Theofilos Spyrou

Sarah A El-Sayed

Engin Afacan

Luis A Camuñas-Mesa

Bernabé Linares-Barranco

Haralampos-G Stratigopoulos

Reliability Analysis of a Spiking Neural Network Hardware Accelerator

Despite the parallelism and sparsity in neural network models, their transfer into hardware unavoidably makes them susceptible to hardware-level faults. Hardware-level faults can occur either during manufacturing, such as physical defects and process-induced variations, or in the field due to environmental factors and aging. The performance under fault scenarios needs to be assessed so as to develop cost-effective fault-tolerance schemes. In this work, we assess the resilience characteristics of a hardware accelerator for Spiking Neural Networks (SNNs) designed in VHDL and implemented on an FPGA. The fault injection experiments pinpoint the parts of the design that need to be protected against faults, as well as the parts that are inherently fault-tolerant.

I. INTRODUCTION

Spiking Neural Networks (SNNs) offer a promising computing paradigm inspired by the inner workings and efficiency of the biological brain [START_REF] Maass | Networks of spiking neurons: The third generation of neural network models[END_REF], [START_REF] Pfeiffer | Deep learning with spiking neurons: opportunities and challenges[END_REF]. Compared to classical Artificial Neural Networks (ANNs), SNNs exhibit properties such as faster recognition speed, higher time resolution, lower latency, and lower energy consumption, making them an interesting candidate for machine learning tasks related to sensory information processing. The design of efficient SNN hardware accelerators is an intense on-going research field [START_REF] Akopyan | TrueNorth: Design and tool flow of a 65 mW 1 million neuron programmable neurosynaptic chip[END_REF]- [START_REF] Camuñas-Mesa | Spiking neural networks and their memristor-CMOS hardware implementations[END_REF]. It is frequently cited that SNN hardware accelerators inherit the remarkable fault-tolerance capabilities of the biological brain, offering resilience to hardware-level faults induced by manufacturing defects, reduced-voltage memory operations, radiation, and aging. However, this assumption has been proven false according to recent fault injection experiments [START_REF] Spyrou | Neuron fault tolerance in spiking neural networks[END_REF]- [START_REF] Putra | ReSpawn: Energyefficient fault-tolerance for spiking neural networks considering unreliable memories[END_REF].

Resilience characteristics of SNN hardware accelerators to hardware-level faults are expected to be dependent on the network topology, circuital implementation (e.g., digital, mixed analog-digital, memristor-based synapses) and size, as well as on the training algorithm, the cognitive task being executed, and the foreseen fault rates. For example, memristor-based synapses show low manufacturing yield and endurance [START_REF] Liu | Rescuing memristor-based neuromorphic design with high defects[END_REF]. Resilience characteristics also show a large variance according to fault type and location. Assessing the reliability and locating the critical faults and the more vulnerable parts of the design is important for developing better targeted and lower-cost self-testing and fault-tolerance capabilities, where the cost is expressed by the area and power overheads and performance penalty. Standard fault-tolerance techniques used in traditional computing, such as Triple Modular Redundancy (TMR) and Error Correction Codes (ECCs) for memories, are not effective for Artificial Intelligence (AI) hardware accelerators in general since they incur prohibitive overheads.

The prior art on dependability analysis of SNNs is still at a very early stage. In [START_REF] El-Sayed | Self-testing analog spiking neuron circuit[END_REF], a functional Built-In Self-Test (BIST) is proposed for biologically-inspired spiking neurons. The idea is to test in one-shot with a specially crafted stimulus that the neuron is capable of producing all the expected firing patterns. If one or more firing patterns are missing, then the neuron is declared faulty.

Transistor-level defect and process variation simulations for a single spiking neuron have been performed in [START_REF] El-Sayed | Spiking neuron hardware-level fault modeling[END_REF]. Main observed faulty behaviors include saturated neuron behavior, i.e., nonstop spiking at the absence of excitatory input, dead neuron behavior, i.e., halt in spiking despite the presence of excitatory input, and timing variations, i.e., variations in timeto-first-spike and firing rate.

In [START_REF] Spyrou | Neuron fault tolerance in spiking neural networks[END_REF], such faulty behaviours are mapped in a Python-based SNN model. Fault simulations showed that saturated neuron faults occurring anywhere in the network can be lethal, while all other fault types can impact the classification accuracy if they occur in neurons in the last layers. A two-step neuron fault tolerance approach is proposed. First, training the network with dropout [START_REF] Srivastava | Dropout: A simple way to prevent neural networks from overfitting[END_REF] helps to passively tolerate all except the saturation faults and the faults occurring in the last layer. Then, an active fault tolerance scheme is used based on detecting saturation on a per neuron basis and cancelling it out by the "fault hoping" concept where a saturated neuron is transformed to a dead neuron. TMR is used for the last and most critical layer.

The work in [START_REF] Vatajelu | Special session: Reliability of hardware-implemented spiking neural networks (SNN)[END_REF] presents a fault taxonomy and discusses behavioral-level fault injection experiments for a SNN architecture with memristor-based synapse implementation. The fault model includes worst case faulty behaviours, such as dead neuron and dead synapse faults, where a dead synapse is modeled with a zero weight. Results show that a high fault density is required for noticeable decrease in recognition rate.

The work in [START_REF] Schuman | Resilience and robustness of spiking neural networks for neuromorphic systems[END_REF] studies the resilience of feed-forward SNNs to dead synapse faults when trained with different algorithms. Synapses are selected to be faulty at random with different fault rates. Results show that resilience characteristics depend largely on the training algorithm and in all cases the accuracy drops rapidly with increasing fault rates. It is shown how to modify an evolutionary optimization-based training algorithm so as to improve fault tolerance. In particular, the fitness function is redesigned to become a weighted sum of the baseline accuracy and the average accuracy obtained on versions of the SNN with dead synapse faults. The resilience is improved but the baseline accuracy is not recovered.

In [START_REF] Putra | ReSpawn: Energyefficient fault-tolerance for spiking neural networks considering unreliable memories[END_REF], fault injection is performed in a Python-based SNN model. The fault model is bit-flips in the memories storing the weights of the network. A uniform random distribution with different rates is considered. Fault-tolerance schemes to mitigate memory failures are also proposed, namely Fault-Aware Mapping (FAM) and Fault-Aware Training and Mapping (FATM). First, the memory fault map, i.e., the location of the faulty memory cells, is derived using testing. FAM consists in identifying the memory segment with the highest number of subsequent non-faulty cells and prioritize placing the Most Significant Bits (MSBs) of the weight in this segment, which is done using a circular shift. FATM follows FAM and consists in performing re-training while considering bit-flips for different rates during training epochs. In this way, the network adapts its accuracy to different bit-flip probabilities.

In all the aforementioned works, resilience characteristics of SNNs have been studied by performing fault injection at transistor-level for single neurons or in a behavioral-level model for entire networks. Although experimenting on higher abstraction models allows flexibility, the particularities of a hardware implementation are not taken into consideration.

Similarly, in the case of ANN hardware accelerators reliability analysis has been mainly performed by simulation using a higher abstraction model [START_REF] Li | Understanding error propagation in deep learning neural network (DNN) accelerators and applications[END_REF], [START_REF] Neggaz | Are CNNs reliable enough for critical applications? An exploratory study[END_REF]. Few works exist demonstrating fault injection in actual hardware, in particular fault injection in FPGA implementations [START_REF] Zahid | FAT: Training neural networks for reliable inference under hardware faults[END_REF] and radiation experiments on ANNs running in GPUs [START_REF] Santos | Analyzing and increasing the reliability of convolutional neural networks on GPUs[END_REF], [START_REF] Lotfi | Resiliency of automotive object detection networks on GPU architectures[END_REF] and FPGAs [START_REF] Libano | Selective hardening for neural networks in FPGAs[END_REF].

In this work, we present results of fault injection on actual neuromorphic hardware. For this purpose, we designed in VHDL a convolutional SNN for the poker card symbol recognition task and implemented it on an FPGA. For our implementation, we use the Zynq UltraScale+ MPSoC ZCU104 board from Xilinx. The network parameters are configured automatically with a MATLAB script to allow easy experimentation. The fault injection framework runs on the processor of the board allowing us to perform an accelerated large-scale reliability analysis. Our fault model includes bit-flips in the memories storing the different network parameters. We analyse the resilience of the entire network and layer-by-layer at different fault rates and bit positions. The experiments pinpoint the critical parts of the design for which a fault-tolerance scheme is demanded, as well as the parts that are inherently fault-tolerant.

