The UIUC Plant Physiology journal club reviewed the preprint “α-carboxysome formation is mediated by the multivalent and disordered protein CsoS2” (doi: https://doi.org/10.1101/708164) by Oltrogge et al. 2019. The paper describes the biochemical characterization of the CsoS2 protein involved in carboxysome assembly, identifying a repeat peptide region that makes weak electrostatic interactions with rubisco through the use of bio-layer interferometry (BLI) and x-ray crystallography. The authors identified evolutionary conserved residues through protein sequence comparisons and the sites of interaction between these residues and rubisco through protein X-ray crystallography. We found the paper to be very well written, well presented and valuable addition to knowledge about alpha carboxysome assembly. Our journal club assessed the paper as part of a learning exercise about how to make work accessible to a wide audience. Participants first learned about the “and-but-therefore (ABT)” model of paper writing popularized by Randy Olsen in his freely available book "Huston, we have a narrative", that can be used throughout the manuscript to help maintain the reader’s interest. Focusing on the abstract we found it to contain many of aspects of the ABT model. We also thought it could potentially be strengthened by including a stronger “but” phrase which generally represents the question under consideration. It was suggested that this phrase would start with the fact that there is little knowledge about how the carboxysome is assembled, and some members of the club questioned if the ongoing carboxysome engineering efforts might be mentioned as relevant to the wider importance of the work (either in the abstract or the discussion).One aspect we found particularly interesting was the similarities between CsoS2 and the algal protein EPYC which has been implicated in aggregation of rubisco in the pyrenoid. These appeared to us to an important point, and the reason to include information about CsoS2 as an intrinsically disordered protein (IDP) that could perhaps be emphasized more. As we were not familiar with the PONDR-FIT disorder score, we would have found it helpful to have a little more explanation as to its importance and interpretation. Overall we liked the approach for analyzing IDPs and thought it was an impressive effort to successfully crystallize the CsoS2 peptide with rubisco. In addition, we assessed the presentation of figures, we particularly liked the use of consistent colouring throughout, the choice of clearly legible font sizes on all graphs and the helpful diagrams to illustrate biochemical procedures, such as the BLI procedure in Fig 2b. One consideration is whether the choice of colors is colorblind friendly, using the app color oracle, several of the colors are indistinguishable in all the figures analysed. We also thought inclusion of legend titles would help guide readers on how best to interpret the data. We thought the X-ray crystallography data was presented in a clear and helpful manner, displaying what the individual residue interactions were between the bpeptide and rubisco. If it could be improved further it may be by inclusion of a label of rubisco for non-experts who may not immediately associate CbbL and CbbS as subunits. Finally, we particularly liked Figure 5 as it neatly summarized the proposed role of CsoS2 in carboxysome assembly.Other thoughts included:It would be interesting to include discussion of why the full length CsoS2 peptide does not appear to bind rubisco.The paper tied up loose ends and did a good job of using multiple approaches to build evidence for the direct interaction of CsoS2 and rubisco.
The preprint “Revisiting tradeoffs in Rubisco kinetic parameters” by Flamholz et al. 2018 (https://doi.org/10.1101/470021) investigates the tradeoffs between catalytic efficiency and rate, of the central enzyme in carbon fixation, Ribulose-1,6-bisphosphate Carboxylase/Oxygenase (RuBisCO), using kinetic modeling based on biochemical data. The manuscript builds on previous work from the group (Savir et al. 2010; doi: 10.1073/pnas.0911663107), including an expanded dataset of kinetic parameters of ~250 RuBisCOs from 286 different species extracted from the literature. We thought it was an important topic with potential interest for a wide range of researchers working on photosynthesis and evolution.The main questions the paper seeks to address are:Which trade-offs are inherent in Rubisco kinetics. Has evolution resulted in optimal kinetics within the constraints of those inherent trade-offs.The preprint challenges the theory that increasing RuBisCO activity reduces enzyme specificity as described by Tcherkez et al. (2006), which was based on a model of enzyme activity the discriminates between CO2 and O2 in a transition state. Data supporting this theory has been reported widely in the literature. However, the authors propose that there is little/no correlation between specificity and activity, and most previously-found correlations (KcatC and SC/O etc) are smaller for the new dataset, except for KcatC/KC and KcatO/KO. We really enjoyed reading the manuscript and as it challenged our preconceptions about RuBisCO activity. We found it interesting (and surprising!) that the data contradicts a well-established theory, and it increased our awareness out current models of enzyme activity. We also thought it was interesting that they were able to collect data from so many species across many previous studies and also break down trends/relationships between different clades