extfieldsofstudy:
Computer Science
Medicine

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Automated macrophage counting in DLBCL tissue samples: a ROF filter based approach

Background For analysis of the tumor microenvironment in diffuse large B-cell lymphoma (DLBCL) tissue samples, it is desirable to obtain information about counts and distribution of different macrophage subtypes. Until now, macrophage counts are mostly inferred from gene expression analysis of whole tissue sections, providing only indirect information. Direct analysis of immunohistochemically (IHC) fluorescence stained tissue samples is confronted with several difficulties, e.g. high variability of shape and size of target macrophages and strongly inhomogeneous intensity of staining. Consequently, application of commercial software is largely restricted to very rough analysis modes, and most macrophage counts are still obtained by manual counting in microarrays or high power fields, thus failing to represent the heterogeneity of tumor microenvironment adequately. Methods We describe a Rudin-Osher-Fatemi (ROF) filter based segmentation approach for whole tissue samples, combining floating intensity thresholding and rule-based feature detection. Method is validated against manual counts and compared with two commercial software kits (Tissue Studio 64, Definiens AG, and Halo, Indica Labs) and a straightforward machine-learning approach in a set of 50 test images. Further, the novel method and both commercial packages are applied to a set of 44 whole tissue sections. Outputs are compared with gene expression data available for the same tissue samples. Finally, the ROF based method is applied to 44 expert-specified tumor subregions for testing selection and subsampling strategies. Results Among all tested methods, the novel approach is best correlated with manual count (0.9297). Automated detection of evaluation subregions proved to be fully reliable. Comparison with gene expression data obtained for the same tissue samples reveals only moderate to low correlation levels. Subsampling within tumor subregions is possible with results almost identical to full sampling. Mean macrophage size in tumor subregions is 152.5±111.3 μm2. Conclusions ROF based approach is successfully applied to detection of IHC stained macrophages in DLBCL tissue samples. The method competes well with existing commercial software kits. In difference to them, it is fully automated, externally repeatable, independent on training data and completely documented. Comparison with gene expression data indicates that image morphometry constitutes an independent source of information about antibody-polarized macrophage occurence and distribution. Electronic supplementary material The online version of this article (10.1186/s12575-019-0098-9) contains supplementary material, which is available to authorized users.

Background
Diffuse large B-cell lymphoma (DLBCL), the most frequent mature aggressive B-cell lymphoma in adults, is characterized by very heterogeneous pathological, clinical, and biological features [1]. Additionally to the neoplastic B-cells, cancerous tissue contains high numbers of various subsets of T-cells, macrophages, mast cells and stromal cells [1,2]. The composition of this tumor microenvironment has attracted considerable interest since it turned out to affect the clinical outcome. Besides of overall histological inspection, it has been largely investigated by molecular procedures as gene expression profiling (GEP) [3,4] as well as by morphometric image analysis [5,6]. Based on GEP results, two biologically and clinically distinct molecular subtypes of DLBCL were identified, namely activated B-cell-like subtype (ABC) and germinal center B-cell-like subtype (GCB) [7,8], the latter being associated with a favorable prognosis. Prognostic effects by different signatures of the tumor microenvironment were also found by Lenz et al. [9]. In particular, a signature associated with increased overall survival included components of the extracellular matrix and genes that are characteristically expressed in cells from the monocytic lineage.
An important component of tumor microenvironment are infiltrating tumor-associated macrophages (TAMs). As yet, the role of TAMs and their possible importance for prognosis is a controversially discussed item. Although TAMs have been associated with immunomodulation in other tumor entities [10,11], their functional role in the DLBCL tumor microenvironment is still not fully defined [12][13][14][15]. A typical marker used for its identification is CD163. In the present study, besides of CD163, we use CD14 as a further specific marker for monocytes and macrophages. The choice of this particular marker pair has been motivated by the intention of future testing whether the ratio of CD14/CD163 could be used as a prognostic factor for clinical outcome in DLBCL patients.
Until now, macrophage counts are either inferred from GEP analysis of whole tissue sections or by manual counting in immunohistochemically (IHC) fluorescence stained tissue microarrays (TMA) or high-power fields (HPF) [16,17]. However, due to the heterogeneity of the tumor microenvironment, counts within TMAs and HPFs cannot be considered as representative. Consequently, morphometric image analysis and related macrophage counting should be performed for whole IHC stained tissue slides instead of for small subareals.
For several reasons, fully automated counting of IHC stained macrophages within tissue sections is still a difficult task [18][19][20]. First, the size and shape of the macrophages are highly variable, thus largely impeding a recognition by prior shape information. Second, the intensity of the staining shows a large variation as well, even within a single tissue sample or for different parts of a single macrophage. Third, we must deal with cropped or squeezed cells as well as with macrophages located outside the focal plane, appearing as defocused features within the images. Further, as far as fluorescent-labeled antibodies are used, we must cope with autofluorescence of other structures, e.g. erythrocytes, in the tissue. For these reasons, the most popular strategies for cell segmentation [21], i.e. (fixed or adaptive) intensity thresholding and elementary feature detection, as implemented in most commercial software kits, will be confronted with serious difficulties when applied to macrophage segmentation.
In the present study, therefore, we describe a novel ROF filter based segmentation approach, which allows for fully automated macrophage counting in whole tissue sections, and avoids the above mentioned difficulties, at least in part. More precisely, we will combine a strategy of floating intensity thresholding with a rule-based feature detection in single-channel images. The latter has been suggested e.g. in Steiner et al. [22] for detection of IHC stained leukocytes. Our method is deterministic, fully automated, externally repeatable (no dependence on training data) and -in difference to most commercial software packages -completely documented. It will be validated against manual macrophage counts in a set of 50 test images.
Further, our novel method will be compared with different existing segmentation approaches. For the mentioned test image set, we perform a comparison with the output of two commercial cell segmentation software kits (Tissue Studio 64, Definiens AG, Munich, Germany, and Halo, Indica Labs, Corrales, New Mexico, USA) as well as with a straightforward machine-learning approach (training and application of a region-based convolutional neural network). Next, our method and both commercial packages will be applied to a set of 44 whole tissue sections, and outputs will be compared with each other as well as with GEP data available for the same tissue samples. In a final step, the ROF based segmentation approach will be applied to 44 expert-specified tumor subregions for testing selection and subsampling strategies. To the best of the authors' knowledge, a comparative analysis of automated macrophage segmentation approaches is being conducted for the first time.

Methods
Preparation and staining of tissue samples 44 biopsy specimens of DLBCL were selected from the files of the Lymph Node Registry Kiel based on availability of material. Core needle biopsies were excluded. Formalin-fixed paraffin-embedded (FFPE) tissue was sliced into 2 μm thin slides and, additionally to a conventional HE-staining, an immunohistochemical staining was done with antibodies against CD14 (Clone EPR3653; Cell Marque, Rocklin, CA, USA; 1:10) and CD163 (Clone 10D6; Novocastra, Leica Biosystems, Wetzlar, Germany; 1:100). Briefly, after deparaffinization in xylene and rehydration in alcohol, tissue sections were incubated for 3 min in citrate buffer (pH 6) within a pressure cooker. The slides were washed in PBS and then incubated for 1 h with a mixture of the primary antibodies in antibodydiluent (medac GmbH, Wedel, Germany). After incubation with the primary antibodies, the sections were washed in PBS and then incubated with a mixture of the secondary fluorescent-labeled antibodies in PBS for 1 h. As secondary antibodies, donkey anti rabbit Alexa 488 and donkey anti mouse Alexa 555 were used (both from Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA; 1:100). After washing in PBS the slices were incubated with DAPI (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA; 1:5000) for 2 min, washed in PBS and cover-slipped with mounting medium. Use of tissue was in accordance with the guidelines of the internal review board of the Medical Faculty of the Christian-Albrechts-University Kiel, Germany (No. 447/10).