The rest of the paper is structured as follows. In Section II, we describe the SNN hardware architecture. In Section III, we introduce the fault model. In Section IV, we provide the findings of the fault injection experiments. Section V concludes the paper.

II. SNN HARDWARE ARCHITECTURE

The building block of the SNN is an event-driven configurable convolutional node proposed in [START_REF] Camuñas-Mesa | A configurable eventdriven convolutional node with rate saturation mechanism for modular convnet systems implementation[END_REF] as a generic block that can be used to build multi-layer feature maps for convolutional SNNs communicating through the Address Event Representation (AER) protocol. In this section, we present the node along with a brief description of its most important features. Then, we present the SNN built using this node to classify poker card symbols recorded using a Dynamic Vision Sensor (DVS).

A. The Convolutional Node

The node consists of three main blocks, namely a convolutional unit, an internal configuration block, and a router, as shown in Fig. 1. The ports of the node are optimized for a 2-D layout, an efficiently adopted structure in hardware Convolutional Neural Networks (CNNs) since it optimizes the use of on-chip space. Each node has four bidirectional ports connecting it to its immediate neighbors to the north, south, east, and west.

1) The Convolutional Unit: Shown in Fig. 2, it consists of an array of Integrate & Fire (I&F) neurons representing pixels, three main memory blocks (i.e., kernel memory, neuron memory, and rate-saturation memory), First In, First Out (FIFO) input and output registers, a controller block, and a Serial Peripheral Interface (SPI) block.

The convolutional unit is where the convolution of an input flow of events ev in (t, x, y, p, k) and a kernel w k (x, y) takes place to produce an output flow of events ev out (t, x, y, p), where t is time, x and y are the pixel address coordinates, p is the polarity of the event, and k is the kernel ID in the kernel memory. In addition, the unit has two more important features: global leakage and rate saturation. Leakage is the decay of the neuron membrane potential in between incoming spikes and it is implemented by forcing the neuron state to converge towards the reset value after a certain time interval, which is determined by a global counter. The rate saturation feature, on the other hand, is the imposition of the minimum refractory period property found in biological neural networks, i.e., the neuron is not allowed to produce an output spike for a certain period after the last output spike, hence controlling the maximum spiking frequency of a neuron.

With every incoming event read from the input FIFO register, a convolution operation is executed and the values of the corresponding pixels are updated and compared to the positive and negative thresholds. If the value of a threshold is reached by a pixel and the condition imposed by the rate saturation mechanism is fulfilled, the pixel produces an output event with address (x out , y out ) and polarity p out and writes it in the output FIFO register.

To control the traffic, a signal is activated when the registers get full, and the incoming events are discarded until there is room for more events in the register. While this implies that the output events would get down-sampled and some information will eventually be lost, the spatio-temporal correlation of the passing events is preserved, keeping the integrity of the carried information.

2) The Router: In hardware implementations of neural networks, the highly dense connectivity required between neurons poses a challenge in terms of on-chip area. Routers handle the transmission of events from their origin to their destination, hence providing a practical solution to this problem. In this design, the destination-driven addressing scheme is adopted, which means that for every event, there is a routing header that carries the x and y coordinates of the destination node in the mesh distribution of the network.

3) The Configuration Block: The convolutional node has a set of adjustable parameters that need to be configured prior to the inference operation of the network. Some parameters belong to the convolutional unit, such as the neuron threshold and the kernel weights. Others belong to the router, such as the local address of the node and the routing table information necessary for redirecting events through the ports of the node. Each parameter value is sent to the node through a SPI, with an index indicating its identity. The configuration block interprets the parameter identities and handles their allocation in their corresponding locations in the different memory blocks.

B. The Poker Card Symbols Dataset

The SNN used in this work is built to classify a dataset representing the 4 poker card symbols [START_REF] Pérez-Carrasco | Mapping from frame-driven to framefree event-driven vision systems by low-rate rate coding and coincidence processing-application to feedforward ConvNets[END_REF]. A deck of 40 poker cards was presented in front of a DVS for a period of around 1 s. The events were recorded and processed in order to generate 40 samples of 32×32 pixel windows showing only the centered symbols. The resulting stimulus has a total of 174644 events, a duration of 950 ms, and an average speed of 184K events per second. In our experiments, we use a version of the dataset slowed down to 1% of the original speed in order to ensure a scenario where no input events are discarded.

C. The Convolutional SNN

The convolutional SNN is designed and trained in software in a frame-based format using backpropagation, and then transformed into the equivalent spiking form [START_REF] Pérez-Carrasco | Mapping from frame-driven to framefree event-driven vision systems by low-rate rate coding and coincidence processing-application to feedforward ConvNets[END_REF]. Afterwards, the weights and parameters are scaled, rounded, and then tuned to make up for the discrepancies between hardware and software implementations using simulated annealing as an optimization algorithm [START_REF] Camuñas-Mesa | A configurable eventdriven convolutional node with rate saturation mechanism for modular convnet systems implementation[END_REF].

As shown in Fig. 3, the SNN consists of 4 convolutional layers (C1, C2, C3, and C4) made up of 22 convolutional nodes. The first 2 layers are followed by 2 sub-sampling layers (S1 and S2). The network has 94 kernels in total, where layer C1 has 1 kernel per node, layer C2 has 6 kernels per node, layer C3 has 4 kernels per node, and layer C4 has 8 kernels per node.

The 2-D hardware layout of the FPGA implementation is shown in Fig. 4, where the nodes are arranged in a 6 × 4 mesh with bidirectional connections between the routers of each block and its immediate neighbors. Every node carries an identification corresponding to its x and y address in the mesh, and its color indicates the respective layer in the network. Nodes [START_REF] Camuñas-Mesa | Spiking neural networks and their memristor-CMOS hardware implementations[END_REF][START_REF] Davies | Loihi: A neuromorphic manycore processor with onchip learning[END_REF] and [START_REF] Spyrou | Neuron fault tolerance in spiking neural networks[END_REF][START_REF] Davies | Loihi: A neuromorphic manycore processor with onchip learning[END_REF] are extra nodes added for routing purposes but do not perform any processing.

Input events do not have any specified destination address and they need to be sent to all nodes of the first layer. Therefore, there is an extra splitter block at the input side, which creates 6 copies of every incoming event, adds the address of a node in the input layer to each copy, and delivers them to the corresponding nodes. At the output side, there is a merger block which simply forwards events from the 4 nodes of the output layer to the output of the network without altering them.

D. Embedded system design

To automate the reliability analysis, we built a hardware platform and a software framework to support it. More specifically, we designed a standalone embedded system application where the convolutional SNN is handled by a C program running on the ARM Cortex-A53 APU of the MPSoC. The C program iterates over the fault injection experiments that have been stored to an SD card. For each experiment, the C program coordinates the following actions: (i) configuration of the faulty SNN; (ii) generation of the input spiking events according to the dataset; and (iii) monitoring of the output spiking events and storing to the SD card. The hardware platform communicates with a PC through a serial connection. The software framework consists of three MATLAB scripts: (i) the configurator which generates the configuration data for the nominal SNN; (ii) the injector which performs the fault injection into the configurator's output to generate configuration data for faulty SNN versions which are then written to the SD card; and (iii) the classifier which processes the output spiking events written to the SD card and performs the classification. The winning poker card symbol is the one whose node produces the largest number of spikes. The classifier also calculates the SNN recognition accuracy over the testing set.

III. FAULT MODEL

As discussed in Section II, the generic convolutional node used as a building block in this hardware implementation is configurable through a set of modifiable parameters. These parameters are stored in mutually exclusive memory blocks inside the node, which are the subject of our reliability experiments. The parameters have an 8-bit representation in hardware and can be categorized into:

1) Splitter Parameters: The splitter is parametrized by the number of input event copies to generate and send to each firstlayer node, as well as the node addresses. Splitter parameters make up 0.52% of the used memory.