or physiologies. It was also interesting that given that they were using data from studies that showed the opposite, they were able to come to the conclusion they found. We particularly liked the authors' suggestions about how to move the field forward and the call for an improved understanding of RuBisCO kinetic mechanism.There were a few areas we thought it would be useful to clarify:Providing a cartoon model of the proposed mechanism would help readers unfamiliar with the nuances of the models being assessed. More information about how the authors selected and filtered data collecting from the literature and how the different datasets were taken into account in the statistical analysis. i.e. how many measurements per species, types of values (mean/median) etc.Figure 4 and 5: how was the conclusion reached that there was no correlation between parameters?Organization, it would make things stronger to layout the arguments clearly between what came before and after, for those not familiar with the academic argument.As the paper argues against what most people are reading it might expect, additional text theorizing why this is the case would be useful to guide readers.Minor commentsFigure 2A y-axis labelsPut a key at the top as the symbols can get buried in the text
The UIUC Plant physiology journal club chose to review the preprint “Triose phosphate utilization and beyond: from photosynthesis to end-product synthesis” (http://dx.doi.org/10.1101/434928), by McClain and Sharkey, about the role of phosphate levels in relation to CO2 assimilation during photosynthesis. The paper explains that when the rate of photophosphorylation which produces ATP, exceeds that of starch or sucrose synthesis which consumes ATP, phosphate levels drop causing photosynthetic electron transport rates to slow. This process is defined as Triose phosphate limitation (TPU). We found the paper provided a very thorough definition of TPU and thought it was a really good introduction to the topic. In particular, the explanation about the way TPU limitation can be identified by no change in photosynthesis and decline in PhiPSII at high CO2 was very useful. The paper raises a number of points we were interested to discuss further including why TPU limitation is only slightly more than photosynthetic rate? Are some plants are more subjected to TPU limitation than others? Why is TPU limitation is slightly more than photosynthetic rate (one might predict that natural selection would select for utilization of all their photosynthate)? Why C4 plants do not experience any TPU limitation? Is it more common to see TPU limitation in Li6800s?The question of the potential impact of a rising atmospheric CO2 concentrations on TPU is a fascinating topic. We thought it would be beneficial for readers to include reference to Experimental free air CO2 enrichment (FACE) experiments which have sought to address this issue, and some degree of guidance about what types of increases in atmospheric CO2 would be required to potentially see an effect. In addition it would be useful to mention that many of the TPU studies have been performed in pot bound plants, which experience an altered shoot/root ratio creating an artificial sink limitation, and a bit more discussion about how often TPU limitation might occur under field conditions (Arp 1991). Finally it might be good to include a bit more discussion about how the xanthophyll cycle kicks in to protect the plant under stress and that decreased PhiPSII could be an example of non-photochemical quenching.In summary, we thoroughly enjoyed the paper, we learnt a lot and look forward to seeing it published.Minor pointsIn equation 5 it is not entirely clear what the phi symbol is? The symbol R is used to represent three different variables, could maybe consider changing this to prevent confusion.Many color combinations used in the figures would be unsuitable for those with color blindness, and suggest using color oracle (https://colororacle.org/) to help.Fig 2: Clarification on what ‘a little’ and ‘a lot’ means would be helpfulFig 2: We found it a bit confusing, many members were unclear about what it was demonstrating and suggest considering reformulating the description as we believe it could be useful as a teaching aid.Fig 3: Many members really liked this figure and thought it provided a clear image to explain the various limitations a plant experiences during an A/Ci curve. There was some discussion about where the boundary lines were drawn, this might be addressed by having zones of colour fade into each other rather than represented as sharp boundaries. It was also suggested that some kind of upper bound to J limitation of ETR might be included (possibly fading out to white?)ReferencesArp (1991) https://doi.org/10.1111/j.1365-3040.1991.tb01450.xRodgers et al. (2004) https://doi.org/10.1111/j.1365-3040.2004.01163.x
