Sílvia Mas

A Torro

N Bec

L Fernández

G Erschov

Céline Gongora

C Larroque

Pierre Martineau

A De Juan

S Marco

Use of physiological information based on grayscale images to improve mass spectrometry imaging data analysis from biological tissues

Keywords: MCR-ALS, K-means, local rank constraints, MALDI-MSI, grayscale images

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Mass Spectrometry Imaging (MSI) is a powerful tool extensively applied in the field of biomedical research that provides exceptional advantages to analyze tissue specimens in detail. MSI incorporate information of a broad variety of analytes, ranging from small (e.g., drugs and their metabolites) to large molecules (e.g. proteins, peptides) and provide visualization of their spatial distribution. Mass spectrometry capabilities combined with microscopic information assists in the comprehension of molecular processes in specific cell types within a tissue.

Among MSI techniques, Matrix-Assisted Laser Desorption/Ionization (MALDI)-MSI is the most commonly employed in the field of tissue-based research [START_REF] Seeley | MALDI imaging mass spectrometry of human tissue: Method challenges and clinical perspectives[END_REF][START_REF] Drake | MALDI Mass Spectrometry Imaging of N-Linked Glycans in Cancer Tissues[END_REF][START_REF] Dekker | Multicentre matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) identifies proteomic differences in breast cancer-associated stroma[END_REF][START_REF] Buck | Distribution and quantification of irinotecan and its active metabolite SN-38 in colon cancer murine model systems using MALDI MSI[END_REF]. The method MALDI-MSI, as developed by Caprioli et al. [5], permits the analysis of hundreds to thousands of molecules directly from tissue sections after matrix deposition and introduction of the sample into the ionization chamber. For each pixel, a complete mass spectrum is acquired. Since this spectrum may contain hundreds of distinct biomolecular ions, the information content of a MALDI-MSI data set is extremely complex. Therefore, the application of chemometric tools is necessary to successfully interpret MALDI images [START_REF] Alexandrov | MALDI imaging mass spectrometry: statistical data analysis and current computational challenges[END_REF].

Two of the most important aspects to be investigated on sections of biological material are the identity and distribution of the biological components and the presence of sample regions with similar properties, often defined by tissue boundaries. The ideal approaches to achieve these two objectives are factor analysis and segmentation analysis, respectively. In the framework of MSI data, the most well-known factor analysis method is the principal component analysis (PCA) [START_REF] Mann | On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other[END_REF][START_REF] Hanselmann | Concise representation of mass spectrometry images by probabilistic latent semantic analysis[END_REF][START_REF] Diehl | The challenge of on-tissue digestion for MALDI MSI-a comparison of different protocols to improve imaging experiments[END_REF][START_REF] Gut | Application of chemometric algorithms to MALDI mass spectrometry imaging of pharmaceutical tablets[END_REF]. Other methods have also been applied, such as probabilistic latent semantic analysis [START_REF] Hanselmann | Concise representation of mass spectrometry images by probabilistic latent semantic analysis[END_REF][START_REF] Diehl | The challenge of on-tissue digestion for MALDI MSI-a comparison of different protocols to improve imaging experiments[END_REF], independent component analysis (ICA) [START_REF] Hanselmann | Concise representation of mass spectrometry images by probabilistic latent semantic analysis[END_REF][START_REF] Gut | Application of chemometric algorithms to MALDI mass spectrometry imaging of pharmaceutical tablets[END_REF] and non-negative matrix factorization (NMF) [START_REF] Gut | Application of chemometric algorithms to MALDI mass spectrometry imaging of pharmaceutical tablets[END_REF].

Recently, MCR-ALS has been proven to adapt particularly well to MALDI image resolution due to the ability to incorporate dedicated image constraints and to the chemical meaning of the resolved distribution maps and spectral information associated with the image constituents [START_REF] Gut | Application of chemometric algorithms to MALDI mass spectrometry imaging of pharmaceutical tablets[END_REF][START_REF] Bedia | Analysis of multiple mass spectrometry images from different Phaseolus vulgaris samples by multivariate curve resolution[END_REF][START_REF] Jaumot | Potential use of multivariate curve resolution for the analysis of mass spectrometry images[END_REF].

In the case of segmentation methods, unsupervised algorithms are commonly applied when insufficient or no prior knowledge is available, such as often occurs in tissuebased cancer research. Among them, K-means is widely used for large image data sets because it is computationally lighter than hierarchical approaches [START_REF] Trede | Excluído-Exploring Three-Dimensional Matrix-Assisted Laser Desorption/ Ionization Imaging Mass Spectrometry Data: Three-Dimensional Spatial Segmentation of Mouse Kidney[END_REF][START_REF] Alexandrov | Efficient spatial segmentation of large imaging mass spectrometry datasets with spatially aware clustering[END_REF].

Recently, combination of MCR (factor analysis method) and K-means clustering (segmentation method), has been proposed as general strategy for the characterization of biological tissues in infrared and Raman images [START_REF] Piqueras | Resolution and segmentation of hyperspectral biomedical images by Multivariate Curve Resolution-Alternating Least Squares[END_REF][START_REF] Piqueras | Combining multiset resolution and segmentation for hyperspectral image analysis of biological tissues[END_REF][START_REF] Felten | Vibrational spectroscopic image analysis of biological material using multivariate curve resolution-alternating least squares (MCR-ALS)[END_REF]. Piqueras et al. demonstrated that the use of MCR score as starting information in K-means allows a compound-wise selection and preprocessing of the input information to be submitted to the segmentation algorithm and, for this particular scenario, presents advantages in the interpretability of the class centroids fo [START_REF] Piqueras | Resolution and segmentation of hyperspectral biomedical images by Multivariate Curve Resolution-Alternating Least Squares[END_REF]. MCR scores are chemically meaningful because they are concentrations of the pixel constituents in the images. This could help to explain that the meaning of centroids coming from MCR scores are easy to interpret. Moreover, the application of this strategy in the simultaneous analysis of several images coming from different samples provided a better differentiation among tissue subparts or among tissues in different conditions [START_REF] Piqueras | Combining multiset resolution and segmentation for hyperspectral image analysis of biological tissues[END_REF]. Therefore, the application of this proposed methodology improved the efficiency of the information obtained when K-means method was applied directly on the raw image spectra.

Nevertheless, resolution of hyperspectral images under constraints, like non-negativity, does not always guarantee unique solutions because of the rotation ambiguity inherent to the bilinear model decompositions of MCR [START_REF] Jaumot | MCR-BANDS: A user friendly MATLAB program for the evaluation of rotation ambiguities in Multivariate Curve Resolution[END_REF][START_REF] Tauler | Selectivity, local rank, three-way data analysis and ambiguity in multivariate curve resolution[END_REF]. The introduction of selectivity and local rank constraints, i.e., information on the absence of one or more image constituents in certain pixels, are among the most powerful constraints to decrease the rotational ambiguity in resolution results. Some examples of introduction of these constraints to hyperspectral images have already been reported [START_REF] Juan | Use of local rank-based spatial information for resolution of spectroscopic images[END_REF][START_REF] Zhang | Local rank-based spatial information for improvement of remote sensing hyperspectral imaging resolution[END_REF]. However, the presented work proposes for the first time the introduction of local rank constraints based on the information provided by biological grayscale images (scanned images of the tissues) in the MCR resolution. Thus, the spatial information from biological grayscale images will be used to force certain components to be absent in some pixels.

To do so, grayscale images will be registered to their MALDI-MSI data acquisition. This multimodal registration technique will be used for anatomical labeling of the data and will be later coded under the form of local rank constraints.

Application of the combination of MCR followed by K-means for the simultaneous analysis of MALDI-MSI data and the benefits from setting this local rank constraint to obtain best-defined images and less ambiguous profiles will be shown in a real example corresponding to fifteen tissues of colorectal cancer images.

Experimental

Human colon adenocarcinoma cell lines sensitive HCT116 (S) and resistant HCT116-SN50 (R) to Irinotecan (chemotherapy drug) were used as a model of sensitive and resistant experimental tumors. Clonogenic tumor xenografts were generated by a subcutaneous injection of both unique cell lines in athymic mice whereas a model of heterogeneity was created injecting various mixtures of R and S clones. The tumors were then collected, sliced, and analyzed utilizing MALDI-MSI. The complete methodology is described in more detail in the following subsections.

Materials and Methods

Chemicals and cell lines

Acetonitrile (ACN) and trifluoroacetic acid (TFA) were purchased from ThermoFisher Scientific (France). α-cyano-4-hydroxycinnamic acid was purchased from LaserBio Labs (France). HCT116 (S) cell line was purchased from the American Type Culture Collection (ATCC, Manassas, Virginia). HCT116-SN50 (R) cell line was obtained from a clone of HCT116 as previously described in [START_REF] Candeil | ABCG2 overexpression in colon cancer cells resistant to SN38 and in irinotecan-treated metastases[END_REF][START_REF] Gongora | New Topoisomerase I mutations are associated with resistance to camptothecin[END_REF]. Cells were grown in RPMI1640 with Lglutamin supplemented with 10% fetal calf serum at 37°C under an atmosphere with 5% CO 2 .

Tumor xenografts in nude mice

In vivo experiments were conducted by accredited researchers (Dr Adeline Ayrolles-Torro n°I-34UnivMontp-F1-12, Dr Celine Gongora n° 34-142) in compliance with the French regulation and ethical guidelines for experimental animal studies. Six weeks old female athymic mice were purchased from Harlan laboratories and were maintained in a specific pathogen-free facility in an accredited establishment (Agreement n° 34-172-27). Mice were xenografted subcutaneously in both flanks with 3.10 6 HCT116 or HCT116-SN50 alone or with mixture of these cells lines. Cells of the two lines of interest were mixed in 3 ratios 90% HCT116-10% HCT116-SN50; 50% HCT116-50% HCT116-SN50; 10%HCT116-90%HCT116-SN50. When tumors reached approximately 0.5cm 3 in diameter, mice were euthanized and tumors were excised and frozen in liquid nitrogen.