2) Router Parameters: The router needs two important settings, namely the local address of the node and the routing information necessary for redirecting events through its ports, i.e., addresses of next-layer nodes and the direction towards them (down, right). The router also carries the kernel ID information so that the correct kernels corresponding to each event can be retrieved from the memory. Router parameters make up 15.25% of the used memory.

3) Neuron Parameters: Neuron parameters govern the key features of the I&F neurons within the node. They include the neuron threshold, the leakage pulse amplitude and period, and the refractory period. These parameters are set for the whole node, i.e., they are global to the whole array of neurons inside a node. Neuron parameters make up 7.8% of the used memory.

4) Kernel Parameters: Every node of the network has a specific number of kernels and needs two parameters per kernel, namely the kernel size and the center-shift of the kernel which determines whether the kernel is applied to the pixels in the zone around the one given by the event destination address or it is shifted to another pixel. Kernel parameters make up 10.75% of the used memory.

5) Kernel Weights: The number of kernel weights per kernel is determined by the kernel size, i.e., a n × k kernel has n * k weights. The kernel weights considering all kernels in all nodes of the network make up 68.55% of the used memory. The fault model consists of permanent bit-flips in the aforementioned memories. Bit-flips are injected in two different ways, in particular with a Bit Error Rate (BER) probability, leading to a multiple-bit fault scenario with uniform random distribution of bit-flips, or considering a single-bit fault scenario. In the former scenario, assigning different BER probabilities helps assessing the BER that can be tolerated by the SNN. In the latter scenario, we study the effect of single bitflips parameter-by-parameter, layer-by-layer, and for different bit positions. The goal is to identify critical parts of the design, as well as critical bit positions.

IV. RESULTS

For each fault injection experiment we evaluate the SNN recognition rate and compare it with the baseline value for the nominal design which is 85 ± 2.5%.

Each fault injection experiment takes approximately 2 minutes. In the multiple-fault scenario, for a given BER value, we perform 100 repetitions. As the total memory size is 18464 bits, it is very time-consuming to perform all single bit-flip scenarios even in hardware where run-time is accelerated. Thus, for the kernel weights in the first three layers C1, C2, and C3 that occupy the largest fraction of the memory, we perform fault sampling, randomly selecting 20%, 10%, 10% fault locations, respectively. For the rest of the parameters we perform exhaustive fault injection. In total, we performed 11925 fault injections which took approximately 16.5 days of simulation time.

Fig. 5 shows the accuracy versus BER in the case where bit-flips are injected uniformly at random across the entire network. We visualize summary statistics using box plots. The bottom and top edges of the box indicate the 25th and 75th percentile, respectively, the whiskers extend to the most extreme data points not considered outliers, and the outliers are plotted individually using the 'o' symbol and are not always aligned vertically for illustration purpose. We also report the median shown with a dotted circle and the average accuracy across repetitions of the same experiment. Experiments with 0% accuracy correspond to an application crash as the result of fatal errors which made the system unable to respond to any incoming event activity. As it can be seen from Fig. 5, the accuracy drops with increasing BER and beyond 10 -4 the drop is below the tolerated zone shown with green color. This shows that the maximum tolerated BER is 10 -5 or less. We also observe that for moderate BER values the variance of the accuracy increases as BER increases, which shows that accuracy drop is largely dependent on the combinations of faulty bits and their locations.

The outliers for moderate BER values in Fig. 5 are due to faults occurring is the smaller yet far more critical memory blocks storing splitter, router, and kernel parameters. In fact, the network is susceptible even to single bit-flips affecting these parameters. Faults in the splitter may lead to generated addresses that do not correspond to a valid node inside the mesh, and this leads to input events not reaching any destination address and, thereby, not being processed. Faults in the kernel parameters change the kernel's size and center-shift and faults in the router parameters change the routing of the spikes. Thus, such faults essentially result in a structurally different network architecture. For example, Fig. 6(a) shows single bit-flips in the router parameters layer-by-layer and across different bit positions. The network is very sensitive and the bit position where the flip occurs is irrelevant. Thus, we conclude that splitter, router, and kernel parameters are critical and protecting them is of utmost importance. From Fig. 6(b) we observe that the network performance is sensitive to single bit-flips in the neuron threshold only if these occur in the 3 Most Significant Bits (MSBs), while the last layer C4 shows no vulnerability. From Fig. 7(a) we observe that layers C1 to C3 start showing vulnerability for BER values larger than 10 -2 .

Regarding kernel weights, from Fig. 6(c) we observe that the network sensitivity increases with the layer number. Thus, some faults in the beginning of the network tend not to propagate. Another observation is that single faults affecting Least Significant Bits (LSBs) can be tolerated. From Fig. 7(b) we observe that the network can tolerate up to a BER of 10 -5 . These results show that leaving unprotected the 4 LSBs of the kernel weights, which always occupy the largest fraction of the memory, is feasible, leading to significant cost reduction in fault tolerance schemes.

V. CONCLUSIONS

We presented a fault injection experiment and fault resilience analysis for a SNN hardware accelerator. We developed a fault injection framework that creates and maps SNN faulty instances into the hardware, allowing to accelerate fault injection and assess the fault criticality on actual hardware. Our findings show that certain SNN parameters, i.e., splitter, router, and kernel parameters, are critical and must be protected, while for others, i.e., neuron threshold and kernel weights, the network shows some degree of resilience to faults occurring in LSBs. Therefore, these parameters can be the subject of selective fault tolerance reducing the cost of an all-around fault-tolerance.

Fig. 2 :

 2 Fig. 2: The convolutional unit.

Fig. 4 :

 4 Fig. 4: 2-D mesh SNN implementation on the FPGA.

Fig. 5 :

 5 Fig. 5: Network accuracy for different BER values.

Fig. 6 :

 6 Fig. 6: Single bit-flips layer-by-layer: (a) Router Parameters; (b) Neuron Threshold; (c) Kernel Weights.

Figs. 6

 6 (b)-(c) show results for single bit-flips layer-by-layer for the neuron threshold and kernel weights, respectively. For the single-bit fault scenario, among the different neuron parameters, accuracy drop was observed only for the neuron threshold, thus Fig. 6(b) shows results only for the neuron threshold. Figs. 7(a)-(b) show results for multiple bit-flips with different BER values layer-by-layer for the neuron parameters and kernel weights, respectively.

Fig. 7 :

 7 Fig. 7: Multiple bit-flips for different BER values layer-by-layer: (a) Neuron Parameters; (b) Kernel Weights.

ACKNOWLEDGMENTS

T. Spyrou has a fellowship from the Sorbonne Center for Artificial Intelligence (SCAI). This work has been partially funded by the Penta HADES project and by Junta de Andalucía under contract US-1260118 (Neuro-Radio). L. A. Camuñas-Mesa was funded by the VI PPIT through the Universidad de Sevilla.


Laurence Michel
email: laurence.michel@inserm.fr

P Reygagne

P Benech

S Scalvino

S Ly

Ducos

F Jean-Louis

Ka So

Z Hamidou

S Bianovici

J Pouch

B Ducos

M Bonnet

A Bensussan

A Patatian

E Lati

J Wdzieczak-Bakala

J-C Choulot

E Loing

M Hocquaux

Study of gene expression alteration in male androgenetic alopecia: evidence of predominant molecular signaling pathways

come

Michel et al.

Pascal REYGAGNE : p.reygagne@libertysurf.fr † These authors contribute equally to the work Word count: 3867

INTRODUCTION

Male pattern baldness or male androgenetic alopecia (AGA) is the most common type of baldness, characterized by progressive patterned hair loss from the scalp, starting in the frontal area and the vertex, following a defined pattern [START_REF] Hamilton | Patterned loss of hair in man; types and incidence[END_REF][START_REF] Heilmann-Heimbach | Hunting the genes in male-pattern alopecia: how important are they, how close are we and what will they tell us?[END_REF] . It is caused by a shortening of the anagen phase of hair cycling and progressive miniaturization of the hair follicle (HF), which results in the formation of vellus hairs that eventually atrophy [START_REF] Bergfeld | Androgenetic alopecia: an autosomal dominant disorder[END_REF] . Up to 80% of European men are concerned by AGA during their life. Although the precise etiology and pathogenesis of androgenetic alopecia are still unclear, genetic and androgenic factors play definite roles. .

