White matter hyperintensities (WMH) appear as regions of abnormally high signal intensity on T2-weighted magnetic resonance image (MRI) sequences. In particular, WMH have been noteworthy in age-related neuroscience for being a crucial biomarker for all types of dementia and brain aging processes. The automatic WMH segmentation is challenging because of their variable intensity range, size and shape. U-Net tackles this problem through the dense prediction and has shown competitive performances not only on WMH segmentation/detection but also on varied image segmentation tasks. However, its network architecture is high complex. In this study, we propose the use of Saliency U-Net and Irregularity map (IAM) to decrease the U-Net architectural complexity without performance loss. We trained Saliency U-Net using both: a T2-FLAIR MRI sequence and its correspondent IAM. Since IAM guides locating image intensity irregularities, in which WMH are possibly included, in the MRI slice, Saliency U-Net performs better than the original U-Net trained only using T2-FLAIR. The best performance was achieved with fewer parameters and shorter training time. Moreover, the application of dilated convolution enhanced Saliency U-Net by recognizing the shape of large WMH more accurately through multi-context learning. This network named Dilated Saliency U-Net improved Dice coefficient score to 0.5588 which was the best score among our experimental models, and recorded a relatively good sensitivity of 0.4747 with the shortest training time and the least number of parameters. In conclusion, based on our experimental results, incorporating IAM through Dilated Saliency U-Net resulted an appropriate approach for WMH segmentation. White matter hyperintensities (WMH) are commonly identified as signal abnormalities with intensities higher than other normal regions on the T2-FLAIR magnetic resonance imaging (MRI) sequence. WMH have clinical importance in the study and monitoring of Alzheimer's disease (AD) and dementia progression  is an unsupervised algorithm for detecting tissue irregularities, that successfully has been applied for segmenting WMH on brain T2-FLAIR images  removes cerebrospinal fluid (CSF) signal from the T2-weighted sequence, increasing the contrast between WMH and other brain tissues. Therefore, we have chosen T2-FLAIR MRI as the main source of image data for our experiments.the Alzheimer's Disease Neuroimaging Initiative (ADNI)We obtained T2-FLAIR MRI sequences from the public dataset TM software. This semi-automatic WMH segmentation used the T2-FLAIR images. Intracranial volume (ICV) and CSF masks were generated automatically using optiBET  network often induces the problem of overfitting.Figure 3B). Each branch extracts features from raw image and regional map independently, and the extracted features are fused before the decoding part.Saliency U-Net was first introduced to detect anomalies in medical images using a combination of raw  images and simple regional maps , IAM (model 2), and both (model 3). To feed both T2-FLAIR and IAM together, we integrated T2-FLAIR and IAM as a two-channel input. As mentioned in section 2.3, U-Net architecture has encoding and decoding parts. In the encoding part, input images or feature maps are down-sampled by max-pooling layers to obtain relevant features for WMH segmentation. Then, in the decoding part, reduced feature maps are up-sampled again by up-sampling layers to acquire the original size in the final segmentation map. Max-pooling and Up-sampling layers are followed by two CONV blocks  masks as follows:We normalized the brain tissue voxels on each slice into a distribution with zero-mean and unit variance by subtracting the mean value from each voxel value and dividing the result by the standard deviation.Although WMH segmentation can be regarded as the binary classification of voxels, we re-labeled the ground-truth data assigning voxels one of the three following labels: non-brain, non-WMH brain tissue and WMH. However, when evaluating the segmentation results, we considered both non-brain and non-WMH brain tissue labels as non-WMH labels to calculate sensitivity and Dice similarity coefficient which are metrics for the binary classification. For training, 30 MRI scans of the ADNI dataset described in section 2.1 were randomly selected. These 30 MRI scans were collected from 10 subjects for three consecutive years. We trained our networks with image patches generated from these MRI scans, not slices, to increase the amount of training data. If we train our models using slice images, the amount of training data is only 35 × 30 = 1050 slices, which is not ideal for training a deep neural network architecture. Instead, by extracting 64 × 64 sized patches from each image slice, we could have 30,000 patches for training data.For testing, we used the rest 30 scans of the ADNI sample, which are not used during training. These scans were also obtained from another 10 subjects for three consecutive years. The testing dataset was comprised of image slices without patch extraction. Slice image data is necessary to analyse the results from our models according to the distributions or volumes of WMH. Our testing dataset holds 1050 of 256 × 256 image slices in total as each scan contains 35 slices.e−5 and batch size to 16. As an optimization method, we selected the Adam optimization algorithm  and Dice similarity coefficient (DSC) to evaluate the models. Sensitivity measures the rate of true positives as below:TP means true positive, and FN means false negative. PPV also measures the rate of true positives but from the total of positive calls like below:where where FP refers to false positive. DSC is a statistic method to compare the similarity between two samples of discrete values Dice, . It is oTP and FN are as per Equation 5 and FP means false positive. DSC is interpreted as the overlapping ratio to the whole area of prediction and target objects, while sensitivity measures the correctly predicted region of the target object. If the prediction includes not only true positives but also wrong segmentation results , the DSC score can be low despite the high sensitivity.where Saliency and Dilated Saliency U-Net included considerably less parameters than the three U-Net models. As shown in With regards to training time, although feeding both T2-FLAIR and IAM together into U-Net involved the calculation of more parameters due to the two-channel input, the training time for this model was shorter than that of U-Net(FLAIR) and U-Net(IAM). In deep learning studies, visual attention, which gives larger weight on the region of interest, speeds up learning by leading the model to concentrate on the relevant regions. This has been experimentally demonstrated in previous studies  the Kruskal-Wallis test, as implemented by the MATLAB function kruskalwallis, to evaluate whether the distributions of these DSC values were statistically significantly different between the models. Neither the medians nor the DSC distributions obtained by these five models significantly differed. The result of the Kruskal-Wallis test is shown in p-value obtained from the ANalysis Of VAriance (ANOVA) of the DSC distributions from the five models across all cases is 0.7786, indicating that the results of these five models did not differ significantly from each other in terms of the distribution of DSC across the testing set. This emphasizes that Dilated Saliency U-Net model can produce similar level of performance as the original U-Net models even with less number of parameters and shorter training time. We also evaluated whether the median and the distribution of DSC scores throughout the testing set differed significantly between the five models evaluated. We conducted two tests: (1) the Wilcoxon ranksum, as implemented by the function In this experiment, we evaluate our models based on the WMH volumes of the MRI scan  to examine the influence of WMH burden on the performance of WMH segmentation. The WMH volume of each MRI scan is calculated by multiplying the number of WMH voxels by the voxel size. We grouped MRI scans into three groups according to the range of WMH volume. All models tested in this study showed high median values of DSC scores in the Medium group, for which all models performed better than the other groups. In the Large group, U-Net(FLAIR) and U-Net(F+I) models performed similarly well, while U-Net(IAM) performed worst compared with the rest of the models. Mean, median and standard deviation (std.) values of DSC score distribution in each group are shown in In the Longitudinal evaluation test we addressed the capacity of our five models in predicting WMH in subsequent years after being trained only using the first year samples. Hence, the training set was formed by the first year samples while the testing set was composed by the second and third year samples. In order to evaluate the effectiveness of the Saliency U-Net architecture, we compared the original U-Net and Saliency U-Net models trained with T2-FLAIR and IAM. As shown in We also investigated the change in Saliency U-Net's performance in relation to its complexity when the number of convolution layers increased/decreased. DSC score, training time and model complexity  are compared in As shown in In this experiment, we applied different dilation factors in Dilated Saliency U-Net, which captures multi-context information on image slices without having to change the number of parameters. As per 0, 21, 22 …) (Yu and Koltun, In order to find the most appropriate dilation factors, we compared different sequences of dilation factors. Additionally, we investigated the influence of dilation factors in DSC score performance per WMH volume of MRI scans. Evaluation was conducted on the three groups previously described in In this study, we explored the use of IAM as an auxiliary data to train deep neural networks for WMH segmentation. IAM produces a probability map of each voxel to be considered a textural irregularity compared to other voxels considered “normal" (Rachmadi et al., Dilated convolution can learn spatially multi-context by expanding the receptive field without increasing the number of parameters. We added dilation factors to the convolution layers in the encoding block of Saliency U-Net to improve WMH segmentation, especially due to the high variability in the WMH size. This new model is named “Dilated Saliency U-Net.” Dilated convolution improved both DSC score and sensitivity with shorter training time. Dilated Saliency U-Net also yielded more accurate results in the presence of large WMH volumes and worked well in Medium and Small WMH volume MRI data groups which are more challenging. We identified that dilated convolution is effective when dilation factors are increased and decreased sequentially.To our knowledge, this is the first attempt of successfully combining dilation, saliency and U-Net. We could reduce the