Iulia Floristeanu, Cindy Chan , Steven Burgess (0000-0003-2353-7794), Charles Pignon, Stephanie Cullum, Isla Causon, Pietro Hughes AbstractIn the preprint "StomataCounter: a deep learning method applied to automatic stomatal identification and counting” (doi: https://doi.org/10.1101/327494) Fetter et al. introduced a reliable and automated stomata counting program that is more efficient and accurate than human counting and existing algorithms, with a low false positive rate. The authors report the algorithm can be used for previous uncharacterised species and has a 94.2% transfer accuracy when used on untrained datasets. In addition they provide a publically available webtool which could be very beneficial for researchers working on stomata.ReviewWe really enjoyed the paper and found it to be of high interest as the method presented could make the work of a lot of people easier. We were particularly impressed with the precision score of StomataCounter which compares well with other existing algorithms, especially in terms of the transfer accuracy. To our knowledge this is a novel approach, as there are no automated stomata counting technologies available, and it outperforms existing methods. The DCCN appears to overcomes challenges of stomata counting and it correctly identified stomata showing minimal false positives on non-plant and non-stomata covered tissue.The article was well written and easy to follow. We liked the choice of a Deep Convolutional Neural Network (DCNN) for machine learning and felt the authors could have highlighted the benefits of this method by providing a more detailed justification of why it is superior to other algorithms - as this is what made the paper so interesting to us. The text might be further improved by being more specific about results in the abstract and a clearer statement on supplementary data and sampling used.We were interested to know how the algorithm performs on grass species, as they have “dumbbell-shaped” guard cells and companion cells. It was unclear to us whether grass stomata have been used among the training images so we wondered if the accuracy would be the same for this particular shape of stomata. Including some text in the discussion about this would be illuminating. In addition for the sake of reproducibility and to aid readers comprehension it would be useful to provide (1) a complete list of samples analysed and (2) ideally the whole training dataset in a public repository such as Zendo or Dryad, as it could greatly benefit future comparative studiesQuestions we had it would help to clarifyThe authors state “many researchers are likely to manually count stomata” - is this because other methods are not good enough, user interface is too complicated or just people liking more traditional things?In the methods it is written “final fully connected layers by convolutions”. Does this mean there's no fully-connected layers, only convolutional layers? It sounds like both are included in other parts of the paper. We were confused by the statement “we argue that the current architecture does not transfer well to different scales and images should be pre-processed to match the training scale of the network,” is that not the reason a fully-convolutional network is used? to avoid the inability to manage different input sizes.Minor commentsConsider making legends of Figure 3 and Figure 4 bigger to improve readabilityFigure 5 is very informative and summarizes the data wellFigure 6 might be improved by including an inset to focus on the down number.Figure 7 - scale barsThere was a strong linear correlation at higher magnification between DCNN and human counts is this a standard magnification for this type of experiment?It would be good to include:Quantification of the accuracy of the automatic separation between abaxial and adaxial datasets. A failure of separation could potentially have contributed to finding stomata on the "adaxial" cuticle. Discussion of whether the magnifications (200x and 400x) are suitable for this kind of analysis.Information about why the 4 sources of images were chosen and discussion of whether they provide enough of a range.An explanation of why data was which showed <98% was discardedStats on Gingko and poplar datasets.Might consider rephrasing “the mean number of stomata detected in the adaxial, aorta, and breast cancer image sets was 1.5, 1.4, and 2.4, respectively, while the mean value of the abaxial set was 24.1” to better explain the low frequency: to compare with non-stomata dataset vs dataset that has very low number of stomataCode availability: It would be helpful if the code and custom scripts (such as separation of abaxial and adaxial leaf sides) are made available and linked to a repository.
Steven Burgess (0000-0003-2353-7794), Samuel Fernandes, Antony Digrado, Charles Pignon, Elsa de Becker, Naomi Housego Day, Lusya Manukyan, Stephanie Cullum, Isla Causon, Iulia Floristeanu, Young Cho, Freya Way, Judy Savitskya, Robert Collison, Aoife Sweeney, Pietro Hughes, Cindy Chan AbstractThe paper “Arabidopsis species employ distinct strategies to cope with drought stress” by Bouzid et al. (https://doi.org/10.1101/341859) investigates whether responses to water limitation vary between closely related species by assessing the growth and survival of A. thaliana, A. lyrata and A. halleri accessions in a dry down experiment. By including multiple accessions of each species the authors were able to analyse variation in response to drought stress within and between species based on eight phenotypic parameters. The authors went on to perform comparative transcriptomic analysis between A. lyrata and A. halleri over a time course of drought treatment and identified differentially expressed genes. GO ontology analysis suggest the species analysed adopt different strategies to cope with drought stress, with A. lyrata employing avoidance and tolerance mechanisms, whereas A. thaliana showed strong avoidance but no tolerance. We were impressed with the amount of work performed and thought the study aims to address an interesting question. During the hour long journal club participants were asked to focus on three aspects of the paper as part of a training exercise, including novelty, interest, soundness as well