Androgens, especially the metabolite 5-alpha dihydrotestosterone (DHT), affect genetically susceptible cells of the dermal papilla (DP), specialized mesenchymal derived part of the HF, causing progressive HF miniaturization and hair cycle abnormalities leading to male AGA [START_REF] Rathnayake | Male androgenetic alopecia[END_REF] .

Polymorphism of the androgen receptor (AR) gene was first identified in association with AGA [START_REF] Ellis | Polymorphism of the androgen receptor gene is associated with male pattern baldness[END_REF][START_REF] Hillmer | Genetic variation in the human androgen receptor gene is the major determinant of common early-onset androgenetic alopecia[END_REF] . The paradoxical role of androgens in hair follicle biology remains poorly understood: indeed, androgens trigger hair development at puberty and inversely favor AGA in later life. Kretzchmar et al. [START_REF] Kretzschmar | The Androgen Receptor Antagonizes Wnt/β-Catenin Signaling in Epidermal Stem Cells[END_REF] explored the inhibitory role of androgens by defining a reciprocal relationship between activated Wnt/β-catenin and AR signaling within the HF.

They pinpointed AR as a negative regulator of β-catenin signaling. However, identification of various new susceptibility genes, such as those on chromosomes 3q26 and 20p11 [START_REF] Adhikari | A genome-wide association scan in admixed Latin Americans identifies loci influencing facial and scalp hair features[END_REF][START_REF] Hillmer | Genome-wide scan and fine mapping linkage study of androgenetic alopecia reveals a locus on chromosome 3q26[END_REF] , suggests that non-androgen-dependent pathways are also involved, as reviewed by Heilmann-Heimbach et al [START_REF] Heilmann-Heimbach | Hunting the genes in male-pattern alopecia: how important are they, how close are we and what will they tell us?[END_REF] HF contains a well-described stem cell niche in the bulge area of the hair, close to the sebaceous glands. After the anagen phase, the catagen phase is followed by migration of stem cells (SC) from the bulge to the bulb to regenerate a new hair. In alopecia, the anagen phase becomes shorter and the number of follicles in the anagen phase decreases. It has been suggested that stem cell pool remain unaffected in AGA patient whereas progenitor cells

Michel et al.

arising from stem cells are much more numerous in the follicles of AGA subjects compared to controls [START_REF] Garza | Bald scalp in men with androgenetic alopecia retains hair follicle stem cells but lacks CD200-rich and CD34-positive hair follicle progenitor cells[END_REF] .

Although genetic predisposition and the role of genes in AGA have been documented in several previous reports [START_REF] Ellis | Genetic analysis of male pattern baldness and the 5alphareductase genes[END_REF][START_REF] Nyholt | Genetic basis of male pattern baldness[END_REF] , inheritance of male AGA is not fully understood. Also, the full mechanism of hair growth impairment in this disorder remains to be elucidated. The aim of this study is to focus on biomarkers associated with AGA through transcriptomic analyses of scalp biopsies from AGA subjects

MATERIAL AND METHODS

Selection of individuals with AGA and controls

This monocentric study was approved by at the Ethics Committee of Saint-Louis Hospital and the study described adhered to the tenets of the Declaration of Helsinki. Written informed consent was obtained from all participants. Individuals with any systemic diseases were excluded from the study, and 28 male young Caucasian volunteers were selected for the study and allocated into the two following groups: -group A: Fourteen hairless/bald participants (alopecia, "A"; age: 29.4±3.4), with premature AGA, with stage V to VII according to the Hamilton's classification as modified by Norwood; -group C: Fourteen control subjects (controls, "C"; age: 26.1±3.6) with <2% white hairs and stage I or II in the Hamilton's classification as modified by Norwood .

Gene analysis from cutaneous biopsies.

Two scalp biopsies (2 mm diameter each) were carried out on the vertex, either at the edges of alopecia area in the group A subjects, or at a similar emplacement for the the control group.

Biopsies were immediately frozen and kept for further RNA extraction or immunostaining.

RNA was isolated using a standard RNA extraction protocol (Trizol ® Reagent, Ambion by

Michel et al.

Life Technologies, Courtaboeuf, France). Extracted RNA samples were quality checked usingthe Agilent 2100 Bioanalyzer platform (Agilent Technologies, Les Ulis, France).

Gene expression analysis of human skin biopsies was performed using Agilent Whole Human Genome Oligo Microarrays (onecolor, 8x60K, v2). Bioinformatical analysis of the microarray experiments was obtained from the single-color hybridization of human cDNA samples to Agilent microarrays and was structured in four main parts: -data preprocessing that covers all necessary data transformations as well as an assessment of variability between the samples, and the detection of potential outliers by a global correlation analysis; -discriminatory genes analyses (DGAs) of pairwise comparisons between A vs C groups in order to characterize the differences between the various sample groups based on their gene expression profiles;functional annotation analysis (FAA);-annotation enrichment analysis (AEA) of differentially expressed genes in order to provide an overview of the different biological processes and pathways which are regulated in the discriminatory analyses.

Secondary analysis of the main metabolic pathways and their potential dysfunctions was performed using the Java/Perlsoftware Predictsearch ® (PS-V5) [START_REF] Landel | Temporal gene profiling of the 5XFAD transgenic mouse model highlights the importance of microglial activation in Alzheimer's disease[END_REF] . The gene expression value cutoff was 60 for all the analyses. This text-mining-based software was used to search and retrieve correlations between modulated genes and biological processes to characterize pathways and functional networks.

Real-Time Quantitative PCR Analysis

Validation of some potent genes depicted by DGA, FAA, and PredictSearch analysis of the identified genes using microarrays was subsequently assessed by analysis of 45 genes by Real Time quantitative PCR (RT-qPCR) [START_REF] Bégué | Inducible expression and pathophysiological functions of T-plastin in cutaneous T-cell lymphoma[END_REF] using aliquots of the RNA prepared for the microarrays (details in Supplementary Materials and Methods). Furthermore, to improve our study, the isolated total RNA were also subjected to reverse transcription (Fluidigm™,

Michel et al.

Les Ulis, France) prior to high throughput RT-qPCR analysis using the same primers [START_REF] Livak | Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method[END_REF][START_REF] Pfaffl | A new mathematical model for relative quantification in real-time RT-PCR[END_REF] (Biomark-HD system, Fluidigm™) (Supplementary Materials and Methods).

Histochemical analysis.

The frozen biopsies from the vertex were cut in 7µm sections using a Leica CM 3050 cryostat. Sections were then mounted on Superfrost ® plus silanized glass slides.

RESULTS AND DISCUSSION

Patient cohorts and Gene expression analysis.

Fourteen young volunteers with male AGA (alopecia group: A) with appearance of hair loss since 1 to 18 years ago (median, 4.7 years) and 14 young control volunteers (control and detectability. This returned 325 genes upregulated in the alopecia samples relative to the healthy controls and 390 genes with the opposite expression trend (Table 1). After

Benjamini-Hochberg correction for multiple testing, the analysis evidenced in fine a final number of 184 significantly down-regulated and 149 up-regulated candidate gene sequences in "A" comparing to "C", respectively (Table 1, Fig. 2). Hierarchically clustered heat map representation is shown in Fig. 3. An overview of the different biological processes and pathways significantly enriched among the differentially expressed genes was provided by the functional annotation analysis (Table 2, Fig. S1a andS1b). Based on their fold change, a battery of genes was selected and analyzed for mRNA transcript abundancy by high throughput RT-qPCR, confirming for most of them altered gene expression levels in AGA cohort detected by transcriptomic analysis (Fig. 4). The DGA gene set was submitted to PredictSearch ® analysis for functional interpretation and major

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over-or under-expressed genes were highlighted and associated by gene families (Tables 3A-D). As detailed thereafter, the present transcriptomic data shows an intense activation of immune and inflammatory responses in AGA cohort whereas an inhibition of the Wnt/-catenin signaling pathway, of the TGF-β and BMP signaling pathways as well as an alteration of the vitamin D biosynthesis were detected in the AGA subjects.

Activation of immune and inflammatory responses in AGA subjects.

Our data show strong up-regulation of immune and inflammatory genes, as related to B and T lymphocyte recruitment and activation of mast cell (MC), as well as some inflammatory factors related to extracellular matrix as depicted in Fig. 4 and Table 3A.

