Long-term effects of an egg-protein hydrolysate on cognitive performance and brain vascular function: a double-blind randomized controlled trial in adults with elevated subjective cognitive failures.

PURPOSE: Short-term intake of the egg-protein hydrolysate Newtricious (NWT)-03 improved executive function, but underlying mechanisms and long-term effects, including other cognitive domains, are unknown. METHODS: A 36-week randomized controlled trial involving 44 overweight/obese individuals experiencing elevated Subjective Cognitive Failures (SCF; aged 60-75 years) assessed the impact of daily consumption of 5.7 g of NWT-03 or placebo powders on cognitive performance (psychomotor speed, executive function, memory) and Cerebral Blood Flow (CBF), a marker of brain vascular function. Cognitive performance was evaluated using a neurophysiological test battery (CANTAB) and CBF was measured using magnetic resonance imaging perfusion method Arterial Spin Labeling (ASL). Serum samples were collected to determine brain-derived neurotrophic factor (BDNF) concentrations. RESULTS: Anthropometrics, and energy and nutrient intakes remained stable throughout the trial. NWT-03 was well tolerated, and compliance was excellent (median: 99%; range: 87-103%). No overall intervention effects were observed on cognitive performance or CBF, but post-hoc analyses revealed significant improvements on executive function in women, but not men. Specifically, a reduction of 74 ms in reaction latency on the multitasking task (95% CI: -134 to -15; p = 0.02), a reduction of 9 between errors (95%CI: -14 to -3; p < 0.001), and a reduction of 9 total errors (95%CI: -15 to -3; p < 0.001) on the spatial working memory task were found in women. No intervention effects were observed on serum BDNF concentrations (p = 0.31). CONCLUSION: Long-term consumption of NWT-03 improved multitasking abilities and working memory in women with elevated SCF. Brain vascular function remained unaffected. Sex differences in executive function require additional clarification.

Transcriptome assembly and annotation of johnsongrass (Sorghum halepense) rhizomes identify candidate rhizome-specific genes.

Rhizomes facilitate the wintering and vegetative propagation of many perennial grasses. Sorghum halepense (johnsongrass) is an aggressive perennial grass that relies on a robust rhizome system to persist through winters and reproduce asexually from its rootstock nodes. This study aimed to sequence and assemble expressed transcripts within the johnsongrass rhizome. A de novo transcriptome assembly was generated from a single johnsongrass rhizome meristem tissue sample. A total of 141,176 probable protein-coding sequences from the assembly were identified and assigned gene ontology terms using Blast2GO. Estimated expression analysis and BLAST results were used to reduce the assembly to 64,447 high-confidence sequences. The johnsongrass assembly was compared to Sorghum bicolor, a related nonrhizomatous species, along with an assembly of similar rhizome tissue from the perennial grain crop Thinopyrum intermedium. The presence/absence analysis yielded a set of 98 expressed johnsongrass contigs that are likely associated with rhizome development.

IMPROVED PERFORMANCE OF GENE SET ANALYSIS ON GENOME-WIDE TRANSCRIPTOMICS DATA WHEN USING GENE ACTIVITY STATE ESTIMATES.

Gene set analysis methods continue to be a popular and powerful method of evaluating genome-wide transcriptomics data. These approach require a priori grouping of genes into biologically meaningful sets, and then conducting downstream analyses at the set (instead of gene) level of analysis. Gene set analysis methods have been shown to yield more powerful statistical conclusions than single-gene analyses due to both reduced multiple testing penalties and potentially larger observed effects due to the aggregation of effects across multiple genes in the set. Traditionally, gene set analysis methods have been applied directly to normalized, log-transformed, transcriptomics data. Recently, efforts have been made to transform transcriptomics data to scales yielding more biologically interpretable results. For example, recently proposed models transform log-transformed transcriptomics data to a confidence metric (ranging between 0 and 100%) that a gene is active (roughly speaking, that the gene product is part of an active cellular mechanism). In this manuscript, we demonstrate, on both real and simulated transcriptomics data, that tests for differential expression between sets of genes using are typically more powerful when using gene activity state estimates as opposed to log-transformed gene expression data. Our analysis suggests further exploration of techniques to transform transcriptomics data to meaningful quantities for improved downstream inference.