complexity of a deep neural network architecture while increasing its performance through the integrated techniques and the use of IAM. Due to the trade-off between performance and training time, which is proportional to the model complexity, it is crucial to develop less complex CNN architectures without decreasing their performance.Anomaly detection in the medical imaging field has been broadly studied (Quellec et al., YJ, MR, MV-H, and TK conceived and presented the idea. YJ and MR planned the experiments. YJ carried out the experiments. All authors provided critical feedback and analysis, and edited the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The prolonged use of many currently available drugs results in the severe side effect of the disruption of glucose metabolism leading to type 2 diabetes mellitus (T2DM. Gut hormone receptors including glucagon receptor (GCGR) and the incretin hormone receptors: glucagon-like peptide 1 receptor (GLP1R) and gastric inhibitory polypeptide receptor (GIPR) are important drug targets for the treatment of T2DM, as they play roles in the regulation of glucose and insulin levels and of food intake. In this study, we hypothesized that we could compensate for the negative influences of specific drugs on glucose metabolism by the positive incretin effect enhanced by the off-target interactions with incretin GPCR receptors. As a test case, we chose to examine beta-blockers because beta-adrenergic receptors and incretin receptors are expressed in a similar location, making off-target interactions possible. The binding affinity of drugs for incretin receptors was approximated by using two docking scoring functions of Autodock VINA (GUT-DOCK) and Glide (Schrodinger) and juxtaposing these values with the medical information on drug-induced T2DM. We observed that beta-blockers with the highest theoretical binding affinities for gut hormone receptors were reported as the least harmful to glucose homeostasis in clinical trials. Notably, a recently discovered beta-blocker compound 15 ([4-((2S)-3-(((S)-3-(3-bromophenyl)-1-(methylamino)-1-oxopropan-2-yl)amino)-2-(2-cyclohexyl-2-phenylacetamido)-3-oxopropyl)benzamide was among the top-scoring drugs, potentially supporting its use in the treatment of hypertension in diabetic patients. Our recently developed web service GUT-DOCK (gut-dock.miningmembrane.com) allows for the execution of similar studies for any drug-like molecule. Specifically, users can compute the binding affinities for various class B GPCRs, gut hormone receptors, VIPR1 and PAC1R. The number of diabetic patients is rapidly increasing, reaching 425 million cases in 2018 . Glucosein silico solution to the drug-induced T2DM problem using the concept of drug repurposing or off-target interactions. Off-target interactions or, in other words, interactions with proteins which are not the intended targets (on-targets) of a specific drug, can be either beneficial (drug repurposing) or unwanted (when they cause adverse drug reactions) [In both studies, we proposed an actions) . In our actions) . We seleactions) . Gut horactions) –13.GLP1Ractions) , 15, an actions) . Recentlactions) . Additioactions) . Gut horactions) . In shoractions) .In addition to gut hormone receptors we included in this study two other class B GPCRs that which are also partly expressed in the gastrointestinal tract: vasoactive intestinal polypeptide receptor 1  and pituitary adenylate cyclase-activating polypeptide type I receptor . The impact of these GPCRs on blood glucose levels has been studied to a much lesser extent so far .http://gut-dock.miningmembrane.com) to propose drug-like molecules from a user’s defined set of compounds that bind strongly to incretin receptors. Assuming that the efficacy of these compounds is experimentally confirmed, e.g. by in vitro assays [in-vivo studies. However, cellular assays and coactivators assays used, e.g., in a recent study about the off-target drug activity for nuclear receptors, seem to be less expensive and reliable tools in this area of research [In our study, we first computed theoretical binding affinities corresponding to the strengths of protein-ligand interactions. We then gathered medical data that were available in the literature regarding the influence of beta-blockers on glucose homeostasis. Next, we converted both the gut hormone receptors binding affinities and the medical information into drug rankings. Surprisingly, we observed a significant correlation between these two sets of data leading to the conclusion that beta-blockers with the strongest binding affinities exhibited the smallest negative influence on glucose metabolism. We believe that high binding affinities for incretin receptors, plausibly associated with high efficacy leading to the compensating incretin effect, could be helpful in avoiding or at least decreasing the incidence of drug-induced T2DM while treating hypertension with beta-blockers. Based on the results described here, we developed a web service named GUT-DOCK . The remaining 1500 models were clustered using the Rosetta3.5 cluster application and evaluated with BCL::Score using knowledge-based potentials derived specifically for membrane proteins [Ligand-based filtering of GPCR models—a virtual screening study). In the case of GCGR and GLP1R, their crystal structures were used in 20ns MD simulations to generate an ensemble of receptor conformations (see Molecular dynamics simulations). Then, 2000 snapshots were recorded for each structure/receptor pair  [+ and Cl- ions, with an ionic concentration of 0.15 M. The size of the box was set so that its boundaries were at least 15 Å away from the protein atoms. Each of the generated systems contained approximately 100 POPC and 20 cholesterol molecules, and the total number of atoms ranged from 47213 (5VEW) to 69309 (5XEZ). The applied force field was Charmm36 [-1 Å-2. The positions P atoms in POPC molecules and O3 atoms in cholesterol molecules were restrained with a force constant of 2.5 kcal mol-1 Å-2. In the subsequent six consecutive short runs of the equilibration process, the position constraints were gradually decreased, firstly in two runs of 100 ps in the NVT ensemble, and then in four runs of 100 ps in the NPT ensemble. Temperature was controlled by the Langevin thermostat, with a friction coefficient of 1.0 ps-1, and a value of 310 K. The external pressure was 1 bar controlled by the Berendsen barostat. The time integration step was set to 0.001 ps during the first equilibrium runs and set to 0.002 ps thereafter. The SHAKE algorithm was turned on to constrain bonds involving hydrogen atoms. Finally, an unconstrained 20 ns-long production run was performed for each system. All molecular dynamics simulations were performed with the GPU version of the pmemd module of the AMBER14 package [We performed short, 20 ns molecular dynamics simulations to generate the ensemble of receptor conformations based on the GLP1R and GCGR crystal structures. Each system, containing the protein embedded within the membrane and solvated, was prepared using the CHARMM-GUI Membrane Builder (gui.org) –34. The Charmm36 . Next, eTo assess the accuracy of the modeled external loops of the studied receptors, we applied the following procedure. We defined a three-dimensional Cartesian grid which covered the orthosteric binding site occupied by the endogenous peptide. The spacing between the grid vertices was the same in all directions and was equal to 1 Å. Each of the heavy atoms of these endogenous peptides was assigned to the proper unit cell based on its coordinates, and each of these atoms was treated with the same weight. In this way we calculated the ligand density, which was proportional to the number of ligand atoms within each grid cell for a given set of ligand peptides. The calculated ligand density was then applied to the analysis of the binding pockets of G-protein coupled receptors, the structures of which were predicted using homology modeling. In this way, it was possible to distinguish receptor models with extracellular loops excessively occupying the orthosteric binding site. The ligand density grid was divided into the following three areas. The first area covered the space of grid points assigned with nonzero density values. The second area consisted of the space of grid points with the ligand density equal to zero but with the nonzero neighboring grid points. This area formed a shell surrounding the non-zero ligand density grid point space. The first area was suitable for the assessment of steric hindrances caused by a peptide, while the second area was useful in the case of small-molecule ligands (data not shown). The final steric hindrance score for a peptide and a given homology model of a receptor was equal to the number of nonzero ligand density grid points covered by the receptor (TMD and loops).Models of gut hormone receptors were generated using: crystal template structures, GPCRM, model quality assessment with the BCL::Score scoring function , RosettaIn GPCR models of five class B receptors generated with the procedure described above were implemented in GUT-DOCK to serve for docking purposes. Before implementing these models in GUT-DOCK we used a standalone version of Autodock Tools to generate pdbqt receptor files. The main core of GUT-DOCK includes the docking of user-provided small-molecule ligands to preprepared GPCR models see . Those mThe GUT-DOCK web service is divided into two parts. The first part is a responsive user interface that can run queries and display results. The user interface is a web interface that was constructed using HTML5 with jQuery, jQuery UI, Lightbox, Bootstrap libraries. The web interface was designed to fit both desktop and mobile devices. The web service itself was written in Python using the Django Framework for serving the web application and dynamically generated HTML5 pages for managing the MySQL database. The second part is dedicated to computing user jobs independently from the user’s web actions. A link to the results is sent by email when the job is completed. The computational server runs under Linux on a multi-core CPU and that allows for the Autodock VINA multithreading. The user can run multiple jobs at the same time depending on current computational resources.In GUT-DOCK and in VS we assumed that docking scores obtained with Autodock VINA and Glide correlated with the binding affinities. This assumption was based on the observed high success rates of virtual screening comparing experimental screening (see the accompanying manuscript ). AlthouIntroduction. Indeed, high correlation coefficients for gut hormone receptors confirmed that the compensating incretin effect could be, at least partially, responsible for the decreased risk of hyperglycemia associated with the new generation of beta-blockers. On the other hand, the weak binding affinities of the previous generation of beta-blockers for gut