Indeed, a significant increase of several chemokines, including CCL13, CCL18, CXCL10

and CXCL12, was detected in our AGA subjects (Fig. 3, Table 3A, p<0.01, 0.05, 0.05 and 0.001, respectively). These chemokines play an important role in accumulation of leukocytes during inflammation through potent chemotactic activity, such as CCL13 for recruitment of monocytes, lymphocytes, basophils and eosinophils. Binding of CXCL10 to CXCR3 or CXCL12 to CXCR4 results in pleiotropic effects, including stimulation of monocytes, natural killer and T-cell migration, as well as modulation of adhesion molecule expression. Increased production of CXCL10 from HF has been shown to induce preferential infiltrates of highly chemoattracted Th1 and Tc1 cells during the acute phase of alopecia areata (AA) [START_REF] Ito | CXCL10 produced from hair follicles induces Th1 and Tc1 cell infiltration in the acute phase of alopecia areata followed by sustained Tc1 accumulation in the chronic phase[END_REF][START_REF] Ito | The role of cytokines and chemokines in the T-cell-mediated autoimmune process in alopecia areata[END_REF] . In AGA subjects, its production, as well as that of CCL8, CCL13, CCL26, and IL-8 (which tended to be increased by RT-PCR), should favor inflammation maintenance. Regarding immune response, we also noticed significant upregulation of TNFRSF17 in AGA volunteers (p<0.05)Also known as B cell maturation antigen (BCMA), and member of the TNF-receptor superfamily 20 , this receptor is preferentially expressed in mature B lymphocytes, and may be important for B cell

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development and autoimmune responses. Also, CD38, a multifunctional ectoenzyme widely expressed in leukocytes including B and T lymphocytes, is up-regulated in AGA cohort (p<0.02). It has been shown that CD38 might function in cell adhesion, signal transduction and calcium signalling [START_REF] Salmi | Ectoenzymes in leukocyte migration and their therapeutic potential[END_REF] .

We observed an over-expression of HLA-DPB1 as well as HLA-DQA1, HLA-DRB3 and HLA-DRB4 in AGA volunteers (Fig. 4, Table 3A, p<0.005, 0.01, 0.001 and 0.002, respectively). This is in line with previous reports showing that keratinocytes in the bulb of HF from subjects with AA express class II HLA antigens [START_REF] Khoury | HLA-DR expression by hair follicle keratinocytes in alopecia areata: evidence that it is secondary to the lymphoid infiltration[END_REF] . This suggests that the mononuclear cell activation in AGA might be linked to the expression of class II HLA antigens on HF keratinocytes, thus triggering a putative autoimmune response. The DGA analysis also revealed an up-regulation of genes encoding for B lymphocyte-produced immunoglobulins, including IGHG1, IGHM and IGJ, in some AGA volunteers. Possibly, lymphocytes attack the lower part of the anagen follicle, with ectopic expression of MHC class I and II molecules on the epithelium of affected HF, suggesting that B, T cells and cytokines play an important role in AGA as depicted in AA where chemokines are regarded as key players that mediate autoimmune inflammation [START_REF] Ito | The role of cytokines and chemokines in the T-cell-mediated autoimmune process in alopecia areata[END_REF] . Furthermore, we detected a slight up-regulation of the gene encoding high-affinity IgE-receptor FcεRI/MAS4A2 (membrane-spanning 4-domains, subfamily A, member 2) in AGA subjects (p<0.001), a receptor found on the surface of MCs and basophils and involved in FcεRIdependent signaling. The relationship between mast cells and male pattern hair loss was supported by previous histological evidences showing that perifollicular fibrosis is associated with increase in MC numbers [START_REF] Won | Dermal fibrosis in male pattern hair loss: a suggestive implication of mast cells[END_REF] . Similarly, we noticed up-regulated expression of several genes including COL1A1 (p<0.01) or COL1A2 (p<0.01) and genes of several metalloproteinases (MMPs), especially MMP12 (p<0.02), as observed during inflammatory wound-healing [START_REF] Martins | Matrix metalloproteinases and epidermal wound repair[END_REF] (Fig. 4, Table 3A). Of interest, our data bring evidence of

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an activation of MCs in AGA subjects as illustrated by the significant over-expression of MC markers, including prostaglandin D 2 synthase (PTGDS) (p<0.01), KIT/c-kit, tryptases (TPSAB (p<0.001) and TPSAD (p<0.001)), chymase (CMA1) (p<0.005) and cathespin G (CTSG) (p<0.0005) (Fig. 4, Table 3A). Expression of KIT/c-kit , has been shown to be positively controlled by GATA2 in a hair-cycle-dependent manner [START_REF] Maeda | GATA2 and Sp1 positively regulate the c-kit promoter in mast cells[END_REF] and its significant upregulation in the AGA subjects (p<0.002) reinforces a functional role of MC and epithelial KIT/c-kit expression in hair growth control. We may suggest that PGD 2 synthesized from prostaglandin H 2 by PTGDS, and previously found highly expressed in AGA [START_REF] Garza | ProstaglandinD2 inhibits hair growth and is elevated in bald scalp of men with androgenetic alopecia[END_REF] , could inhibit HF regeneration through binding to the Gpr44 receptor [START_REF] Nelson | Prostaglandin D2 inhibits wound-induced hair follicle neogenesis through the receptor, Gpr44[END_REF] . Altogether, our results reinforce the MC involvement in AGA.

Inhibition of the Wnt signaling in AGA patients.

Among genes eliciting a differential expression pattern in AGA subjects and healthy volunteers, a significant number of genes was related to the Wnt/-catenin signaling pathway as described in Fig. 4 and Table 3B. This pathway plays an important and wellknown role in HF development, hair stem cell maintenance, and HF lineage commitment [START_REF] Alonso | Stem cells in the skin: waste not, Wnt not[END_REF] and is required for the induction of hair keratin genes in HF stem cells co-cultured with DP cells [START_REF] Jansson | Making sense of Wnt signaling-linking hair cell regeneration to development[END_REF] . Moreover, several studies have reported the inhibition of this pathway in AGA [START_REF] Kretzschmar | The Androgen Receptor Antagonizes Wnt/β-Catenin Signaling in Epidermal Stem Cells[END_REF][START_REF] Leirós | Hair follicle stem cell differentiation is inhibited through cross-talk between Wnt/β-catenin and androgen signalling in dermal papilla cells from patients with androgenetic alopecia[END_REF][START_REF] Heilmann | Androgenetic alopecia: identification of four genetic risk loci and evidence for the contribution of WNT signaling to its etiology[END_REF] .

SFRP2 (secreted Frizzled-related protein 2) and DKK1 (dickkopf Wnt signaling pathway inhibitor 1) are the main known inhibitors of the Wnt signaling [START_REF] Kim | Overexpression of Hr links excessive induction of Wnt signaling to Marie Unna hereditary hypotrichosis[END_REF][START_REF] Niida | DKK1, a negative regulator of Wnt signaling, is a target of the beta-catenin/TCF pathway[END_REF] . DKK1-promoting HF regression was shown to be up-regulated in follicular keratinocytes of the late anagen in depilation-induced hair cycle progression [START_REF] Kwack | Dickkopf 1 promotes regression of hair follicles[END_REF] . While DKK1 expression remained unchanged in our study, expression of SFRP2 was significantly increased in more than 80% of our AGA subjects (p<0.002). SFRP2, expressed in the epithelial portion of HF, has been shown to be

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involved in hair fiber differentiation [START_REF] Rutberg | Differences in expression of specific biomarkers distinguish human beard from scalp dermal papilla cells[END_REF] and as well as in the catagen phase by inhibiting keratinocyte differentiation [START_REF] Kim | Expression of sfrp2 is increased in catagen of hair follicles and inhibits keratinocyte proliferation[END_REF] .

In accordance with this report, we found that expression of SFRP2 and Wnt target genes such as LGR5 (p<0.001), ID3 (p<0.05), MSX2 (p<0.1), ALAD (p<0.05), and BAMBI (BMP and activin membrane-bound inhibitor) (p<0.05) exhibited an inverse relationship (Fig. 3, Table 3B).