MALDI-MSI analysis

Sample preparation

Frozen xenograft tumors were cut at 10µm thick slices with a HM 550 OVPD cryostat (Fisher Scientific, Illkirch, France) operating at -20°C. Consecutive sections were mounted on ITO coated conductive glass slides for IMS and on Superfrost Plus slides (Microm) for immunochemistry experiments. Then, ITO slides were allowed to thaw and were desiccated in a vacuum desiccator. The MALDI matrix application was as follows: a 5 mg/ml solution of α-cyano-4-hydroxycinnamic acid dissolved in 50% ACN and 0.1% TFA was sprayed on each tissue section (SunChrom SunCollect Maldi spotter) with a distance in x of 0.5mm and in y of 2mm, Z offset at 1 and speed low7.

Twenty-two layers were applied at 10, 15, 20, 25 µl/min with a pause of 15 seconds between each layer.

Data acquisition

Scanned images

The scanned images were acquired at 2400 dpi resolution with an Epson Perfection 4990 Photo Scanner (Seiko Epson, Nagano, Japan) into a tiff format as RGB images stored in a x×y×3 matrix with data type uint8 (containing all whole numbers from 0 to 255). These scanned images were prior to MALDI-MSI acquisition.

MALDI images

All imaging mass spectrometry experiments were performed with a 4800 Plus MALDI For each analysis, one scanned image and one MALDI image were provided. The size (x×y number of pixels) of both scanned and MALDI images is also described in table 1 3. Data analysis

Data pretreatment

In MALDI-MSI, the data generated can be arranged in a data cube in which the x-and y-axis correspond to the pixel coordinates and the z-axis corresponds to the m/z values registered in each pixel MS. Data preprocessing is needed to improve the signal quality and to compress the raw data acquired into a list of useful peaks associated with relevant m/z values for further analysis. In this work, almost all the data preprocessing was performed using the MALDIquant R package [START_REF] Gibb | Maldiquant: A versatile R package for the analysis of mass spectrometry data[END_REF].

First, the raw data was imported into the R environment and, for each pixel, the spectrum was smoothed using a moving average filter and the baseline was corrected using the Statistics-sensitive Non-linear Iterative Peak-clipping algorithm (SNIP) [25].

Afterwards, the Median Absolute Deviation (MAD) was adopted as a noise estimation method, and all masses with signals above a signal-to-noise (S/N) ratio threshold of 2.5MAD, were identified as relevant peaks. This led to the identification of a list of masses and associated intensities for each pixel and helped to reduce the size of the data. Peaks of different m/z spectra of the same image or different images are associated with the same nominal m/z value by using a tolerance of 0.002 Da. Finally, the dataset including all 15 images was further reduced by keeping only the peaks present in at least 30 % of the spectra and, hence, avoiding artifacts and noise contributions The final dataset contained 758 different peaks between 250 and 1200 Da. For each pixel, the number of detected and preserved peaks ranged between 132 and 667 with a median value of 477. The most frequent peak (798.50 Da) was found in 98.9 % of the analyzed pixels, and 60.7 % of the peaks were present in at least half of the pixels.

For further analysis, this data set was imported to Matlab R2013b environment using a homemade script. A matrix D (n,m) of dimension n equal to (x × y) pixels by m m/z values was generated per each image. A multiset structure containing all 15 images was built, appending the 15 blocks of individual image pixel mass spectra one on top of each other.

Image registration

Spatially-related properties identified in the grayscale images from tissue slices could be used to label the related parts in the MALDI images. Therefore, grayscale images were registered to their related MALDI-MSI by using the MATLAB Image Processing Toolbox 8.3 (R2013b) "imregister" function. This intensity-based automatic registration uses an optimization algorithm to find the best transformation to register two input images [START_REF] Sharma | Automating Image Registration with MATLAB[END_REF]. We used the "rigid" transformation (only involving translation and rotation transforms) to register all grayscale images to MSI.

Before applying the registration, the pixels in the grayscale images were binned by a factor of nine in x and y to have the same pixel size as the MALDI images. RGB

FIGURE 1

This difference among gray color intensity in the physiological grayscale image was used as a criterion for tissue recognition. Histograms were computed from all the pixels in the physiological grayscale images and the peaks and valleys in the histogram were used to locate the boundaries among clusters related to MALDI matrix, tumor parts and necrotic parts. The tissue assignment in the physiological images was then used to label the related MALDI pixels. Figure 1d shows the histogram corresponding to the registered grayscale image in Figure 1c. The black rectangle includes all the pixels with information of the biological material that was considered for further analysis. The region framed in blue represents the grey intensity range in which tumor parts are clearly located and the red framed region includes the grayscale values associated with necrotic pixels. Pixels with grayscale intensities between both regions were not assigned either to tumor or to necrotic parts to avoid incorrect labeling. Pixels outside the black rectangle are related to background. This background would correspond to MALDI matrix pixels, which give no information; therefore, they were not used for further analysis. It is worth to mention that visually inspection of selected pixels was made to ensure the correct labeling.

Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS)

MCR-ALS decomposes the matrix of MS pixel spectra D into a bilinear model, where all MS spectra in the image can be described by the sum of the MS fingerprints of the different pure image constituents weighted according to their relative concentrations in the different pixels. This model is expressed by the following equation in matrix form:

D(n,m)=C(n,n c )S T (n c ,m)+E(n,m) Equation 1
where D (n,m) is the matrix of MS pixel spectra obtained after the pretreatment described in section 3.1, C(n,n c ) is the matrix of the relative amounts or concentration of the n c components in the n pixels and S T is the pure MS spectra matrix associated with these n c components. E is the matrix associated with noise or experimental error More details about the MCR-ALS method are given in [START_REF] Tauler | Multivariate curve resolution applied to second order data[END_REF][START_REF] De Juan | Two-Way Data Analysis: Multivariate Curve Resolution -Iterative Resolution Methods[END_REF][START_REF] Jaumot | MCR-ALS GUI 2.0: New features and applications[END_REF] and a GUI to use the algorithm is freely available at http://mcrals.info.

K-means (segmentation analysis)

K-means clustering is an algorithm which works by partitioning the observations of a dataset into k clusters [START_REF] Tran | Clustering multispectral images: a tutorial[END_REF]. In image analysis, the pixels in the dataset are partitioned into groups according to their similarity, expressed by a similar spectral shape, composition, chemical and/or biological properties.

In this study, a single K-means analysis was performed on the 15 images simultaneously. This multiset segmentation analysis allowed finding clusters consistently present in all images and, thus, related to relevant biological structures of the samples analyzed. MCR scores coming from the augmented C aug matrix obtained by MCR-ALS have been used as the pixel input information for K-means analysis because they are excellent compressed and noise-filtered representations of the pixel composition. Besides, each column in C aug has constituent-specific information and this allows performing K-means on some or all the signal contributions resolved by MCR-ALS. When using C aug for segmentation, the number of sought clusters should be equal or slightly higher than the number of MCR contributions used in the K-means analysis.

The K-means algorithm basically consists of three steps: 1. Initialization, where few pixels are randomly selected and used as cluster centroids. 2. Assignment of every pixel of the data set to its nearest centroid, and 3. Updating of the centroid position based on the groups formed. Steps 2 and 3 are iteratively performed until centroids do not shift any longer and clusters are stable or when a present number of iterations is exceeded. In this work, 100 replicates with random initial centroid positions were performed to achieve more reliable results and Euclidean distance was used as a similarity criterion for pixel assignment to clusters. Finally, suitable segmentation maps were obtained refolding the part of the segmentation vector that relates to each image. Segmentation maps can be used to be compared with physiological images.

Moreover, the use of MCR scores provides chemically meaningful spectra of the components and, hence, allows interpreting easily the centroid properties in each segmentation class. This is an advantage in comparison with the centroids coming from PCA scores, which are difficult to interpret because these composition-related values are abstract and lack chemical sense.

Figure 2 presents a schematic illustration of the combination of MCR-ALS with Kmeans for the simultaneous analysis of MALDI images previously described. First, multiset MCR-ALS is applied to the images of biological tissues to provide basic spectral signatures and distribution maps of the biological contributions. Finally, multiset K-means analysis is applied to obtain consistent tissue clusters (relevant biological parts) common to all samples. MCR scores from biological constituents in MSI images were used as input pixel information for segmentation analysis.

Autoscaling was applied to each concentration profile in each submatrix C i of the C aug matrix, to balance the importance of all biological constituents present in each image in the segmentation analysis. This strategy might be considered as generally applicable for mass spectrometry imaging data analysis.

FIGURE 2 4. Results and discussion

This section will include the results obtained from both image resolution and segmentation approaches. Firstly, the information gathered from the comparison of the analysis of the two types of tumor by MCR-ALS will be showed. Secondly, advantages of the implementation of the new constraint in the simultaneous MCR-ALS analysis of all 15 images will be presented. Finally, the results achieved from the multiset image segmentation and a final interpretation of the chemical results will be offered. 4.1.

Resolution analysis (MCR-ALS)

The first MCR-ALS analysis was oriented to identify significant contributions with a specific "mass signature" for each tumor type (R or S). To do so, two multisets as described in section 3. S1 in Supplementary information for resolved spectra and distribution maps for these two MCR-ALS analyses). Four contributions were related to biological regions of the tissues; two associated with tumoral parts and two corresponding to necrotic parts, according to the morphology seen in the related grayscale images. The need of two biological contributions to describe the tumoral part of the sample and two additional ones for the description of the necrotic regions of the samples is not surprising if we take into consideration the heterogeneity associated with this kind of tissues. Moreover, an additional non-biological contribution was needed to improve the resolution results in both multiset structures. This extra contribution could be attributed to instrumental background or could be originated to compensate for variations linked to slight differences of sample preparation. Actually, the high noise of MSmass spectra and possible experimental differences in the tissue preparation also justify that the explained variance by the models does not exceed 90%.