Image acquisition, selection of tumor subregions and ROIs
Images were generated by Hamamatsu Nanozoomer 2.0 RS slide scanner (Hamamatsu Photonics, Ammersee, Germany) with 20 × magnification. For every fluorescent immunostained tissue slide, the whole tissue sample as well as a tumor subregion were imaged, resulting in single images for the Alexa 488, Alexa 555, and DAPI channel, respectively, and an overlay picture of the channels. Raw image data were saved in .ndpi format (single-channel images) or .ndpis format (overlay image), respectively. Pixel size is 0.45 μm × 0.45 μm in all images.
In order to select a tumor subregion within a whole tissue sample, the tumor area was defined and marked by a pathologist by inspection of the HE-stained slice. Subsequently, within the immunostained slice, a suitable subregion of the tumor area not larger than 10 mm 2 has been selected depending on tissue and staining quality (no tissue artifacts, no scratches or folding in the tissue, no overstaining) and captured. The position of the selected tumor subregion has been marked within the raw data by use of the software kit NDP.view 2 (Hamamatsu Photonics, Ammersee, Germany), which is available as freeware [23].
From 25 randomly selected tumor subregions, ROIs of 900 × 600 px (0.109 mm 2 ) size for manual counting and comparison of image analysis methods have been singled out (CD14 + /488 nm and CD163 + /555 nm channels). Note that the ROIs have been selected under the viewpoint of reflecting the several difficulties of automated macrophage recognition, see Fig. 1.
In order to prepare the scans for image analysis, raw data were converted into uncompressed .tif format and, in Fully automated ROF filter based segmentation a) Method description. The described method originates as a substantial further development of the approach presented in Bredies et al. [25], where IHC stained photoreceptor segmentation was performed with data-dependent but fixed intensity thresholding and without application of geometric rules for feature segmentation. Some of the steps described below are visualized in Fig. 2.
After initialization of the parameters (Step 0), subtraction of a median-filtered version I (1) from the original image I (0) (Step 1), which results in a brightnessnormalized, unsharply masked image I (2) = max ( I (0) − I (1) , 0 ), we apply the Rudin-Osher-Fatemi (ROF) filter [26] (Step 2), ending up with I (3) . ROF filtering constitutes a well-established standard procedure in image processing, resulting in a sligthly coarsened, cartoon-like version of the input image which, nevertheless, conserves the original edge structure. The procedure allows for a surprisingly efficient numerical realization [27], pp. 175 ff.
Steps 0 − 2 are analogous to the algorithm described in Bredies et al. [25]. We refer to the appendix of this paper for an outline of the mathematical background of the ROF approach.
Next, we extract the evaluation subregion to which the macrophage segmentation has to be applied (i.e., the part of the image where tissue is present). For this purpose, we apply Steps 1 and 2 to the DAPI image, which is available together with I (0) . From the obtained DAPI cartoon, we generate a black-and-white mask I eval by masking all pixels with intensity less than 10 at 8bit scale with black and covering every remaining pixel with a white 31 × 31 px square centered at the given position (Step 3). In the case of application of the method to the ROIs, this step is being skipped, and the evaluation subregion is assumed to coincide with the ROI image as a whole. Note that, in difference to the following step, the application of a fixed threshold is possible due to the much more regular structure of the DAPI image. The threshold value has been experimentally chosen.
In difference to [25], the cartoon I (3) will be segmented with a floating intensity threshold instead of a fixed one, and features will be identified as macrophages by application of a set of several geometrical rules. This subprocedure, which has been newly developed, will be described in more detail. For the geometrical description of a feature F, we employ the following variables: the size s(F) of the feature itself, the size c(F) of the convex hull of the feature, the ratio r(F) of the principal axes' lengths of the smallest ellipse covering the feature, the perimeter p 1 (F) of the feature and the perimeter p 2 (F) of a circle with equal area to the feature F. Further, we define the parameters s min and s max -minimal and maximal feature size (in px), c min -minimal area excess of the convex hull (in percent), r max -maximal ratio of axes, and p max -maximal excess of the feature perimeter p 1 when compared with the perimeter of a circle with equal area p 2 .
We start at the intensity threshold i, which will be given as the mean intensity of I (3) , rounded to the next integer value, and the feature mask I (3) (i) := I (3) . Using I eval , we mask in I (3) (i) all pixels outside the obtained evaluation subregion (Step 4). Now we perform the first segmentation step by masking in I (3) (i) all pixels with intensity less than i, subsequent labeling (Step 5) and inspecting the connected features F j , j = 1 , ... , N(i), in I (3) (i) (Step 6). Each of the features F j will be classified by the following rules.
1) If s max < s(F j ) then do nothing, reserving the too large feature for further analysis with incremented intensity threshold (Step 7). 2) If s(F j ) < s min then neglect the feature as too small and mask it in I (3) 3) If s min ≤ s(F j ) ≤ s max then test whether the feature satisfies . d -Feature classification in c (Steps 7 − 9): saved by Rule 1 for further processing (yellow); excluded by Rule 2 (red), Rule 3a (purple) or Rule 3b (pink); accepted as macrophages (green). Rule 3c caused no exclusions here. e -Features to be examinated in b after masking with incremented threshold i = 4 (white); pixels saved in d but masked now (grey) (Step 10). f -Feature classification in e, color encoding as before. Rule 3c caused no exclusions again all of the following three criteria: 3a) c(F j )/s(F j ) ≥ 1 + c min /100 (the feature is not too round), 3b) r(F j ) ≤ r max (the feature is not too elongated), and 3c) p 1 (F j )/p 2 (F j ) ≤ p max (the feature's boundary is regular enough). If yes, save the feature F j into the output mask I segm , interpreting it as macrophage, and mask it in I (3) (i). If at least one of the three criteria fails then neglect the feature and mask it in I (3) (i) as well (Steps 9 and 10).
As a result of the classification, we end up with a masked version I (3) (i) of the cartoon and (possibly) a set of features to be interpreted as macrophages, written into the output mask I segm . Now the segmentation step is repeated with incremented intensity threshold i = i + 1, further application of masking to I (3) Step 11) and geometrical analysis of the remaining features. Thus we repeat subsequent segmentation steps until the maximal intensity is reached. The complete algorithm is summarized in Fig. 3 again. b) Input, output and implementation. As input for the method, a single-channel greyscale image is required. In the case of whole tissue samples and tumor subregions, the related greyscale DAPI image must be provided as well. The output of the procedure are three black-and-white masks. I eval , the first one, contains the evaluation subregion. Into I segm , all detected macrophages are plotted as white features which are, as a consequence of the organization of the processing steps, mutually disjoint, see Fig. 4c. Into the third mask I conv , we plot all convex hulls conv (F) of the detected macrophages F. All result images are of the same size as the input image. Further, the method provides the total area of the evaluated subregion marked in I eval , the number of features in I segm as macrophage count and the total area marked in I conv , i.e. the cumulative area of the convex hulls of the obtained features, as macrophage area. We refer to the obtained count as to method (S1) and to the obtained cumulative area as to method (S2).
The algorithm has been implemented as a series of MATLAB procedures. They have been tested on MAT-LAB 9.4.0.813654 (R2018a) and require the MATLAB Image Processing Toolbox [28,29]. For the ROF filtering in Step 2, the numerical method from [30] is applied. The window size for the median filter (31×31 px) as well as the internal parameters of the ROF filtering are being fixed from the outset. The geometrical parameters from Steps 7 − 9 must be initialized as well. For the analysis of the ROIs, we used s min = 140, s max = 800, c min = 7.5, r max = 3 and p max = 2. For the analysis of the whole tissue samples and the tumor subregions, we set the parameters to s min = 160, s max = 1500, c min = 7.5, r max = 3 and p max = 2.5.
The parameter s min has been set above 140 px in order to exclude the misidentification of erythrocytes (with a mean diameter of about 6 μm and a corresponding mean area of ca. 100 px) as (parts of ) macrophages. The setting of s max is well in agreement with the mean macrophage area reported in the "Results" section below. The values of the parameters c min , r max and p max have been experimentally found. No particular attempts for performance tuning have been made.
Let us remark that dependency on proprietary software can be completely removed, e.g., by reimplementation of the ROF segmentation procedures in the freeware environment OCTAVE [31].
c) Availability and usage. We made the MATLAB procedures publicly accessible (CC0 1.0 Universal Public Domain Dedication or GNU General Public License v3) at the Leipzig Health Atlas repository under the address [32]. Execution assumes that a single image set, consisting of three greyscale images representing the CD14 + /488 nm, CD163 + /555 nm and DAPI channels, as well as the procedures are stored in the MAT-LAB working directory. Output images and logfile will be saved at the same location. To start the analysis, type rof_segm_public_step_00_masterfile, which subsequently calls the other procedures, within the MAT-LAB command window. You will be asked to enter the image filenames and to confirm the parameter settings. Progress of segmentation can be traced by display messages. Parameters are set by default to the values used for the analysis of the whole tissue samples and the tumor subregions as described in the subsection above. They can be changed within the file rof_segm_public_step_01_parameters.m. Modification of the basic procedure in order to enforce batch processing may be easily effected but is left to the user as it depends strongly on the particular structure of the dataset to be analyzed.

Other segmentation methods
Mutual comparison of the segmentation methods a) Manual count as reference basis. Within single channel images of the ROIs (at CD14 + /488 nm and CD163 + /555 nm), macrophage cells were marked with a 3 × 3 px cross and manually counted (see Fig. 4b, wherein, for better visibility, the cross-shaped detection marks have been replaced by squares centered at the same pixel). Tags have been saved into a black-and-white mask of equal size as the original image. We refer to the manual count as to method (MC). b) Method comparison by means of the ROIs. To the ROI image set, segmentation methods (S1) − (S6) have been applied and subsequently compared. For this comparison, the relative error turns out to be an inadequate measure. Indeed, since manual counts range from 8 to 311 macrophages per ROI, the relative error would vary from 0.32 % to 12.5 % per erroneously counted single feature, thus considerably overweighing errors made within ROIs with small macrophage numbers. Instead, we will use the Pearson correlation coefficients between the methods' outputs for the complete sample of ROIs. Since the manual count as reference method gives no information about the area of the tagged cells, this measure has the further advantage to allow for an immediate comparison of count or area information without the necessity of a normalization of the latter.
For (S1) and (S5), we will further provide the percentage of manually counted macrophages which are exactly matched by the output of the respective method. Due to the reasons mentioned in the "Background" section, the relation between a detected feature and a manually tagged macrophage is to be considered as a matching not only in the case if the marking cross falls inside the convex hull of the detected feature. A matching is given nonetheless if the tag and the convex hull of the feature are mutually disjoint but visual inspection reveals that the convex hull covers the marked macrophage at least partly. c) Method comparison by means of the whole samples. To the whole samples, methods (S1) − (S4) have been applied and subsequently compared. We provide first the Pearson correlation coefficients for the methods' output for the CD14 + /488 nm and CD163 + /555 nm channels. Since, however, the evaluation subregions as well as the overall density of cells contained within them show considerable variation between the samples, the outputs will be appropriately normalized and then compared again. As normalizations for (S1), we calculate the density, which is given as total macrophage count divided by area of evaluation subregion, cf. Step 3 of Algorithm 1 above, as well as the cell percentage, which is given as total macrophage count diveded by estimated total number of cell nuclei within the evaluation subregion. The latter is obtained from the cartoon of the DAPI channel by masking all pixels with intensity less than 10 and dividing the number of the remaining pixels by 100. As normalizations for (S2) − (S4), we calculate the area percentages, which are given as cumulative macrophage area divided by the area of the corresponding evaluation subregion.
We consider a feature detected within the CD14 + / 488 nm channel as double-stained if at least 20 % of the area of its convex hull is covered by convex hulls of some features detected within corresponding CD163 + /555 nm channel image. Note that the presence of a double staining does not influence the detection of a feature by methods (S1) − (S4) since the channels are analyzed independently from each other. However, the more completely and uniformly a given macrophage is stained, the more probable is the recognition of a possible double staining. d) Analysis of tumor subregions. The tumor subregions have been analyzed with method (S1) only. Here, we will compare the full output with its 50 % and 25 % downsampling, considering only one half or one quarter of the tiles of the given tumor subregion dataset for evaluation. Further, we provide a comparison with the outputs of (S1) and (GE) for the corresponding whole tissue sample. The analysis is repeated with the normalized outputs of (S1), calculated as densities. All comparisons will be given in terms of Pearson correlation. Moreover, the percentage of double-stained features according to the above given definition will be recorded. Finally, we characterize the distribution of the feature sizes, which will be derived from the analysis of the CD14 + /488 nm channel. Frequencies are obtained by counting up all features of a given size and subpopulation over the outputs for all 44 datasets.