hormone receptors could indicate that their chemical structures do not fit the receptor binding sites, and thus the compensating incretin effect could not be enhanced.It was suggested in  that betAs we mentioned above, allosteric and orthosteric binding sites slightly differed in the strength of drug-receptor interactions. In almost all cases, ligands were bound more strongly to the more spacious orthosteric site than to the allosteric site. To date, all available crystal structure of class B GPCRs contain only antagonists or negative allosteric modulators, and no orthosteric nonpeptide small-molecule ligands mimicking the mechanism of action of the endogenous peptide have been reported in the PDB. In contrast, analogs of endogenous peptides have been reported . HollenIn general, if a drug binds to an orthosteric GPCR site, it may block that binding site from its endogenous ligand and thus may demonstrate an inhibitory (antagonistic) effect on the receptor signaling pathway. Nevertheless, it may also act as an agonist, activating the receptor similarly to the endogenous ligand. In the case of GCGR, the former effect would be highly desirable due to the inactivation of the diabetogenic mechanism associated with glucagon . AllosteCompound 15 demonstrated similar binding affinities for gut hormone receptors similar to carvedilol and nebivolol see . In almoIt is noteworthy that the computational engine of GUT-DOCK is not a novel one. We simply implemented the well-known programs and methods for small-molecule docking that were tested and assessed elsewhere  that were extracted as three-dimensional structures deposited as SDF files in the PubChem Database . In the The results of the self-docking study for GCGR were less worse than for GLP1R. The crystal orientation of the GCGR ligand was not very accurately predicted . However, also in this case, the best receptor for the given ligand was accurately predicted based on the Autodock VINA scoring . We believe that the results, for both GLP1R and GCGR confirmed the usefulness of Autodock VINA for the prediction of the best GPCR receptor (with the highest binding affinity) for a given ligand, even if the binding mode was not accurate. However, a more detailed study could be carried out on this subject in the future. Radar charts presented in In our study, we hypothesized that off-target interactions of specificdrugs with gut hormone receptors GLP1R, GIPR and GCGR could be a way to compensate for the negative influence of each on glucose homeostasis leading to drug-induced diabetes. For such off-target interactions to be beneficial, they should result in the enhancement of the incretin effect, e.g., when a drug acts as PAM or an agonist of GIPR and GLP1R and NAM or an inhibitor of GCGR. Thus, improvements in insulin secretion and normal glucose serum levels could be observed. In contrast, the inhibition of GLP1R increases blood glucose levels , and agoThe current study on beta-blockers and a second study  describihttp://sideeffects.embl.de), e. g., angiopathy, that should be taken into account while optimizing pharmacotherapy for diabetic patients.Our work is an example of a novel computational approach to the fast and low-cost prediction of the off-target drug interactions with class B GPCRs. Such information on the enhancement of the compensating incretin effect is especially important for diabetic patients. However, we did not intend for this study to be the only guidance for modification of pharmacotherapies for DM, as there are many other side effects (see A freely available web service developed during this study (GUT-DOCK) can also be used to discover new therapeutics and to predict off-target interactions of any small molecule or small peptide with class B GPCRs. Easy-to-download GPCR models can also be used in further drug discovery studies.S1 FileA file including: active compounds lists, sequence identity between templates and targets from the current study, figures presenting GUT-DOCK results for compound 15 and GLP1R.(PDF)Click here for additional data file.S2 File(XLSX)Click here for additional data file.


Early in the course of psychosis, alterations in brain connectivity accompany the emergence of psychiatric symptoms and cognitive impairments, including processing speed. The clinical-staging model is a refined form of diagnosis that places the patient along a continuum of illness conditions, which allows stage-specific interventions with the potential of improving patient care and outcome. This cross-sectional study investigates brain connectivity features that characterize the clinical stages following a first psychotic episode. Structural brain networks were derived from diffusion-weighted MRI for 71 early-psychosis patients and 76 healthy controls. Patients were classified into stage II (first-episode), IIIa (incomplete remission), IIIb (one relapse), and IIIc (two or more relapses), according to the course of the illness until the time of scanning. Brain connectivity measures and diffusion parameters  were investigated using general linear models and sparse linear discriminant analysis (sLDA), studying distinct subgroups of patients who were at specific stages of early psychosis. We found that brain connectivity impairments were more severe in clinical stages following the first-psychosis episode  than in first-episode psychosis (stage II) patients. These alterations were spatially diffuse but converged on a set of vulnerable regions, whose inter-connectivity selectively correlated with processing speed in patients and controls. The sLDA suggested that relapsing-remitting  and non-remitting (stage IIIa) patients are characterized by distinct dysconnectivity profiles. Our results indicate that neuroimaging markers of brain dysconnectivity in early psychosis may reflect the heterogeneity of the illness and provide a connectomics signature of the clinical-staging model. Although the outcome after a first episode of psychosis is better than traditionally thought2, relapses within 5 years from the initial episode are up to 80%3. Current definitions of psychotic illnesses are mainly based on relatively short-term evaluations and only poorly consider the long-term, potentially progressive evolution of the illness. Clinical staging, which has proven useful in somatic medicine, is an alternative approach, which captures the degree of disease progression in a given patient and places the person along the continuum of the course of illness4. Introduced to psychiatry by Fava and Kellner5, clinical staging was developed and applied to psychotic disorders by McGorry and collegues8. This approach allows to distinguish earlier and more benign states from more chronic states, with the aim of selecting the right treatment according to the clinical stage. As a framework, it has proven to be useful in the implementation of early intervention in psychosis by distinguishing an ultra-high risk phase from first-episode and chronic phases. Mapping neurobiological markers, such as brain imaging features, onto clinical stages could further allow us to refine the model and validate the boundaries of the clinical groups, thus broadening our understanding of psychotic disorder pathophysiology10.Psychotic disorders, including schizophrenia, are characterized by heterogeneity in terms of etiopathology, clinical presentation and outcome12. For instance, WM microstructural properties assessed by diffusion-weighted imaging (dMRI) are important in explaining deficits in the processing speed15, which is one of the most impaired cognitive dimensions in first-episode psychosis and established illness17 and predicts the functional outcome in patients18.Abnormalities in white matter (WM) and structural brain connectivity are well documented in schizophrenia and have been related to the expression of clinical symptoms, cognitive deficits and differential functional outcome21. From a whole-brain connectivity (or connectome) perspective, patients present a less efficient brain-network organization and a decentralization of core brain regions and hubs24, which are important in maintaining proper information integration underlying cognitive functions in the brain26. Individuals in the early stages of psychosis appear to have less consistent WM (as well as grey matter (GM)) changes than chronic patients. This observation has led to the hypothesis that there might be a progression or a differentiation of brain connectivity impairments across stages and over the course of the illness from prodromal symptoms to the first-episode of psychosis and, finally, to the relapsing and chronic phases30. Not all patients necessarily progress from one stage to another, which adds complexity to the model. Only a few studies have looked at the early phases after a first episode of psychosis34, or compared a first episode with multiple episodes35 or with non-remitting patients. Indeed, there is a 5-year period following the first psychotic episode, called the ‘critical period' by Birchwood and colleagues36, during which the most severe brain changes appear to occur10.WM alterations are widespread in schizophrenia and affect most of the cerebral lobes in chronic patients38 of early psychosis patients classified in different stages  according to their clinical profiles. Given the tight link between the WM characteristics and the processing speed, we also tested the relationship between the network properties and the processing speed. Our cross-sectional analyses are a first step toward a neuroimaging investigation and validation of the clinical staging model for psychosis illness.The objective of the present work is to investigate brain connectomes39) of the Lausanne University Hospital, Switzerland. The entry criteria into the TIPP were the following: between 18 and 35 years of age; residence in the catchment area; and meeting the threshold criteria for psychosis according to CAARMS psychosis-threshold subscale41.A total of 147 subjects (71 early-psychosis patients (EPPs) and 76 healthy controls (HCs)) were included in this cross-sectional study  were exclusion criteria for all subjects.A total of 76 age, gender and handedness-matched HCs  were recruited from the same catchment area; they were not affected by any mood, psychotic or substance-use disorderAll of the participants provided informed written consent for this study, and the procedure was approved by the Ethics Committee of Clinical Research of the Faculty of Biology and Medicine, University of Lausanne, Switzerland.42. The patients were stratified into four distinct groups . Patients in stage III were defined as follows: IIIa: incomplete remission from stage II at 12 months after entry to care and following a reasonable course of treatment (>3 months), with duration of illness no longer than 5 years; IIIb: recurrence or relapse of a psychotic episode ; IIIc: two or more relapses after stage II with remission between episodes. Further