TABLE 2

A correlation coefficient matrix generated by comparing by pairs the resolved spectra of the components of both multisets (S T R and S T S ) by Pearson correlation shows a high correlation between analogous biological components of the two tumor cell lines R and

S (see correlation matrix shown below).

Where Rc1/Rc2 and Sc1/Sc2 were the spectra corresponding to tumoral contribution 1/2 of D R and D S , respectively. Rn1/Rn2 and Sn1/Sn2 were the spectra corresponding to necrotic contribution 1/2 of D R and D S , respectively. Finally, Rb and Sb were the spectra corresponding to background contribution of D R and D S , respectively.

No significant differences between the composition of tumors R and S were found.

Thus, resolved mass spectral signatures of the four biological contributions related to tumor and necrotic parts were rather similar in both R and S tumoral tissues. Indeed, in tumoral contributions, Rc and Sc, correlation coefficient values were equal or higher than 0.9, whereas the lowest correlation coefficient between necrotic contributions, Rn and Sn, was 0.8. Only the non-biological contributions, Rb and Sb, presented expected differences in the spectral signatures, due to the noise random nature. Limits in the selectivity of the MS detection system and the fact that both R and S clones are closely related, since they derived from the same original HCT116 cell line, could be the reasons to explain why a specific distinction and identification of "mass signatures" for each tumor type was not possible. Owing to this limitation, the interpretation of the differences among the R and S tumor compounds obtained was no longer the main goal Rc1 Rc2 Rn1 Rn2 Rb Sc1 0,9 0,0 0,4 0,1 0,0 Sc2 0,0 1,0 0,1 0,1 -0,1 Sn1 0,6 0,0 0,8 0,4 0,4 Sn2 0,1 0,2 0,7 0,9 -0,1 Sb 0,5 0,0 0,6 0,4 0,2 of the work. However, some general interpretation, valid for R and S tumors, can still be done. Therefore, salient masses in the tumor signatures to masses around 750-800 could be assigned to glycosyldiradylglycerols, glycerophosphocholines or glycerophosphoglycerols and around 520-525 with oxidized glycerophospholipids or fatty acyl glycosides. Relevant masses in the necrotic signatures to masses around 400-430 could be assigned to sterols/fatty esters.

The spectral similarity between both tumors R and S suggested the possibility of performing a simultaneous treatment of all 15 images corresponding to homogeneous (100% R or S) and heterogenous (different percentages of R and S) tumor tissues in order to obtain more reliable mass spectral signatures and distribution maps to integrally describe the tumor and necrotic contributions common to both kind of R and S tumor samples [START_REF] Tauler | Selectivity, local rank, three-way data analysis and ambiguity in multivariate curve resolution[END_REF][START_REF] Tauler | Multivariate curve resolution applied to second order data[END_REF][START_REF] Tauler | Multiset Data Analysis: Extended Multivariate Curve Resolution[END_REF]. Therefore, MCR-ALS analysis of the multiset formed by the 15 images was performed. Figures 3a andb show the MCR-ALS resolved distribution maps and pure spectra of the analyzed multiset. Results were clearly showed through the example of two images (100S3a and 100S3b) with a higher magnification and the full set of magnified distribution maps were provided as supplementary information (see Figure S2). Related scanned grayscale images of all samples analyzed with MSI are also shown (left plots in figure 3a). As mentioned in section 3.2, these physiological grayscale pictures show the region in which tumoral cell populations have grown (dark gray color) and the region where necrosis has been produced (light gray color).

Therefore, these grayscale images help to associate each resolved MCR contribution with a particular kind of tissue types (tumoral or necrotic). A After the preliminary simultaneous MCR-ALS analysis of all 15 images, visual inspection of the distribution maps showed that the contributions 1 (blue) and 6 (orange) corresponding to a tumor contribution and a necrotic contribution, respectively, were blending with each other throughout the resolution. In some images corresponding to homogenous tumors (both 100% R and 100% S) the tumor contribution 1 was found in regions associated with necrotic tissue that should not be present and likewise for the necrotic contribution 6 that was present in parts of the tissues attributed to tumor tissue (see distribution maps of Figure 3).

The preliminary MCR-ALS analysis of the fifteen images was not fully satisfactory but provided better spectral signatures for the contributions to be modelled than the raw image and an approximate identification of the biological and non-biological contributions of the data set. The resolved spectral signatures were used as initial estimates in a second MCR-ALS analysis and the information provided by the concentration profiles was used as an auxiliary source of information to set local rank constraints, as described below.

Since spatial information of tumor and necrotic parts can be identified in the grayscale images of tissue slices, this information was used to force the presence/absence of tumor/necrotic contributions throughout the resolution. Once tumor and necrotic pixels were identified using the image registration between grayscale and MS images (See section 3), a masking local rank matrix was built to be used as a constraint (see section 3.2 for more detail in the implementation of this constraint). Taking as a basis the preliminary results obtained by MCR-ALS analysis using only non-negativity constraints, pixels related to necrotic parts were forced to be absent in component 1

(blue tumor contribution) and pixels corresponding to tumor cell populations were forced to be absent in component 6 (orange necrotic contribution) in the images corresponding to both 100% R and 100 % S. Please note that the use of this constraint does not need either that all pixels in all images be constrained nor that all biological contributions be subject to local rank conditions. The use of this constraint in small areas clearly characterized as necrotic or tumor in simpler samples, such as 100% R or 100% S, is sufficient to improve significantly the results of the whole multiset.

Figure 4 shows the resolution results for the simultaneous analysis of all 15 images using non-negativity, normalization and local rank information. The variance explained obtained in the resolution using only non-negativity and normalization constraints was 86.8 % and now with the use of the local rank constraint is 85.5 %. The similarity between the two values indicates that the new constraint introduced is correct and does not perturb the natural behavior of the data set. Resolved distribution maps corresponding to 100S3a and 100S3b were again zoomed for better visualization and the full set of magnified distribution maps were provided as supplementary information (see Figure S3). Moreover, pixel selection from the registered scanned images of necrotic (green color) and tumoral regions (red color) were also showed for these two examples (left side of the zoomed plot in Figure 4).

FIGURE 4

By examination of the MCR-ALS results, the positive effect of local rank constraints in the definition of MS signatures and in the morphology of the distribution maps of the different biological contributions can be clearly seen . Now, in contrast to the resolution only using non-negativity and normalization constraints, tumor contribution 1 and necrotic contribution 6 are no longer mixed with each other and are located in their correct physiological regions. Besides, the resolved profiles are more accurate and show less ambiguity due to the effect of the local rank constraint.

Once the MCR-ALS analysis on the all 15 images was performed, the MCR scores (C aug , distribution maps of sample constituents) were used for segmentation analysis.

Multiset image segmentation. K-means cluster analysis.

Multiset image segmentation analysis was the next step in the strategy proposed (see figure 2), mainly performed to look for clusters present in all analyzed images. As mentioned before, K-means analysis was carried out using resolved MCR scores of the multiset structure (see Figure 4). The fact of using MCR scores allows selecting the information to be included in the segmentation process. In this case, only the four biological contributions related to tumor and necrotic parts (contributions 1, 3, 4 and 6)

were selected and the background contributions (2 and 5) were left aside.

The individual profiles of the four biological contributions of each submatrix C i of the C aug matrix were autoscaled to balance the importance of each biological contribution present in each image of the multiset in the segmentation analysis. Doing it by block (each C i separately), global intensity differences among MS images were also compensated. Autoscaled concentration profiles C i were segmented simultaneously into four classes. In this case, a number of clusters equal to the number of biological contributions was selected. No reliable patterns of necrotic and cancer regions were achieved when the number of clusters in the segmentation procedure was increased.

Figure 5 shows the segmentation results obtained by using the preprocessed MCR scores previously described. Zoom in an example (image 100S3a) was provided in Figure 5 for a better evaluation of the results and magnified results of the full set can be seen in supplementary information (see Figure S4).

FIGURE 5

The fact that K-means assign each pixel to only one cluster [START_REF] Tran | Clustering multispectral images: a tutorial[END_REF] helps to attribute that cluster to a single tissue type and, hence, be more easily comparable with the grayscale images. In figure 5 grayscale images were presented (left side of the pair images in Figure 5) for comparison with their related segmentation maps (right side of the pair images in Figure 5). It can be observed that the grayscale images match the location of tumor and necrotic parts with their related segmentation maps. Clusters 2 (green) and 3

(orange) correspond to the tumor parts, they have the same spatial distribution of the dark grey regions in the scanned image. Similarly, cluster 1 (blue) and 4 (brown) were associated with the necrotic parts. However, this information is richer than that provide by the grayscale images, since heterogeneity within the same kind of tissue (tumor or necrotic) can be appreciated.

Centroid profiles using MCR scores in segmentation analysis are interpretable and, hence, it is possible to know which component or mixture of components is represented in each cluster. In our case, cluster 2 and 3 display dominance of contribution 1 and 3 of MCR contributions (tumor contributions), respectively, and low relative presence of other compounds, as expected. Likewise, cluster 1 and 4 show dominance of contribution 4 and 6 of MCR contributions (necrotic contributions). It must be reminded that segmentation clusters can be potentially formed by pure compound pixels or by pixel mixtures, whereas distribution maps from image resolution display the distribution of each pure image constituent. Since image constituents overlap (specially species 1 and 4), clusters are formed by some pixels with constituent mixtures of different composition. As mentioned before, an important asset is that MCR scores only include information related to biological contributions of the samples and discard information associated with background contributions. This benefit cannot be obtained if segmentation procedures are directly applied to raw MS image spectra.