Comparison with gene expression data for the whole samples
Digital-multiplexed gene expression (DMGE) profiling was performed with the nCounter platform (NanoString, Seattle, OR, USA), targeting the genes of interest by digitally color-coded oligonucleotides. For a detailed description of the procedure, see [39,40]. The data were further processed and normalized by the following three steps. First, we performed quality controls using the R package NanoStringQCPro [41]. Here, four samples were flagged and removed from subsequent analysis. Second, we added a pseudo count and normalized the data by dividing sample-wise through the geometric mean of the housekeeper genes (B2M, MTMR14, PGK1, ABCF1, EIF2B4, LDHA, CTCF, TBP, WDR55, POLR2B), and third, we multiplied the data with a factor of 1000 to bring them on a natural scale. We refer to the normalized gene expression values as to method (GE). Below, the normalized counts will be compared with the outputs of image morphometry in terms of Pearson correlation coefficients.

Summary of methods' application
In Tables 1 and 2, we provide a summary of the properties of the described macrophage counting approaches and Abbreviations: (MC) -manual count, (S1) -automated macrophage count from ROF filter based segmentation approach, (S2) -cumulative macrophage area from ROF filter based segmentation approach, (S3) -cumulative macrophage area from Tissue Studio software, (S4) -cumulative macrophage area from Halo software, (S5) -automated macrophage count from Mask R-CNN machine learning approach, (S6) -cumulative macrophage area from Mask R-CNN machine learning approach, (GE) -normalized gene expression values from nCounter platform the experiments performed with them. Note that, for the whole tissue samples, comparison of results of (S1) − (S4) is possible for 40 datasets, and of (S1) − (S4) and (GE) for 35 datasets while (S5) and (S6) have not been applied.

Application to ROIs
a) Application of segmentation methods and its mutual correlation. First, we present the results of the methods' application to the ROIs. In Table 3, we describe the parameters of the outputs (minimal/maximal value, mean, median, standard deviation). Calculation comprises all 50 ROIs for (MC), (S1) − (S4) and a subset of 35 ROIs for (S5) − (S6) while the remaining 15 images have been used for the generation of training and validation data. Table 4 contains the survey of the Pearson correlation coefficients between manual count (MC) and output of methods (S1) − (S6). Again, the mutual correlations between (MC), (S1) − (S4) have been calculated on the base of the complete ROI dataset while correlations involving (S5) and (S6) are calculated on the subset of 35 ROIs where the outputs of the latter were available. Complete results of methods' application to the ROIs are provided in Additional file 1.
We observe that the ROF filter based segmentation method (S1) shows the best correlation with the manual count (MC), namely 0.9297. This correlation is slightly better than (S3) and (S2) and clearly superior to (S4), (S5) and (S6). The relative order of the correlations between (S1) − (S4) is 0.9661 : 0.8901 : 0.6898. b) Exact matching of manually counted macrophages. In Table 5, we provide the analysis of exact feature matchings between (MC) − (S1) resp. (MC) − (S5). Here, the total number of macrophages counted in (MC) is summed up over all 50 ROIs for the comparison with (S1) (column 2) and over the 35 ROIs available for analysis with (S5) (column 5).  Application to whole tissue samples a) Mutual correlation between segmentation methods. For the application of (S1) − (S4) to the whole tissue samples, we compare first the obtained evaluation subregions in terms of Pearson correlation coefficients, see Table 6. For (S1), we include the estimated number of cell nuclei as well.
In Table 7, we show the Pearson correlation coefficients between the outputs of methods (S1) − (S4) and the gene expression data (GE) for the CD14 + /488 nm and CD163 + /555 nm channels, respectively. In Table 8, we repeat the survey with the normalized outputs of (S1) − (S4). Calculations comprise 40 datasets for the mutual correlations between (S1) − (S4) and 35 datasets for correlations involving (GE).
Macrophage densities, as observed by (S1) in all 44 datasets, range from 353.6 to 1374.6 cells/mm 2 with a mean of 847.9 ± 269.3 cells/mm 2 for the CD14 + /488 nm channel, and from 325.7 to 1715.4 cells/mm 2 with a mean of 833.9 ± 328.2 cells/mm 2 for the CD163 + /555 nm channel. Macrophage cell percentages resulting from (S1) range from 2.42 % to 11.29 % with a mean of 5.56 ± 2.05 % for the CD14 + /488 nm channel, and from 2.23 % to 10.87 % with a mean of 5.47 ± 2.35 % for the CD163 + /555 nm channel. Complete results of methods' application to whole tissue samples are provided in Additional file 2.
b) Correlation with gene expression data. In Tables 7 and 8, (GE) is correlated with the output of (S1) with Table 4 Correlation between segmentation methods (ROIs)  Table 7, column 4, this is the best value, while in Table 7, column 9, and Table 8, methods (S3), (S2) and (S2) are slightly better correlated with coefficients of 0.7099, 0.6184 and 0.7924, respectively. Otherwise, correlation between (GE) and the commercial software kits (S3) and (S4) is rather poor. c) Double-stained features. In the output of (S1), we observed considerable numbers of double-stained features. Percentages range from 25.72 % to 77.68 % of the detected CD14-positive macrophages within a single dataset bearing CD163-positive staining as well. In the mean, 55.51 % of the macrophages per dataset detected by (S1) were double-stained.

Application to tumor subregions
a) Results of subsampling. In Table 9, we show the Pearson correlation coefficients between the output of methods (S1) and (GE) for the whole tissue samples and the output of (S1) for the respective tumor subregions selected within them, subjected to 100 %, 50 % and 25 % sampling rate. In Table 10, we repeat the analysis with the macrophage densities instead of the counts. Calculations comprise 44 datasets for the mutual comparisons of (S1) and 37 datasets for the comparison with (GE). Note that the correlations between (S1) and (GE) in Tables 9 and 10 differ slightly from those in Tables 7 and 8 because of additional data involved in the calculation of the latter (37 instead of 35 datasets).
Macrophage densities, as observed by (S1) in all 44 fully evaluated datasets, range from 463.3 to 1574.9 cells/mm 2 with a mean of 907.7 ± 325.3 cells/ mm 2 for the CD14 + /488 nm channel, and from 371.3 to 1758.9 cells/mm 2 with a mean of 836.9 ± 376.9 cells /mm 2 for the CD163 + /555 nm channel. Macrophage cell percentages resulting from (S1) range from 2.17 % to 13.99 % with a mean of 5.93 ± 2.62 % for the CD14 + /488 nm channel, and from 1.98 % to 14.36 % with a mean of 5.46 ± 2.82 % for the CD163 + /555 nm channel.
Complete results of application of (S1) to tumor subregions are provided in Additional file 3.
b) Double-stained features. As to expect from our observations for the whole tissue samples above, double-stained features are fairly common in the output of (S1). Percentages range from 25.37 % to 75.95 % per fully evaluated dataset, with a mean percentage of 53.41 %.
c) Distribution of feature sizes. Within the counts of features and convex hulls of features, we distinguish subpopulations with or without double staining. The properties of the obtained distributions (minimal/maximal value, mean, median, standard deviation, 95 % quantil) are summarized in Table 11. All feature sizes are given in px. The minimal feature sizes result from the choice of parameters s min = 160 and c min = 7.5, the maximal feature sizes in columns 2 − 4 reflect the setting of the parameter s max = 1500. Figure 5 shows the histogram of the feature sizes.
From Table 11, we observe a mean macrophage size of 152.5 ± 111.3 μm 2 . For the single-stained subpopulation, the mean size is 133.6 ± 101.5 μm 2 , slightly differing from the double-stained subpopulation with a mean size of 167.9 ± 116.5 μm 2 .

