Enhanced expression of vacuolar H+-ATPase subunit E in the roots is associated with the adaptation of Broussonetia papyrifera to salt stress.

Vacuolar H(+)-ATPase (V-H(+)-ATPase) may play a pivotal role in maintenance of ion homeostasis inside plant cells. In the present study, the expression of V-H(+)-ATPase genes was analyzed in the roots and leaves of a woody plant, Broussonetia papyrifera, which was stressed with 50, 100 and 150 mM NaCl. Moreover, the expression and distribution of the subunit E protein were investigated by Western blot and immunocytochemistry. These showed that treatment of B. papyrifera with NaCl distinctly changed the hydrolytic activity of V-H(+)-ATPase in the roots and leaves. Salinity induced a dramatic increase in V-H(+)-ATPase hydrolytic activity in the roots. However, only slight changes in V-H(+)-ATPase hydrolytic activity were observed in the leaves. In contrast, increased H(+) pumping activity of V-H(+)-ATPase was observed in both the roots and leaves. In addition, NaCl treatment led to an increase in H(+)-pyrophosphatase (V-H(+)-PPase) activity in the roots. Moreover, NaCl treatment triggered the enhancement of mRNA levels for subunits A, E and c of V-H(+)-ATPase in the roots, whereas only subunit c mRNA was observed to increase in the leaves. By Western blot and immunocytological analysis, subunit E was shown to be augmented in response to salinity stress in the roots. These findings provide evidence that under salt stress, increased V-H(+)-ATPase activity in the roots was positively correlated with higher transcript and protein levels of V-H(+)-ATPase subunit E. Altogether, our results suggest an essential role for V-H(+)-ATPase subunit E in the response of plants to salinity stress

Ab initio prediction of metabolic networks using Fourier transform mass spectrometry data

Fourier transform mass spectrometry has recently been introduced into the field of metabolomics as a technique that enables the mass separation of complex mixtures at very high resolution and with ultra high mass accuracy. Here we show that this enhanced mass accuracy can be exploited to predict large metabolic networks ab initio, based only on the observed metabolites without recourse to predictions based on the literature. The resulting networks are highly information-rich and clearly non-random. They can be used to infer the chemical identity of metabolites and to obtain a global picture of the structure of cellular metabolic networks. This represents the first reconstruction of metabolic networks based on unbiased metabolomic data and offers a breakthrough in the systems-wide analysis of cellular metabolism. KEY WORDS: Fourier transform mass spectrometry; metabolic networks; network reconstruction; computational methods. 1