LGR5 (Leucin-Rich repeat containing G protein-coupled receptor 5) is a major Wnt target gene in hair cell progenitors [START_REF] Haegebarth | Wnt signaling, lgr5, and stem cells in the intestine and skin[END_REF][START_REF] Wang | Lgr5+ cells regenerate hair cells via proliferation and direct transdifferentiation in damaged neonatal mouse utricle[END_REF] , and ID3 (Inhibitor of DNA binding 3) encodes a marker in dermal papilla at the middle anagen and telogen stages [START_REF] Shaughnessy | The role of BMP signalling in the control of ID3 expression in the hair follicle[END_REF] . Noteworthy, BAMBI [START_REF] Sekiya | Transcriptional regulation of the TGF-beta pseudoreceptor BAMBI by TGF-beta signaling[END_REF] encodes the bone morphogenic protein and activin membrane-bound inhibitor, which is known to enhance the Wnt/-catenin signalling in various cell types [START_REF] Lin | The pseudoreceptor BMP and activin membrane-bound inhibitor positively modulates Wnt/beta-catenin signaling[END_REF] .

In line with reduced expression of these Wnt/-catenin target genes, expression of LEF1

(lymphoid enhancer binding factor 1) and CTNNB1 (β-catenin1), although to a lesser extent, is decreased in the AGA patients (Fig. 3, Table 3B, p<0.1 and 0.01, respectively). The products of these genes are known to interact and, after nuclear translocation, to bind to promoter regions of Wnt target genes 42,[START_REF] Huber | Nuclear localization of beta-catenin by interaction with transcription factor LEF-1[END_REF] . LEF1 is considered as the key transducer of the Wnt signaling pathway, and a potent regulator of hair development [START_REF] Kratochwil | Lef1 expression is activated by BMP-4 and regulates inductive tissue interactions in tooth and hair development[END_REF] while CTNNB1 is the major downstream effector of this pathway [START_REF] Ahmed | MicroRNA-214 controls skin and hair follicle development by modulating the activity of the Wnt pathway[END_REF][START_REF] Huelsken | beta-Catenin controls hair follicle morphogenesis and stem cell differentiation in the skin[END_REF] .

Noteworthy, our data suggest that alteration of hair regeneration might result also from a dysregulation of proopiomelanocortin (POMC) expression, which was reduced in 70% of the AGA cohort compared to controls (Fig. 4, Table 3B, p<0.001). POMC is the precursor of two hormones: Adrenocorticotropic hormone (ACTH) known as an activator of hair growth [START_REF] Ito | Human hair follicles display a functional equivalent of the hypothalamic-pituitary-adrenal axis and synthesize cortisol[END_REF] , and α-melanocyte stimulating hormone-MSH), which triggers eumelanin synthesis and can protect CTNNB1 from degradation by ubiquitinylation through its inhibitory effect on GSK3  .

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Moreover, our data showed that one of the highly repressed gene expression in the AGA subjects affected VGF (p<0.02), an inducible member of the Nerve Growth Factor family that might also contribute to POMC downregulation. Indeed, invalidation of this gene has been shown to lead to a strong alteration of POMC expression as well as of neuropeptide Y and AGRP (agouti-related peptide), an antagonist of the -MSH receptor [START_REF] Hahm | Targeted deletion of the Vgf gene indicates that the encoded secretory peptide precursor plays a novel role in the regulation of energy balance[END_REF] .

On the other hand, ASIP (agouti signaling protein), encoding another antagonist of the -MSH receptor, was found highly expressed in 86% of the AGA subjects (Fig. 4, Table 3B, p<0.005). Thus, it can be suggested that a low level of -MSH together with high levels of ASIP contributes to impaired CTNNB1 stabilization. Of note, our data in AGA did not only evidence the up-regulation of ASIP but also that of its negative regulator CORIN, which encodes a transmembrane serine protease expressed specifically in the DP (Fig. 4, Table 3B, p<0.001). However, it was reported in mice that -catenin activity in the DP suppresses ASIP expression and activates CORIN while regulating melanocyte activity by a mechanism independent of both ASIP and CORIN [START_REF] Enshell-Seijffers | Beta-catenin activity in the dermal papilla of the hair follicle regulates pigment-type switching[END_REF] . The opposite regulation of ASIP and CORIN by catenin in mice is not in line with their co-expression seen nearly in all AGA subjects and such a discrepancy might illustrate distinct regulations among species. Alternatively, activities of the respective gene products may not follow gene expression.

Besides RT-qPCR analysis of LEF1, POMC, ASIP and CORIN gene expression to strengthen bioinformatics analysis of transcriptomic data (Fig. 4), a histochemical analysis was performed to evaluate whether some of the gene modulations could be detected at protein levels. While both POMC and ASIP expression levels were strongly impaired in AGA patients as shown in figure 5, with scorage intensity in Table 4, POMC was barely detectable in AGA subjects, in line with the results from the transcriptomic data. In contrast, ASIP was more expressed in all HF areas of controls (Fig. 5 and Table 4). Thus, a decrease of POMC at both mRNA and protein levels should have a more drastic effect on -catenin stabilization

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than the increased expression of ASIP. Noteworthy, a lower size of the bulb was observed in AGA subjects as compared to controls.

Furthermore, we found that SRD5A2 encoding the Steroid 5α-reductase was up-regulated in AGA (Table 3B, p<0.01). This enzyme, known to suppress hair growth activity and to be highly active in balding HF [START_REF] Sawaya | Different levels of 5alpha-reductase type I and II, aromatase, and androgen receptor in hair follicles of women and men with androgenetic alopecia[END_REF] , is involved in the conversion of testosterone into DHT.

Interestingly, DHT was reported to activate the interaction between -catenin and AR [START_REF] Kitagawa | Keratinocyte growth inhibition through the modification of Wnt signaling by androgen in balding dermal papilla cells[END_REF] .

Such an interaction leads to suppress the -catenin transcriptional activity, in line with the inhibition of Wnt signaling pathway by androgens in DP cells [START_REF] Leirós | Hair follicle stem cell differentiation is inhibited through cross-talk between Wnt/β-catenin and androgen signalling in dermal papilla cells from patients with androgenetic alopecia[END_REF] .

TGF-β and BMP signaling pathways in AGA patients.

TGF-β stimulates hair follicle regeneration through Wnt signaling activation and this is achieved via the inhibitory effect of TGF-β on DKK1 [START_REF] Akhmetshina | Activation of canonical Wnt signalling is required for TGF-β-mediated fibrosis[END_REF][START_REF] Xie | Integrin β6-deficient mice show enhanced keratinocyte proliferation and retarded hair follicle regression after depilation[END_REF] . Among the different members of the TGF- family, only the expression of TGFB2 was found negatively modulated in our AGA patients (Fig. 4, Table 3C, p<0.0001). Several reports have evidenced the role of TGF-β2 in hair physiology and in particular in HF morphogenesis [START_REF] Foitzik | The TGF-beta2 isoform is both a required and sufficient inducer of murine hair follicle morphogenesis[END_REF] .

TGF-β can be activated through its interaction with Integrin Beta 6 (ITGB6) whose gene, known to be up-regulated in the outer root sheath during early hair regeneration [START_REF] Xie | Integrin β6-deficient mice show enhanced keratinocyte proliferation and retarded hair follicle regression after depilation[END_REF] , was under-expressed in AGA (Fig. 4, Table 3C, p<0.001). Thus, the reduced expression of ITGB6 in AGA is in line with an inhibition of the TGF-β 2 signaling, which impacts negatively folliculogenesis and maturation [START_REF] Inoue | TGF-beta is specifically expressed in human dermal papilla cells and modulates hair folliculogenesis[END_REF] . Moreover, expression of other related ITGB6 or TGF-β 2 genes were found underexpressed. Indeed, as ITGB6, its complex associate, psoriasin (S100A7) [START_REF] Morgan | Psoriasin (S100A7) associates with integrin β6 subunit and is required for αvβ6-dependent carcinoma cell invasion[END_REF] , exhibited a decreased expression in our AGA patients together with several other S100A proteins (Fig. 4, p<0.05). The expression of these S100A proteins closely coincides

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with epithelial Ca ++ -regulation, which governs cornified envelope formation in hair follicles [START_REF] Kizawa | S100 and S100 fused-type protein families in epidermal maturation with special focus on S100A3 in mammalian hair cuticles[END_REF] .