Discussion
• Our results show that the ROF filter based segmentation method (S1) may be considered as fairly reliable and well-comparable with with other existing methods. Besides of showing the best correlation with the manual count (MC), the mean and median of (S1) and (MC) are closely related. Further, we see that the automated determination of evaluation subregions in (S1)/(S2) based on DAPI channel information is fully reliable. The relative order of correlations between (S1) − (S4) is comparable for the applications to ROIs and whole tissue samples. Our results further indicate that the different normalizations of (S1) (density and cell percentage) contain different information and must be indeed distinguished. As to expect, the percentage of exact matches between the features detected by (S1) and manually counted macrophages is lower than in situations where more regular shaped and uniformly stained cells are targeted. In view of the difficulties described in the "Background" section, the absolute and relative percentages of 66.1 % and 76.1 % of exactly matched macrophages, respectively, although moderately underestimating the absolute number of macrophages, are still fairly large. For large numbers of macrophages, cell counts by (S1) and area determination by (S2) turn out to be largely equivalent. Of course, within the outputs of method (S1), one may observe the typical errors in automated cell counting, which would be avoided by a human examiner (cf. [25], p. 11, Fig. 4). While, on the one hand, tightly packed and uniformly stained macrophages may be lumped into a single feature, nonuniform staining of single macrophages may cause, on the other hand, a "breaking" of the cell image, resulting in a double or multiple count. For the same reason, many macrophages will be recognized only partly, thus be properly counted but inaccurately masked. The setting of the parameter s max may exclude large single macrophages or aggregates of squeezed macrophages from counting. Background structures may be misidentified as macrophages as well.
Nevertheless, method (S1) shows considerable robustness when dealing with scratches, folds, overstainings or splatters of staining liquid (which were excluded when selecting ROIs and tumor subsections but are present in the whole tissue samples). In Fig. 6, some typical examples are shown.
For the obtained cell counts, no stereological corrections [42] have been applied since the mean size of target macrophages largely exceeds the thickness of tissue slides.
• The application of commercial software kits to macrophage segmentation is confronted with serious difficulties. The above described selection of the analysis modes and parameters has been performed to the best of the authors' experience. In particular, due to the heterogeneity of the data, the use of fixed thresholds turned out to be inappropriate. For the same reasons, we refrained from the application of cell counting modes with prior nucleus detection (based on synchronous DAPI staining of the samples) and subsequent colocalization of stained area around the nuclei. As a consequence, we must restrict ourselves to detection modes analyzing the stained area in single-channel fluorescence images, and the necessity of repeated manual interventions for parameter adaptation had to be accepted. Even under these preconditions, both software packages cope very poorly with artifacts in tissue preservation (typical examples are shown in Fig. 6). For the analysis of whole tissue samples Nos. 23 and 35, both suffering from overstaining and widespread presence of erythrocytes, application of (S4) (in the above described analysis mode) failed at all. Let us further remark that our results reveal a considerable disagreement between the outputs of both commercial software kits with a correlation of 0.7719 for the ROIs and correlations ranging from 0.4077 to 0.7612 for the whole tissue samples.
Compared with the commercial software kits applied in this study, the ROF filter based segmentation method has the advantages of full automatization, complete documentation of the algorithm and exact repeatability. Tissue preservation artifacts are handled in a much more robust way. Moreover, shapes, sizes, positions and colocalization of macrophages can be observed from the method's output.
• Straightforward application of the Mask R-CNN machine learning approach (S5)/(S6) leaded to very poor results in terms of correlation with (MC) as well as of the absolute percentage of exact matches between (MC) and (S5). The relative percentage of artifacts (6.7 %) generated by (S5), however, is considerably lower than in (S1). Nevertheless, although we used the common ratio of 20 %:10 %:70 % between training, validation and analysis data, it is obvious that the application of the neural network suffered from a strong deficiency of training items. As a consequence, we refrained from an application of (S5)/(S6) to whole tissue samples.
The window size for the training items has been selected in agreement with the mean macrophage area observed in Table 11.
• For the whole tissue samples as well as for the tumor subregions, correlation coefficients for the CD163 staining are slightly larger than for CD14 staining for all surveyed methods. This observation may be explained by the fact that the CD14 staining appears weaker than the CD163 staining. In general, such differences depend on the distribution of the epitop on the cell surface and the binding  Table 9 Correlations under subsampling (whole tissue samples and tumor subregions) CD14 + /488 nm channel CD163 + /555 nm channel (S1) (GE) (S1) (S1) (S1) (S1) (GE) (S1) (S1) (S1) of the primary antibody. Experiments during the staining process revealed that the combination of the primary antibody CD14 with the fluorophore Alexa 488 resulted in the clearest possible images. With regard to the possible nonuniformity of the staining of single macrophages, it is obvious that the distribution of the macrophage sizes should be observed from the convex hulls of the features rather than from the features themselves. The slighty increased mean size of the doublestained subpopulation may simply reflect the fact that the detection of a double staining is less probable for small cell fragments, dissected or cropped cells.
Subsampling within the tumor subregions leads to almost perfectly correlated results, which are mutually correlated with coefficients greater than 0.99. On the other hand, the discrepancies between the counts and densities obtained for the whole tissue samples and the tumor subregions cannot be neglected.
• In general, comparison between image morphometry and gene expression analysis reveals moderate to low correlation levels, regardless whether (GE) is compared with (S1)/(S2) or with the outputs of the commercial software kits (S3) and (S4). Further, we may observe that normalization of the outputs of (S1) − (S4) improves the correlations to a moderate level at best, and that correlations for the CD163 staining/expression are better than those for the CD14 staining/expression.
If tumor subregions are piloted instead of whole tissue samples, correlations shift in a nonuniform way without a considerable improvement.
• We may conclude that the ROF filter based segmentation method constitutes a solid approach to obtain reliable counts and distributions for different macrophage types in IHC stained whole tissue samples. Compared with counts of high power fields, the new method provides an easy access to a complete representation of the heterogeneous tumor microenvironment. In terms of Pearson correlation, results of gene expression profiling are not reproduced by morphometrical image analysis. In difference to GEP, ROF filter based segmentation is able to identify and to count doublelabeled macrophages, thus enabling the study of diverse macrophage subpopulations. Moreover, the method allows for a systematic study of the local distribution of the macrophages, thus enabling subsequent investigations of Table 10 Correlations under subsampling (whole tissue samples and tumor subregions), normalized outputs CD14 + /488 nm channel CD163 + /555 nm channel (S1) (GE) (S1) (S1) (S1) (S1) (GE) (S1) (S1) (S1)  macrophage clustering and applications of point pattern statistics.
As a future challenge, the detailed information about macrophage counts and distribution obtained by the ROF filter based segmentation method has to be tested for its prognostic potential in different lymphoma diseases. In a first step, we carried out a clinical application of the ROF method to a large cohort of DLBCL patients (N > 400). Based on IHC stained TMAs, image data for the Alexa 488, Alexa 555 and DAPI channels were generated by the same protocol as described above. These images have been analyzed in full analogy to the tumor subsections, obtaining counts and densities for CD14-and CD163positive macrophages, to be investigated for possible correlations with the documented clinical outcome. Again, we observed a fairly robust behaviour of the method, coping well with folds, scratches and overstainings in the tissue cores. Results will be reserved for a forthcoming publication.

Conclusions
To the detection of IHC stained macrophages (CD14, CD163) in DLBCL tissue samples, a ROF filter based segmentation method has been successfully applied. The method, providing number, area, shape, and location of stained macrophages, is deterministic, fully automated, externally repeatable, independent on training data as well as on particular markers and completely documented. Comparison of macrophage counts obtained by ROF filter based segmentation with gene expression data reveals only moderate levels of correlation, thus indicating that image morphometry constitutes an independent source of information about antibody-polarized macrophage occurence and distribution.   (42,16), CD163 + /555 nm), contrast enhanced by factor 1.5, scale bar 45 μm. b -Result of (S1), (S2); vessel as a hyperfluorescent feature removed, erythrocytes partly ignored. c -Result of (S3), contrast enhanced by factor 1.5; vessel erroneously marked as target area, erythrocytes partly ignored. d -Result of (S4), contrast enhanced by factor 1.5; vessel as well as erythrocytes erroneously marked as target area. e -h Sample with tissue fold. e -Original single-channel image (whole tissue sample No. 31, cutout from tile No. (3,20), CD14 + /488 nm), contrast enhanced by factor 2, scale bar 45 μm. f -Result of (S1), (S2); fold as a strongly fluorescent feature removed, macrophages under the fold partly detected. g -Result of (S3), contrast enhanced by factor 2; fold erroneously marked as target area. h -Result of (S4), contrast enhanced by factor 2; fold erroneously marked as target area. i -l Sample with staining artifact (splatter of staining liquid). i -Original single-channel image (whole tissue sample No. 11, cutout from tile No. (18,21), CD163 + /555 nm), scale bar 45 μm. j -Result of (S1), (S2); splatter as a hyperfluorescent feature removed, macrophages close to its border properly detected. k -Result of (S3); splatter erroneously marked as target area. l -Result of (S4); splatter erroneously marked as target area


extfieldsofstudy: Medicine Biology

text:
A geminivirus betasatellite encoded βC1 protein interacts with PsbP and subverts PsbP‐mediated antiviral defence in plants

Summary Geminivirus disease complexes potentially interfere with plants physiology and cause disastrous effects on a wide range of economically important crops throughout the world. Diverse geminivirus betasatellite associations exacerbate the epidemic threat for global food security. Our previous study showed that βC1, the pathogenicity determinant of geminivirus betasatellites induce symptom development by disrupting the ultrastructure and function of chloroplasts. Here we explored the betasatellite‐virus‐chloroplast interaction in the scope of viral pathogenesis as well as plant defence responses, using Nicotiana benthamiana—Radish leaf curl betasatellite (RaLCB) as the model system. We have shown an interaction between RaLCB‐encoded βC1 and one of the extrinsic subunit proteins of oxygen‐evolving complex of photosystem II both in vitro and in vivo. Further, we demonstrate a novel function of the Nicotiana benthamiana oxygen‐evolving enhancer protein 2 (PsbP), in that it binds DNA, including geminivirus DNA. Transient silencing of PsbP in N. benthamiana plants enhances pathogenicity and viral DNA accumulation. Overexpression of PsbP impedes disease development during the early phase of infection, suggesting that PsbP is involved in generation of defence response during geminivirus infection. In addition, βC1‐PsbP interaction hampers non‐specific binding of PsbP to the geminivirus DNA. Our findings suggest that betasatellite‐encoded βC1 protein accomplishes counter‐defence by physical interaction with PsbP reducing the ability of PsbP to bind geminivirus DNA to establish infection.

