Nathan Cebron

Sarah Maman

Sarah Walachowski

Blandine Gausserès

Patricia Cunha

Pascal Rainard

Gilles Foucras
email: gilles.foucras@envt.fr

Th17-related mammary immunity, but not a high systemic Th1 immune response is associated with protection against E. coli mastitis

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INTRODUCTION

Mastitis is a major disease of dairy cows [START_REF] Ruegg | A 100-year review: mastitis detection, management, and prevention[END_REF] , which develops upon infection of the mammary gland by various bacterial species. Clinical cases are mainly due to Escherichia coli and Streptococcus uberis, while Staphylococcus aureus is more frequently isolated from chronic subclinical mastitis. Infection can easily be detected due to leukocyte recruitment in mammary tissue, and ultimately in milk, leading to an increased milk somatic cell count (SCC), an indicator widely implemented in dairy control programmes [START_REF] Martin | Symposium review: novel strategies to genetically improve mastitis resistance in dairy cattle[END_REF] .

Mastitis prevention and cure heavily rely on antibiotics, so that mastitis is the main reason for their common usage in dairy cattle [START_REF] Suojala | Treatment for bovine Escherichia coli mastitis-an evidence-based approach[END_REF] . Alternatives to antibiotic treatment are needed to reduce their use and the spread of antibiotic resistance [START_REF] Krömker | Mastitis treatment-reduction in antibiotic usage in dairy cows[END_REF] . Development of strategies to increase mammary defences is a major possibility, and among immune-based approaches, vaccination would be a good path to follow [START_REF] Sordillo | Mammary gland immunobiology and resistance to mastitis[END_REF] . Unfortunately, marketed vaccines based on bacterins, such as the E. coli J5, suffer a certain number of limitations that lessen their generalized utilization in dairy herds. These vaccines have shown a low reduction of clinical mastitis incidence upon natural exposure, and they mainly reduce the severity of mastitis and milk losses [START_REF] Wilson | Comparison of J5 vaccinates and controls for incidence, etiologic agent, clinical severity, and survival in the herd following naturally occurring cases of clinical mastitis[END_REF][START_REF] Steele | The effect of J5 bacterins on clinical, behavioral, and antibody response following an Escherichia coli intramammary challenge in dairy cows at peak lactation[END_REF] . Since new developments are needed to overcome the low efficacy of available commercial vaccines, the slow progress is partly due to the lack of knowledge about the way vaccines operate in the mammary gland and about the exact protective mechanisms against infection.

In effect, the local immune response relies essentially on early and efficient recruitment of neutrophil granulocytes to control bacterial infections. Antibodies in the IgG1 and G2 isotypes, and to a lesser extent IgA since their concentration is lower in ruminants compared to other mammals, do not seem to play a major role although they are generally induced by current vaccines.

Vaccine delivery via mucosal routes is an interesting alternative to parenteral administration [START_REF] Lycke | Recent progress in mucosal vaccine development: potential and limitations[END_REF] . Local immunization through respiratory, digestive, and genital mucosal surfaces has been shown to induce local immunity associated with tissue-resident CD4 and CD8 memory T cells [START_REF] Schenkel | T cell memory. Resident memory CD8 T cells trigger protective innate and adaptive immune responses[END_REF][START_REF] Stary | A mucosal vaccine against Chlamydia trachomatis generates two waves of protective memory T cells[END_REF][START_REF] Shin | A vaccine strategy that protects against genital herpes by establishing local memory T cells[END_REF] . The mammary gland does not have a mucosal surface strictly speaking, although it is sometimes affiliated to the mucosal common immune system. Surprisingly, the induction of a robust immune response against mammary pathogens through intramammary (IMM) immunization has rarely been reported in dairy ruminants. Pioneering work by Lascelles 12 used living staphylococci in dairy ewes, but the main limitation of these studies is that no information is available on the mammary status and ensuing inflammation before injection of the challenge inoculum. The presence of high SCC at the time of experimental infection may have prevented the proliferation of the bacterial inoculum and thus the development of mastitis. More recently, immunization locally with killed Streptococcus uberis had favorable effects on the reduction of mastitis frequency on the next lactation in dairy cows [START_REF] Finch | Local vaccination with killed Streptococcus uberis protects the bovine mammary gland against experimental intramammary challenge with the homologous strain[END_REF] . Local injection with a low number of dead E. coli bacteria also had some protective effects, although it is doubtful if immunity was really present at the time of challenge due to the low severity of the induced mastitis in the control group [START_REF] Pomeroy | Intramammary immunization with ultraviolet-killed Escherichia coli shows partial protection against late gestation intramammary challenge with a homologous strain[END_REF] .

