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Poplar has emerged as a model plant for understanding molecular mechanisms of tree growth, development and response to environment. Long-term application of different forms of nitrogen (such as NO3--N and NH4+-N) may cause morphological changes of poplar roots; however, the molecular level changes are still not well known. In this study, we analyzed the expression profiling of poplar roots treated by three forms of nitrogen: S1 (NH4+), S2 (NH4NO3) and S3 (NO3-) by using RNA-SEQ technique. We found 463 genes significantly differentially expressed in roots by different N treatments, of which a total of 116 genes were found to differentially express between S1 and S2, 173 genes between S2 and S3, and 327 genes between S1 and S3. A cluster analysis shows significant difference in many transcription factor families and functional genes family under different N forms. Through an analysis of Mapman metabolic pathway, we found that the significantly differentially expressed genes are associated with fermentation, glycolysis and tricarboxylic acid cycle (TCA), secondary metabolism, hormone metabolism, and transport processing. Interestingly, we did not find significantly differentially expressed genes in N metabolism pathway, mitochondrial electron transport / ATP synthesis and mineral nutrition. We also found abundant candidate genes (20 transcription factors and 30 functional genes) regulating morphology changes of poplar roots under the three N forms. The results obtained are beneficial to a better understanding of the potential molecular and cellular mechanisms regulating root morphology changes under different N treatments.

Secreted proteins including cytokines, chemokines and growth factors represent important functional regulators mediating a range of cellular behavior and cell-cell paracrine/autocrine signaling, e.g. in the immunological system, tumor microenvironment or stem cell niche. Detection of these proteins is of great value not only in basic cell biology but also for diagnosis and therapeutic monitoring of human diseases such as cancer. However, due to co-production of multiple effector proteins from a single cell, referred to as polyfunctionality, it is biologically informative to measure a panel of secreted proteins, or secretomic signature, at the level of single cells. Recent evidence further indicates that a genetically-identical cell population can give rise to diverse phenotypic differences. It is known that cytokines, for example, in the immune system define the effector functions and lineage differentiation of immune cells. In this Perspective Article, we hypothesize that protein secretion profile may represent a universal measure to identify the definitive correlate in the larger context of cellular functions to dissect cellular heterogeneity and evolutionary lineage relationship in human cancer.