SUMMARY
Geminivirus disease complexes potentially interfere with plants physiology and cause disastrous effects on a wide range of economically important crops throughout the world. Diverse geminivirus betasatellite associations exacerbate the epidemic threat for global food security. Our previous study showed that βC1, the pathogenicity determinant of geminivirus betasatellites induce symptom development by disrupting the ultrastructure and function of chloroplasts. Here we explored the betasatellitevirus-chloroplast interaction in the scope of viral pathogenesis as well as plant defence responses, using Nicotiana benthamiana-Radish leaf curl betasatellite (RaLCB) as the model system. We have shown an interaction between RaLCB-encoded βC1 and one of the extrinsic subunit proteins of oxygen-evolving complex of photosystem II both in vitro and in vivo. Further, we demonstrate a novel function of the Nicotiana benthamiana oxygenevolving enhancer protein 2 (PsbP), in that it binds DNA, including geminivirus DNA. Transient silencing of PsbP in N. benthamiana plants enhances pathogenicity and viral DNA accumulation. Overexpression of PsbP impedes disease development during the early phase of infection, suggesting that PsbP is involved in generation of defence response during geminivirus infection. In addition, βC1-PsbP interaction hampers non-specific binding of PsbP to the geminivirus DNA. Our findings suggest that betasatellite-encoded βC1 protein accomplishes counter-defence by physical interaction with PsbP reducing the ability of PsbP to bind geminivirus DNA to establish infection.

I N T RO D U C T I O N
Viruses as intracellular obligate parasites usurp the host machinery at each step of infection cycle such as replication, viral movement and pathogenesis (Whitham and Wang, 2004). To establish infection in permissive hosts, viruses take over highly orchestrated cellular events by interacting with various host factors. In addition, interactions with the specific host factor affect disease development, sustained systemic infection and host antiviral defence (Kong et al., 2014). Immune response of plants to viruses is mediated through complex host-virus interactions that are often difficult to understand mechanistically. Considerable progress has been made in elucidating the plant defence response, and an increasing number of host factors that negatively regulate virus accumulation have been identified (Bhat et al., 2013).
The antiviral response against virus systemic infection is known to be significantly associated with chloroplast function (Bhattacharyya and Chakraborty, 2017;Bhattacharyya et al., 2015). Conversely, plant viruses primarily target and exploit chloroplasts to establish viral pathogenesis and symptom induction (Takahashi and Ehara, 1992). Various plant RNA virus encoded proteins interact with the proteins of chloroplastic machinery and polypeptides of the photosystem (Jang et al., 2013). The photosynthetic electron transport activity is hampered by RNA viruses such as Tobacco mosaic virus (TMV) and Cucumber mosaic virus (CMV).
Our previous study provided the initial evidence of chloroplast localization of a DNA virus encoded protein, which affected photosynthesis (Bhattacharyya et al., 2015). Radish leaf curl betasatellite (RaLCB)-encoded βC1 protein is localized into the chloroplasts of the infected N. benthamiana plants and causes damage to the OEC of PSII. The veinal chlorosis symptom induction by betasatellite infection was associated with βC1-mediated disruption of the chloroplast structure and function. However, the mechanistic interaction between βC1 and chloroplast protein(s), and the resulting effect on the disease development remains undetermined. In this present study, we further explored the virus-chloroplast interaction in the scope of viral pathogenesis as well as plant defence response. Here, we identified interaction of RaLCB-encoded βC1 protein with the OEC protein subunit 2, PsbP and decreased DNA binding activity of PsbP. Interestingly, PsbP expression was negatively correlated with disease development. Further, the study demonstrates that βC1, the pathogenicity determinant of betasatellites, overcomes the PsbP-mediated defence response during the later phases of geminivirus infection.

RaLCB-encoded βC1 protein interacts with host PsbP
Betasatellite infections, as well as transient expression of βC1 protein, intrude plant's physiology by causing structural and functional damage to chloroplast (Bhattacharyya et al., 2015). To explore the molecular basis of βC1-induced chloroplast damage we identified a N. benthamiana-encoded protein interacting with βC1 by immunoprecipitation assay. The 23 kDa protein that immunoprecipitated with the βC1 protein was found to be a N. benthamiana chloroplastic photosynthetic oxygen-evolving protein 23 kDa subunit, PsbP1 by sequencing through MALDI-TOF MS analysis (Fig. S1a). We confirmed the interaction between βC1 and NbPsbP1 (referred here as PsbP) by yeast two-hybrid analyses. Yeast transformants carrying BD-βC1 and AD-PsbP plasmids were able to grow on SD-Leu-Trp-His selection plates supplemented with 5 mM 3-amino-1,2,4-triazole, whereas the yeast transformants carrying negative control plasmids (AD and BD, AD-PsbP and BD or AD and BD-βC1) were unable to grow (Fig. 1a). Similarly, yeast transformants carrying positive control plasmids AD-T Ag and BD-P 53 grew on selection plates (Fig. 1a). Similarly, we confirmed the interaction between βC1 and PsbP encoded by N. tabacum Samsun NN (referred here as NtPsbP) by yeast two-hybrid analysis (Fig. S1b). These results confirmed interaction of βC1 with PsbP proteins in yeast cells.
In vitro GST pull-down assays were performed to confirm the direct interaction between βC1 and PsbP. The results showed that MBP-PsbP was able to pull-down GST-βC1 but not with GST. In addition, the MBP protein was unable to pull-down either GST-βC1 or GST. These results indicate specific interaction between βC1 and PsbP in vitro (Fig. 1b). Further, we carried out bimolecular fluorescence complementation (BiFC) assays in N. benthamiana plants using BiFC vectors, pVYNE and pVY-CE(R) encoding the N-terminal and C-terminal sequences of the Venus fluorescent protein, respectively. Venus fluorescent protein is a variant of Yellow fluorescent protein (YFP), which exhibits brighter fluorescence than YFP and Cyan fluorescent protein (Waadt et al., 2008). The reconstituted Venus fluorescence was observed in the epidermal cells of leaves co-infiltrated with Agrobacterium carrying pVYNE-βC1 and pVYCE(R)-PsbP constructs (Fig. 1c). However, no fluorescence was observed in the epidermal cells of leaves co-infiltrated with pVYNE and pVY-CE(R), pVYNE and pVYCE(R)-PsbP or pVYNE-βC1 and pVYCE(R) (Fig. 1c). The nuclei of the epidermal cells were detected by 4, 6-diamidino-2-phenylindole (DAPI) staining.
To identify the region of PsbP that interacts with βC1, yeast two-hybrid analyses were carried out with C-terminal deletion constructs PsbPD12 and PsbPD1 for the expression of amino acids 1-200 and 1-100, respectively of the PsbP protein (Fig. 1d). The results indicated that βC1 interacts with PsbPD12 but not with PsbPD1 (Fig. 1e). The interaction of βC1 with PsbPD12 was further validated by BiFC assay in N. benthamiana. The fluorescence was observed in the epidermal cells of leaves co-infiltrated with pVYNE-βC1 plus pVYCE(R)-PsbP and pVYNE-βC1 plus pVYCE(R)-PsbPD12 indicating the interaction between βC1 and PsbPD12 in planta (Fig. 1f). Taken together, these results demonstrated that βC1 interacts with PsbPD12 in the epidermal cells of N. benthamiana.

PsbP binds to RaLCB DNA in vitro
To elucidate the role of PsbP in plant-geminivirus interaction, we investigated functionality of the protein by in silico analysis. Prediction by helix-turn-helix motif tool shows the presence of helix-turn-helix motif at amino acid position 39 to 60, indicating a possible DNA binding feature of the PsbP protein. To confirm the ability of PsbP to bind RaLCB DNA in vitro, we performed electrophoretic mobility shift assay (EMSA) using the purified recombinant MBP-PsbP protein and DNA fragments corresponding to the SCR region of the betasatellite. EMSA results showed that MBP-PsbP binds to DNA in the presence of ATP (Fig. 2a) and the DNA binding activity of PsbP was found to increase with the increasing concentration of ATP (Fig. S2a). PsbP protein also showed binding to ssDNA corresponding to SCR (Fig. 2b) and βC1 (Fig. 2c) regions of betasatellite as well as to the CR region ( Fig. 2d) of helper virus DNA. Moreover, a ssDNA probe located at positions 1210 to 1325 in the betasatellite genome, and a double-stranded DNA (dsDNA) probe located at position 1103 to 50 in the betasatellite genome, were also used for EMSA assays. Results showed that MBP-PsbP protein can bind to both ssDNA ( Fig. 2e) and dsDNA ( Fig. 2f) corresponding to betasatellite genome, while MBP protein used as negative control did not bind to either ssDNA or dsDNA. Addition of 100-fold molar excesses of ssDNA competitor (NbActin ssDNA oligo) (Fig. 2e, lane 4) competed for binding with PsbP. Likewise, 100-fold molar excesses of dsDNA competitor (pMAL-c2X plasmid DNA) (Fig. 2f, lane 4) also competed for binding with PsbP. Together, these results indicate that in vitro PsbP binding to ssDNA and dsDNA is sequence non-specific.