The results from the combination of MCR with segmentation analysis could be potentially compared with conventionally stained sections to support determination of both anatomical and histopathological features and to provide a better chemical characterization of the compounds or mixtures of compounds involved in every sample region.

In future works, the incorporation of more selective MS detection systems and the use of conventionally stained sections is planned to allow for a more specific identification and location of biological regions to incorporate local rank constraints and for a better definition of the MS spectral signatures obtained to describe tumor heterogeneity.

Conclusions

The strategy of combining multiset resolution and segmentation has been shown to work for the analysis of biological tissues based on the use of hyperspectral images. For the first time, this strategy was applied to MALDI-MSI data and spatial information from grayscale images was used to set local rank constraints in the MCR-ALS resolution. This constraint improved significantly the morphology of the constituent distributions maps resolved by the MCR-ALS, helped in the suppression of the ambiguity in the results obtained and, therefore, contributed to a better definition of the related MS signatures for every biological contribution.

From the grayscale images, relevant features corresponding to anatomical areas of interest (tumoral or necrotic areas) could be identified. The use of this information to force certain components to be absent in particular pixels through MCR-ALS multiset analysis allowed providing reliably spectral signatures to define biological parts common in all the samples and to provide MCR scores for subsequent segmentation analysis. The results of image segmentation distinguished different tissue types (within tumor or necrotic areas) and their spatial distribution matched consistently with the grayscale images. Therefore, the segmentation maps and the chemical information linked to them could be eventually compared with the outcomes of standard histopathological staining protocols.

It is essential to stress the twofold benefit of using in a combined way physiological grayscale images and MSI measurements. On the one hand, the results obtained from MSI analysis by MCR-ALS are clearly improved by the easy setting of local rank constraints based on the visual location of different tissues in grayscale images. On the other hand, the plain information in grayscale images, only marking the boundaries among tissue regions, is greatly enhanced by the MS signatures provided after MCR-ALS application associated with the different tissue regions. Such a detailed MSI analysis also provides more insight on the intrinsic heterogeneity of tissue regions, often necessarily described by more than one biological contribution.

The strategy proposed was shown to work for a case study including fifteen experimental tumors with different grade of heterogeneity but can be generally applicable in any study of biological material based on mass spectrometry imaging data analysis.

Acknowledgements

This Pure spectra 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0 9 0 0 1 0 0 0 1 1 0 0 1 2 0 0 1 3 0 0 1 4 0 0 0 0.5 1 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0 9 0 0 1 0 0 0 1 1 0 0 1 2 0 0 1 3 0 0 1 4 0 0 0 0.5 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0 9 0 0 1 0 0 0 1 1 0 0 1 2 0 0 1 3 0 0 1 4 0 0 0 0.2 0.4 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0 9 0 0 1 0 0 0 1 1 0 0 1 2 0 0 1 3 0 0 1 4 0 0 0 0.5 1 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0 9 0 0 1 0 0 0 1 1 0 0 1 2 0 0 1 3 0 0 1 4 0 0 0 0.5 1 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0 9 0 0 1 0 0 0 1 1 0 0 1 2 0 0 1 3 0 0 1 4 0 0 0 0.5 1 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0 9 0 0 1 0 0 0 1 1 0 0 1 2 0 0 1 3 0 0 1 4 0 0 0 0.5 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0 9 0 0 1 0 0 0 1 1 0 0 1 2 0 0 1 3 0 0 1 4 0 0 0 0.2 0.4 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0 9 0 0 1 0 0 0 1 1 0 0 1 2 0 0 1 3 0 0 1 4 0 0 0 0.5 1 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0 9 0 0 1 0 0 0 1 1 0 0 1 2 0 0 1 3 0 0 1 4 0 0 0 0.5

  scanned images were converted to grayscale images with gray levels from 0 to 255 by rbg2gray MATLAB function. This function converts RGB images to grayscale by eliminating the hue and saturation information while retaining the luminance. MALDI image cubes (x × y × z) were used to obtain 2D global intensity maps by adding all the mass intensity values for each image pixel. Finally, the MALDI 2D global intensity maps were converted to an image containing values in the range 0 (black) to 1.0 (white) by mat2gray MATLAB function. Afterwards, we rescaled this intensity image multiplying by 255 to be matched with the analogous grayscale images coming from the scanner of tissue slices. As an example, figure 1a, b and c show an original scanned grayscale image of 100R1a, its related MALDI 2D global intensity map and its corresponding registered scanned image, once binned to the MALDI pixel size, respectively. The scanned image in figure 1a reveals three different pixel types according to the gray intensity. Black color pixels correspond to MALDI matrix, the dark gray pixels (external region) are attributed to the region in which tumor cells have grown and light gray pixels (central region) correspond to necrotic tissue.

(

  variance not explained by the n c resolved components). The indexes n and m refer to the number of pixels analyzed from the image and the number of m/z channels in each MS spectrum, respectively.With the aim to recover the bilinear model expressed in Equation1, an iterative alternating least squares optimization under constraints is used to obtain both C and S T matrices with a minimum error in the reproduction of the original data set (D). In order to initialize the iterative procedure, a previous determination of the number of pure signal contributions in the raw data set and the generation of C or S T estimates is required.During the alternating least-squares optimization, constraints are used to introduce information useful to provide chemically meaningful C and S T profiles and to decrease the ambiguity in the final solutions obtained[START_REF] Tauler | Selectivity, local rank, three-way data analysis and ambiguity in multivariate curve resolution[END_REF][START_REF] Juan | Chemometrics applied to unravel multicomponent processes and mixtures[END_REF]. In image resolution, nonnegativity in the concentration and in the spectral direction are the most commonly used constraints. Normalization of pure spectra in S T is also a common constraint used to avoid scaling fluctuations in the profiles during the optimization. Moreover, the use of local rank constraints can be extremely helpful to improve the reliability of MCR-ALS solutions. Local rank information indicates the absence of one or more constituents in the different pixels. In tissue-based cancer research, local rank information about the presence/absence of different tissues (tumor or necrotic) in samples can be obtained from the grayscale images, as mentioned in section 3.2. In these cases, the use of this physiologically-based local rank information can help to decrease the inherent MCR ambiguity and, consequently, to define better the location and chemical structure of tissue type. The local rank information in terms of compound identification is translated into a 'mask' matrix. This matrix, sized as C (n pixels × n c of components), is used to encode the local rank constraints, i.e., the information on the missing components in the different pixels, setting them to have a concentration value equal to zero or smaller than a very low predefined value[START_REF] Tauler | Selectivity, local rank, three-way data analysis and ambiguity in multivariate curve resolution[END_REF][START_REF] Juan | Chemometrics applied to unravel multicomponent processes and mixtures[END_REF].A great advantage of MCR-ALS is the possibility to analyze simultaneously several images in a single multiset structure to provide more reliable results, less affected by ambiguity phenomena[START_REF] Tauler | Selectivity, local rank, three-way data analysis and ambiguity in multivariate curve resolution[END_REF][START_REF] Tauler | Multivariate curve resolution applied to second order data[END_REF][START_REF] Tauler | Multiset Data Analysis: Extended Multivariate Curve Resolution[END_REF]. In a biomedical context, this means that resolved spectral contributions will define much better general trends of the population of samples analyzed together than if images were analyzed individually. In this study, the multiset structure was obtained by appending the pixel MS blocks of the different MALDI images one on top of each other to form a column-wise augmented matrix, D aug . The decomposition D aug = C aug S T + E provides a single matrix S T of pure spectra, valid for all the images in the multiset, and a matrix C aug , formed by as many submatrices C i as images in the multiset. The profiles in each of these C i submatrices can be refolded conveniently to recover the related distribution maps of every constituent in each image. A multiset structure also obeys the bilinear model seen in Equation 1. The percentage of explained variance is used to indicate the fit quality of the MCR-ALS results. This parameter is calculated according to the expression: d ij * are the values of the data set reproduced by the bilinear model and d ij the original values in the raw data set D. The number of components included in the MCR-ALS model should be compromise between this maximum variance explained by the model, model simplicity, and model interpretability

2 D

 2 3. were built, each containing images related to a particular kind of tumor and were analyzed separately. The first multiset was formed by the four images corresponding to the tumors of 100% R, D aug R= [D100R1a; D100R1b; D100R1c; D100R2], and the second multiset by the four images corresponding to the tumors of 100% S, DS= [ D100S3a; D100S3b; D100S3c; D100S4]. MCR-ALS was applied separately to each multiset structure (D R and D S ) following the steps explained in section 3.3 and the following bilinear models were obtained: D augR = C augR *S T R Equation augS = C augS *S T S Equation3and using non-negativity constraints in concentration and spectra profiles and spectra normalization in matrix S T R or S (using the Euclidean norm).

Now, in contrast

  to the analysis of either D R or D S multiset structures, resolution of six components is proposed. As in the resolution of D R and D S multisets, the four components associated with relevant biological parts (tumor and necrotic) were observed. Looking at their related grayscale images, blue (component 1) and red (component 3) could be approximately associated with tumor contributions and cyan (component 4) and orange (component 6) with necrotic contributions. Moreover, two non-biological contributions (green and violet) were again required to perform the analysis and to cope with spectral differences due to instrumental and experimental variability. As expected, two non-biological spectral contributions were needed instead of one, in agreement with the lack of correlation between the Rb and Sb contribution resolved in the D R and D S multisets.

  Figure 1. a) Original RGB image of 100R1a sample, b) MALDI image of 100R1a after

1 Figure 2 .

 12 Figure 2. Graphical representation of the combination of MCR-ALS with K-means for

1 Figure 3 .

 13 Figure 3. MCR-ALS results for the multiset analysis of 15 tumor images using non-

4

  Photographic images

Table 2

 2 lists the number of resolved components and the explained variance obtained from the MCR-ALS analyses of both multisets. Resolution of five contributions was necessary in both cases. The inclusion of a different number of contributions gave worse data fits or unreliable spectra or distribution maps (see Figure

Table 1 .

