Deep Splicer: A CNN Model for Splice Site Prediction in Genetic Sequences.

Many living organisms have DNA in their cells that is responsible for their biological features. DNA is an organic molecule of two complementary strands of four different nucleotides wound up in a double helix. These nucleotides are adenine (A), thymine (T), guanine (G), and cytosine (C). Genes are DNA sequences containing the information to synthesize proteins. The genes of higher eukaryotic organisms contain coding sequences, known as exons and non-coding sequences, known as introns, which are removed on splice sites after the DNA is transcribed into RNA. Genome annotation is the process of identifying the location of coding regions and determining their function. This process is fundamental for understanding gene structure; however, it is time-consuming and expensive when done by biochemical methods. With technological advances, splice site detection can be done computationally. Although various software tools have been developed to predict splice sites, they need to improve accuracy and reduce false-positive rates. The main goal of this research was to generate Deep Splicer, a deep learning model to identify splice sites in the genomes of humans and other species. This model has good performance metrics and a lower false-positive rate than the currently existing tools. Deep Splicer achieved an accuracy between 93.55% and 99.66% on the genetic sequences of different organisms, while Splice2Deep, another splice site detection tool, had an accuracy between 90.52% and 98.08%. Splice2Deep surpassed Deep Splicer on the accuracy obtained after evaluating C. elegans genomic sequences (97.88% vs. 93.62%) and A. thaliana (95.40% vs. 94.93%); however, Deep Splicer's accuracy was better for H. sapiens (98.94% vs. 97.15%) and D. melanogaster (97.14% vs. 92.30%). The rate of false positives was 0.11% for human genetic sequences and 0.25% for other species' genetic sequences. Another splice prediction tool, Splice Finder, had between 1% and 3% of false positives for human sequences, while other species' sequences had around 4% and 10%.

Down regulation of tumour biomarkers in colon cancer cells with IRNA PFK-1 plus metformin.

PURPOSE: Metformin has been widely used for the treatment of Type 2 Diabetes Mellitus (T2DM), hyperglycemia and polycystic ovarian syndrome. Recent studies have suggested the potential of this substance as a cancer chemopreventive agent. We evaluated the antitumoral effect of iRNA-PFK-1 and the combined therapy iRNA-PFK-1 + metformin in RKO p53-positive cells. METHODS: mRNA levels of tumor suppressor genes AMPK, APC, and c-MYC, KRAS oncogenes were measured by qRT-PCR in RKO cells treated with 25 µM metformin alone or combined with iRNA-PFK-1, to evaluate the effect of both treatments. RESULTS: At 72 h after treatment with either 25 µM metformin, 150 nM iRNA-PFK-1, or the combined treatment, the transcriptional levels of these biomarkers were decreased by ~73% (p˂0.05), ~99.9%, (p˂0.01), and ~76% (p˂0.05), respectively. CONCLUSION: These in vitro results support the potential therapeutic role of metformin and PFK-1 in the treatment of colon cancer via down-modulation of the expression of several important cancer biomarkers.

Response to Edaphoclimatic Conditions and Crop Management of the Bacterial Microbiome of Musa acuminata Rhizosphere Profiled by 16S rRNA Gene Amplicon Sequencing.

Bacterial rhizospheric microbiomes of Musa acuminata cultivated in farms close to the west and east Mexican coasts and with different climate, soils, and crop management practices were characterized by 16S rRNA gene amplicon sequencing. Results showed that rhizospheric microbiome composition changed along with seasonal weather but were mostly indifferent to soil type.

Cell surface display of proteins on filamentous fungi.

Protein display approaches have been useful to endow the cell surface of yeasts with new catalytic activities so that they can act as enhanced whole-cell biocatalysts. Despite their biotechnological potential, protein display technologies remain poorly developed for filamentous fungi. The lignocellulolytic character of some of them coupled to the cell surface biosynthesis of valuable molecules by a single or a cascade of several displayed enzymes is an appealing prospect. Cell surface protein display consists in the co-translational fusion of a functional protein (passenger) to an anchor one, usually a cell-wall-resident protein. The abundance, spacing, and local environment of the displayed enzymes-determined by the relationship of the anchor protein with the structure and dynamics of the engineered cell wall-are factors that influence the performance of display-based biocatalysts. The development of protein display strategies in filamentous fungi could be based on the field advances in yeasts; however, the unique composition, structure, and biology of filamentous fungi cell walls require the customization of the approach to those microorganisms. In this prospective review, the cellular bases, the design principles, and the available tools to foster the development of cell surface protein display technologies in filamentous fungi are discussed.

Thymidylate synthase polymorphism in Mexican patients with colon cancer treated with 5-fluorouracil.

PURPOSE: We analyzed the genotype and allele frequency of variable number tandem repeats (VNTR)-thymidylate synthase (TS) and its relationship with the disease evolution in colon cancer patients. METHODS: We selected 24 paraffin-embedded colon cancer tissue samples from Mexican patients who received a 5-fluorouracil (5-FU)-based chemotherapy regimen. Tumor tissue was digested with proteinase K and genomic DNA was isolated by the standard method with phenol-chloroform extraction. Polymerase chain reaction (PCR) was performed for TS genotyping of VNTR and the results were evaluated directly in a stained agarose gel. RESULTS: The allele frequency of 2 repeats (2R) was greater (0.66) than 3R (0.34) in metastatic colon cancer (x2=10.24; p=0.001)) however, no difference in allelic distribution between 2R (0.54) and 3R (0.46) in non metastatic patients was observed (x2=0.640; p=0.424). CONCLUSION: Our results suggest that Mexican patients with colon cancer present differences in the allelic distribution, the 2R allele being the most frequent.