βC1-PsbP interaction interferes with the ability of PsbP to bind viral DNA
Since PsbP can bind to DNA and also interacts with βC1 protein, we tested whether the βC1-PsbP interaction affects DNA binding ability of PsbP. Previous studies showed that βC1 protein can bind to DNA in vitro (Cui et al., 2005) and forms a multimeric complex in in vitro conditions (Cheng et al., 2011). The results of EMSA performed with purified recombinant GST-βC1 protein confirms the DNA binding ability of βC1 protein ( Fig.   Fig. 1 The βC1 protein of the Radish leaf curl betasatellite (RaLCB) interacts with the nuclear-encoded chloroplast protein; PsbP. (a) Yeast strain AH109 cells were co-transformed with different combinations of yeast two-hybrid constructs as mentioned. The co-transformed AH109 cells were grown on either synthetic dropout medium SD-Leu-Trp or selection medium SD-Leu-Trp-His supplemented with 5 mM 3-amino-1,2,4-triazole as serial dilution from cultures OD 600 of 1.0. Yeast cells co-transformed with AD plus BD or AD-T Ag plus BD-P 53 act as negative and positive controls, respectively. (b) An equal amount of purified GST/GST-βC1 protein was used to pull-down MBP/MBP-PsbP protein. Immunoblotting was carried out using either anti-GST or anti-MBP antibodies. Coomassie-stained gel served to monitor the amount of protein present in input and pull-down. (c) Different combinations of Agrobacterium cells harbouring bimolecular fluorescence complementation (BiFC) constructs were co-infiltrated into the leaves of 3 to 4 weeks old N. benthamiana plants as mentioned. The lower epidermal cells of co-infiltrated leaves were visualized under a confocal microscope. The reconstituted Venus protein fluorescence was observed in the periphery of the epidermal cell of leaves co-infiltrated with Agrobacterium harbouring both pVYNE-βC1 and pVYCE(R)-PsbP constructs. Nuclei of the epidermal cells were stained with 4, 6-diamidino-2-phenylindole (DAPI). Scale bar represents 20 μM. (d) Schematic representation of the C-terminal deletion constructs of PsbP protein used for yeast two-hybrid assay and BiFC assay. (e) Yeast two-hybrid assay carried out with βC1 and different domains of PsbP. (f) BiFC assay was performed on N. benthamiana plants leaves infiltrated with Agrobacterium carrying plasmid combinations as mentioned. Scale bar represents 20 μm. S2b). It is important to note that βC1-DNA complex migrate at the same rate as PsbP-DNA complex electrophoresed on 3.7% native polyacrylamide gel, probably due to similar electrostatic properties of those complexes (Fig. S3a,b). To study the effect of βC1 on the DNA binding activity of PsbP, we analysed the PsbP-DNA binding ability with increasing concentration of purified recombinant GST-βC1 by competition experiment. For this purpose, MBP/MBP-PsbP protein was added to GST/ GST-βC1 protein pre-incubated with radiolabelled DNA-probe or vice versa. Although, EMSA results could not differentiate PsbP-DNA and βC1-DNA complexes, the amount of DNAprotein complex was found to be decreased with increasing concentration of βC1 protein (Fig. S3a,b). The protein competitive EMSA autoradiographs were quantified using the ImageJ software and plotted (Fig. 3a,b). These results indicate that the equilibrium towards βC1-PsbP interaction is favoured rather than βC1 and/or PsbP-DNA interactions.
The 3D structures of PsbP and βC1 proteins were not available in Protein Data Bank (PDB) and therefore, we have used Phyre2 to predict 3D structures of these proteins. The MD simulated structure showed stable conformation of PsbP and βC1. The complex of PsbP-βC1 was generated using HADDOCK, which suggest that PsbP amino acid residues corresponding to 57-72 and 141-172 are involved in strong interaction with βC1 ( Fig. S4a). After each successful docking result, the structures are clustered and the resulting clusters are ranked according to the HADDOCK score. The best docking solution is considered based on the docking scores. Further, hydrogen bond analyses were carried out to compare the different docking complexes. Additionally, we predicted the region encompassing amino acid residues 17-42 as potential DNA binding sites in PsbP and the crucial ssDNA binding residues were highlighted ( Fig. S4b). We predicted a stronger interaction between ssDNA and PsbP as compared to that of ssDNA with PsbP-βC1 complex (Fig. 3c). Similarly, while docking with dsDNA we predicted stronger interaction between dsDNA and PsbP as compared to that of dsDNA with PsbP-βC1 complex (Fig. S4c,d,e). Thus, βC1 probably interacts with the α-helix (amino acids 36-60) region of PsbP and interferes with its DNA binding ability (Figs 3d and S4e).

Silencing of PsbP enhances viral pathogenesis in N. benthamiana plants
Transient silencing of the PsbP gene was achieved in N. benthamiana plants using the pTRV-PsbP-silencing construct, while pTRV-infiltrated plants served as the control. The leaves of PsbPsilenced plants remained yellowish green as compared to the pTRV-control plants (Figs 4a and S5a,b). Transcript level of PsbP in systemic leaves of PsbP-silenced and pTRV-control plants at 10 days post-inoculation (dpi) was measured by RNA blot analysis. Silencing of the PsbP gene was confirmed by detection of marked reduction of PsbP mRNA in the pTRV-PsbP infiltrated plants as compared to pTRV-control plants (Fig. 4b).
To decipher the role of PsbP during geminivirus infection, we co-inoculated the PsbP-silenced and pTRV-control N. benthamiana plants with Tomato leaf curl New Delhi virus (ToLCNDV) DNA-A (A) and RaLCB (β). A + β-inoculated wild-type and pTRV-control plants showed downward leaf curling symptom at around 13 dpi, while symptom appearance was noticed much earlier (at 8 dpi) on PsbP-silenced plants (Figs 4a,c and S5b,c; Supplementary Table S1). The accumulation of viral DNA in the systemic leaves of betasatellite infected PsbP-silenced plants was found to be significantly higher than the pTRV-control plants (Fig. 4d). Similarly, PsbP-silenced plants showed higher accumulation of helper virus DNA at 8 dpi as well as 15 dpi compared to that of pTRV-control plants ( Fig. 4e; Supplementary Table S1). These results revealed that transient silencing of PsbP greatly enhances the symptom induction and geminivirus DNA accumulation, indicating that PsbP plays an antiviral role during geminivirus infection.

Overexpression of PsbP delays disease development and reduces viral DNA accumulation during betasatellite infection
To demonstrate the critical role of PsbP during betasatellite infection, we co-inoculated wild-type and 35S-PsbP transgenic N. benthamiana plants with A + β. Wild-type plants inoculated with A + β showed downward leaf curling at 9 dpi and developed severe vein clearing symptom at 14 dpi. However, the betasatellite-mediated symptom induction was found to be delayed (by 2 days) on the 35S-PsbP transgenic plants compared with A + β inoculated wild-type plants (Fig. 5a,b; Supplementary Table S2). Furthermore, the accumulation of both viral genome and betasatellite DNA was assessed by Southern blotting analysis. At 14 dpi, accumulation of both helper virus and betasatellite DNA was found to be reduced in 35S-PsbP transgenic plants compared to that of the wild-type plants. On the contrary, at 21 dpi the level of helper virus and betasatellite DNA is comparable in A + β co-inoculated wildtype and 35S-PsbP transgenic N. benthamiana plants (Fig. 5c,d; Supplementary Table S2). Taken together, these results suggest that PsbP-mediated defence response delays symptom induction and reduces viral titre during the early stage of geminivirus infection. However, the PsbP-mediated defence response can be attenuated by the virus during the later stage of infection.

PsbP isoforms contribute to defence against geminivirus
An earlier study reported that, in PsbP 1Air lines, two isoforms (PsbP1A and PsbP5B) were down-regulated, whereas, in PsbP 2FAir lines, three isoforms (PsbP2AF, PsbP3F and PsbP5B) were down-regulated (Ishihara et al., 2005). Although the photosynthetic function of PsbP is known to be dependent on the total amount of PsbP protein irrespective of its isoform type, the role of different PsbPs in plant defence remain elusive. To explore the function of different PsbP isoforms in the perspective of plant defence against geminiviruses, we inoculated the wild-type, and transgenic PsbP dRNAi (IAir and 2FAir) Nicotiana tabacum Samsun NN plants with A + β. Upon co-inoculation with A + β, the wild-type, IAir and 2FAir lines showed downward leaf curling, mild vein clearing, and stem bending (Fig. 6a). However, symptoms on transgenic PsbP dRNAi tobacco lines appeared earlier (by 2 days to 3 days) as compared to inoculated wild-type tobacco plants ( Fig. 6b; Supplementary Table S3). In addition, higher accumulation of betasatellite DNA was found at 14 dpi in IAir and 2FAir lines as compared to the wild-type plants (Fig. 6c). However, at 21 dpi, the betasatellite accumulation in wild-type plants showed minor reduction as compared to IAir and 2FAir lines (Fig. 6d). These observations correlate with the results obtained from the earlier experiments and conjointly confirm that effect of PsbP-mediated defence response is diminished during the later stage of virus infection. Likewise, accumulation of helper virus DNA in dRNAi lines (in IAir and 2FAir) at 14 dpi was found to be higher than in wild-type plants (Fig. S6a), while the helper virus DNA accumulations at 21 dpi were only slightly altered in wild-type plants compared to that of PsbP dRNAi lines (Fig.  S6b). These results are consistent with the negative correlation of the PsbP protein accumulation with betasatellite-mediated disease development. These results suggest that both PsbP group-I and PsbP group-II isoforms contribute to PsbPmediated antiviral state against geminivirus in tobacco.
Betasatellite infections affect the photosynthetic function and disrupt the ultrastructure of chloroplast structure in N. benthamiana plants (Bhattacharyya et al., 2015). In comparison to A + β-infected wild-type plants, the photosynthetic activity of PSII was found to be reduced in either mock or A + β-inoculated transgenic 1Air and 2FAir lines (Fig. S7a). However, the chloroplasts of mock-inoculated transgenic 1Air and 2FAir lines remain largely unaltered while the chloroplasts from A + β-infected wild-type plants show severe ultrastructural damage (Fig. S7b). Taken together, these results demonstrate that mere reduction of photosynthetic activity of PSII by modulating the PsbP is not sufficient to cause the ultrastructural damage of chloroplast.

