CDH1, MAPK14, TP53, MAPK1, SRC, RAC3, PLCG1, GRB2, MED1, TOP2A, GATA3, BCL2, CTNNB1, EGFR and CDK2. According to the Open Targets Platform, there are 111 drugs that are currently being analyzed in 3151 clinical trials in 39 genes. Lastly, there are more than 800 clinical annotations associated with 94 genes in BC pharmacogenomics.

genomics↗

bioRxiv2019

Pharmacogenomics, biomarker network and allele frequencies in colorectal cancer

Colorectal cancer (CRC) is one of the leading causes of cancer death worldwide. Over the last decades, several studies have shown that tumor-related genomic alterations predict tumor prognosis, drug response and toxicity. These observations have led to the development of a number of precision therapies based on individual genomic profiles. As part of these approaches, pharmacogenomics analyses genomic alterations that may predict an efficient therapeutic response. Studying these mutations as biomarkers for predicting drug response is of a great interest to improve precision medicine. Here we conduct a comprehensive review of the main pharmacogenomics biomarkers and genomic alterations affecting enzyme activity, transporter capacity, channels and receptors, and therefore the new advances in CRC precision medicine to select the best therapeutic strategy in populations worldwide, with a focus on Latin America.

genomics↗

bioRxiv2019

Genomic, Ancestral and Networking Analyses of a High-Altitude Native American Ecuadorian Patient with Congenital Insensitivity to Pain with Anhidrosis

Congenital insensitivity to pain with anhidrosis (CIPA) is an extremely rare autosomal recessive disorder characterized by insensitivity to pain, inability to sweat and intellectual disability. CIPA is caused by mutations in the neurotrophic tyrosine kinase receptor type 1 gene (NTRK1) that encodes the high-affinity receptor of nerve growth factor (NGF). Patients with CIPA lack the primary afferents and sympathetic postganglionic neurons leading to lack of pain sensation and the presence of anhidrosis, respectively. Herein, we conducted a genomic analysis of 4,811 genes and 18,933 variants, including 54 mutations of NTRK1 in a high-altitude indigenous Ecuadorian patient with CIPA. As results, the patient presented 87.8% of Native American ancestry, 6.6% of African ancestry and 5.6% of European ancestry. The mutational analysis of the kinase domain of NTRK1 showed two pathogenic mutations, rs80356677 (Asp674Tyr) and rs763758904 (Arg602*). The genomic analysis showed 68 pathogenic and/or likely pathogenic variants in 45 genes, and two variants of uncertain significance in CACNA2D1 (rs370103843) and TRPC4 (rs80164537) genes involved in the pain matrix. The GO enrichment analysis showed 28 genes with relevant mutations involved in several biological processes, cellular components and molecular functions. In addition, the protein-protein interaction (PPi) networking analysis showed that NTRK1, SPTBN2 and GRM6 interact with several proteins of the pain matrix. In conclusion, this is the first time that a study associates genomic, ancestral and networking data in a high-altitude Native American Ecuadorian patient with consanguinity background in order to better understand CIPA pathogenesis.

genetics↗