Journal: bioRxiv

Article Title: Multi-tissue transcriptomic aging atlas reveals predictive aging biomarkers in the killifish

doi: 10.1101/2025.01.28.635350

Figure Lengend Snippet: (a) Workflow of BayesAge 2.0, a Bayesian and locally weighted scatterplot smoothing (LOWESS) regression model behind the aging clocks. To train a tissue clock, Leave One Sample Out Cross-Validation (LOSO-CV) was used to generate testing-training splits of the data. In each iteration of LOSO-CV, one sample was used as a test set, while the rest of the tissue samples were used for training. This was performed k times, where k is the number of tissue samples available. Each time LOSO-CV was performed, a set of top age-associated genes (the highest absolute Spearman’s rank correlation values) was selected for the feature set. Then, the probability that the sample in the test set was a given age was calculated from the probability of the observed expression value for each selected gene in the sample at that age, assuming a Poisson distribution. The product of each gene-wise probability was computed to determine the age probability. The result was an age-probability distribution from which the age prediction was the highest probability age in this distribution. (b) Bar plots of the performance metrics for the BayesAge sex-combined tissue clocks, using the coefficient of determination (R 2 ) for the relationship between chronological and predicted age and the mean absolute error (MAE). (c) Scatterplot of gut clock chronological age vs. the ‘transcriptomic age’ (tAge) for measuring the prediction accuracy of the highest performing gut sex-combined tissue clock. The ‘optimal’ BayesAge clock is defined as the model with the most concordance between chronological and predicted age among all the gene number tested. Bottom, the gene frequency scatterplots of the top 10 overall age-correlated genes trained on the sex-combined gut samples are shown. The pink line is the locally estimated scatterplot smoothing (LOESS) regression fit across time. (d) Bar plots of R 2 and MAE values for select clocks trained on sex-combined data (left, ‘S-C’), female data (middle, ‘F’), and male data (right, ‘M’). Selected tissues include highly transcriptionally sex-dimorphic tissues (gonad, kidney, liver), moderately transcriptionally sex-dimorphic tissues (gut, skin), and one weakly sex-dimorphic tissue (brain). (e) Accuracy of tAge predictions for the optimal sex-combined (left), male-only (middle), and female-only liver clocks (right). (f) Predicted ages for liver samples from male and female killifish fed on ad libitum (AL) or dietary restricted (DR) diets using sex-dimorphic liver clocks (data from a published dataset ). Age prediction was performed using three different modeling strategies, BayesAge 2.0 (left), Elastic Net regression (middle), and Principal Component regression (right). Each dot in each box plot represents the predicted tAge for the liver transcriptome of an individual fish (4 fish per condition) and the gene set size or number of principal components used for age prediction is listed. For each model, Mann-Whitney test was used to test the significance of difference between the AL and DR conditions.

Article Snippet: This method utilizes a Bayesian framework to estimate the most likely transcriptomic age of a sample (‘tAge’) and employs locally weighted scatterplot smoothing (LOWESS) regression to model the nonlinear dynamics of gene expression, enabling age prediction between 47 to 163 days of age at day-level resolution.

Techniques: Biomarker Discovery, Expressing, MANN-WHITNEY

Journal: bioRxiv

Article Title: Multi-tissue transcriptomic aging atlas reveals predictive aging biomarkers in the killifish

doi: 10.1101/2025.01.28.635350

Figure Lengend Snippet: (a) Scatterplot of the tissue transcriptomic age (tAge) vs. chronological age for measuring the prediction accuracy of the optimal brain sex-combined tissue clock, which is the model that corresponds to the most concordance between chronological and predicted age among all the gene number tested. The coefficient of determination (R 2 ) between chronological and predicted age, as well as the mean absolute error (MAE), is listed in graphs. (b) The gene frequency scatterplots of the top 10 overall age-correlated genes trained on the sex-combined brain samples are shown. The black line is the locally weighted scatterplot smoothing (LOWESS) regression fit across time. (c, d) The scatterplots of tAge vs. chronological age (c) and gene frequency (d) were generated as in panels a and b, but for the testis.

Article Snippet: This method utilizes a Bayesian framework to estimate the most likely transcriptomic age of a sample (‘tAge’) and employs locally weighted scatterplot smoothing (LOWESS) regression to model the nonlinear dynamics of gene expression, enabling age prediction between 47 to 163 days of age at day-level resolution.