D I SC U SS I O N
Geminivirus disease complexes cause disastrous effects on a wide range of economically important crops throughout the world. The severe epidemic threat for the crops aroused since the betasatellite associated with the geminivirus disease complex could be trans-replicated by diverse helper begomoviruses. Plants have evolved diverse defence responses against geminivirus infection by instigating ubiquitin/26S proteasome system, RNA silencing machinery and numerous host cellular factors associated with innate immunity (Cui et al., 2005;Eini et al., 2009;Kushwaha et al., 2015;Li et al., 2014a;Sahu et al., 2016;Shen et al., 2011Shen et al., , 2016. In an earlier study, we reported that RaLCB in association with ToLCNDV DNA-A induces severe downward leaf curling, veinal chlorosis and stunting of N. benthamiana plants (Singh et al., 2012).
Systemic infection of RNA viruses often induces chlorosis and mosaic pattern on the leaves of infected plants. Although mechanism of systemic symptom expression remains elusive, several studies associate the viral symptom induction with deterioration of pigment composition and chloroplast structure and function (Bhattacharyya et al., 2015;Hodgson et al., 1989;Takahashi and Ehara, 1992). TMV inhibits electron transport rate of PSII but not PSI in the infected leaves of spinach. The association of TMV encoded CP with PSII complex contributes to the reduced Fv/Fm ratio in the infected leaves (Hodgson et al., 1989;Reinero and Beachy, 1989). Similarly, CMV infection significantly reduces the level of the PsbP polypeptide in OEC, oxygen evolution by OEC and electron transport rate of PSII in the infected tobacco leaves, whereas the level of PsbO polypeptide of OEC remains largely unaltered (Reinero and Beachy, 1989). Similar to plant RNA viruses, betasatellite infection alters the composition of the extrinsic protein of OEC, drastically reduces the number of active reaction centres and subsequently affects the electron transfer rate of PSII. Betasatellite infection down-regulates the expression of genes involved in chlorophyll biosynthesis, plastid translocation and chloroplast development (Bhattacharyya et al., 2015).
In addition to the photosynthetic function of PsbP regulating OEC, its role in plant defence response has recently been recognized. Earlier studies on plant RNA viruses indicate PsbPmediated antiviral defence response that remains elusive at the molecular level (Balasubramaniam et al., 2014;Kong et al., 2014;Takahashi and Ehara, 1992). Silencing of PsbP also causes increased symptom expression and viral accumulation in plants infected with AMV and RSV (Balasubramaniam et al., 2014;Kong et al., 2014). The current study showed PsbP-silencing resulted Purified GST protein was used as negative control. The 32p-labelled single-stranded oligonucleotide substrate from the SCR region of betasatellite (1303 nt-1326 nt) was used as DNA-probe. The amount of protein bound DNA was quantified using the ImageJ software and plotted using the Graph Pad Prism 6 software. Asterisks indicate reactions in which amount of protein bound DNA was significantly decreased compared with the amount of protein bound DNA in the reaction with PsbP (considered as 100%). (*, P < 0.05; **, P < 0.01; ***, P < 0.001) as determined by Dunnett's multiple comparisons test by analysis of variance (ANOVA). The values are mean of the per cent of protein bound DNA from three independent experiments. (c) In silico docking of ssDNA (located at positions 1303 to 1326 in the betasatellite genome) with PsbP and PsbP-βC1 complex. (d) In silico docking to identify potential residues of PsbP that interacts with either βC1 protein or ssDNA.  in enhanced disease development on infected N. benthamiana plants (Fig. 4). Following infection, the transgenic PsbP dRNAi tobacco plants showed earlier symptom induction and enhanced viral DNA accumulation in comparison to the wild-type plants (Fig. 6). Correspondingly, overexpression of PsbP delays symptom induction and reduces viral DNA accumulation during the initial phase of disease progression. In contrast, overexpression of PsbP in N. benthamiana does not affect either symptom expression or the accumulation of viral DNA during later stage of infection (Fig. 5). These results suggest that geminiviruses mitigate the PsbP-mediated antiviral defence response in order to establish pathogenesis.
The movement protein of a bipartite geminivirus (Abutilon mosaic virus, AbMV) interacts with nuclear-encoded and chloroplast localized heat shock cognate 70 kDa protein (Krenz et al., 2010). Bipartite geminivirus (AbMV) as well as betasatellite (RaLCB) infection affect the structure of chloroplasts (Bhattacharyya et al., 2015;Krenz et al., 2012;Schuchalter-Eicke and Jeske, 1983). PsbP, a nuclear-encoded component of OEC of PSII localizes into the chloroplast and cytoplasm  (Balasubramaniam et al., 2014;Kong et al., 2014). We have previously demonstrated that RaLCB-βC1 localizes into the nucleus and as well as chloroplast (Bhattacharyya et al., 2015). In this study, for the first time, we demonstrate the interaction between geminivirus betasatellite encoded protein and PsbP. Although geminiviruses are known to replicate in the nucleus of the infected cells and migrate to the cytoplasm, the virions as well as viral genome, have also been detected in chloroplasts and other plastids (Groning et al., 1987). EMSA results showed binding of PsbP to geminivirus as well as betasatellite DNA (Figs 2 and S2) in a sequence non-specific manner. DNA binding activity of PsbP may have additional role in host biology, which needs further investigation. Together, it appears that chloroplast and/or cytoplasm serve as the possible site(s) where βC1-PsbP and PsbP-DNA interaction might occur. However, further investigations are required to ascertain precise site(s) of these interactions.
Various studies demonstrate the role of βC1 in mediating viral counter-defence to suppress plant defence response. The interaction of TYLCCNB-βC1 with ASYMMETRIC LEAVES1 (AS1) in the molecular disguise of ASYMMETRIC LEAVES2 (AS2) selectively suppresses the JA response (Yang et al., 2008). TYLCCNB-βC1 interaction with S-adenosyl homocysteine hydrolase (SAHH) limits the methyl group donor for methylation by inhibiting the SAHH activity. Thus, either betasatellite infection or only transient expression of TYLCCNB-βC1 protein reduces both host and viral genome methylation globally and stabilizes geminivirus disease complex (Yang et al., 2011). The βC1 protein interacts with MYC2 transcription factor and interferes with dimerization of MYC2 essential for JA-mediated defence response in Arabidopsis thaliana (Li et al., 2014b).
Plant RNA viruses pre-empt the PsbP-mediated antiviral state by deploying viral protein that interact with the chloroplast-targeted PsbP protein in the cytosol. The CP of AMV and diseases specific protein of RSV interact with PsbP in the cytosol, thereby modulating symptom induction and viral pathogenesis (Balasubramaniam et al., 2014;Kong et al., 2014). The βC1-PsbP interaction possibly blocks the PsbP-role in the electron transport of PSII; this interaction might block the PSII dependent pathogen restriction too. To overcome the PsbP-mediated antiviral state, the βC1 protein interacts and interferes with PsbP binding to geminivirus DNA.
Our previous study demonstrates that either betasatellite infection or transient expression of the βC1 protein disrupts the electron transport rate by PSII and damage the photosynthetic machinery. Studies on plant RNA viruses show evidence that interruption of the PSII activity by viral proteins leads to the suppression of defence response, defence-associated reactive oxygen species (ROS) production, and programmed cell death (Balasubramaniam et al., 2014;Kong et al., 2014;Takahashi and Ehara, 1992). The ability of PsbP to interact with geminivirus DNA leads to delayed symptom induction and reduced viral DNA accumulation in PsbP overexpressing N. benthamiana plants. On the contrary, overexpression of PsbP could not effectively eradicate the virus infection suggesting ability of the virus to overcome PsbP-mediated defence. To understand the significance of βC1-PsbP interaction in virus infection, we analysed the DNA binding ability of PsbP in the presence of increasing concentration of the βC1 protein. Our competitive EMSA result shows that the βC1-PsbP interaction interferes with the DNA binding ability of PsbP.
With insights from current and previous studies, we hypothesized the working model for βC1-PsbP interaction (Fig. 7). Our study highlighted novel DNA binding property of PsbP protein.
Enhanced symptom induction and viral DNA accumulation in infected PsbP-silenced N. benthamiana plants suggest that PsbP mediates an antiviral defence response. Furthermore, binding of PsbP protein to the geminivirus DNA in infected N. benthamiana plants may possibly interfere with replication, transcription and cell-to-cell movement of viral DNA. On the other hand, autophagy plays role in antiviral defence, in addition to its contribution to plant immunity against necrotrophic or biotrophic pathogen (Haxim et al., 2017). PsbP facilitates the formation and stabilization of active PSII super complex in the grana thylakoid membrane (Ido et al., 2009). Proper functioning of OEC and PSII leads to defence-related ROS production against pathogen, which activates autophagy (Rodriguez-Herva et al., 2012).
To overcome the defence responses generated by plants, viral encoded multifunctional proteins adopt counteracting strategies. The interaction studies showed that betasatellite-encoded βC1 protein interacts with PsbP. PsbP overexpressing N. benthamiana plants showed reduced viral titre at early stage of betasatellite infection and not during the later stage. In this scenario, βC1-PsbP interaction interferes with the DNA binding ability of PsbP and probably attenuates the PsbP-mediated antiviral response. Although betasatellite infection damages the PSII activity, the higher rate of damage of PSII activity in transgenic PsbP dRNAi N. tabacum plants signifies the positive correlation of PsbP level and PSII activity. These results suggest that βC1-PsbP interaction destabilizes the active PSII super complex, thereby suppressing the defence-related ROS production and possibly exhibiting compromised cell death. The βC1-PsbP interaction subverts the impediment on viral pathogenesis mediated by PsbP binding to geminivirus DNA.
In summary, βC1 protein interacts with host PsbP protein. Additionally, PsbP binds to both helper virus and betasatellite DNA. PsbP-mediated antiviral response impedes symptom induction and viral DNA accumulation in geminivirus infected plants. Irrespective of PsbP isoform types, both PsbP group-I and PsbP group-II isoform expression negatively correlates with the betasatellite infection. However, geminivirus generated counter-defence attenuates the PsbP-mediated defence response during the later stage of viral infection. The βC1 protein interacts with PsbP and affects its ability of binding to viral DNA. In addition, betasatellite infection damages the ultrastructure and function of the chloroplast, thereby overcoming the plant defence response. However, PsbP-mediated modulation of PSII activity is not solely responsible for the chloroplast ultrastructural damage caused during betasatellite infection. In conclusion, βC1 subverts the PsbP-mediated impediment on symptom induction and viral pathogenesis during geminivirus infection. In future, the delicate regulation of host and viral protein during infection and its effect on assembly of OEC and PSII activity need to be investigated in detail.