as writing and presentation.ReviewThere are several published papers looking at the effect of drought stress in Arabidopsis species including A. lyrata (Sletvold and Agren 2011; Paccard et al. 2014) and A. thaliana (Ferguson et al. 2018; Kalladan et al. 2017). We suggest toning down the assertion on Line 28 that little is known about the physiological response to drought in closely related species. Although not in a single paper, this issue has been addressed within a species by Sletvold and Agren (2012) and Davila Olivas et al. (2017) and there is a fairly large collection of papers looking at this within a species. To our knowledge, the novelty of this work lies in analysis of drought responses within and between several species of Arabidopsis in one article. We thought this was an interesting approach and that the authors can make more of this point, highlighting the new information that it yields. The article is well written in clear sentences and it was easy to read. We felt the authors had collected a lot of data and believe this could be explored further in the discussion, particularly the differential expression data. There is a lot of microarray and transcriptomic data available for the response of A. thaliana to drought conditions and would like to have seen some form of comparison between these data and that collected for A. lyrata and A. halleri.In addition, it would help to provide more information about why analysis of Arabidopsis accessions was limited to late flowering varieties. Does excluding accessions which can terminate life cycle early bias the experiment? Termination is a major strategy for survival and may impacts upon the conclusion that “response to depletion in SWC did not reveal significant differences between accessions (line 591).” Further discussion of this conclusion in the context of previous studies would be illuminating as it appears to contrast with some findings, for example Bouchabke et al. (2008) which suggested there are differences in response to depletion of SWC between A. thaliana accessions. Readers would also benefit from discussion about how the results from the phenotypic analysis relates to other studies which have implicated trichome production, rosette leaf size and flowering time as drivers for drought tolerance.We commend the fact the methods are detailed which should aid anyone wanting to replicate the study. Several aspects were highlighted as excellent practices, in particular: the fact that all program versions are provided, software parameters are included and accessions and materials are well catalogued. To build on this we recommend depositing the data (particularly transcriptomic analysis) in a public repository such as GEO, SRA or Zendo as required by some journals. This means data can be built upon in future studies and could increase the likelihood of the paper being cited. Inclusion of extended methods in an accompanying Bioprotocol paper or on sites such as Protocols.io and sharing custom scripts in a github repository (or on Zendo) would make the reporting of methods outstanding. Minor commentsThe paper may benefit from clarify a number of questions raised by participants: How was soil mixture chosen?Line 192 under what conditions were the plants grown in the greenhouse? Line 206: How were the first signs of wilting defined? It might be worth mentioning this earlier in the manuscript (line 206)Line 232 - How was loss of turgidity measured?The colors used in the figures will create difficulty for those individuals with color blindness, using color oracle (https://colororacle.org/) can help address this issue.Figure 2 - no scale bars are includedFigure 2 - how many plants per pot were analyzed? This could have impacted on measurements displayed images suggested this was variable.Figure 5 - we thought it was excellent that unit level data is provided, this could be extended to the other figures too.Figure legends could be improved e.g. by changing “halleri” to “A. halleri”*Formating of legend (line 1326 )extra space and period.The article might benefit from consistent formatting/presentation of figuresA table might be more appropriate for Figures 7 and 8Inclusion of n= numbers in the figures would help readers assess the data.We were confused about how the experiment was designed, why is data for only on biological replicate displayed in figures 7 and 8.References Bouchabke et al. 2008 https://doi.org/10.1371/journal.pone.0001705 Davila Olivas et al. (2017) https://doi.org/10.1111/mec.14100Des Marais, et al. (2012) https://doi.org/10.1105/tpc.112.096180 Huttunen et al. 2010 https://doi.org/10.5735/085.047.0304Kalladan et al. (2017) https://doi.org/10.1073/pnas.1705884114 Paccard et al. (2014) doi: 10.1007/s00442-014-2932-8 Sletvold and Agren (2011) https://doi.org/10.1007/s10682-011-9502-x