details on the patients’ assessment can be found in SI.1.The clinical stage was rated as the highest stage achieved at the time of imaginga-c Fig. ) based o43, and the functioning level was assessed with the Global Assessment of Functioning (GAF) scale44. For each patient, a consensus diagnosis45 was realized by a senior psychiatrist and a senior psychologist in charge of scale-based assessment over the treatment period, based on DSM-IV criteria41. The levels of cannabis and alcohol use were assessed with the Case Manager Rating Scale (CMRS) and ranked as none, mild, moderate, severe or extremely severe46. None of the patients had extremely sever level of cannabis or alcohol use. Antipsychotic doses were converted to chlorpromazine equivalents48. All of the subjects were assessed with the MATRICS Consensus Cognitive Battery50.In patients, the symptoms severity was assessed with the Positive and Negative Syndrome Scale (PANSS)2, 2.2 × 2.2 × 3 mm3 resolution). During the study, there was a routine MRI-system upgrade from the MAGNETOM-Trio to the MAGNETOM-Prisma Siemens system. Imaging parameters were precisely matched before and after the upgrade, and the same 32-channel head coil was used.Each subject underwent an MR-imaging session on a 3-Tesla Siemens scanner, including magnetization-prepared rapid acquisition gradient echo (MPRAGE) sequence  and diffusion spectrum imaging (DSI) sequences  regions based on the Desikan-Killiany atlas52. Reconstructed DSI-data were used to compute generalized fractional anisotropy (gFA)53 and apparent diffusion coefficient (ADC)23 scalar maps, and for deterministic streamline tractography. The structural connectivity between each pair of cortical and subcortical regions was quantified as the number of streamlines connecting the two regions, which resulted in 82-nodes, weighted undirected brain-networks. For consistency reasons and to limit possible biases in the network analyses55, connections that were present in less than 50% of the subjects were discarded.Individual connectomes were estimated by combining MPRAGE and DSI dataFurther details regarding MRI acquisitions’ parameters, data processing and connectome reconstruction can be found in the SI.2, SI.3.53 and ADC values were computed for each connection of individual brain networks. gFA and ADC values relate to organizational and microstructural properties of the WM, including myelination levels, axonal packing and fibre coherence56. The network efficiency and clustering coefficient57 were investigated in supplementary analyses  connectivity measures were considered. The overall brain network strength was quantified as the total streamline count in the network. The centrality of each brain region in the network was quantified with the nodal strength, which was defined as the weighted sum of the node’s connections. Tract-average gFAd coefficient58 between GLM residual distributions, after correcting for covariates. Continuous variables’ cross-group progression was tested with the Jonckheere-Terpstra (JT) test for ordered alternative hypotheses59. The false discovery rate correction for multiple comparisons was applied when indicated60. Logistic regression was used to assess the cognitive impairments (MATRICS scores) of patients with respect to healthy controls (see SI.4). Relationships between neuroimaging and clinical/cognitive variables were assessed with Pearson’s correlation coefficient (r). A sparse linear discriminant analysis (sLDA)62 on nodal strength values was performed on patients only to investigate whether distinct brain connectivity features can be associated with distinct clinical stages. Leave-one-out cross validation (LOOCV) error and inter-class distances in the sLDA feature space were used as indicators of classes’ separability. In these analyses, sLDA was meant to explore the neuroimaging patterns characterizing the different early-psychosis stages, and not to generalize to a prediction setting. For further details see SI.5, Figure Statistical differences between the subjects’ groups were assessed with multi-factor ANCOVA within a general linear model (GLM) framework. Age, gender, handedness and a ‘scanner-upgrade’ variable were added as co-variates in all of the analyses. Global network analyses were repeated on data acquired on the MAGNETOM-Trio or on the MAGNETOM-Prisma system only to further exclude major effects of the scanner upgrade on the results. The effect size was quantified with the Cohen’s We investigated the brain connectivity alterations that occur in young adults in the early phases of psychosis (EPPs), classified into stages II, IIIa, IIIb and IIIc, and in comparison with healthy controls (HCs). The demographics of the patients and controls, the cognitive and clinical scores, and the related statistical comparisons are reported in Table In patients, there were no inter-group differences in the GAF, processing speed, PANSS scores, CMRS levels or medication dose  as assessed at the time of MR-imaging. The EPPs had lower GAF and processing speed scores compared to the HCs.There were no significant differences in the age, gender or handedness between the EPPs and HCs. Stage-II patients were on average younger than stage-III patients, while no age-difference was present among the different sub-groups of the stage-III patients. There was an unbalance in the proportion of right-handed subjects across the patient sub-groups.p = 0.00086, d = 0.55), and trend-level alterations of the whole-brain tract-average gFA  and ADC  values  . No significant pair-wise differences were found between stage-II patients and HCs for the other connectivity measures or between stage-II and stage-III patients. Consistent results were found when analyzing only data acquired before or after the MRI scanner upgrade, or when including right-handed subjects only   Fig. . Vulnera05) Fig. . A total05) Fig. . A compa05) Fig. .Fig. 3Co62. The leave-one-out cross-validation (LOOCV) error of the sLDA classification for stages II/IIIa/IIIb/IIIc was 0.60 , suggesting that the different clinical stages are characterized by distinct nodal connectivity patterns. Figure To explore the heterogeneity of brain connectivity alterations across psychosis stages II/IIIa/IIIb/IIIc, which are possibly associated with different temporal and/or clinical pathways of pathological evolution, we performed an sLDA on the nodal strength values. SLDA is a multivariate technique that identifies directions  and EPPs . This correlation was driven by the connectivity strength between the 22 vulnerable regions  ; the first-LDD scores correlated with the processing speed . We did not find any relationship between the connectivity measures  and the duration of illness, duration of untreated psychosis, PANSS scores, GAF scores, CMRS levels and medication dose in patients.Among the MATRICS domains, the processing speed was the most impaired in the patients  compared to the controls . There was a significant positive correlation between the processing speed and the overall network strength in both HCs (12) Fig. , and no 63. In this cross-sectional work, we investigated the white-matter connectivity of early-psychosis patients (EPPs) in different clinical stages after the first psychotic episode and in comparison with healthy controls (HCs). We found that the EPPs have reduced brain connectivity strength, lower gFA and higher ADC values compared to the HCs. Such connectivity impairments are more severe in the advanced (early) stages than in the first-episode of psychosis (stage II), and they converge on a set of vulnerable brain regions whose connectivity strength selectively correlates with the processing speed. Finally, using a linear discriminant analysis technique, we showed that clinical subtypes are characterized by distinct brain-connectivity profiles.White-matter alterations that occur in the early stages of psychosis may parallel the course of the illness and differentiate the clinical subtypes26, and progressive dysconnectivity from recent-onset to chronic schizophrenia patients64. While our data, being cross-sectional, cannot directly confirm a progressive change of WM-connectivity over the early course of the pathology, they reveal an association between WM-connectivity alterations and clinical stages. These alterations are spatially diffuse in the brain, but they converge on a vulnerable subnetwork that spans frontal, inter-hemispheric, cortico-thalamic and striatal circuits. This vulnerable subnetwork spatially aligns with the literature on WM-impairments in schizophrenia65 and partially overlaps (~40%) the brain’s ‘affected-core’ that we detected in previous work on chronic patients23. Within the subnetwork, the most significant effects were found in the left hemisphere, in the superior parietal and frontal cortices  and in the pars opercularis of the inferior frontal gyrus . The vulnerable subnetwork includes the main brain-network hubs , which are consistently implicated in schizophrenia pathophysiology22 and whose early impairment might relate to genetic and clinical risk factors71. Our findings corroborate the dysconnectivity hypothesis of schizophrenia pathophysiology and suggest that the early decentralization of vulnerable brain regions, including hubs, might play a role in the aetiology of the disorder.Our results on brain connectivity alterations across early-psychosis stages complement recent studies that indicate progressive brain tissue atrophy across clinical stages14 but also in first-episode and clinical high-risk populations73, and it could mediate working memory impairments15. In our sample, the processing speed was impaired in EPPs compared to HCs, with a statistical effect that exceeded that of other cognitive domains. Our results suggest that inter-subject WM variability in connections vulnerable to psychosis could mediate inter-subject variations in the processing speed.The connectivity strength between vulnerable brain regions selectively correlates with the processing speed, in both patients and controls, which indicates an involvement of these regions and connections in maintaining fast response and proper information integration in the brain. The processing speed is a core dimension of the cognitive deficit that is observed not only in chronic schizophrenia74. Alterations in NOS, gFA and ADC are compatible with neurobiological processes that have been associated with schizophrenia, such as neuroinflammation, myelin impairment and loss of tissue volume77. It has been suggested that oxidative stress is an important risk factor for developing schizophrenia. Redox dysregulation interacts with neuroinflammation and glutamatergic hypofunction to impair oligodendrocyte maturation and myelination80. In early psychosis, WM gFA values relate to brain concentrations of glutathione, a fundamental antioxidant that prevents oxidative stress and oligodendrocytes sufferance78. Elevated ADC values have been