The selection of a DNA aptamer through the Systematic Evolution of Ligands by EXponential enrichment (SELEX) method involves multiple binding steps, in which a target and a library of randomized DNA sequences are mixed for selection of a single, nucleotide-specific molecule. Usually, 10 to 20 steps are required for SELEX to be completed. Throughout this process it is necessary to discriminate between true DNA aptamers and unspecified DNA-binding sequences. Thus, a novel machine learning-based approach was developed to support and simplify the early steps of the SELEX process, to help discriminate binding between DNA aptamers from those unspecified targets of DNA-binding sequences. An Artificial Intelligence (AI) approach to identify aptamers were implemented based on Natural Language Processing (NLP) and Machine Learning (ML). NLP method (CountVectorizer) was used to extract information from the nucleotide sequences. Four ML algorithms (Logistic Regression, Decision Tree, Gaussian Naïve Bayes, Support Vector Machines) were trained using data from the NLP method along with sequence information. The best performing model was Support Vector Machines because it had the best ability to discriminate between positive and negative classes. In our model, an Accuracy (A) of 0.995, the fraction of samples that the model correctly classified, and an Area Under the Receiving Operating Curve (AUROC) of 0.998, the degree by which a model is capable of distinguishing between classes, were observed. The developed AI approach is useful to identify potential DNA aptamers to reduce the amount of rounds in a SELEX selection. This new approach could be applied in the design of DNA libraries and result in a more efficient and faster process for DNA aptamers to be chosen during SELEX.

Gold nanoparticles (AuNPs) are the most thoroughly studied nanoparticles because of their remarkable optical properties. Color changes in assays that use AuNPs can be easily observed with the naked eye, resulting in sensitive colorimetric methods, useful for detecting a variety of biological molecules. However, while AuNPs represent an excellent nano-platform for developing analytical methods for biosensing, there are still challenges that must be overcome before colloidal AuNPs formulation can be successfully translated into practical applications. One of those challenges is the ability to immobilize AuNPs in a solid support. There are many difficulties with controlling both the cluster size and the adhesion of the coatings formed. In addition, many of the techniques employed are expensive and time-consuming, or require special equipment. Thus, a simple and inexpensive method that only requires common lab equipment for immobilizing AuNPs on a surface using Starch Hydrogels has been developed. Starch hydrogels confer a 400% increase in stability to the nanoparticles when exposed to changes in the environment while also allowing for macromolecules to interact with the AuNPs surface. Several starch derivatives were tested, including, dextrin, beta-cyclodextrin and maltodextrin, being dextrin the one that conferred the highest stability. As a proof-of-concept, a SlipChip microfluidic sensor scheme was developed to measure the concentration of DNA in a sample. The detection limit of our biosensor was found to be 25 ng/mL and 75 ng/mL for instrument and naked eye detection, respectively.

FAP1 is a cytoplasmic protein found in Saccharomyces cerevisiae that can bind Fpr1p for conferring Rapamycin’s resistance to yeast. Rapamycin is lethal to yeast because it can also bind Fpr1p, and this complex interferes negatively with the TORC1 function, which is responsible for nutrient-regulated gene expression. Currently, the FAP1’s function remains poorly understood, but it has been suggested that under nutrient-deficient conditions, the fap1 gene is over-expressed and becomes essential for yeast to survive. In this study, the expression levels of the fap1 gene were monitored when yeast cells were exposed to Nitrogen-limited media for 0 h, 3 h, 6 h and 9 h. After the harvesting yeast cells, total RNA was extracted and treated with Reverse Transcriptase to generate complementary DNA (cDNA), after which concentrations ranging from 1939 ng/µL (0 h), 1608 ng/µL (3 h), 1845 ng/µL (6 h) and 1521 ng/µL (9 h) were obtained. Real-time (quantitative) PCR experiments were performed using these cDNA samples and those obtained after yeast cells were grown in YPD (control media) at 0 h. This experimental method was used to validate the amplification efficiency of the fap1 gene relative to the endogenous fba1 gene, by establishing the conditions necessary for acquiring experimental cycle threshold (CT) values. The obtained results suggest that the expression levels of the fap1 gene increase, under different Nitrogen-limited media, as time progresses.