Techniques: Generated

Journal: Science Advances

Article Title: Nitrate limitation in early Neoproterozoic oceans delayed the ecological rise of eukaryotes

doi: 10.1126/sciadv.ade9647

Figure Lengend Snippet: Red points in ( A ) to ( C ) denote samples with Fe T > 0.5%; blue points, Fe T 0.4 to 0.5%; black points, Fe T < 0.4%. Blue band in (B) marks sediments deposited under oxic conditions, and green band marks sediments deposited under anoxic conditions. In (C), green band represents ferruginous conditions, and orange band represents euxinic conditions. In ( D ), green band marks near-zero δ 15 N bulk values indicative of dominance of nitrogen fixation. Color and size of data points are keyed to reflect TN and TOC contents. Blue line is LOWESS regression fit with 95% CI (gray shadow) calculated from SEs.

Article Snippet: We note that more data from the 800- to 900-Ma time bin are needed to accurately define the rise of δ 15 N. Sensitivity tests of LOWESS regression and changepoint detection analysis on the lowest 50% data and the entire dataset (fig. S6) also revealed a stepwise rise at around 800 Ma, although these analyses show more temporal variation.

Techniques:

Journal: Science Advances

Article Title: Nitrate limitation in early Neoproterozoic oceans delayed the ecological rise of eukaryotes

doi: 10.1126/sciadv.ade9647

Figure Lengend Snippet: ( A ) Compilation of published nitrogen isotope data from Mesoproterozoic to Neoproterozoic. Boxplots show distribution of δ 15 N values in each 100-Ma time bin. Blue line is LOWESS regression fit of lowest 25% δ 15 N data in every 100-Ma time bin; see fig. S6 for LOWESS regression fit of lowest 50% data and the entire dataset. Gray shade is 95% CI calculated from 10,000 bootstrapping experiments. Red triangle marks stepwise increase from a changepoint analysis of lowest 25% δ 15 N data. Most significant change in both mean and variance occurs at ca. 820 Ma. The green shade marks δ 15 N values characteristic of a nitrogen cycle dominated by nitrogen fixation (e.g., f assimilator < 0.16; see Fig. 5 ). ( B ) Frequency distributions of δ 15 N values from bootstrapping experiments ( n = 10,000) of pre–800 Ma (blue) and post–800 Ma (orange) samples. ( C ) Frequency distributions of mean δ 15 N values from bootstrapping experiments ( n = 10,000) of pre–800 Ma (blue) and post–800 Ma (orange) samples.

Article Snippet: We note that more data from the 800- to 900-Ma time bin are needed to accurately define the rise of δ 15 N. Sensitivity tests of LOWESS regression and changepoint detection analysis on the lowest 50% data and the entire dataset (fig. S6) also revealed a stepwise rise at around 800 Ma, although these analyses show more temporal variation.

Techniques:

Journal: Science Advances

Article Title: Nitrate limitation in early Neoproterozoic oceans delayed the ecological rise of eukaryotes

doi: 10.1126/sciadv.ade9647

Figure Lengend Snippet: ( A ) LOWESS regression curve of lowest 25% δ 15 N data in every 100-Ma time bin. A stepwise rise in δ 15 N at ca. 800 Ma coincides with breakup of supercontinent Rodinia and implies increasing marine nitrate availability. ( B ) Phosphorus concentrations of marine siliciclastic sediments through time ( , ). LOWESS regression is based on the entire dataset. An increase in late Tonian implies greater phosphorus availability in the surface ocean. ( C ) Biomarker data (see the Supplementary Materials) . Note oldest steranes at ca. 820 Ma, increasing sterane/hopane ratios, and inferred dominance of various primary producers. ( D ) Approximate stratigraphic ranges of major eukaryotic clades .

Article Snippet: We note that more data from the 800- to 900-Ma time bin are needed to accurately define the rise of δ 15 N. Sensitivity tests of LOWESS regression and changepoint detection analysis on the lowest 50% data and the entire dataset (fig. S6) also revealed a stepwise rise at around 800 Ma, although these analyses show more temporal variation.

Techniques: Biomarker Discovery