 1 work is part of the BEST Postdoctoral Program, funded by the European Percentage of both R and S cell lines in xenografts, replicate number of either

	Commission under Horizon 2020's Marie Skłodowska-Curie Actions COFUND
	scheme (Grant Agreement no. 712754) and by the Severo Ochoa program of the
	Spanish Ministry of Science and Competitiveness (Grant SEV-2014-0425 (2015-2019)).
	A.J. acknowledges financial support from the Catalan government through project 2017
	SGR 753 and the Spanish government through project CTQ2015-66254-C2-2-P.7. We
	would like to acknowledge, the Departament d'Universitats, Recerca i Societat de la
	Informació de la Generalitat de Catalunya (expedient 2017 SGR 1721); the Comissionat
	per a Universitats i Recerca del DIUE de la Generalitat de Catalunya; and the European
	Social Fund (ESF). Additional financial support has been provided by the Institut de
	Bioenginyeria de Catalunya (IBEC). IBEC is a member of the CERCA
	Programme/Generalitat de Catalunya. This publication has been also funded with

Table 2 .

 2 Number of resolved components and variance explained by MCR-ALS

	Multiset	Explained	Resolved
		variance	components
	DR= [D100R1a; D100R1b; D100R1c; D100R2]	89.7%	5
	DS= [ D100S1a; D100S1b; D100S1c; D100S2]	87.5%	5
	8. Figures

12

analysis of D r and D s multiset structures.

the image sections analyzed.


Deepika Watts

Johanna Stein

Ana Meneses

Nicole Bechmann

Ales Neuwirth

Denise Kaden

Anja Krüger

Vasileia Anupam Sinha

Ismini Alexaki

Gustavo Luis

Perez-Rivas

Vasileia Ismini Alexaki

Stefan Kircher

Antoine Martinez

Marily Theodoropoulou

Graeme Eisenhofer

Mirko Peitzsch

Ali El-Armouche

Triantafyllos Chavakis

Ben Wielockx
email: ben.wielockx@tu-dresden.de

HIF1α is an essential regulator of steroidogenesis in the adrenal gland

Keywords: Hypoxia, adrenocortical steroids, cytokines, HIF, PHD

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Steroidogenesis in the adrenal gland is a complex process of sequential enzymatic reactions that convert cholesterol into steroids, including mineralocorticoids and glucocorticoids [START_REF] Miller | The Molecular Biology, Biochemistry, and Physiology of Human Steroidogenesis and Its Disorders[END_REF]. While glucocorticoids are regulated by the hypothalamic-pituitary-adrenal axis (HPA axis) and are essential for stress management and immune regulation [START_REF] Cain | Immune regulation by glucocorticoids[END_REF][START_REF] Straub | Glucocorticoids and chronic inflammation[END_REF], aldosterone, the primary mineralocorticoid, regulates the balance of water and electrolytes in the body [START_REF] Faught | The mineralocorticoid receptor is essential for stress axis regulation in zebrafish larvae[END_REF]. As steroidogenesis is a tightly regulated process, proper control of adrenal cortex function relies on appropriate endocrine signaling, tissue integrity, and homeostasis [START_REF] Gallo-Payet | Steroidogenesis-adrenal cell signal transduction[END_REF]. Accordingly, it has been suggested that inappropriately low pO2, or hypoxia, can lead to both structural changes in the adrenal cortex and interfere with hormone production [START_REF] Raff | The Effect of Oxygen on Aldosterone Release from Bovine Adrenocortical Cells in Vitro:PO2 versus Steroidogenesis*[END_REF][START_REF] Tan | Overexpression and Knockdown of Hypoxia-Inducible Factor 1 Disrupt the Expression of Steroidogenic Enzyme Genes and Early Embryonic Development in Zebrafish[END_REF][START_REF] Nusrin | Regulation of CYP11B1 and CYP11B2 steroidogenic genes by hypoxia-inducible miR-10b in H295R cells[END_REF][START_REF] Bruder | Oxygen-dependence of ACTH-stimulated aldosterone and corticosterone synthesis in the rat adrenal cortex: developmental aspects[END_REF][START_REF] Lorente | Chronic hypoxia induced ultrastructural changes in the rat adrenal zona glomerulosa[END_REF].

Hypoxia inducible factors (HIFs) are the main transcription factors that are central to cellular adaptation to hypoxia in virtually all cells of our body. The machinery that directly controls HIF activity consists of the HIF-prolyl hydroxylase domain-containing enzymes (PHDs 1-3), which are oxygen sensors that hydroxylate two prolyl residues in the HIFα subunit under normoxic conditions, thereby marking the HIFs for proteasomal degradation. Conversely, oxygen insufficiency renders these PHDs inactive, leading to the binding of the HIF-complex to hypoxia responsive elements (HRE) in the promotor of multiple genes that ensure oxygen delivery and promote adaptive responses to hypoxia such as hematopoiesis, blood pressure regulation, and energy metabolism (reviewed in [START_REF] Sormendi | Hypoxia Pathway Proteins As Central Mediators of Metabolism in the Tumor Cells and Their Microenvironment[END_REF]12)). Apart from directly activating hypoxia-responsive genes [START_REF] Schodel | High-resolution genome-wide mapping of HIF-binding sites by ChIP-seq[END_REF][START_REF] Smythies | Inherent DNAbinding specificities of the HIF-1alpha and HIF-2alpha transcription factors in chromatin[END_REF], HIFs also indirectly influence gene expression by interfering with the activity of other transcription factors or systems. Of the most intensively studied HIFα genes, HIF1α has a ubiquitous pattern of expression in all tissues, whereas expression of the paralogue HIF2α is restricted to a selection of cell types [START_REF] Stroka | HIF-1 is expressed in normoxic tissue and displays an organ-specific regulation under systemic hypoxia[END_REF][START_REF] Wiesener | Widespread hypoxia-inducible expression of HIF-2alpha in distinct cell populations of different organs[END_REF].

Recent in vitro and zebrafish studies have revealed a continuous cross talk between HIF and steroidogenesis pathways, along with potential interference in the production of aldosterone and glucocorticoids [START_REF] Wang | HIF 1 inhibits StAR transcription and testosterone synthesis in murine Leydig cells[END_REF](18)(19)[START_REF] Marchi | Bidirectional crosstalk between HIF and Glucocorticoid signalling in zebrafish larvae[END_REF]. There is also evidence suggesting a role for the hypoxia pathway in modulating glucocorticoid/glucocorticoid receptor (GR) signaling [START_REF] Kodama | Role of the glucocorticoid receptor for regulation of hypoxia-dependent gene expression[END_REF][START_REF] Zhang | Effects of hypoxia inducible factor-1alpha on apoptotic inhibition and glucocorticoid receptor downregulation by dexamethasone in AtT-20 cells[END_REF]. Importantly, these observations indicate a possible interplay of HIFs and PHDs in modulating the immune-regulatory actions of the HPA axis. Currently, there is huge interest in the development of HIF inhibitors and HIF stabilizers, and their influence on medicine is expected to become significant in the near future [START_REF] Semenza | Pharmacologic Targeting of Hypoxia-Inducible Factors[END_REF]. However, as the role of HIFs/PHDs is both central and manifold with respect to maintaining oxygen homeostasis, a better understanding of the true impact of Hypoxia Pathway Proteins (HPPs) in the complex interplay of different essential physiological and pathological conditions, including in the adrenal cortex, assumes great importance.

We describe the creation and use of a unique collection of transgenic mouse lines that enabled an investigation of the role of HIFα subunits and PHDs in adrenocortical cells. Our results point towards a central role for HIF1α in the direct regulation of steroidogenesis in the adrenal gland and consequent changes in circulatory hormone levels. Importantly, chronic exposure of mice to such altered hormone levels eventually led to a dramatic decrease in essential inflammatory cytokines and profound dysregulation of circulatory immune cell profiles.

Materials and Methods

Mice

All mouse strains were housed under specific pathogen-free conditions at the Experimental Centre of the Medical Theoretical Center (MTZ, Technical University of Dresden -University Hospital Carl-Gustav Carus, Dresden, Germany). Experiments were performed with male and female mice aged between 8-16 weeks. No significant differences between the genders were observed.

Akr1b7:cre-PHD2/HIF1 ff/ff (P2H1) or Akr1b7:cre-PHD2/PHD3 ff/ff (P2P3) lines were generated by crossing Akr1b7:cre mice (24) to PHD2 f/f , HIF1α f/f or PHD2 f/f ; PHD3 f/f as previously reported by us [START_REF] Lambert-Langlais | A transgenic mouse line with specific Cre recombinase expression in the adrenal cortex[END_REF], and/or the reporter strain mTmG [START_REF] Muzumdar | A global doublefluorescent Cre reporter mouse[END_REF]. All mice described in this report were born in normal Mendelian ratios. Mice were genotyped using primers described in supplementary Table 1. Histological analysis of the adrenal gland of Akr1b7:cre-mTmG f/f reporter mice revealed zonal variation in the penetrance of cre-recombinase activity in the adrenal cortex of all individual mice (GFP + staining). Peripheral blood was drawn from mice by retro-orbital sinus puncture using heparinized micro hematocrit capillaries (VWR, Darmstadt, Germany) and plasma separated and stored at -80 °C until further analysis. Mice were sacrificed by cervical dislocation and adrenals were isolated, snap frozen in liquid nitrogen, and stored at -80°C for hormone analysis or gene expression analysis. All mice were bred and maintained in accordance with facility guidelines on animal welfare and with protocols approved by the Landesdirektion Sachsen, Germany.

Blood analysis

White blood cell counts were measured using a Sysmex automated blood cell counter (Sysmex XE-5000) [START_REF] Mitroulis | Secreted protein Del-1 regulates myelopoiesis in the hematopoietic stem cell niche[END_REF].

