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      CYCLOPS reveals human transcriptional rhythms in health and disease.

      Proceedings of the National Academy of Sciences of the United States of America
      Proceedings of the National Academy of Sciences
      autoencoder, biological rhythms, circadian rhythms, gene expression, machine learning

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          Abstract

          Circadian rhythms modulate many aspects of physiology. Knowledge of the molecular basis of these rhythms has exploded in the last 20 years. However, most of these data are from model organisms, and translation to clinical practice has been limited. Here, we present an approach to identify molecular rhythms in humans from thousands of unordered expression measurements. Our algorithm, cyclic ordering by periodic structure (CYCLOPS), uses evolutionary conservation and machine learning to identify elliptical structure in high-dimensional data. From this structure, CYCLOPS estimates the phase of each sample. We validated CYCLOPS using temporally ordered mouse and human data and demonstrated its consistency on human data from two independent research sites. We used this approach to identify rhythmic transcripts in human liver and lung, including hundreds of drug targets and disease genes. Importantly, for many genes, the circadian variation in expression exceeded variation from genetic and other environmental factors. We also analyzed hepatocellular carcinoma samples and show these solid tumors maintain circadian function but with aberrant output. Finally, to show how this method can catalyze medical translation, we show that dosage time can temporally segregate efficacy from dose-limiting toxicity of streptozocin, a chemotherapeutic drug. In sum, these data show the power of CYCLOPS and temporal reconstruction in bridging basic circadian research and clinical medicine.

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          Most cited references27

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          Efficacy of a tyrosine kinase inhibitor in idiopathic pulmonary fibrosis.

          Idiopathic pulmonary fibrosis is a progressive lung disease with a high mortality rate. Because the signaling pathways activated by several tyrosine kinase receptors have been shown to be involved in lung fibrosis, it has been suggested that the inhibition of these receptors may slow the progression of idiopathic pulmonary fibrosis. In a 12-month, phase 2 trial, we assessed the efficacy and safety of four different oral doses of the tyrosine kinase inhibitor BIBF 1120 as compared with placebo in patients with idiopathic pulmonary fibrosis. The primary end point was the annual rate of decline in forced vital capacity (FVC). Secondary end points included acute exacerbations, quality of life (measured with the St. George's Respiratory Questionnaire [SGRQ]), and total lung capacity. A total of 432 patients underwent randomization to receive one of four doses of BIBF 1120 (50 mg once a day, 50 mg twice a day, 100 mg twice a day, or 150 mg twice a day) or placebo. In the group receiving 150 mg of BIBF 1120 twice a day, FVC declined by 0.06 liters per year, as compared with 0.19 liters per year in the placebo group, a 68.4% reduction in the rate of loss with BIBF 1120 (P = 0.06 with the closed testing procedure for multiplicity correction; P = 0.01 with the hierarchical testing procedure). This dose also resulted in a lower incidence of acute exacerbations, as compared with placebo (2.4 vs. 15.7 per 100 patient-years, P = 0.02) and a small decrease in the SGRQ score (assessed on a scale of 0 to 100, with lower scores indicating better quality of life) as compared with an increase with placebo (-0.66 vs. 5.46, P = 0.007). Gastrointestinal symptoms (which led to more discontinuations in the group receiving 150 mg twice a day than in the placebo group) and increases in levels of liver aminotransferases were more frequent in the group receiving 150 mg of BIBF 1120 twice daily than in the placebo group. In patients with idiopathic pulmonary fibrosis, BIBF 1120 at a dose of 150 mg twice daily, as compared with placebo, was associated with a trend toward a reduction in the decline in lung function, with fewer acute exacerbations and preserved quality of life. (Funded by Boehringer Ingelheim; ClinicalTrials.gov number, NCT00514683 .).
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            Nonlinear principal component analysis using autoassociative neural networks

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              Neural networks and principal component analysis: Learning from examples without local minima

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                Author and article information

                Journal
                28439010
                10.1073/pnas.1619320114
                5441789

                autoencoder,biological rhythms,circadian rhythms,gene expression,machine learning

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