directly associated with demyelination processes in animal models with myelin deficiency81, and both gFA and ADC impairments in schizophrenia have been related to deficits in the density or myelination of axonal fibers82. In schizophrenia, NOS-connectivity relates to cytoarchitectonic abnormalities of pyramidal neurons, which are centrally implicated in the disorder83. In this study, we found that NOS-connectivity bears the largest effect for progressive connectivity changes across psychosis stages. While this result suggests that NOS might be sensitive to pathological processes that occur early and persist over the development of the disease, this measure remains an unspecific marker of WM-organization. Future studies that employ additional dMRI-techniques for WM microstructural assessment85 could better characterize the neurobiological processes that underlie macro-scale connectivity alterations across psychosis stages.To assess the connectivity strength and microstructural properties of WM tracts in patients and controls, we investigated the number of streamlines (NOS), average gFA and average ADC values. These measures indirectly relate to the tract volume and to tissue microstructural properties such as local axonal packing, membrane permeability and myelin properties34. A second complementary explanation is that different clinical subtypes can experience different spatio-temporal patterns of WM alteration. For example, it is plausible that an early-psychosis patient with a non-remitting course of illness could present a different or more severe dysconnectivity pattern than a relapsing-remitting patient, with duration of illness being equal. A large longitudinal study on first-episode schizophrenia found that individuals with extended periods of relapse had more severe brain-tissue atrophy compared to brief- or no-relapse conditions86, while GM-volume alterations measured at the time of the first psychotic episode partially predicted the course of psychosis into continuous or remitting illness34. Moreover, WM-changes detected after a first psychotic episode can partially reverse with the remission of acute psychotic symptoms87.Although our analyses suggest a progressive WM-connectivity impairment from the first episode to more advanced (early) psychosis stages, we did not find in the first instance direct correlations between neuroimaging markers and duration of illness. A possible explanation for this finding is that connectivity alterations follow spatially non-homogeneous and temporally non-linear patterns over the course of the pathology, with periods of faster and slower WM changes localized in different areas of the brainst and 3rd LDDs  on nodal connectivity-strength values. Our analysis suggests that distinct brain connectivity features characterize the clinical stages and identifies axes of pathological evolution in terms of both  temporal progression  and the corresponding neuroimaging markers. Our data are cross-sectional and it is not possible to foresee the pathological evolution of single individuals. In addition, our sample-size is relatively small, which limits the statistical power and the generalizability of our exploratory sLDA analysis to a classification or predictive setting. Finally, although no relationship was found between brain-connectivity measures and antipsychotic dose, and no differences in CPZ equivalents were found between patient subgroups, possible iatrogenic or other secondary effects on the reported neuroimaging measures cannot be completely excluded. While some studies have shown that medication can negatively impact WM connectivityIn conclusion, our work shows that neuroimaging markers of brain dysconnectivity and core-network decentralization are more severe in more advanced early-psychosis stages and typify relapsing-remitting and non-remitting clinical profiles, thus providing a connectomics perspective of the clinical-staging model. Based on these results, we expect that the investigation of large datasets , the inclusion of additional clinical, neurobiological and multimodal brain-connectivity features, and longitudinal designs combined with machine learning approaches will significantly contribute to model and understand the unfolding of psychosis-spectrum disorders across clinical stages.Supplemental Information


Bradyrhizobium sp. DOA9 strain displays the unusual properties to have a symbiotic plasmid and to fix nitrogen during both free-living and symbiotic growth. Sequence genome analysis shows that this strain contains the structural genes of dinitrogenase (nifDK) and the nifA regulatory gene on both the plasmid and chromosome. It was previously shown that both nifDK clusters are differentially expressed depending on growth conditions, suggesting different mechanisms of regulation. In this study, we examined the functional regulatory role of the two nifA genes found on the plasmid (nifAp) and chromosome (nifAc) that encode proteins with a moderate level of identity (55%) and different structural architectures. Using gusA (β-glucuronidase) reporter strains, we showed that both nifA genes were expressed during both the free-living and symbiotic growth stages. During symbiosis with Aeschynomene americana, mutants in only one nifA gene were not altered in their symbiotic properties, while a double nifA mutant was drastically impaired in nitrogen fixation, indicating that the two NifA proteins are functionally redundant during this culture condition. In contrast, under in vitro conditions, the nifAc mutant was unable to fix nitrogen, and no effect of the nifAp mutation was detected, indicating that NifAc is essential to activate nif genes during free-living growth. In accordance, the nitrogenase fixation deficiency of this mutant could be restored by the introduction of nifAc but not by nifAp or by two chimeric nifA genes encoding hybrid proteins with the N-terminus part of NifAc and the C-terminus of NifAp. Furthermore, transcriptional analysis by RT-qPCR of the WT and two nifA mutant backgrounds showed that NifAc and NifAp activated the expression of both chromosome and plasmid structural nifDK genes during symbiosis, while only NifAc activated the expression of nifDKc during free-living conditions. In summary, this study provides a better overview of the complex mechanisms of regulation of the nitrogenase genes in the DOA9 strain that involve two distinct NifA proteins, which are exchangeable during symbiosis for the activation of nif genes but not during free-living growth where NifAc is essential for the activation of nifDKc.The  In addition, this enzymatic complex is highly sensitive to molecular oxygen, which irreversibly inactivates the enzyme. Diazotrophic bacteria have evolved sophisticated regulatory circuits of their nitrogen fixation (nif) genes in response to oxygen and nitrogen availability to prevent unnecessary energy consumption and permit the synthesis of the nitrogenase complex only during the proper environmental conditions  to activate the expression of nif genes by binding to an upstream activating sequence . The NifA proteins show a typical three-domain structure. The N-terminal GAF domain is a ubiquitous signaling motif found in signaling and sensory proteins from all three kingdoms of life  motif involved in DNA-binding. The activity of NifA is directly sensitive to molecular oxygen, and in some cases, is also directly affected by combined nitrogen  and the nifA found on the plasmid (nifAp) specifically regulated the nifDK cluster found on the replicon where nifA is present. However, crosstalk between these two regulatory circuits would also be expected, given that NifDKp proteins require the expression of the accessory nif genes to form a functional nitrogenase. In particular, the nifENX genes whose products are needed for synthesis of the iron-molybdenum cofactor of nitrogenase, exist as a unique copy and are found downstream of the nifDKc cluster.Another example of the presence of two 9 strain . This bap legume , displayif genes . Second,nd nifDK . Third, c states . Data frnditions . These dnifA genes identified for the Bradyrhizobium sp. DOA9. In a first approach, we analyzed the expression levels of both nifA genes under different culture conditions using translational fusions to gusA (β-glucuronidase). We then analyzed the contribution of each regulatory protein to the control of bacterial nitrogenase activity under free-living and symbiotic states by constructing single and double nifA mutants. Finally, the expression level of several nif genes in three different backgrounds, the DOA9 wild-type (WT), ΔnifAc, and ΔnifAp mutant strains, were analyzed to identify which genes are activated by NifAc and NifAp.Thus, in the present work, we aimed to investigate in more detail the regulatory functions of the two Bradyrhizobium sp. DOA9 WT was obtained from the School of Biotechnology, Suranaree University of Technology, Thailand, while all mutants were constructed in the Laboratoire des Symbioses Tropicales et Méditerranéennes (LSTM), France. These bacterial strains were grown at 28°C for 4 days in Yeast extract-mannitol (YM) medium  medium  or a BNM) medium . EscheriSupplementary Table S1. To construct the reporter strains, DOA9-Pm-fixRnifAc and DOA9-Pm-nifAp, the 500-bp upstream region of fixR and the nifAp operon were amplified by PCR and cloned into the plasmid pVO155-npt2-cefo-npt2-gfp. This plasmid, which is a derivative of the pVO155 plasmid  mutants, 300 to 400 base pairs (bp) of the internal sequence of each nifA gene were amplified by PCR and cloned into the plasmid pVO155-npt2-cefo-npt2-gfp. To construct the DOA9ΔnifA deletion mutants, the upstream and downstream regions (between 700 and 1000-bp) of each nifA gene were amplified and merged using overlap extension PCR. Then, the fragment was cloned into the plasmid pK18 mob-cefo-sacB. This plasmid carries the sacB gene, which induces bacterial death in the presence of sucrose and the kanamycin-resistance gene  (The symbiosis efficiency of the um (BNM) . Plants um (BNM) . The expTo follow the GUS activity in the nodules elicited by the reporter strains, 30- to 40-μm-thick sections from fresh nodule samples were prepared using a vibratome  and incubated at 37°C in the dark in GUS assay buffer for 1 h, as described in Bradyrhizobium sp. strain DOA9 and derivatives were grown in 10-ml test tubes hermetically closed  containing 2 ml of semisolid BNM-B medium (agar 0.8% w/v). The BNM-B medium is a synthetic plant growth medium  as described by The two DOA9 reporter strains were grown for 4 days in YM medium, collected, and washed with BNM-B medium as described above. Bottles of 150-ml sealed with rubber stoppers and containing 55-ml of BNM-B medium and 95-ml of air were then inoculated with DOA9 reporter strains to an initial ODA. americana under symbiotic conditions. For free-living conditions, the bacterial cells were grown in 150-ml bottles as described