ACTH measurements

Plasma ACTH was determined using a radioimmunoassay, as per manufacturer's instructions (ImmuChem Double Antibody hACTH 125 I RIA kit; MP Biomedicals Germany GmbH, Eschwege, Germany) [START_REF] Castillo | Effect of SOM230 (pasireotide) on corticotropic cells: action in dogs with Cushing's disease[END_REF].

Hormone detection

Adrenal glands were incubated in disruption buffer (component of Invitrogen TM Paris TM Kit, AM 1921, ThermoFisher Scientific, Dreieich, Germany) for 15min at 4°C, homogenized in a tissue grinder, followed by incubation for 15 min on ice and further preparation. Adrenal steroid hormones were determined by LC-MS/MS as described elsewhere [START_REF] Peitzsch | An LC-MS/MS method for steroid profiling during adrenal venous sampling for investigation of primary aldosteronism[END_REF]. Catecholamines, norepinephrine, epinephrine, and dopamine were measured by high pressure liquid chromatography (HPLC) coupled with electrochemical detection, as previously described (30).

RNA extraction and qPCRs

RNA from adrenal glands and sorted cells was isolated using the RNA Easy Plus micro kit (Qiagen) (Cat. # 74034Qiagen). cDNA synthesis was performed using the iScript cDNA Synthesis Kit (BIO-RAD, Feldkirchen, Germany). Gene expression levels were determined by performing quantitative real-time PCR using the 'Ssofast Evagreen Supermix' (BIO-RAD, Feldkirchen, Germany). Sequences of primers used are provided in supplemental Table 2. Expression levels of genes were determined using the Real-Time PCR Detection System-CFX384 (BIO-RAD, Feldkirchen, Germany). All mRNA expression levels were calculated relative to β2M or EF2 housekeeping genes and were normalized using the ddCt method. Relative gene expression was calculated using the 2(-ddCt) method, where ddCT was calculated by subtracting the average WT dCT from dCT of all samples individually.

Immunohistochemistry and immunofluorescence

For preparation of paraffin sections, adrenal glands were isolated, incubated in 4% formaldehyde at 4°C overnight, dehydrated, embedded in paraffin and cut into 5µm sections using a microtome. Sections were rehydrated and subjected to hematoxylin and eosin staining (H&E).

For frozen sections, adrenal glands were embedded in O.C.T Tissue-Tek (A. Hartenstein GmbH, Würzburg, Germany) and stored at -20°C. For H&E staining of frozen sections (7µm), samples were first fixed in cold acetone before staining. For immunofluorescence, sections were fixed in cold acetone, air-dried, washed with phosphate-buffered saline containing 0.1% Tween-20, blocked with 5% normal goat serum followed by primary antibody staining (CD31/PECAM -1:500 (31)) or GFP Polyclonal (Antibody ThermoFischer Scientific -1:200) overnight at 4°C and subsequent secondary antibody staining. After counterstaining with DAPI, slides were mounted in fluorescent mounting medium and stored at 4 °C until analysis.

Microscopy

Both brightfield and fluorescent images were acquired on an ApoTome II Colibri (Carl Zeiss, Jena, Germany). Images were analyzed using either Zen software (Carl Zeiss, Jena, Germany) or Fiji (ImageJ distribution 1.52K). Fiji was used to quantify lipid droplet sizes and CD31 staining.

Meso Scale Discovery

Meso Scale Discovery (MSD, Rockville, Maryland) was used to measure cytokines in plasma samples using the MSD plate reader (QuickPlex SQ 120). Cytokine concentrations were calculated by converting the measured MSD signal to pg/ml using a standard curve. All values below that of blank (control) were considered as zero. Finally, all cytokine concentrations in individual P2H1 mice were normalized to the average value of WTs for every independent experiment; and the average WT value was set as 1.

Next generation sequencing

For RNAseq analysis, adrenal glands from Akr1b7:cre-PHD2/HIF1/mTmG fff/fff and Akr1b7:cre-mTmG f/f (control) mice were isolated directly into the lysis buffer of the RNeasy Plus Micro Kit, RNA was isolated according to manufacturer's instructions, and SmartSeq2 sequencing was performed (SmartSeq2 and data analysis in Supplemental Data). Flow cytometry and cell sorting were performed as described previously [START_REF] Singh | HIF prolyl hydroxylase 2 (PHD2) is a critical regulator of hematopoietic stem cell maintenance during steady-state and stress[END_REF].

Read Quantification

Kallisto v0.43 was first used to generate an index file from the transcript file, which can be downloaded from :ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M12/gencode.vM12.transcri pts.fa.gz. Kallisto v0.43 was then run on all the fastq files using parameters "quant --single -l 75s 5 -b 100" to quantify reads for the genes.

Differential Gene Expression Quantification

Complete cDNA sleuth v0.30.0 (an R package) was used to evaluate differential expression. The command "sleuth_prep" was run with parameter "gene_mode=TRUE". Two separate error models were fit using "sleuth_fit" wherein the first was a "full" model with gender and experimental condition as covariates, while the second was a "reduced" model with only gender as the covariate.

"sleuth_lrt" (Likelihood Ratio Test) was used to evaluate differential gene expression by comparing the full model and the reduced model.

Statistical analyses

All data are presented as mean ± SEM. Data (WT control versus transgenic line) were analyzed using the Mann-Whitney U-test, unpaired t-test with Welch's correction as appropriate (after testing for normality with the F test) or as indicated in the text. All statistical analyses were performed using GraphPad Prism v7.02 for Windows (GraphPad Software, La Jolla California USA, www.graphpad.com). Significance was set at p<0.05; "n" in the figure legends denotes individual samples.

Results

A new mouse model to study the effects of alterations in hypoxia pathway proteins (HPPs) in the adrenal cortex

We took advantage of the adrenal cortex-specific Akr1b7:cre recombinase mouse line [START_REF] Lambert-Langlais | A transgenic mouse line with specific Cre recombinase expression in the adrenal cortex[END_REF] to investigate the effects of adrenocortical HPPs on the structure and functions of the adrenal gland.

When combined with the mTmG reporter strain (26), we show up to 40% targeting among all cortical cells (Figure 1A). Next, we generated the Akr1b7:cre-PHD2/HIF1 ff/ff mouse line (henceforth designated P2H1) by combining Akr1b7:cre mice with PHD2 and HIF1α floxed mice [START_REF] Franke | HIF-1alpha is a protective factor in conditional PHD2-deficient mice suffering from severe HIF-2alphainduced excessive erythropoiesis[END_REF]. Genomic PCRs on DNA and qPCR analysis using mRNA from whole adrenal glands revealed targeting of PHD2 and HIF1α, when compared to WT littermates (Figure 1B-C).

Importantly, in P2H1 mice, we even detected a significant increase in HIF2α mRNA but not of PHD3, which is in line with our earlier report of enhanced HIF2α-activity in PHD2/HIF1αdeficient cells [START_REF] Franke | HIF-1alpha is a protective factor in conditional PHD2-deficient mice suffering from severe HIF-2alphainduced excessive erythropoiesis[END_REF]. Therefore, we explored the expression profile of a number of downstream genes known to be transactivated by HIF2α [START_REF] Wiesener | Induction of endothelial PAS domain protein-1 by hypoxia: characterization and comparison with hypoxia-inducible factor-1alpha[END_REF][START_REF] Bertout | HIF2alpha inhibition promotes p53 pathway activity, tumor cell death, and radiation responses[END_REF][START_REF] Rankin | Hypoxia-inducible factor-2 (HIF-2) regulates hepatic erythropoietin in vivo[END_REF] and found a significant increase in Vegfa, Hmox1, and to a lesser extent Bnip3 levels, underscoring the functionality of the P2H1 mouse line (Figure 1E).

Morphological changes in the adrenal cortex of P2H1 mice

To evaluate the impact of changes in HIF1α and/or HIF2α activity in adrenocortical cells, we analyzed adrenal gland morphology using H&E staining on paraffin sections but found no differences between P2H1 mice and WT littermates in the structure of the adrenal gland, especially, at the side of the cortex of P2H1 mice in comparison to WT littermates (Figure 1F). As we detected a significant increase in Vegfa in the adrenal glands of P2H1 mice, we used CD31 staining to quantify endothelial cells but detected no significant differences between P2H1 and WT mice (Figure 1G). Remarkably, H&E staining on cryosections of P2H1 adrenal glands revealed significantly smaller lipid droplets in the adrenocortical cells (Figure 1H), an effect that is reported to be correlated with greater conversion of cholesterol into pregnenolone [START_REF] Lorente | Chronic hypoxia induced ultrastructural changes in the rat adrenal zona glomerulosa[END_REF].

Modulation of HPPs in the adrenal cortex enhances synthesis and circulatory levels of steroid hormones

Next, to verify if the observed changes in lipid droplets indeed led to changes in steroidogenesis, we quantified steroid hormones and their precursor levels by LC-MS/MS in the adrenal gland and in plasma. Quantification revealed a significant increase in virtually all of the hormones tested in P2H1 adrenal glands compared to WT littermates (Figure 2A), and importantly, a corresponding increase of progesterone, corticosterone, and aldosterone was found in the plasma (Figure 2B).

These observations clearly indicate that central HPPs have an impact on steroidogenesis in the murine adrenal gland and on circulatory levels of steroid hormones.

