Genome-wide network model capturing seed germination reveals coordinated regulation of plant cellular phase transitions.

Seed germination is a complex trait of key ecological and agronomic significance. Few genetic factors regulating germination have been identified, and the means by which their concerted action controls this developmental process remains largely unknown. Using publicly available gene expression data from Arabidopsis thaliana, we generated a condition-dependent network model of global transcriptional interactions (SeedNet) that shows evidence of evolutionary conservation in flowering plants. The topology of the SeedNet graph reflects the biological process, including two state-dependent sets of interactions associated with dormancy or germination. SeedNet highlights interactions between known regulators of this process and predicts the germination-associated function of uncharacterized hub nodes connected to them with 50% accuracy. An intermediate transition region between the dormancy and germination subdomains is enriched with genes involved in cellular phase transitions. The phase transition regulators SERRATE and EARLY FLOWERING IN SHORT DAYS from this region affect seed germination, indicating that conserved mechanisms control transitions in cell identity in plants. The SeedNet dormancy region is strongly associated with vegetative abiotic stress response genes. These data suggest that seed dormancy, an adaptive trait that arose evolutionarily late, evolved by coopting existing genetic pathways regulating cellular phase transition and abiotic stress. SeedNet is available as a community resource (http://vseed.nottingham.ac.uk) to aid dissection of this complex trait and gene function in diverse processes.

Transcranial Magnetic Stimulation Indices of Cortical Excitability Enhance the Prediction of Response to Pharmacotherapy in Late-Life Depression.

BACKGROUND: Older adults with late-life depression (LLD) often experience incomplete or lack of response to first-line pharmacotherapy. The treatment of LLD could be improved using objective biological measures to predict response. Transcranial magnetic stimulation (TMS) can be used to measure cortical excitability, inhibition, and plasticity, which have been implicated in LLD pathophysiology and associated with brain stimulation treatment outcomes in younger adults with depression. TMS measures have not yet been investigated as predictors of treatment outcomes in LLD or pharmacotherapy outcomes in adults of any age with depression. METHODS: We assessed whether pretreatment single-pulse and paired-pulse TMS measures, combined with clinical and demographic measures, predict venlafaxine treatment response in 76 outpatients with LLD. We compared the predictive performance of machine learning models including or excluding TMS predictors. RESULTS: Two single-pulse TMS measures predicted venlafaxine response: cortical excitability (neuronal membrane excitability) and the variability of cortical excitability (dynamic fluctuations in excitability levels). In cross-validation, models using a combination of these TMS predictors, clinical markers of treatment resistance, and age classified patients with 73% ± 11% balanced accuracy (average correct classification rate of responders and nonresponders; permutation testing, p < .005); these models significantly outperformed (corrected t test, p = .025) models using clinical and demographic predictors alone (60% ± 10% balanced accuracy). CONCLUSIONS: These preliminary findings suggest that single-pulse TMS measures of cortical excitability may be useful predictors of response to pharmacotherapy in LLD. Future studies are needed to confirm these findings and determine whether combining TMS predictors with other biomarkers further improves the accuracy of predicting LLD treatment outcome.

Combining classifiers to predict gene function in Arabidopsis thaliana using large-scale gene expression measurements.

BACKGROUND: Arabidopsis thaliana is the model species of current plant genomic research with a genome size of 125 Mb and approximately 28,000 genes. The function of half of these genes is currently unknown. The purpose of this study is to infer gene function in Arabidopsis using machine-learning algorithms applied to large-scale gene expression data sets, with the goal of identifying genes that are potentially involved in plant response to abiotic stress. RESULTS: Using in house and publicly available data, we assembled a large set of gene expression measurements for A. thaliana. Using those genes of known function, we first evaluated and compared the ability of basic machine-learning algorithms to predict which genes respond to stress. Predictive accuracy was measured using ROC50 and precision curves derived through cross validation. To improve accuracy, we developed a method for combining these classifiers using a weighted-voting scheme. The combined classifier was then trained on genes of known function and applied to genes of unknown function, identifying genes that potentially respond to stress. Visual evidence corroborating the predictions was obtained using electronic Northern analysis. Three of the predicted genes were chosen for biological validation. Gene knockout experiments confirmed that all three are involved in a variety of stress responses. The biological analysis of one of these genes (At1g16850) is presented here, where it is shown to be necessary for the normal response to temperature and NaCl. CONCLUSION: Supervised learning methods applied to large-scale gene expression measurements can be used to predict gene function. However, the ability of basic learning methods to predict stress response varies widely and depends heavily on how much dimensionality reduction is used. Our method of combining classifiers can improve the accuracy of such predictions - in this case, predictions of genes involved in stress response in plants - and it effectively chooses the appropriate amount of dimensionality reduction automatically. The method provides a useful means of identifying genes in A. thaliana that potentially respond to stress, and we expect it would be useful in other organisms and for other gene functions.

Association of poor glycemic control with prolonged hospital stay in patients with diabetes admitted with exacerbation of congestive heart failure.

OBJECTIVE: To establish a relationship between the control of blood glucose levels and the severity of congestive heart failure (CHF) in a retrospective review of medical records of patients with diabetes admitted with acute exacerbation of CHF and to assess the potential correlation between the number of days of hospitalization and the baseline and in-hospital glycemic status. METHODS: Medical records were reviewed to identify patients with diabetes admitted to a tertiary care center with exacerbation of CHF. Patients in whom any new complications developed that could have prolonged the hospitalization were excluded from the study. The number of days of hospitalization attributable to CHF were noted and statistically correlated with the glycemic control. RESULTS: Data on 100 patients included in the study are presented. The duration of hospitalization ranged from 1 day to 2 weeks (mean, 4.79 +/- 3.03 days). The in-hospital glycemic control strongly correlated positively with the number of days of hospitalization (r = 0.499; 95% confidence interval [CI], 0.325 to 0.643). The admission blood glucose level also showed a strong positive correlation with the days of hospitalization (r = 0.587; 95% CI, 0.426 to 0.720). The mean hemoglobin A1c (HbA1c) correlated positively with the number of days in the hospital (r = 0.653; 95% CI, 0.508 to 0.764). The 51 patients with uncontrolled diabetes (HbA1c >7%) were hospitalized for a mean period of 6.3 +/- 3.2 days, in comparison with a mean duration of 3.2 +/- 1.9 days for the 49 patients with good outpatient glycemic control (HbA1c < or =7%). CONCLUSION: Patients with diabetes admitted with exacerbation of CHF who have poor baseline or in-hospital glycemic control have a prolonged hospitalization.

Modeling protein tandem mass spectrometry data with an extended linear regression strategy.

Tandem mass spectrometry (MS/MS) has emerged as a cornerstone of proteomics owing in part to robust spectral interpretation algorithm. The intensity patterns presented in mass spectra are useful information for identification of peptides and proteins. However, widely used algorithms can not predicate the peak intensity patterns exactly. We have developed a systematic analytical approach based on a family of extended regression models, which permits routine, large scale protein expression profile modeling. By proving an important technical result that the regression coefficient vector is just the eigenvector corresponding to the least eigenvalue of a space transformed version of the original data, this extended regression problem can be reduced to a SVD decomposition problem, thus gain the robustness and efficiency. To evaluate the performance of our model, from 60,960 spectra, we chose 2,859 with high confidence, non redundant matches as training data, based on this specific problem, we derived some measurements of goodness of fit to show that our modeling method is reasonable. The issues of overfitting and underfitting are also discussed. This extended regression strategy therefore offers an effective and efficient framework for in-depth investigation of complex mammalian proteomes.