just above. For harvesting, cultures were added to a 1:10 volume of “stop solution” [10% Tris-HCl-buffered phenol (pH 8) in ethanol], and cells removed from the liquid medium by centrifugation for 10 min . The cell pellets were frozen in liquid nitrogen and stored at -80°C. Analysis under symbiotic conditions and RNA isolation from bacteroids were processed from approximately 1 g of frozen nodules by homogenization with a tungsten carbide bead  in 2-ml microcentrifuge tubes. Total RNA was isolated from the free-living bacterial cells, and the nodules were disrupted with a hot (65°C) phenol-extraction procedure that was previously described , and the appropriate amount of specific primers (listed in Supplementary Table S2) were used in the qRT-PCR analyses using an annealing temperature at 55°C for all reactions. The expression of target genes was relatively compared with the expression of the housekeeping gene, dnaK, using QuantStudio Design & Analysis Software from Applied Biosystems.The expression of genes involved in nitrogen fixation of strain DOA9 was determined from cells grown under free-living conditions and bacteroids obtained from nodules of escribed . RNA wasnifA homologous genes can be identified in the Bradyrhizobium DOA9 strain. One copy found on the chromosome, termed nifAc, is located approximately 6 kb from the nifDKENX operon and found just downstream of the fixR gene . In B. japonicum, nifA is also found downstream of fixR. It has been shown that the two genes are part of the same transcript  encoding a 4Fe-4S ferredoxin and a suf operon composed of four genes  were identified and have been shown to function in the assembly of iron-sulfur clusters  and NifAp (503 aa) display only 52% identity. A Pfam analysis to identify functional domains showed that NifAc displays a classical NifA architecture with a N-terminal GAFdomain, a central sigma 54 interaction domain and a C-terminal HTHdomain. NifAp shows a less classical structure with only the presence of the central and HTH domains . The divergence of NifAp is not limited to the absence of the N-terminal GAF domain, since phylogenetic analysis showed that this protein formed an outgroup that was well separated from the NifA proteins identified in Bradyrhizobium strains .Two anscript , suggestclusters . The othfixRnifAc operon and nifAp permitted the identification by manual analysis of a putative NifA and a RpoN binding sites, suggesting that both NifA proteins could autoregulate their own expression level and that of their homolog .Interestingly, in both cases, a close examination of the promoter regions of the nifA genes identified in the Bradyrhizobium DOA9 strain, we constructed two reported strains (DOA9-Pm-fixRnifAc and DOA9-Pm-nifAp) by integrating the nonreplicative plasmid pVO155-npt2-cefo-npt2-gfp, which carries a promoterless gusA gene . Cytological analysis revealed a β-glucuronidase activity in the nodules, which was elicited by the two reporter strains, in contrast to the WT-nodules for which no activity could be detected . Although X-gluc  staining is a qualitative measurement of gene expression, the naked eye could observe that the nodules elicited by DOA9-Pm-nifAp displayed a more intense blue color than those elicited by the DOA9-Pm-fixRnifAc reporter strain , which indicates slight differences in the expression of the two nifA genes.To analyze the expression of the two usA gene  downstrePm-nifAp (30 Miller unit) was higher than that detected for the DOA9-Pm-fixRnifAc reporter strain (22 Miller Unit) . Taken together, these data indicate that the two nifA genes identified in the DOA9 strain are expressed during symbiotic and free-living conditions and that in both conditions, the level of expression of nifAp is slightly higher than that of nifAc.Similar observations were also made during free-living growth under microaerobic conditions and the absence of a combined nitrogen source. Indeed, after 7 days of culture in these conditions, the β-glucuronidase activity measured for DOA9-nifA gene identified in DOA9 during symbiosis, we constructed various nifA mutants, single nifA mutants, either by insertion (DOA9ΩnifAc and DOA9ΩnifAp) or deletion (DOA9ΔnifAc and DOA9ΔnifAp), and a double nifA mutant (DOA9ΔnifAp::ΩnifAc). As shown in Figures C and Supplementary Figure S1, the plants inoculated with the different single mutants displayed no significant difference from those inoculated with the WT strain in terms of their growth, the number of nodules formed or the measured nitrogenase activity indicating that the single mutation of the nifAc or nifAp gene had no impact on the symbiotic performance of the strain. In contrast, the plants inoculated with the double nifA mutant (ΔnifAp::ΩnifAc) displayed a strict fix minus phenotype , and the growth of the plants was similar to that of the non-inoculated plants. Notably, the double nifA mutant induced nodules that were smaller and displayed symptoms of senescence  . Taken together these data suggest that the two NifA proteins identified in the DOA9 strain are functionally redundant during symbiosis and that at least one functional NifA protein is absolutely required for symbiotic nitrogenase activity, as observed in other rhizobia.The NifA protein has been shown to be essential for symbiotic nitrogen fixation in several rhizobia . To apprnifA genes were also exchangeable during free-living conditions, we analyzed the nitrogenase activity of the different constructed nifA mutants after 7 days of culture in semisolid BNM medium. The nifAc mutants including DOA9ΩnifAc and DOA9ΔnifAc were obviously unable to fix nitrogen in their free-living state (Table 1). In contrast, DOA9ΩnifAp and DOA9ΔnifAp mutants displayed nitrogenase activity similar to the WT strain. These data indicate that NifAc is essential for nitrogenase activity during the free-living condition, while NifAp does not play a significant role in this condition. As expected, it was found that the double nifA mutant was not able to fix nitrogen in the free-living state (Table 1).To determine whether the two npt2 promoter and reintroduced into the DOA9ΔnifAc mutant . As controls, we also reintroduced the complete nifAc or nifAp gene using the same plasmid and npt2 promoter. As shown in Table 1, only the reintroduction of the complete nifAc gene completely restored the nitrogenase activity of the DOA9ΔnifAc mutant. No gain of function was observed for all other constructs. This suggests that simple addition of the GAF domain is not sufficient to rebuild functional activity in NifAp protein under free-living conditions.Because NifAp lacks the N-terminal GAF domain, a simple hypothesis would be to postulate that NifAp protein is not active under free-living conditions, due to the absence of this functional domain. To check this hypothesis, we constructed two chimeric NifA hybrid proteins, one corresponding to the almost complete NifAp, to which has been added the first 70 AA of NifAc, the second corresponding to the sigma 54 interaction domain and HTH domain of NifAp (from the AA 150 to 503), to which was added the complete GAF domain of NifAc (the first 220 AA). These constructs were cloned into the pMG103 plasmid under the constitutive nifA mutants on plants suggested that the two NifA proteins are functionally redundant, but considering that a functional redundancy of the nifHDK genes found on the chromosome and the plasmid was also reported  as well as controls, namely, the two regulatory nifA genes  in both the WT and the two ΔnifAc and ΔnifAp mutants. As shown in Figure 4A, during symbiosis, the expression profiles of all structural nif genes is well conserved in the WT and the two nifA mutants. This clearly demonstrates that in this condition, the two NifA proteins can activate the expression of the two nifHDK sets, definitively confirming their functional redundancy in this condition. In contrast, during free-living growth, we observed that the ΔnifAc mutant differed drastically from the WT and the ΔnifAp mutant . Indeed, in the ΔnifAc mutant, no expression of nifDKc and p was detected and the expression levels of nifHc and p were also extremely low. These data, which were in concordance with the absence of nitrogenase activity detected in this mutant, confirms the essential role of NifAc protein in the activation of nif genes under free-living conditions. In addition, for this mutant, no expression of nifAp could be detected, indicating that NifAc also activates the expression of nifAp which is in accordance with the presence of putative NifA and RpoN binding sites found in the nifAp promoter region.To answer this question, we used quantitative reverse transcription PCR (qRT-PCR) to analyze the level of expression of the structural nif genes remains very similar between the WT strain and the ΔnifAp mutant during free-living conditions, confirming that NifAp is dispensable during this condition. Interestingly, in these culture conditions, nifDKc was expressed at a far higher level than nifDKp. These data are in agreement with our previous study, which showed that NifDKc was the major contributor to the bacterial nitrogenase activity under free-living conditions . Furthermore, in the only other example rhizobial strain reported to contain two nifA genes, Mesorhizobium loti, there was no functional redundancy observed between these two NifA proteins since the ΔnifA2 mutant gave a Fix- phenotype, while no symbiotic defect was observed for the ΔnifA1 mutant  clearly showed that NifAp forms an outgroup from the bradyrhizobial NifA proteins. Furthermore, nifAp is found in an unusual genomic context, as no known nif or fix genes are found in the vicinity, in contrast to the other rhizobial nifA genes that were always found associated with genes involved in nitrogen fixation (nifAp suggests the possibility that nifAp could have been separated from nif genes by a transposition event (Supplementary Text S1).The origin of these two fixation . The prenifA has been studied, it has been shown that NifA is absolutely required to activate nitrogen fixation during symbiosis , for which NifA naturally lacks this GAF domain and which, despite this, maintains its essential role in the activation of nif genes during symbiosis indicating that this domain is dispensable, at least under this culture condition , only a nonconventional site differing by one nucleotide has been identified in the upstream regions of nifDKp, nifHp . However, at the same time, under free-living growth, nifDKc was more highly expressed than nifDKp. Therefore, we cannot exclude the hypothesis that these slight variations in the upstream activating sequence differentially impact the affinity of NifA (at least for NifAc) according to the environmental conditions. Behind the control of nif genes, the NifA protein influences various cellular processes in rhizobia (The mechanism of regulations involving NifA in the DOA9 strain are certainly more complex