bioRxiv Science⌕ Search

Biology subjects

Yeo, S. H.

Publications and source records attributed to Yeo, S. H..

2 recordsLinked to original sources

Unexpected plasma gonadal steroid and reproductive hormone levels across the mouse estrous cycle

Despite the importance of the mouse in biomedical research, the levels of circulating gonadal steroids across the estrous cycle are not established with any temporal precision. Using liquid chromatography-mass spectrometry, now considered the gold standard for steroid hormone analysis, we aimed to generate a detailed profile of gonadal steroid levels across the estrous cycle of C57BL/6J mice. For reference, luteinizing hormone (LH) and prolactin concentrations were measured in the same samples by sandwich ELISA. Terminal blood samples were collected at 8-hour intervals (10 am, 6 pm, 2 am) throughout the four stages of the estrous cycle. As expected, the LH surge was detected at 6 pm on proestrus with a mean ({+/-}SEM) concentration of 11{+/-}3 ng/mL and occurred coincident with the peak in progesterone levels (22{+/-}4 ng/mL). Surprisingly, estradiol concentrations peaked at 10 am on diestrus (51{+/-}8 pg/mL), with levels on proestrus 6 pm reaching only two-thirds of this value (31{+/-}5 pg/mL). We also observed a proestrous peak in prolactin concentrations (132.5{+/-}17 ng/mL) that occurred earlier than expected at 2 am. Estrone and androstenedione levels were often close to the LOD and showed no consistent changes across the estrous cycle. Testosterone levels were rarely above the LOD (0.01 ng/mL). These observations provide the first detailed assessment of fluctuating gonadal steroid and reproductive hormone levels across the mouse estrous cycle and indicate that species differences exist between mice and other spontaneously ovulating species.

physiology↗

ClusterEnG: An interactive educational web resource for clustering big data

SummaryClustering is one of the most common techniques used in data analysis to discover hidden structures by grouping together data points that are similar in some measure into clusters. Although there are many programs available for performing clustering, a single web resource that provides both state-of-the-art clustering methods and interactive visualizations is lacking. ClusterEnG (acronym for Clustering Engine for Genomics) provides an interface for clustering big data and interactive visualizations including 3D views, cluster selection and zoom features. ClusterEnG also aims at educating the user about the similarities and differences between various clustering algorithms and provides clustering tutorials that demonstrate potential pitfalls of each algorithm. The web resource will be particularly useful to scientists who are not conversant with computing but want to understand the structure of their data in an intuitive manner.\n\nAvailabilityClusterEnG is part of a bigger project called KnowEnG (Knowledge Engine for Genomics) and is available at http://education.knoweng.org/clustereng.\n\nContactsongi@illinois.edu

bioinformatics↗