As previously reported in cultured DP cells from AGA [START_REF] Midorikawa | Different gene expression profile observed in dermal papilla cells related to androgenic alopecia by DNA macroarray analysis[END_REF] , an underexpression of bone morphogenetic protein 2 (BMP2) was observed in our subjects (Table 3C, p<0.02). Together with TGF-β, this reduced expression might decrease the activation of SMAD4 and its target Distal-less homeobox 3 (DLX3) [START_REF] Park | Bone morphogenetic protein-2 (BMP-2) transactivates Dlx3 through Smad1 and Smad4: alternative mode for Dlx3 induction in mouse keratinocytes[END_REF] as well as it may explain the under-expression of desmoglein 4 (DSG4) we observed in AGA patients (Fig. 4, Table 3C, p<0.05 and p<0.02, respectively). Indeed, ablation of SMAD4 was reported to inhibit the expression of DSG4, involved in hair follicle integrity and hair shaft differentiation 61 . Of interest, Wnt-, BMP-and TGF-β signaling pathways are all potent transcriptional inducers of BAMBI [START_REF] Sekiya | Transcriptional regulation of the TGF-beta pseudoreceptor BAMBI by TGF-beta signaling[END_REF] . Conversely, inhibition of BAMBI expression as well as that of DLX3 and KRT16 may be triggered by Mir-31, shown to be markedly increased during the anagen and decreased during the telogen and catagen phases [START_REF] Mardaryev | Micro-RNA-31 controls hair cycle-associated changes in gene expression programs of the skin and hair follicle[END_REF] . Accordingly, KRT16 was also decreased in our AGA patients (Table 3C, p<0.001).

Interestingly, DLX3 ablation has been reported to lead to alopecia through a loss of BMP signaling while DLX3 activates hair formation and differentiation, towards its ability to induce the expression of GATA3 and homeobox C13 (HOXC13) [START_REF] Hwang | Dlx3 is a crucial regulator of hair follicle differentiation and cycling[END_REF] . HOXC13, whose expression was reduced also in our AGA patients (Table 3C, p<0.01), plays a role in the expression of keratin genes [START_REF] Jave-Suarez | HOXC13 is involved in the regulation of human hair keratin gene expression[END_REF] and in hair follicle differentiation [START_REF] Kulessa | Inhibition of Bmp signaling affects growth and differentiation in the anagen hair follicle[END_REF] . In addition to KRT16, some AGA patients exhibited an underexpression of a subset of genes encoding keratins (KRT19, KRT26, KRT39, KRT72, KRT81) and keratin-associated proteins (KRTAPs) that play a crucial role in hair shaft formation [START_REF] Fujikawa | Characterization of the human hair keratin-associated protein 2 (KRTAP2) gene family[END_REF][START_REF] Fujimoto | Krtap11-1, a hair keratin-associated protein, as a possible crucial element for the physical properties of hair shafts[END_REF] . We also observed a down-regulation of mesenchymal stem cell markers (Fig. 4) such as SOX9 (p<0.01) and of Hedgehoginteracting protein (HHIP) (p<0.02), which is a potent morphogen acting in a wide range

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of developmental processes, involved in the Hedgehog signaling and the epithelial-tomesenchymal transition [START_REF] Kasper | GLI transcription factors: mediators of oncogenic Hedgehog signalling[END_REF] .

Vitamin D3 signaling in AGA patients.

It has been shown that CTNNB1 and LEF1 can interact with vitamin D receptor (VDR) to form a complex [START_REF] Ryan | Pleiotropic Activities of Vitamin D Receptors -Adequate Activation for Multiple Health Outcomes[END_REF] , and VDR gene was found differentially expressed between AGA and controls (Fig. 4, p<0.05). Interestingly, VDR-null mice elicit an alopecia phenotype due to a defect in epithelial mesenchymal communication that is required for normal hair cycling [START_REF] Sakai | Metabolic and cellular analysis of alopecia in vitamin D receptor knockout mice[END_REF] .

However, this activity was shown to be independent of the binding of its ligand [START_REF] Malloy | The role of vitamin D receptor mutations in the development of alopecia[END_REF] . On the other hand, calcitriol, the active form of vitamin D3, through binding to VDR, was reported to be responsible for the suppressed expression of DKK1 and SFRP2 in murine bone marrow stromal cells [START_REF] Cianferotti | VDR-mediated inhibition of DKK1 and SFRP2 suppresses adipogenic differentiation of murine bone marrow stromal cells[END_REF] .

The notion that AGA exhibit an alteration of the vitamin biosynthesis was supported by the decreased expression of CYP27B1 (Cytochrome p450 family 27 subfamily B, polypeptide 1), in 86% of the AGA subjects (Table 3D, p<0.02). Indeed, expression of CYP27B1 is involved in the synthesis of calcitriol, which elicits a protective role in radiation-induced alopecia [START_REF] Baltalarli | The preventive effect of vitamin D3 on radiation-induced hair toxicity in a rat model[END_REF] .

Such reduced expression of CYP27B1 in AGA can be related to the underexpression of tyrosine hydroxylase (TH) we observed (Table 3D, p<0.05). Known as a transcriptional target of vitamin D3 [START_REF] Puchacz | Vitamin D increases expression of the tyrosine hydroxylase gene in adrenal medullary cells[END_REF] , TH is involved in the first step of L-dopa synthesis that activates tyrosinase leading to melanogenesis [START_REF] Marles | Tyrosine hydroxylase isoenzyme I is present in human melanosomes: a possible novel function in pigmentation[END_REF] . Thus, in addition to the role of POMC and ASIP in hair differentiation and melanogenesis towards their respective impacts on CTNNB1, a decrease of TH might explain the progressive replacement of terminal hairs by smaller hairs with less pigmentation observed in AGA.

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CONCLUSION

Although RNAs were extracted from whole follicle and not specific follicular compartments, the resulting expression data might reflect the combined amount of distinct RNA populations extracted from different cell types. However, integrating genes within functional networks indicate that the different events observed in our study recapitulate what is known in AGA and therefore ensures consistencies of our results. The present transcriptomic analysis allows us to highlight several specific molecular pathways discriminating AGA individuals from controls as schematized in Fig. 6 and to add two new genes POMC and VGF as markers of AGA. It is presently unclear how these events are related to inflammation, mast cell activation, leukocyte recruitment and dysregulation of the immune system. Nevertheless, the activation of these processes in AGA is illustrated by the over-expression of genes encoding chemokines, immunoglobulins, mast cell markers and MHC class II molecules. Our results also bring evidence of the crucial role of the Wnt/β-catenin signaling pathway in hair physiology, indicating that its inhibition in AGA subjects can be triggered by an increased expression of SFRP2 as well as in some extent by a decreased CTNNB1 and LEF1 expression. A reduced -catenin activity should indeed impair HF integrity and regeneration. Moreover, the alteration of -catenin stabilization might be linked to the decreased expression of POMC at both RNA and protein levels through the loss of the inhibitory effect of -MSH on GSK3β. Inhibition of -catenin activity can be also achieved by an increase production of DHT in the AGA group as a consequence of the higher expression of SRD5A2. In addition, we showed that other components might contribute to this inhibition. Indeed, a reduced expression of BMP2 and ITGB6 should impact negatively the TGF-β transcriptional activity and TGF-β activation of the Wnt/β-catenin signaling, respectively. Although the upstream mechanisms

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by which modulation of these genes is achieved in AGA remain to be determined, the increased SFRP2 expression might result in part from an altered metabolism of vitamin D3. The under-expression of CYP27B1 leading to a reduced production of calcitriol, known to inhibit DKK1 and SFRP2 expression, might contribute to reduce the -catenin activity.

Altogether, these data bring new evidences that distinct altered mechanisms converging to inhibit Wnt/β-catenin signaling account for AGA in young male subjects. They also evidence B and T cell involvement suggesting an underlying autoimmune-related mechanism that remains to be further highlighted.

CONFLICT OF INTEREST

"The authors state no conflict of interest."

ACKNOWLEDGEMENT

This study was financed by IEB-Lucas Meyer cosmetics and Laboratoire BIO-EC for the project entitled ALOCA (Consortium Cosmetic Valley -Ile de France). High throughput RT-qPCR analysis was carried out on the qPCR-HD-Genomic Paris Centre platform and was supported by grants from Région Ile-de-France and Fondation Pierre-Gilles de Gennes. Stefan

Tomiuk from Miltenyi Biotec was in charge of the bioinformatical analysis of whole genome microarray data.