than expected, considering that this strain also contains two ith RpoN . We can p, nifHp . Analysirhizobia . Thus, iJW, NT, NB, PT, and EG conceived the experiments. JW, NT, PT, and EG conducted the experiments. JW, NT, PT, and EG analyzed the results and wrote the paper. All authors reviewed the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The ABP dendrimer, which is built on a phosphorus-based scaffold and bears twelve azabisphosphonate groups at its surface, is one of the dendrimers that has been shown to display immuno-modulatory and anti-inflammatory effects towards the human immune system. Its anti-inflammatory properties have been successfully challenged in animal models of inflammatory disorders. In this review, we trace the discovery and the evaluation of the therapeutic effects of the ABP dendrimer in three different animal models of both acute and chronic inflammatory diseases. We emphasize that its therapeutic effects rely on the enhancement of the production of Interleukin-10, the paradigm of anti-inflammatory cytokines, by different subsets of immune cells, such as monocytes/macrophages and CD4+ T lymphocytes. All are necessary to eradicate the aggressive agent.Inflammation is a physiological process raised by the body in response to any disturbing agent, either of exogenous or endogenous origin. These immuno-stimulatory agents can be living micro-organisms that express at their surface pathogen-associated molecular patterns (PAMPs). It can also be either physically- and chemically-induced damages of a tissue or inappropriately self-modified molecules. These tissue damages and self-modified molecules are referred to as damage-associated molecular patterns (DAMPs). PAMPs and DAMPs are recognized by pattern recognition receptors (PPRs), which are expressed by immunocompetent cells within the circulating blood and the tissues. Most of these cells are professional phagocytic cells, and they are mainly monocytes and their tissue counterpart, macrophages . FollowiThis effector phase is soon accompanied by the onset and the implementation of the resolution phase  mouse lymphocytes (“anti-inflammatory” cells) that regulate CD4+ T helper 1 (Th1) lymphocytes (“pro-inflammatory” cells) ,6. IL10 IL10 gene). Inhibition of ROCK2 promotes the phosphorylation of STAT5, therefore the production of IL10. Based thereon, it has been shown that the oral administration of a selective inhibitor of ROCK2 reduces the severity of psoriasis in humans [In diseases in which the reinforcement of the IL10 pathway is rationally expected to be beneficial, several therapeutic strategies have been assessed. An obvious strategy is to administer recombinant human (rh) IL10 to patients. Indeed, the subcutaneous injection of rh IL10 has shown a partial therapeutic response in patients with melanoma or renal cell cancer . Rho-assn humans . A thirdn humans .On the same track, we have discovered and documented the unprecedented immuno-modulatory properties of a particular phosphorus-based dendrimer of the first generation capped with twelve azabisphosphonate (ABP) groups, and that is called the ABP dendrimer. Over the years, we have shown that this unique molecule has anti-inflammatory effects towards several subsets of immune cells . The anti-inflammatory properties of the ABP dendrimer have been challenged in several animal models of inflammatory diseases, both acute and chronic ones. This mini-review recapitulates the results we have obtained since 2006 and puts in perspective that a common feature of the therapeutic effects of the ABP dendrimer is the enhancement of the production of IL10 via the activation of different subsets of immune cells, namely monocytes/macrophages and IL10-producing CD4+ T lymphocytes.3P3) core on which one series of six phenoxymethyl-methylhydrazone (PMMH) branches are linked. At the end of each branch, the point of divergence is a dichlorothiophosphorus (PSCl2) group that enables the doubling of the number branches at the next generation, if any, or the addition of twelve (2 × 6) ABP end groups in the case of the ABP dendrimer  or an aThe anti-inflammatory activation of human monocytes has been a test that we have implemented to screen different sets of dendrimers synthesized over the years to depict the relations between the structure and the bio-activity of these molecules. Human monocytes are easily available from blood samples of healthy volunteers. The monocytes are purified from peripheral blood mononuclear cells (PBMC) isolated from the blood by a density gradient. They are then plated in culture wells and cultured for four or five days in the presence of dendrimers in a concentration range of 0.2 to 20 µM. Morphological and phenotypical changes of monocytes are assessed by flow cytometry. Starting from the ABP dendrimer, we have varied four chemical and structural features of dendrimers to uncover the key parameters underpinning the anti-inflammatory activity of dendrimers: the surface functions, the size/generation of the dendrimers, the density of the surface functions at the outer shell of the dendrimers, and the dendritic internal scaffold.3P3 core/PMMH branches of the dendritic scaffold shown in 3P3/PMMH dendrimers. We have shown that the first-generation dendrimer of this series, with six PMMH branches and twelve ABP groups at its surface (the ABP dendrimer) is the most active, [Regarding the surface functions, we have assessed the bio-activity of several monophosphonate , azamono active, . Interes active, ,27.3P3/PMMH dendrimers, we have shown that a strong directionality and a low flexibility guarantee a maximization of multivalency, and thereby the anti-inflammatory activity of the molecules [Finally, to understand the role of the dendritic skeleton on the bio-activity of dendrimers, we have prepared a series of thirteen dendrimers with ABP surface groups borne on seven different dendritic scaffolds of the first- and second-generations. The molecular dynamics simulations of these compounds gave an unexpected, but clear, answer: the three-dimensional structure of dendrimers is key to their bio-activity . Indeed,olecules .3P3/PMMH dendrimers capped with ABP groups and identified as bio-active compounds in [Aside from its anti-inflammatory properties towards human monocytes described above, we have shown that the ABP dendrimer has several other immuno-modulatory effects. Very early, we showed that in long-term cultures of human PBMCs, the ABP dendrimer promotes the specific amplification of Natural Killer (NK) cells . NK cellounds in  were ablounds in .In addition to the anti-inflammatory activation triggered on monocytes, the ABP dendrimer displays its anti-inflammatory effects towards to other immune populations. Firstly, it is able to inhibit the proliferation of human CD4+ T lymphocytes in vitro . This pr−/−) [−/− mice, starting at the age of eight weeks, when the incidence of arthritis is 100%. The molecule was injected intravenously at doses between 0.01 and 10 mg/kg/week, the highest doses at 1 and 10 mg/kg/week being efficient [−/− mice. MMP are responsible for the degradation of joint cartilage, therefore, cartilage is preserved in treated mice when compared to untreated animals. In addition, the ABP dendrimer inhibits both the differentiation of monocytes and the trans-differentiation of myeloid dendritic cells in osteoclasts. These giant multi-nucleated cells are responsible for bone resorption, of which treated IL1-ra−/− mice are thus preserved, contrary to untreated mice. These results have reinforced the relevance of monocytes/macrophages as a cellular therapeutic target for the treatment of RA [−/− mice treated with the ABP dendrimer for twelve weeks secrete less pro-inflammatory Th1 cytokines , whereas the secretion of anti-inflammatory Th2 cytokines, such as IL4 and IL10, was dramatically increased, when compared with age-matched untreated IL1-ra−/− mice [−/− mouse model when given per os , solubilized in phosphate buffer without any dedicated formulation, at the dose of 10 mg/kg administered weekly [The first model we have chosen is a mouse model of experimental arthritis. The mice are genetically modified, namely, knocked-out for the antagonist to the receptor to IL1 (IL1-ra−/−) . Therefo−/−) ,39. The fficient . In thisnt of RA . Finally−/− mice . Of noted weekly . The thed weekly . In thisd weekly ,40. Thand weekly ,45.Then we have decided to assess the therapeutic efficacy of the ABP dendrimer in a model of acute inflammatory disease cured through topical application of the drug, instead of systemic administration in the above arthritic models. We have chosen Endotoxin-Induced Uveitis (EIU) in the rat, which is considered a relevant model for human anterior uveitis . Uveitis−/− mouse model of arthritis, we have shown that splenocytes from EAE mice treated with the ABP dendrimer produce significantly lower quantities of IFN-γ and IL17 after ex vivo stimulation with the MOG peptide. Conversely, they produce higher amounts of IL10. To explain these observations, we have unveiled that several cellular mechanisms are operating in vivo in the mouse model of EAE. The ABP dendrimer skews the production of cytokines by splenocytes from diseased mice from an inflammatory pattern to an anti-inflammatory one. It also inhibits the accumulation of antigen-presenting cells (APC) in the spleen under inflammatory conditions, and prevents APC activation. Most interestingly, the ABP dendrimer also redirects the deleterious immune response of MOG-specific CD4+ T lymphocytes towards a benefic production of IL10. This latter effect is, at least in part, indirect through the action of the ABP dendrimer on APC. Taken together, these data strongly suggest that the ABP dendrimer, by down-regulating the activity of APC, could strongly impact the pathogenic profile of autoreactive CD4+ T cells interacting with these APC. Consequently, autoreactive CD4+ T cells produce lesser amounts of Th1/Th17 pro-inflammatory cytokines and are redirected to produce the anti-inflammatory IL10.Experimental Auto-immune Encephalomyelitis (EAE) induced in wild-type mice upon injection of myelin oligodendrocyte (MOG) peptide and pertussis toxin is recognized as a relevant preclinical model of human multiple sclerosis (MS) . As autoFor years dendrimers have been considered promising players of the nanomedicine landscape . HoweverAside from the assessment of the therapeutic efficacy of drug-candidates in relevant preclinical animal models of diseases, the safety of the compounds has to be documented as early as possible. That is the reason why we have performed a general and immuno-safety study in non-human primates . Using a


