MNEMONIC: MetageNomic Experiment Mining to create an OTU Network of Inhabitant Correlations.

BACKGROUND: The number of publicly available metagenomic experiments in various environments has been rapidly growing, empowering the potential to identify similar shifts in species abundance between different experiments. This could be a potentially powerful way to interpret new experiments, by identifying common themes and causes behind changes in species abundance. RESULTS: We propose a novel framework for comparing microbial shifts between conditions. Using data from one of the largest human metagenome projects to date, the American Gut Project (AGP), we obtain differential abundance vectors for microbes using experimental condition information provided with the AGP metadata, such as patient age, dietary habits, or health status. We show it can be used to identify similar and opposing shifts in microbial species, and infer putative interactions between microbes. Our results show that groups of shifts with similar effects on microbiome can be identified and that similar dietary interventions display similar microbial abundance shifts. CONCLUSIONS: Without comparison to prior data, it is difficult for experimentalists to know if their observed changes in species abundance have been observed by others, both in their conditions and in others they would never consider comparable. Yet, this can be a very important contextual factor in interpreting the significance of a shift. We've proposed and tested an algorithmic solution to this problem, which also allows for comparing the metagenomic signature shifts between conditions in the existing body of data.

Age-Related Alterations in Gait Function in Freely Moving Male C57BL/6 Mice: Translational Relevance of Decreased Cadence and Increased Gait Variability.

Age-related gait dysfunction and balance disorders are a major cause of falls and injury in the elderly population. Epidemiological studies have shown that disturbances in gait coordination which manifest with age are associated with increased morbidity and mortality, impaired cognitive capacity, as well as reduced level of function and loss of independence. In geroscience, mice are the most frequently used model system to test efficiency of antiaging interventions. Despite the clinical importance of age-related gait abnormalities in older adults, the impact of aging on mouse gait coordination is not well documented in the literature. To characterize the effect of aging on mouse gait, we assessed gait function in young (3-month-old) and aged (24-month-old) freely moving C57BL/6 mice using the semiautomated, highly sensitive CatWalk XT system for quantitative assessment of footfall and motor performance. We found that aged mice exhibited significantly decreased cadence and increased stride time variability. Aging also tended to alter footfall patterns. In aged mice, speed, swing speed, stride length, duty cycle, base of support, terminal dual stance, the regularity index, and the gait symmetry index were unaltered. Thus, aging is associated with characteristic alterations in gait function in C57BL/6 mice, which could potentially be assessed as clinically relevant endpoints in geroscience studies testing the effects of antiaging interventions.

Treatment with the mitochondrial-targeted antioxidant peptide SS-31 rescues neurovascular coupling responses and cerebrovascular endothelial function and improves cognition in aged mice.

Moment-to-moment adjustment of cerebral blood flow (CBF) via neurovascular coupling has an essential role in maintenance of healthy cognitive function. In advanced age, increased oxidative stress and cerebromicrovascular endothelial dysfunction impair neurovascular coupling, likely contributing to age-related decline of higher cortical functions. There is increasing evidence showing that mitochondrial oxidative stress plays a critical role in a range of age-related cellular impairments, but its role in neurovascular uncoupling remains unexplored. This study was designed to test the hypothesis that attenuation of mitochondrial oxidative stress may exert beneficial effects on neurovascular coupling responses in aging. To test this hypothesis, 24-month-old C57BL/6 mice were treated with a cell-permeable, mitochondria-targeted antioxidant peptide (SS-31; 10 mg kg-1  day-1 , i.p.) or vehicle for 2 weeks. Neurovascular coupling was assessed by measuring CBF responses (laser speckle contrast imaging) evoked by contralateral whisker stimulation. We found that neurovascular coupling responses were significantly impaired in aged mice. Treatment with SS-31 significantly improved neurovascular coupling responses by increasing NO-mediated cerebromicrovascular dilation, which was associated with significantly improved spatial working memory, motor skill learning, and gait coordination. These findings are paralleled by the protective effects of SS-31 on mitochondrial production of reactive oxygen species and mitochondrial respiration in cultured cerebromicrovascular endothelial cells derived from aged animals. Thus, mitochondrial oxidative stress contributes to age-related cerebromicrovascular dysfunction, exacerbating cognitive decline. We propose that mitochondria-targeted antioxidants may be considered for pharmacological microvascular protection for the prevention/treatment of age-related vascular cognitive impairment (VCI).

ALE: automated label extraction from GEO metadata.

BACKGROUND: NCBI's Gene Expression Omnibus (GEO) is a rich community resource containing millions of gene expression experiments from human, mouse, rat, and other model organisms. However, information about each experiment (metadata) is in the format of an open-ended, non-standardized textual description provided by the depositor. Thus, classification of experiments for meta-analysis by factors such as gender, age of the sample donor, and tissue of origin is not feasible without assigning labels to the experiments. Automated approaches are preferable for this, primarily because of the size and volume of the data to be processed, but also because it ensures standardization and consistency. While some of these labels can be extracted directly from the textual metadata, many of the data available do not contain explicit text informing the researcher about the age and gender of the subjects with the study. To bridge this gap, machine-learning methods can be trained to use the gene expression patterns associated with the text-derived labels to refine label-prediction confidence. RESULTS: Our analysis shows only 26% of metadata text contains information about gender and 21% about age. In order to ameliorate the lack of available labels for these data sets, we first extract labels from the textual metadata for each GEO RNA dataset and evaluate the performance against a gold standard of manually curated labels. We then use machine-learning methods to predict labels, based upon gene expression of the samples and compare this to the text-based method. CONCLUSION: Here we present an automated method to extract labels for age, gender, and tissue from textual metadata and GEO data using both a heuristic approach as well as machine learning. We show the two methods together improve accuracy of label assignment to GEO samples.