Respiratory metabolism in macroalgae: Using the respiratory electron transport system (ETS) to detect stress and different physiological states

G DAD DE LAS PALMAS DE GRAN CANARIA Bioges ' ·r ... ro ..... I .. ....,,'-....i_ ..... ,.,..¡,,." .. Jl 1 r l: r .. iIm""'t.W,,, .. 1 Respiratory metabolism in macroalgae: Using the respiratory electron transport IDeAO system (ETS) to detect stress and different physiological states. ~ ~ M.T. Asenslo1 , F. Maldonado1, M. Gómez 1, T. T. Packard 1 and M.A. Vlera-Rodriguez2 bi<D>cean I Grupo de Ecofisiologla Planct6nica, Instituto de Oceanografl'a y cambio Gl obal . Un i versidad de Las Pal m as de Gra n cana ri a, Sp aln. SQauho n s Introduction Metabollsm study of green algae (Ulva) communitles, Inhabitlng InterUdal pools of Gran canaria. As an Index of metabollc status and stress we used tIle electron tnmsport system (ETS) ro dlfferentlate between different growlng conditlons In tIle nlltul8l envlronment. lhls technlque has been successfully used ro study many different marine planktonlc organlsms Indudlng bacteria, phytoplankton and zooplankton, but it has not been used ro study marine macroalgae. In tIlIs flrst phase of our research we have developed tIle methodology for homogenlzlng U/va and have used a standard spectrophotomeb1c-based klnetic enzyme assay ro describe tIle Impact of nutrlent IImitatlon on tIle metabollc capacity In sampes collected In tIle wild and malntalned In controlled culture. 1. Choice of the homogenlzlon method: The difference between botll methods was slgnlflcant, In additlon, we declded ro use tIle tlssue-grlndlng method because it was less expenslve and easler. 2. Correlatlon between dlf'f .. ent blomass proxles. lhere 15 a goo<l correlatlon between dry welght, optical density and chlorophyll a & b (Ags S, 6, & 7. .-.. 0,9 ~ 0,_ 2 0,7 .., o,e f 0,5 0,4 0,3 i§ 0,2 ¡ O,! +. O 20 40 Ory welght (mg) 30 25 1 20 'O 15 - ~10 6 5 O 60 . -; + .. .. . . - O 20 40 Ory wleght (mg) + . .. 60 .... ~I Figure 1: M ap of (¡a n canaria. We co U ected samples at Sa n Oi stobal and Ta H arte (Te l de ). 0,015 ..-- - -------- -- - --..., i 0,04 B 0, 03 ! 0,02 ~ 0,01 0,00 "------ _ Grinder - L1quld Nitrogen figure 4: Comparison between ETS total in both homogenization methods. • Chl a • Chl b 30 Z5 {zo :!!.15 ... .!: 10 6 5 . .. . . • • O ¡ .t' ~ . o 0,2 0,4 0,6 O ,. Optlaol deNl¡ly (670 nm ) + Ch lll -Cht b Mean 0>1 3. Evolutfon of ETS wlth time 4. Relatlon between Blomass and Resplratory actlvlty (Klelber) i' f 1,2 1,1 t 1,0 ~ 0,11 0,_ ! 0,7 E 0,6 I 0,5 0,4 O Z _ 10 We collected lile algae (day 1) and stored lIlem at 200c for 8 days without nutrients with a 10-14 photoperiod. In spite of lile apparent differences between Ihe two areas they were not statistically different because of lile large standard deviation in lile San Cristobal data alter 5th day . (We used PERMANOVA v.1.6 software (M.J. Anderson, 2(05). Figure 8: ETS time-course in Iocations. erencea Kenne r and Ahmed, 19 75. Masurements of Electron Transport Activ i ties in marine Phytoplankton . Mari i ne Biology 33, 119-127. Mitchell and Kiefer , 1984 . Determination of absorption and fluorescence excitation spectra for phytoplankton. Lecture notes on costal and estuarines studies, VOL 8, PP . 157 - 169 . Anderson, M.J. 2005 . PERMANOVA : a FORTRAN computer program for penmutational multivariate analysis of variance . Department of Statistics, University T, y • 0,5211x - 2, 3324 y • 0, 35+1>< - 2, 1173 R'·O,5664 R' · 0, 419 T. y • 0,61S31t - 2,f1J2 y • 0, 1S82x • 2p m R :l. O,7828 R' • 0, 1316 T. y • .o,26Jk • 1p7S7 y • 0,1688x · 2P'ffl R'.Op391 R' · Op593 T.bIe 1: The Kleiber coeffident clecreases from Ihe flrst to Ihe last day due to starvation. 1. compartson between ETS melIns In both homogenlzatlon methods demollstlated a slgnlflamt dllference. We used tIle tlssue-gr1ndlng method because It was Iess expensIve and easIer. 2. There Is a good correIaaon between dry welght, optIcal density and Chlorophyll 10 we used the optIaIl cIensIty at 670 nm as a reference ro meaure bIomass. 3. The ditferences In the 8-cIay ETS time courses for the two areas were not statIstIaIlly dlfferent. 4. Due tIle st3rved condltlons we could see a decrease In the Klelber