E X PE R I M E N TA L PROC E D U R E S Plant inoculation and source of plant material and infectious virus clones
The 35S-PsbP transgenic N. benthamiana plant seeds (Kong et al., 2014) were kindly gifted by Professor Xueping Zhou, Institute of Biotechnology, and Zhejiang University, China. The seeds of wild-type and stable transgenic PsbP dRNAi Nicotiana tabacum cv. Samsun NN lines, namely, IAir and 2FAir (Ishihara et al., 2005) were kindly provided by Dr. Kentaro Ifuku, Kyoto University, Japan. Infectious constructs of ToLCNDV (referred as A) (GenBank accession no. U15015) (Chakraborty et al., 2008), RaLCB (referred as β) (GenBank accession no. EF175734) (Singh et al., 2012), and Radish leaf curl betasatellite mutant (RaLCBHAβC1: referred as βHAβC1) (Bhattacharyya et al., 2015) were already available in our laboratory. Inoculation of tobacco plants (N. benthamiana and N. tabacum cv. Samsun NN) was done as previously described (Bhattacharyya et al., 2015). In each case, at least five plants were inoculated following standard procedures and each experiment was repeated three times. Symptom severity on test plants was recorded according to Chakraborty et al. (2008).

Yeast two-hybrid assay
Yeast two-hybrid assays were carried out by co-transforming Saccharomyces cerevisiae strain AH109 cells with plasmid BD-βC1 plus AD-PsbP/AD-NtPsbP. AD-T Ag and BD-P 53 act as a positive control, while AD and BD, AD-PsbP/AD-NtPsbP and BD, and AD and BD-βC1 acted as negative controls. Further, the plasmids combinations BD-βC1 plus PsbP 1-200 , and BD-βC1 plus PsbP 1-100 were co-transformed into yeast AH109 cells. BD plus PsbP 1-200 , and BD plus PsbP 1-100 served as negative control. The interaction was checked by growing the transformants on synthetic dropout media lacking Leu, Trp, His and containing 5 mM 3-amino-1,2,4triazole at 30 °C for 96 h.

BiFC assay
BiFC assays were performed as described by Waadt et al. (2008). The lower epidermis of leaves of 3-4 weeks old N. benthamiana Fig. 7 Betasatellite-encoded βC1 protein subverts PsbP-mediated defence response against geminivirus infection. (a) PsbP-mediated defence response against geminivirus infection. During the early stage of viral infection, plant innate immunity necessitates the oxygen-evolving enhancer protein 2, PsbP to exhibit an antiviral response in the infected plant cells. PsbP binding to viral DNA possibly interferes with systemic infection and/or movement of viral DNA from the infected cells. Consequently, PsbP binding to geminivirus DNA impedes symptom induction and viral DNA accumulation, thereby hampers viral pathogenesis. In addition, the OEC of PSII facilitated defence-related reactive oxygen species (ROS) production lead to an antiviral state and subsequently impedes the viral infection cycle. (b) βC1 attenuates the PsbP-mediated defence response during betasatellite infection. During the later stage of viral infection, βC1 protein being pathogenicity determinant targets the OEC of PSII and disturbs the photosynthetic function and ultrastructure of chloroplasts. The damage caused by βC1 on the OEC of PSII possibly suppresses defence-related reactive oxygen species (ROS) production, subsequently exhibit compromised cell death supporting viral pathogenesis. Betasatellite-mediated veinal chlorosis symptom induction is associated with damage in the chloroplastic structure and function. Further, βC1 protein interacts with PsbP protein and hampers its DNA binding ability. The βC1-PsbP interaction subverts the impediment on viral pathogenesis mediated by PsbP binding to geminivirus DNA. However, precise subcellular locations of these interactions are not known.

Recombinant protein expression, purification and GST pull-down assay
Escherichia coli strain Arctic express (DE3) carrying either pGEX-6p-2 vector or pGEX-6p-2-βC1 expression construct were used to express either GST or GST-βC1 fusion protein, respectively. Expression of GST/GST-βC1 fusion was achieved after 24 h of incubation at 12 °C after 0.1 mM isopropyl β-D-thiogalactoside (IPTG) induction. GST/GST-βC1 proteins were further purified from the bacterial culture using glutathione resin (G-Biosciences, St. Louis, USA). Similarly, MBP/MBP-PsbP fusion protein was expressed and purified from E. coli transformed with either pMAL-C2X vector or pMAL-C2X-PsbP constructs. Expression of MBP/MBP-PsbP was optimized by incubating at 12 °C for 16 h after 0.1 mM IPTG induction. MBP and MBP-PsbP fusion proteins were purified using Amylose resin (New England Biolabs, Massachusetts, USA) following manufacturer's protocol.
An equal amount of bacterially purified proteins either GST and MBP or GST-βC1 and MBP or GST and MBP-PsbP or GST-βC1 and MBP-PsbP were mixed and incubated with pre-equilibrated glutathione resin for 4 h at 4 °C. After incubation, unbound proteins were removed by washing five times with wash buffer and the bound proteins were then eluted. Then 10% of the input protein and eluted proteins were analysed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and Western blotting assay using an anti-GST antibody (Sigma, Saint Louis, USA) and an anti-MBP antibody (Sigma, Saint Louis, USA).

Virus-induced gene silencing of endogenous PsbP of N .benthamiana
A Tobacco rattle virus (TRV)-based gene silencing vector was used in this study for transient silencing. pTRV1 (CD3-1039), pTRV2 (CD3-1040) and pTRV2-NtPDS (CD3-1045) vectors (Liu et al., 2002) were procured from the Arabidopsis Biological Resource Center (ABRC). The pTRV1, pTRV2 and pTRV2-PsbP constructs were mobilized into A. tumefaciens strain EHA105. Silencing of the PsbP gene was achieved by co-infiltrating the lower leaves of 3 weeks old N. benthamiana plants with Agrobacterium cells carrying pTRV1 + pTRV2-PsbP. Plants co-infiltrated with Agrobacterium carrying pTRV1 and pTRV2 served as a negative control for PsbP-silencing. Inoculated plants were grown at 22 °C, 70% relative humidity, and in 16 h of light/8 h of dark photoperiod.

Southern and northern RNA blotting
Total DNA was extracted from the uppermost leaves of the inoculated plants and Southern blotting was performed as described previously by Singh et al (2012). Total RNA was extracted from the uppermost leaves of the test plants using a standard protocol (Singh et al., 2016). RNA blotting analysis was performed using the protocol as described earlier (Xiong et al., 2009). RNA samples were isolated from three different plants for each combination and the experiment was repeated three times.

Computational analysis
BLASTp analysis with protein sequence of PsbP and βC1 against PDB (https://www.rcsb.org/) databank gave significant hits for PsbP but not for βC1. Since partial PsbP structure is available in PDB (1V2B_A) with help of in silico tools we generated the computational model. The protein structure of PsbP and βC1 is modelled using Phyre2 (http://www.sbg.bio.ic.ac.uk/phyre2/). Further, obtained 3D structures of these proteins were subjected to 20 ns MD simulation using GROMACS. DNA binding region of PsbP was predicted using the DISLPAR (DNA-Interaction Site Prediction from a List of Adjacent Residues) tool (http://pipe.scs.fsu.edu/displar.html). Protein-Protein interaction site of PsbP and βC1 were predicted using cons-PPISP (consensus Protein-Protein Interaction Site Predictor) tool (http://pipe.scs.fsu.edu/ppisp.html). The docking studies were carried out using HADDOCK server (http://milou. science.uu.nl/services/HADDOCK2.2/haddock.php).