Michel et al. The average of gene expression values (A.U.) and the means of fold change (fc) were calculated as in Table 3A.

Michel et al. The average of gene expression values (A.U.) and the means of fold change (fc) were calculated as in Table 3A. The average of gene expression values (A.U.) and the means of fold change (fc) were calculated as in Table 3A.

Table 3C -TGBβ/BMP-signaling related genes

Genes

Table 3D -Vitamin D3 -related genes

Genes

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LEGENDS TO FIGURES Figure 1. Photographs of two representative AGA subjects (A) and two representative control volunteers (C).

Arrows indicate the site where the scalp biopsies were carried out. Histogram of differentially expressed genes between alopecia and control volunteers depicts a quite similar number of significantly upregulated and downregulated genes. Blue bar charts and orange bar charts represent Up-regulated and Down-regulated genes in group A versus group C, respectively. Legend shows 3 groups of fold changes (fc): fc > 2.5, fc > 2 and < 2.5 and fc >1.5 and <2 for up-regulated genes and fc < -2.5, fc > -2.5 and < -2 and fc < -1.5 and >-2. Schematic representation of functional networks. Functional correlations between the modulated genes was designed with the help of the text-mining software PredictSearch (see materials and methods). Modulated genes in AGA are indicated in blue when overexpressed or in red when underexpressed in AGA versus volunteers.

Figure count: 5 Table count: 3

 53 Figure count: 5Tablecount: 3

  Pathway (only 4 genes) Induced by IL1 Pathway (only 5 genes) immunoglobulins (antibody chains) pos. regulation of leukocyte/T-cell activation some MHC II molecules with various associated categories some cytokines/chemokines associated with various signaling pathways cellular

  The average of gene expression values (A.U.) were obtained for selected genes from bioinformatical analysis, with p values established between AGA and control (CTRL) subjects. The mean of fold change (fc) in all subjects and standard deviations were deduced from the mean of the ratios (value of AGA subjects versus controls) for each AGA subject. The number of patients eliciting a modulated expression according to the selected threshold is indicated.p values: *< 0.05; ** <0.01; *** < 0.001.

Figure 2 .

 2 Figure 2. Transcriptomic analysis of scalp biopsies from subjects with AGA (A) and controls (C).

Figure 3 -Figure 4 -

 34 Figure 3 -Hierarchically clustered heat maps (Euclidean distance, complete linkage) of reporters with higher expression in the alopecia group (A) relative to the healthy controls (C) (two-dimensional clustering)

Figure 5 -

 5 Figure 5 -Immunohistochemistry staining

Figure 6 -

 6 Figure 6 -Schematic representation of signaling pathways in AGA.

Table 1 -

 1 TABLES Number of candidate reporters returned by the DGAs (FC: fold-change).

Table 2 -

 2 Synopsis of up-or down-regulated gene family. Most prominent findings of the Functional Annotation (FA) analysis, highlighted in beige and of the Annotation Enrichment (AEA) analysis, shown with blue background.

Table 3A -Differential gene expression between AGA patients and controls.

 3A

Table 4 -Score of staining intensity of POMC and ASIP/PARD3 according to their distribution along the hair follicle.

 4

	Score of staining intensity
	0 1 2 3	4	5 6 7 8 9
	Volunteer	POMC Infundibulum + Upper root sheath	POMC Bulge	POMC Low er root sheath	POMC Bulb	ASIP/PARD3 Infundibulum + Upper root sheath	ASIP/PARD3 Bulge	ASIP/PARD3 Low er root sheath	ASIP/PARD3 Bulb
	Control 1	8		7	5	4	6	5	6	4
	Control 2	8		5	5	2	5	6	5	5
	Control 3	7		4	6		5	4	5	6
	Control 4	7		7	3	2	4	3	3	5
	Control 5	6		2	2	2	3	4	4
	Control 6	7		5	4	4	2	4	4	7
	Control 7	7		5	4		3	5	2
	Control 8	7		5	4	3	2		2	7
	Control 9	6		5	2	3	2	1	3	7
	Control 10	7		5	2	3	2	5	3	5
	Control 11	8		7	5	6	2	4	4	2
	Control 12	5		2	2		3	4	3	2
	Control 13	7		4	2		3	4	4
	Control 14	7		4	2	2	3	4	3	3
	AGA 1	4		2	2	3	3	3	4
	AGA 2	6		2	2	1	2	3	4	3
	AGA 3	4		1	1	1	3	1	2
	AGA 4	4		1	2	4	2	3	4	2
	AGA 5	4		1	1	2	2	2	3	1
	AGA 6	4		1	3	0	2	2	3	2
	AGA 7	5		2	2	1	2	3	2	1
	AGA 8	3		1	1		3	1	2	3
	AGA 9	4			1		3	3	4	1
	AGA 10	3		2	2	1	4	4	4	1
	AGA 11	6		2	2	1	4	5	5	3
	AGA 12	2		2	1	1	4	2	3	2
	AGA 13	2		1	1		1	2	4	5
	AGA 14	2		1			2	2	3	2
		POMC Infundibulum + Upper root sheath	POMC Bulge	POMC Lower root sheath	POMC Bulb	ASIP/PARD3 Infundibulum + Upper root sheath	ASIP/PARD3 Bulge	ASIP/PARD3 Lower root sheath	ASIP/PARD3 Bulb
	Ratio AGA/CT	0,6		0,3	0,5	0,5	0,8	0,6	0,9	0,4
	p value	0,000001	0,000001	0,0002	0,005	0,10	0,001	0,21	0,0005

Michel et al.

Michel et al.

*, **, ***, **** indicate in which compartment(s) of the hair follicle (hair matrix, dermal papilla, outer root sheath or inner root sheath, respectively), the gene expression was reported to be detected, with respective references as following: ASIP [START_REF] Enshell-Seijffers | The serine protease Corin is a novel modifier of the Agouti pathway[END_REF] , BAMBI [START_REF] Rendl | BMP signaling in dermal papilla cells is required for their hair follicle-inductive properties[END_REF] , BMP2 [START_REF] Rendl | BMP signaling in dermal papilla cells is required for their hair follicle-inductive properties[END_REF] , CORIN [START_REF] Enshell-Seijffers | The serine protease Corin is a novel modifier of the Agouti pathway[END_REF] , CTNNB1 [START_REF] Huelsken | beta-Catenin controls hair follicle morphogenesis and stem cell differentiation in the skin[END_REF][START_REF] Enshell-Seijffers | Beta-catenin activity in the dermal papilla of the hair follicle regulates pigment-type switching[END_REF] , DLX3 [START_REF] Hwang | Dlx3 is a crucial regulator of hair follicle differentiation and cycling[END_REF] , DSG4 [START_REF] Bazzi | Desmoglein 4 is expressed in highly differentiated keratinocytes and trichocytes in human epidermis and hair follicle[END_REF] , HOXC13 [START_REF] Qiu | Hoxc13 is a crucial regulator of murine hair cycle[END_REF] , ID3 80 , ITGB6 81 , KRT16 82 , LEF1 83 ,

LGR5 [START_REF] Jaks | Lgr5 marks cycling, yet long-lived, hair follicle stem cells[END_REF] , MSX2 [START_REF] Ma | Cyclic alopecia' in Msx2 mutants: defects in hair cycling and hair shaft differentiation[END_REF] , POMC [START_REF] Kono | In situ expression of corticotropin-releasing hormone (CRH) and proopiomelanocortin (POMC) genes in human skin[END_REF][START_REF] Böhm | Detection of functionally active melanocortin receptors and evidence for an immunoregulatory activity of alpha-melanocyte-stimulating hormone in human dermal papilla cells[END_REF] , SFRP2 [START_REF] Rutberg | Differences in expression of specific biomarkers distinguish human beard from scalp dermal papilla cells[END_REF] , SRD5A2 88 , TGFB2 [START_REF] Soma | Involvement of transforming growth factor-beta2 in catagen induction during the human hair cycle[END_REF][START_REF] Inoue | TGF-beta is specifically expressed in human dermal papilla cells and modulates hair folliculogenesis[END_REF] , TH [START_REF] Bernot | Keratin 16 expression defines a subset of epithelial cells during skin morphogenesis and the hair cycle[END_REF] .