Triple-negative breast cancer (TNBC) is among the most notorious types of breast cancer, the treatment of which does not give consistent results due to the absence of the three receptors (estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) as well as high amount of molecular variability. Drug resistance also contributes to treatment unresponsiveness. We studied differentially expressed genes, their biological roles, as well as pathways from RNA-Seq datasets of two different TNBC drug-resistant cell lines of Basal B subtype SUM159 and MDA-MB-231 treated with drugs JQ1 and Dexamethasone, respectively, to elucidate the mechanism of drug resistance. RNA sequencing(RNA-Seq) data analysis was done using edgeR which is an efficient program for determining the most significant Differentially Expressed Genes (DEGs), Gene Ontology (GO) terms, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. iPathway analysis was further used to obtain validated results using analysis that takes into consideration type, function, and interactions of genes in the pathway. The significant similarities and differences throw light into the molecular heterogeneity of TNBC, giving clues into the aspects that can be focused to overcome drug resistance. From this study, cytokine-cytokine receptor interaction pathway appeared to be a key factor in TNBC drug resistance. Breast cancer represents one of the most common cancers in women and is a major life-threatening disease. In certain cases, due to the development of drug resistance, treatment becomes unsuccessful . Triple-The subtypes of TNBC are Basal-like , immune-modulatory, luminal androgen receptor, mesenchymal, and mesenchymal/stem-like subtype and claudin low subtype . SUM 159In view of this, the present study aimed at studying the gene expression and pathways in TNBC cell lines SUM159 and MDA-MB-231 that show drug resistance and identify the top differentially expressed ones so that appropriate drug targets can be designed for overcoming the mechanism of resistance.p-value of 0.05 and log fold change (log FC) of 1. At a 4-h time interval of Dex treatment, out of the 13,280 genes differentially expressed between Dex-treated MDA-MB-231 cell lines and vehicles (control samples), a total of 563 genes were observed to be significantly differentially expressed (325 upregulated and 238 downregulated genes) at a p-value of 0.05 and log FC of 1  were observed to be significantly differentially expressed between SUM159R and SUM159 (891 upregulated and 1791 downregulated genes) at a  FC of 1 .p-values were SLC37A2 and CADM1 in SUM159R cells treated with JQ1 at 3 and 24 h, respectively, whereas the downregulated genes with the lowest p-values were DCN and CDH11. In MDA-MB-231 cells treated with Dexamethasone at two and four hours, the upregulated gene with lowest p-values was PDK4 in both time intervals, whereas the downregulated genes with lowest p-values were PLK2 and PRAG1. The top most significantly up-and downregulated differentially expressed genes are given in The upregulated genes with lowest A multidimensional scaling (MDS) plot was generated which gives relative positions of each sample a,b. ThisFunctional annotation was done using gene ontology (GO) enrichment analysis for studying the biological roles of significant differentially expressed genes in the study. Broad classification of GO terms includes three groups: biological processes (BP), cellular components (CC), and molecular function (MF). The most significantly enriched processes found out in the study were multicellular organismal process, cell cycle, regulation of signal transduction, and response to stimulus. All of them belonged to biological processes . The topThe importance of the cytokine-cytokine receptor interaction pathway has been highlighted by these studies and further investigation is required to target this pathway for therapeutic and diagnostic purposes.p-value of 5.588 × 10−6 and 1.120 × 10−8 for JQ1 and dexamethasone, respectively. The PI3K-Akt signaling pathway was also found to be significantly altered. The list of top statistically significantly altered pathways is given in The iPathway Guide analysis was used for further validation of our findings of significance. This analysis revealed that the most significant altered pathway at all time intervals for both of the drug treatments was cytokine-cytokine receptor interaction with a The aim of the present study was to explore changes at the molecular level in the drug-resistant TNBC cell lines, which included the study of differentially expressed genes, cellular processes, pathways, and pathway interactions. The difference seen in these molecular aspects of sensitive and resistant cells could throw light on the mechanism of resistance. The study could also give an insight into the major pathways and altered gene functions to be targeted for diagnostic and prognostic purposes in future. Among the different subtypes of TNBC, the study focused on Basal B subtypes and, therefore, two different cell lines (SUM159 and MDA-MB-231) belonging to the same subtype were used and checked for consistency as well as disparity between the two. The drugs used were JQ1 and Dexamethasone.JQ1 is a bromodomain or extra terminal inhibitor or BETi and it stops cancer cells from adapting to hypoxia. Therefore, it acts as an important therapy agent in hard-to-treat breast cancers . Dexametp-values were SLC37A2, PNMA2, and CADM1. SLC37A2 is a gene that codes for a protein that is involved in sugar-phosphate exchange [SLC37A2 gene in contributing to drug resistance. The significantly downregulated genes with the lowest p-values were CDH11 and HGF. CDH11, also known as OB Cadherin, has a role in the metastasis process and has already been established as an epigenetic biomarker of drug resistance in acute myeloid leukemia. The class of proteins to which CDH11 belongs has already been proposed as novel targets for anticancer therapy [p-values were PDK4, TSC22D, and KLF9. PDK4-Pyruvate Dehydrogenase Kinase 4 acts as a gatekeeper of the tricarboxylic acid (TCA) cycle by inactivating pyruvate dehydrogenase (PDH). PDK4 expression is downregulated dramatically in most tumor types. PDK4 is found to be a critical metabolic regulator of epithelial-mesenchymal transition (EMT) seen in acquired anticancer drug resistance [p-values were ZNF703, SOX9, and PRAG1. The ZNF703 gene is a novel oncogene seen in a small percentage of breast cancers—the ones harboring 8p12 amplifications. Luminal breast cancer cell lines in which the ZNF703 is overexpressed are seen to be resistant to tamoxifen through the activation of Akt/mTOR signaling [Only the significant factors common to both time intervals of each sample were considered. The upregulated genes with lowest exchange . A studyexchange . Therefo therapy ,21. Thisignaling .p-value for JQ1 treatment at three hours was cytokine-cytokine interaction, which came under the broader category of environmental information processing. At 24 h of JQ1 treatment, the most significantly enriched pathway was the cell cycle. The cytokine-cytokine interaction pathway was repetitively seen to be significantly enriched in both 2-h and 4-h treatment of MDA-MB-231 cells with Dex.Gene ontology (GO) analysis was performed for elucidation of biological roles of the differentially expressed genes. The most significantly enriched GO category for JQ1 at three hours was multicellular organismal processes which come under the category of biological processes, whereas at 24 h, it was cell cycle which again came under biological processes. In MDA-MB-231 cells treated with Dex at two hours, the most significantly enriched GO category was regulation of signal transduction, and at four hours it was response to stimulus; both belong under biological processes. KEGG pathway analysis was conducted in order to study the biological roles of the differentially expressed genes. The pathway with the lowest Cytokine signaling is said to direct tumor cell proliferation and stromal blood vessel network formation. There is evidence that cancer cells and their associated stroma secrete cytokines that play a key role in a number of drug-resistant mechanisms . Cancer Crucial information, like the type of each gene, the position of each gene on each pathway, and the type and directions of the interactions with the other genes is essential to carry out a more meaningful pathway analysis.iPathway Guide is an implementation of a pathway analysis approach  introducThe limitation of the present study was the unavailability of datasets of similar nature viz. RNASeq data of TNBC resistant samples. More studies on the transcriptome drug-resistant samples in TNBC would have provided greater heterogeneity in the sample subsets, and hence given a broader perspective on the molecular aspects. The results of this study give the molecular characteristics of the drug-resistant cell lines as compared to drug-sensitive, and these can be considered for determining therapeutic targets. Further studies on the cytokine-cytokine interaction pathway and its components should be carried out for arriving at further conclusions regarding overcoming of drug resistance for successful treatment of TNBC.In conclusion, the present study performed an analysis of the RNAseq datasets available of drug-resistant TNBC cell lines to identify the top differentially expressed genes, pathways, as well as other biological functions. The cytokine-cytokine interaction pathway showed a consistent significant differential expression in both the different cell lines. With evidence from other published literature and our findings, we were able to conclude that this pathway could be targeted for therapy. Further functional studies can give an insight into the role of the significantly expressed biological factors in the resistant samples to throw light on the mechanism of resistance and how to overcome the same using targeted therapies. Sun X. et al. confirmed that crosstalk inhibition diminishes the response of signaling output to the external stimuli . TherefoGene expression profiling studies related to drug resistance in TNBC were identified by searching the Gene Expression Omnibus (GEO) database . The fol(1)The first study  was base(2)The second experimental study  focused The characteristics of the experimental setups that generated the datasets were as follows:RNASeq data are usually very large and require efficient algorithms which use minimum computing resources for their analysis . The majThe RNASeq reads were assessed for their quality using the Fast QC 0.11.6  program Top differentially expressed genes were identified in both of the experimental datasets (SUM159R vs. SUM159 and MDA-MB-231 vs. control) at different time intervals of drug treatment. Drug-sensitive samples corresponding to each treatment were taken as controls.The edgeR program in R studio was used to determine how the genes are differentially expressed in the drug-resistant cells when compared to the sensitive ones. edgeR uses probabilistic methods for determining differential expression. The genes and pathways affected were determined based on a false discovery rate (FDR) of 0.05 and a log FC of 1 . edgeR f