Downstream effects of the chronic increase in the steroidogenesis

Previous reports have stated that glucocorticoids can regulate catecholamine production in the adrenal medulla [START_REF] Busceti | Corticosterone Upregulates Gene and Protein Expression of Catecholamine Markers in Organotypic Brainstem Cultures[END_REF][START_REF] Nguyen | Regulation of the phenylethanolamine N-methyltransferase gene in the adrenal gland of the spontaneous hypertensive rat[END_REF]; therefore, we also measured dopamine, norepinephrine, and epinephrine levels in the samples used to quantify steroid levels (as above). However, we found no difference between P2H1 and WT littermates in any of the catecholamines quantified (Supplementary Figure 1A). Further, although increased steroid levels often result in a negative feedback loop affecting ACTH secretion from the pituitary [START_REF] Newell-Price | Cushing's syndrome[END_REF], P2H1 mice displayed no such differences compared to WT littermates (Supplementary Figure 1B), nor did they have any difference in serum potassium levels or blood glucose levels (Supplementary Figure 1C-D). Taken together, in contrast to the systemic effects induced by acute and high levels of circulatory cortical hormones (e.g. corticosterone, aldosterone) [START_REF] Straub | Glucocorticoids and chronic inflammation[END_REF][START_REF] Faught | The mineralocorticoid receptor is essential for stress axis regulation in zebrafish larvae[END_REF], the P2H1 mice display moderate but chronically enhanced levels of cortical hormones at the described time points.

Loss of PHD2/HIF1α in adrenocortical cells impacts gene expression related to steroidogenesis

Previous in vitro studies and reports on HIF1α alterations in zebrafish larvae have suggested negative regulation of StAR, the mitochondrial cholesterol transporter [START_REF] Tan | Overexpression and Knockdown of Hypoxia-Inducible Factor 1 Disrupt the Expression of Steroidogenic Enzyme Genes and Early Embryonic Development in Zebrafish[END_REF][START_REF] Wang | HIF 1 inhibits StAR transcription and testosterone synthesis in murine Leydig cells[END_REF][START_REF] Marchi | Bidirectional crosstalk between HIF and Glucocorticoid signalling in zebrafish larvae[END_REF]. However, data on the effects of HPP alterations in adrenal cortex of mice is scant at best. Therefore, to assess the impact of HIF1α-deletion and/or HIF2α-upregulation in adrenal cortical cells, we performed broad transcription analysis of proteins/enzymes involved in steroidogenesis using mRNA from whole adrenals. Our results reveal that almost all of the gene products tested showed either a significant increase or a tendency to do so, including key enzymes like StAR, Cyp11a1, Cyp21a1 and Cyp11b1

(Figure 3A).

To further characterize this phenotype driven by the HPPs, we performed next generation sequencing (NGS) and compared the steady state transcriptomes of P2H1 and WT littermate mice (Figure 3B). For this, we specifically created the Akr1b7:cre-PHD2/HIF1/mTmG fff/fff mouse line (P2H1 reporter mice) to study only targeted adrenal cortex cells, with Akr1b7:cre-mTmG f/f animals used as controls. Bulk RNAseq was performed on GFP + -sorted adrenal gland cells as described previously [START_REF] Ramasz | Hematopoietic stem cell response to acute thrombocytopenia requires signaling through distinct receptor tyrosine kinases[END_REF] and gene signatures of the various lineages were evaluated using Enrichr or gene set enrichment analyses (GSEA). Concurring with the previous results, we found a number of significant signatures related to the process of steroid synthesis in adrenocortical cells or their response to it (Figure 3C-D). Notably, GSEA also revealed known HIF-dependent associations including, actin cytoskeleton [START_REF] Zieseniss | Hypoxia and the modulation of the actin cytoskeleton -emerging interrelations[END_REF]41), adipogenesis (42) and oxidative phosphorylation [START_REF] Thomas | Exploring the molecular interface between hypoxia-inducible factor signalling and mitochondria[END_REF] (Figure 3E). Furthermore, P2H1 cortical cells also displayed a positive signature related to the regulation of nuclear β-catenin signaling, which is known to be primarily activated in the zona glomerulosa with potential hyperplasic effects (44) (Figure 3F).

Modulated adrenocortical HPPs skew cytokine production and leukocyte numbers.

As several studies have reiterated a crucial role for glucocorticoids in immunomodulation [START_REF] Straub | Glucocorticoids and chronic inflammation[END_REF][START_REF] Brattsand | Cytokine modulation by glucocorticoids: mechanisms and actions in cellular studies[END_REF], and Cushing's syndrome has been described to be accompanied by immune deficiency [START_REF] Straub | Glucocorticoids and chronic inflammation[END_REF][START_REF] Newell-Price | Cushing's syndrome[END_REF]46), we measured circulatory cytokine levels. We report a substantial overall decrease in the levels of both pro-and anti-inflammatory cytokines, with the exception of the chemokine and neutrophil attractant CXCL1, which increased almost 2-fold (Figure 4A). Glucocorticoids have been repeatedly shown to promote apoptosis-mediated reduction of lymphocytes [START_REF] Smith | Glucocorticoid-Induced Apoptosis of Healthy and Malignant Lymphocytes[END_REF] and eosinophil reduction [START_REF] Lee | The Eosinophil Count Tends to Be Negatively Associated with Levels of Serum Glucose in Patients with Adrenal Cushing Syndrome[END_REF], along with neutrophilia due to enhanced recruitment from the bone marrow [START_REF] Ronchetti | How Glucocorticoids Affect the Neutrophil Life[END_REF].

Therefore, we enumerated the various white blood cell (WBC) fractions in P2H1 mice and compared it with that of their WT littermates, which revealed a significant reduction in both lymphocyte and eosinophil fractions (Figure 4B) accompanied by marked elevation in neutrophils (>70% compared to WT) (Figure 4C). Taken together, our data reveal a critical role for HPPs in steady-state cytokine levels and leukocyte numbers, probably through alterations in steroidogenesis pathways.

HIF1α inversely regulates steroidogenesis

To extend our understanding of the role of HIF1α and/or HIF2α in adrenocortical cells, we created the Akr1b7:cre-PHD2/PHD3 ff/ff mouse line (designated as P2P3), which showed adequate activation efficiency upon genomic PCRs of whole adrenal tissue (supplementary Figure 2).

Intriguingly and in contrast to hormone levels in the adrenal glands of the P2H1 mice, P2P3 adrenal glands displayed a marked decrease in corticosterone and aldosterone levels, along with a cognate reduction in their precursors, both in the adrenal gland (Figure 5A) and in circulation (Figure 5B).

These results clearly suggest that steroidogenesis is dependent on HIF1α but not HIF2α. To further confirm this observation, we performed mRNA expression analyses to identify the levels of central enzymes, similar to that performed in P2H1 mice, and demonstrate an overall decrease in these enzymes (Figure 6A). This observation is contrary to that seen in the P2H1 mice but fits neatly with the observed reduction in steroid levels in the P2P3 mice, thereby underscoring the central role of HIF1α (Figure 6B).

Discussion

Here, by using a unique collection of adrenocortical-specific transgenic mouse lines, we identify HIF1α as a central transcription factor that regulates the steroidogenesis pathway by regulating key enzymes. Notably, this directly modifies the entire spectrum of steroid hormones, both in the adrenal gland and in circulation, which eventually impacts the availability of a variety of cytokines.

Studies on the role of HIFs in the regulation of steroidogenesis in vitro are few, apart from those in zebra fish larvae that describe differential regulation of the enzymes involved in the steroid pathway [START_REF] Tan | Overexpression and Knockdown of Hypoxia-Inducible Factor 1 Disrupt the Expression of Steroidogenic Enzyme Genes and Early Embryonic Development in Zebrafish[END_REF]18,[START_REF] Marchi | Bidirectional crosstalk between HIF and Glucocorticoid signalling in zebrafish larvae[END_REF]. However, to the best of our knowledge, there are no mouse models to study the role of HPPs in adrenal cortical cells. Undoubtedly, such models would help us to better understand the crosstalk between HPPs and adrenal steroid metabolism, while simultaneously serving as an essential tool to study the pathophysiology of multiple conditions associated with dramatically altered steroid hormone levels [START_REF] Cain | Immune regulation by glucocorticoids[END_REF]. Ablation of HIF1α revealed an important role for this transcription factor in steroidogenesis, which concurs with results from previous studies [START_REF] Marchi | Bidirectional crosstalk between HIF and Glucocorticoid signalling in zebrafish larvae[END_REF]50). However, our findings that HIF1α deletion results in the upregulation of mRNA of a vast majority of steroid-related enzymes is counterintuitive to the nature of this transcription factor (12, 51), and therefore we believe this effect is most likely indirect with potential involvement of one or more transcriptional repressors [START_REF] Schodel | High-resolution genome-wide mapping of HIF-binding sites by ChIP-seq[END_REF][START_REF] Yun | Inhibition of PPAR gamma 2 gene expression by the HIF-1-regulated gene DEC1/Stra13: a mechanism for regulation of adipogenesis by hypoxia[END_REF][START_REF] Fecher | Inverse Correlation between IL-10 and HIF-1alpha in Macrophages Infected with Histoplasma capsulatum[END_REF]. This type of transcriptional regulation of adrenal steroidogenesis has already been suggested with miRNAs, which are endogenous noncoding single-stranded small RNAs that suppress the expression of various target genes [START_REF] Azhar | The role of miRNAs in regulating adrenal and gonadal steroidogenesis[END_REF]. Hu and colleagues have demonstrated that a HIF1α-dependent miRNA, miRNA-132, attenuates steroidogenesis by reducing StAR protein levels [START_REF] Hu | Regulation of adrenal and ovarian steroidogenesis by miR-132[END_REF], and similar mechanisms have reported for

Cyp11B2 via miR-193a-3p (56, 57), and Cyp11B1 and Cyp11B2 via miR-10b (8). Thus, these new mouse lines will be of great value for in-depth studies on the complex background of HIF1α involvement in the expression patterns of steroidogenesis-related miRs.

