Browsing by Author "Polson, Shawn W."
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Item A short-term, randomized, controlled, feasibility study of the effects of different vegetables on the gut microbiota and microRNA expression in infants(Frontiers in Microbiomes, 2024-03-01) Ferro, Lynn E.; Bittinger, Kyle; Trudo, Sabrina P.; Beane, Kaleigh E.; Polson, Shawn W.; Kim, Jae Kyeom; Trabulsi, Jillian C.The complementary diet influences the gastrointestinal (gut) microbiota composition and, in turn, host health and, potentially, microRNA (miRNA) expression. This study aimed to assess the feasibility of altering the gut microbial communities with short-term food introduction and to determine the effects of different vegetables on the gut microbiota and miRNA expression in infants. A total of 11 infants were randomized to one of the following intervention arms: control, broccoli, or carrot. The control group maintained the milk diet only, while the other groups consumed either a broccoli puree or a carrot puree on days 1–3 along with their milk diet (human milk or infant formula). Genomic DNA and total RNA were extracted from fecal samples to determine the microbiota composition and miRNA expression. Short-term feeding of both broccoli and carrots resulted in changes in the microbiota and miRNA expression. Compared to the control, a trend toward a decrease in Shannon index was observed in the carrot group on days 2 and 4. The carrot and broccoli groups differed by weighted UniFrac. Streptococcus was increased on day 4 in the carrot group compared to the control. The expression of two miRNAs (i.e., miR-217 and miR-590-5p) trended towards decrease in both the broccoli and carrot groups compared to the control, whereas increases in eight and two different miRNAs were observed in the carrot and broccoli groups, respectively. Vegetable interventions differentially impacted the gut microbiota and miRNA expression, which may be a mechanism by which total vegetable intake and variety are associated with reduced disease risk.Item Improvements in viral gene annotation using large language models and soft alignments(BMC Bioinformatics, 2024-04-25) Harrigan, William L.; Ferrell, Barbra D.; Wommack, K. Eric; Polson, Shawn W.; Schreiber, Zachary D.; Belcaid, MahdiBackground The annotation of protein sequences in public databases has long posed a challenge in molecular biology. This issue is particularly acute for viral proteins, which demonstrate limited homology to known proteins when using alignment, k-mer, or profile-based homology search approaches. A novel methodology employing Large Language Models (LLMs) addresses this methodological challenge by annotating protein sequences based on embeddings. Results Central to our contribution is the soft alignment algorithm, drawing from traditional protein alignment but leveraging embedding similarity at the amino acid level to bypass the need for conventional scoring matrices. This method not only surpasses pooled embedding-based models in efficiency but also in interpretability, enabling users to easily trace homologous amino acids and delve deeper into the alignments. Far from being a black box, our approach provides transparent, BLAST-like alignment visualizations, combining traditional biological research with AI advancements to elevate protein annotation through embedding-based analysis while ensuring interpretability. Tests using the Virus Orthologous Groups and ViralZone protein databases indicated that the novel soft alignment approach recognized and annotated sequences that both blastp and pooling-based methods, which are commonly used for sequence annotation, failed to detect. Conclusion The embeddings approach shows the great potential of LLMs for enhancing protein sequence annotation, especially in viral genomics. These findings present a promising avenue for more efficient and accurate protein function inference in molecular biology.