Steven Burgess (0000-0003-2353-7794), Samuel Fernandes, Antony Digrado, Charles Pignon, Elsa de Becker, Naomi Housego Day, Lusya Manukyan, Stephanie Cullum, Isla Causon, Iulia Floristeanu, Young Cho, Freya Way, Judy Savitskya, Robert Collison, Aoife Sweeney, Pietro Hughes, Cindy Chan AbstractThis review is compiled from notes taken by the UIUC Plant Physiology preprint journal club during a one hour session on 2018-07-30. The review refers to the preprint "Natural variation in stomata size contributes to the local adaptation of water-use efficiency in Arabidopsis thaliana" by Hannes Dittberner, Arthur Korte, Tabea Mettler-Altmann, Andreas Weber, Grey Monroe, and Juliette de Meaux ( doi: https://doi.org/10.1101/253021) published on BioRxiv.ReviewIn the paper “Natural variation in stomata size contributes to the local adaptation of Water use efficiency in Arabidopsis thaliana” Dittberner et al. (2018) investigated the genetic basis of water use efficiency using genome wide association analysis. Findings included (1) that there is substantial variation in stomatal size and patterning in A. thaliana accessions (2) decreased stomata size correlates with increased water use efficiency (3) two novel QTL affecting WUE independently of stomatal patterning (4) water use efficiency is a polygenic trait and (5) natural selection contributed to the establishment of variation in WUE in accordance with climatic conditions.The findings are in accordance with previous studies looking at genetic variation in stomatal size and patterning in A. thaliana (Delgado et al. 2011; Monda et al. 2016), the role of natural selection in WUE of tomato (Muir et al. 2014), analysis of genomic variation in WUE in A. thaliana (Easlon et al. 2014; Aliniaeifard and van Meeteren 2014) and the extensive literature correlating a decrease in stomatal size with increased water use efficiency in many species.We were impressed by the significant technical advance presented in the form of automated stomatal counting and suggest more could be made of this aspect of the paper. For example the we suggest inclusion of the term ‘high-throughput’ in the title, and think it would be helpful to include a picture of how the screening system works in the introduction.Further, we found the conclusion that water use efficiency is a polygenic trait very interesting. The implications are important for studies aimed at the improvement of photosynthesis/WUE through conventional breeding or genetic manipulation. We wonder if there are other papers which have already discussed the potential polygenic nature of stomatal traits? Yoo et al. (2011) mentions different genes involved in WUE and stomatal patterning. This kind of paper could have been worth-mentioning in the discussion.The identification of two novel QTL, not otherwise known to influence water use efficiency or to be related to stomatal patterning and regulation was particularly intriguing, and these two genes provide a great starting point for further analysis in future publications.Major commentsWe believe the kinship matrix accounts for relatedness, and is usually called the k model. To account for population structure one could use principal components, combining these two approaches is known as the Q+K model. It would be helpful to include a citation for calculating the kinship matrix as there are multiple methods available. It might also be worth providing QQplots to show whether including population structure is necessary or not for this study.We found the use of a Bonferroni correction in the analysis quite conservative, there are other options such as Benjamini-Hochberg adjusted p-values that could lead to declaring other SNPs as significant. Also, from the GWAS results, there seem to be other SNPs popping up - although, not significant. It might be worth using an alternative to p-values when investigating the possible biological significance of outstanding SNPs on a Manhattan plot - such as selecting the top 10 hits for investigation.Minor comments:Keywords: Consider revisiting, as many of the keywords are already on the title. Additionally, it would be helpful to include terms rather than acronyms which are not familiar to non-experts.Introduction: Would be good to include examples of some of the limitations to automated confocal microscopy approaches to orientate readers.Line 143: (Atwell et al., 2010). This citation doesn't seem to be right. If the author is referring to genomic heritability it could cite de los Campos et al. (2015).Methods: Would like to see further information on the methodology used for carbon isotope discrimination in the main textResults: Might consider using the term ‘genetic-heritability’ rather than ‘pseudo-heritability’Results: Direct comparison with previously reported dC13 values for accessions that have been demonstrated to differing WUE would be helpful.Figure 4: the text on the figure is likely to be too small to see properlyLine 505: “soil composition...” This is a really interesting point about the complexity of environmental effects on plant physiology. It might be helpful to provide some explanation for readers less familiar with soil content. (e.g. water holding content and effect on root structure - e.g.https://doi.org/10.1073/pnas.1721749115)Line 499-500. Repetition of text on 498-499Results: It would be helpful to share where the functional annotation for genes come from. This gene appears to be involved in mRNA decapping. There is only one publication, which found it is involved in PAMP triggered immunity, plants were dwarf in stature. TAIR does not annotate a role in cell differentiation (10.15252/embj.201488645)Discussion: It is interesting to see a comparison with a related species and how the observed pattern is consistent between the two.Data accessibility: This is excellent. Great to see that all data and code will be made available and deposited in a permanent repository!ReferencesAliniaeifard and van Meeteren (2014) J Exp Bot. 65(22): 6529–6542.de los Campos et al. (2015) PLoS Genet 11(5): e1005048. doi:10.1371/journal.pgen.1005048Delgado et al. (2011) Ann Bot.; 107(8): 1247–1258Easlon et al. (2014) Photosynth Res 119: 119. https://doi.org/10.1007/s11120-013-9891-5Monda et al. (2016) Plant Physiol, 170: 1435–1444Muir et al. (2016) Genetics. 2014 Dec; 198(4): 1629–1643.