Our RNAseq analysis of Akr1b7 + P2H1 adrenocortical cells not only unearthed several genetic signatures directly associated with steroidogenesis, but a number of GSEAs revealed prominent HIF-dependent phenotypes previously identified in a variety of other cell types. Recently, we have described a significant role for HIF2α in the regulation of the actin cytoskeleton, especially in facilitating enhanced neutrophil migration through very confined environments (41), HIF1α has also been associated with cytoskeleton structure and functionality in a number of cell lineages (reviewed in ( 40)); this is apart from its role in energy metabolism wherein enhanced oxidative phosphorylation has been demonstrated in various HIF1α-deficient cell lineages [START_REF] Thomas | Exploring the molecular interface between hypoxia-inducible factor signalling and mitochondria[END_REF]. Therefore, it will be of interest to further explore changes in multiple metabolites that are directly or indirectly-associated with the TCA cycle to find a potential link with the overall changes described here.

Glucocorticoids and aldosterone are both essential for homeostasis and their substantial increase in P2H1 mice was intriguing, given their pivotal role in immune suppression [START_REF] Straub | Glucocorticoids and chronic inflammation[END_REF][START_REF] Coutinho | The anti-inflammatory and immunosuppressive effects of glucocorticoids, recent developments and mechanistic insights[END_REF] and blood pressure regulation, respectively. Previous studies have shown that aldosterone not only increases the expression of the potassium channels that secrete potassium but also stimulates K-absorptive pumps in the renal cortex and medulla, thereby stabilizing and maintaining renal potassium excretion (59), a situation we also observed in the P2H1 mice. The significant increase in glucocorticoids upon HIF1α deletion was clearly associated with immunosuppression, as demonstrated by an overall decrease in both pro-and anti-inflammatory cytokines in circulation, and these observations mirror other reports of immune modulation due to enhanced glucocorticoid levels. Such glucocorticoid elevation can eventually even result in dramatic immune deficiency, for example, as seen in Cushing's disease [START_REF] Straub | Glucocorticoids and chronic inflammation[END_REF][START_REF] Newell-Price | Cushing's syndrome[END_REF][START_REF] Brattsand | Cytokine modulation by glucocorticoids: mechanisms and actions in cellular studies[END_REF][START_REF] Coutinho | The anti-inflammatory and immunosuppressive effects of glucocorticoids, recent developments and mechanistic insights[END_REF].

Intriguingly, we found serum CXCL1 to be significantly enhanced in P2H1 mice, probably because as a central neutrophil attractant it was associated with the massive increase in circulatory neutrophils seen in these mice. It is known that enhanced neutrophil recruitment from the bone marrow is directly associated with glucocorticoids (49), as is their overall survival [START_REF] Saffar | The molecular mechanisms of glucocorticoids-mediated neutrophil survival[END_REF][START_REF] Kato | Inhibition by dexamethasone of human neutrophil apoptosis in vitro[END_REF].

An essential role of HIF1α, but not HIF2α, in the modulation of enzymes and adrenocortical hormones could be further corroborated by the contrasting results seen in the P2P3 mice.

Specifically, compared to P2H1 mice, the expression profile of virtually all steroidogenesis regulating enzymes was dramatically inverted in the P2P3 mice, which resulted in an overall impairment of the steroidogenesis pathway. Therefore, these mouse lines will also be helpful to study the potential impact of dramatically modulated steroid levels in a variety of clinically relevant diseases including metabolic and auto-immune disorders.

In summary, we reveal a prominent role for HIF1α as a central regulator of steroidogenesis in mice as two distinct transgenic mouse lines showed persistent but contrasting changes in corticosterone and aldosterone concentrations at levels sufficient to modulate systemic cytokine levels and leukocyte numbers. These P2H1 and P2P3 mouse strains are of significant importance in further exploring the impact of HIF1α in adrenocortical cells and as an essential component in regulation of steroidogenesis-mediated systemic effects.

measurements and contributed to the discussion. B.W. designed and supervised the overall study, analysed data, and wrote the manuscript.

Figure 1 .

 1 Figure 1. Characterization of the Akr1b7:cre-P2H1 ff/ff mouse line with cortex-specific targeting of hypoxia pathway proteins. A: Representative immunofluorescent image of anti-GFP stained (GFP+) area in the adrenal cortex of the Akr1b7:cre-mTmG mouse line. Region enclosed within the white dotted line represents the medulla and it demarcates the medulla from the cortex (scale bar, 100 μm). B: qPCR-based mRNA expression analysis of PHD2 and HIF1α in entire adrenal tissue from P2H1 mice and WT littermates (n=10-13). Relative gene expression was calculated using the 2^( -ddCt) method. The graphs represent data from 2 independent experiments. C: Genomic PCRs for Akr1b7:cre (650bp), PHD2 LoxP (400bp), and PHD2 KO (350bp) in DNA derived from whole adrenal glands of WT and P2H1 mice. D-E: Relative gene expression analysis using mRNA from the entire adrenal tissue in P2H1 mice and their WT counterparts (n=10-13). All graphs represent data from 2 independent experiments. F: Representative images (magnification 20x) of paraffin sections of adrenal glands (H&E) from 8-week old WT and P2H1 mice (scale bars represent 100μm). G: Representative immunofluorescent images of CD31 + endothelial cell staining in adrenal gland sections from WT and P2H1 mice (scale bars represent 50μm). Graph in the right-side panel represents quantification of CD31 + area as a fraction of total tissue area. Each data point represents a single measurement of the cortical area in the adrenal gland (collection of n=6 vs 11 individual mice). H: Representative images of cryo-sections of WT and P2H1 adrenal glands (H&E) (scale bars represent 50μm). Graph in the right-side panel represents the normalized average size of an individual lipid droplet per section of adrenal gland tissue in WT versus P2H1 mice. Measurements were made from 6 sections per mouse. (n=8 individual adrenals per genotype). The graphs in panels G and H are representative of 2 independent experiments. Statistical significance was defined using the Mann-Whitney U test (*p<0.05; **p<0.005; ***p<0.001; ****p<0.0001).

Figure 2 :

 2 Figure 2: Adrenal cortex-specific loss of PHD2 and HIF1 leads to enhanced steroidogenesis in P2H1 mice. A: Box and whisker plots showing steroid hormone measurements in adrenal glands from WT mice and compared to littermate P2H1 mice (n=20-31 individual adrenal glands). B: Box and whisker plots showing steroid hormone measurements in the plasma of individual mice (n=5-17). All data were normalized to average measurements in WT mice. The graphs are a

Figure 3 :

 3 Figure 3: Gene expression analysis of P2H1 adrenocortical cells. A: Gene expression analysisof enzymes involved in the steroidogenesis pathway using mRNA from whole adrenals from P2H1 mice and WT counterparts (n=10-13). All graphs are the result of 2 independent experiments. Statistical significance was defined using the Mann-Whitney U test (*p<0.05; **p<0.005). B: Schematic overview of the RNAseq approach which compared sorted GFP + cells from WT controls and P2H1 mice (n=3). C: Gene signature analysis using Enrichr. D. Gene set enrichment analyses (GSEA) showed positive signatures for steroidogenesis related pathways. E: prominent HIF-related pathways. F: the β-catenin nuclear pathway.

Figure 4 :

 4 Figure 4: Immune system changes in P2H1 mice. A: Box and whisker plots representing levels of pro/anti-inflammatory cytokines measured in the plasma of P2H1 mice and WT littermate controls (n=7-12). All data were normalized to the average value seen in WT mice. Each dot represents data from one animal. B: Box and whisker plots showing percentage lymphocytes and eosinophils in circulation which revealed reduced fractions in P2H1 mice compared to WT controls. C: Greater numbers of circulating neutrophils in P2H1 mice compared to WT littermates. All graphs represent pooled results of 2 independent experiments. Statistical significance for cytokines in panels A and B was defined using the Mann-Whitney U test, except for TNFα, where the Unpaired t test with Welch's correction was used after verifying data normality. (*p<0.05; **p<0.005; ***p<0.001).

Figure 5 :

 5 Figure 5: Adrenal cortex-specific loss of PHD2 and PHD3 leads to reduced steroidogenesis in mice. A: Box and whisker plots showing steroid hormone levels in the adrenal glands of WT mice and compared to that of littermate P2H1 mice (n=14-16 individual adrenal glands). B: Box and whisker plots showing steroid hormone measurements in the plasma of individual mice (n=10-12). All data were normalized to the average value of WT mice and graphs are representative of at least 3 independent experiments. Statistical significance was defined using the Mann-Whitney U test for progesterone, 11-deoxycorticosterone, and 18-OH corticosterone. Unpaired t test with Welch's correction was used for corticosterone and aldosterone after verification of data normality (*p<0.05; **p<0.005; ***p<0.001).

Figure 6 :

 6 Figure 6: Inverse regulation of steroidogenesis in P2P3 mice compared to P2H1 mice A: Gene expression analysis of enzymes involved in the steroidogenesis pathway in P2P3 mice and theirWT counterparts (n=12-13) was performed in mRNA from entire adrenal glands. All graphs represent pooled data from at least 3 independent experiments. Statistical significance was defined using the Mann-Whitney U test (*p<0.05). B: Relative expression profile of all genes analyzed from the adrenal glands of P2H1 and P2P3 mice and compared to their respective WT littermates.Statistical significance was defined using an unpaired multiple t-test (n=13; Benjamini, Krieger and Yekutieli method; *p<0.0001 for all individual genes). C: schematic overview of all changes in adrenocortical enzymes and their corresponding hormones and intermediates reported here in P2H1 (red) and P2P3 (yellow) mice.

  FIGURE 1

Acknowledgments

This work was supported by grants from the DFG (German Research Foundation) within the CRC/Transregio 205/1, Project No. 314061271 -TRR205, "The Adrenal: Central Relay in Health and Disease" (A02) to B.W., T.C, A-E-A.; B.W. was supported by the Heisenberg program, DFG, Germany; WI3291/5-1 and 12-1). We would like to thank Dr. Vasuprada Iyengar for English Language and content editing.

Conflict-of-interest

The authors have declared that no conflict of interest exists.