In a vaccination trial against bovine E. coli mastitis previously reported by us [START_REF] Herry | Local immunization impacts the response of dairy cows to Escherichia coli mastitis[END_REF] , we used a local administration of E. coli crude antigens after a primary intramuscular (IM) immunization, also known as 'prime and pull' strategy. Compared to IM route alone, we showed that the booster injection via the ductal route improved the immunization protocol efficacy. The antibody response did not seem to play a significant role in the immune defences in this case, and the development of a cell-associated immunity with interferon-γ (IFNγ) production in the milk of immunized cows was evident. Indeed, the cell-mediated response may be essential to improve the protective response against E. coli mastitis.

To improve our knowledge about vaccine-based cellular immunity against E. coli infection, we performed a whole-blood transcriptome analysis on the samples collected during the mastitis trial, and also the immunization phase of the study previously described [START_REF] Herry | Local immunization impacts the response of dairy cows to Escherichia coli mastitis[END_REF] . By combining high-throughput assessment of whole-blood transcriptome and cytokine protein expression, we describe here the systemic response during E. coli mastitis. Furthermore, we provide evidence that the settlement of interleukin-17 (IL-17)-producing T helper cells in the mammary tissue improves immunity to E. coli mammary infections.

RESULTS

Clinical and biological differences among experimental groups upon E. coli mammary challenge

The mastitis trial contained two different immunization treatment groups (IM or IMM, n = 6 per group), and a control (CONT, n = 6) group that only received the same adjuvant intramuscularly at the indicated dates (Fig. 1a). The ensuing results have been previously described in more details over a course of 10 days [START_REF] Herry | Local immunization impacts the response of dairy cows to Escherichia coli mastitis[END_REF] . Here, we summed up the main clinical and biological differences observed during the challenge. Indeed, in order to look for mechanisms and the specific effects of each immunization scheme, three time points (0, 12, and 40 hpi) were selected as the most discriminant among the groups, where significant differences were evidenced (Kruskal-Wallis, p < 0.05), and corresponding to the acute (12 hpi) and the late (40 hpi) phases of the E. coli mastitis. Several criteria are considered: the systemic clinical score (Fig. 1b), white blood cell (Fig. 1c), and neutrophil (Fig. 1d) counts; milk production (Fig. 1e); the mammary clinical score (Fig. 1f), and milk bacteriological load (Fig. 1g).

In addition, blood samples collected at day 0 (I0), and 7 days after the IM immunization (I7) and the booster injections (B7) were included in the analysis. In total, 107 samples from six dates and six animals in each of the three groups were prepared for transcriptome analysis; one sample (CONT, 12 hpi) was missing due to the low amount of RNA recovered after extraction. The whole-blood transcriptome was established using RNA sequencing with an average value of 10.3 million (R1 + R2) reads for each sample (Supplementary Table 1). The dataset obtained with FeaturesCount after STAR alignment comprised 24,617 bovine genes. At the acute phase of E. coli mastitis (12 hpi), the number of reads was significantly lower than at the other time points (Kruskal-Wallis, p < 0.05), except in the IMM group (Supplementary Table 1). One sample (IM, B7) was removed from the dataset due to an outlier profile in the principal component analysis (PCA). In total, 106 samples were available for the statistical analysis.

Whole-blood transcriptome through RNA-seq identifies the main biological functions altered by E. coli mastitis As a blood transcriptome related to bovine E. coli mastitis has never been reported before, we first investigated changes in gene expression in the CONT group. A PCA showed that the expression profile varied according to the time, with a clear separation between 0 and 12 hpi (PCA axis 1: 53.8%), and between 0 and 40 hpi (PCA axis 2: 24.3%) (Fig. 2a). Differentially expressed genes (DEGs) were identified with DESeq2 at these two time points in a comparison with the pre-inoculation time point. The numbers of DEGs were 5214 (with 2653 over-and 2561 under-expressed genes), and 3385 genes (2202 over and 1183 under), at 12 and 40 hpi, respectively. DEG lists were mostly different at the two times, as a minority of genes was common between the two sets as shown in a Venn diagram (with 832 over and 447 downregulated DEGs) (Fig. 2b). Volcano plots showed determining DEGs for the 12 hpi (Fig. 2c) and 40 hpi (Fig. 2d) comparisons, and the lists of the top 10 over-or under-expressed DEGs are available in Supplementary Table 2 for 12 and 40 hpi time points. To further depict the response and identify blocks of commonly regulated genes and functions, a heatmap was prepared (Fig. 2e), with samples clustering according to the time. The top 500 most DEGs were divided into four modules that typified individual time points. The function modules were determined with ToppFun software and are presented with a dendrogram to evaluate correlations among functions. At 12 hpi, Cellular response to lipopolysaccharide and Regulation of cytokine mediated signaling pathway were the most activated functions, while Humoral response and Macrophage activation characterized the late phase at 40 hpi. Neutrophil migration and Cellular response to reactive nitrogen species were significantly detected at both times. Plasma concentrations of IL-6, tumor necrosis factor-α (TNFα), IL-10 cytokines, and CCL2, CCL4, CXCL10 chemokines were significantly increased at 12 hpi (Kruskal-Wallis, p < 0.01), and returned to initial values at 40 hpi; only IL-1RA plasma concentration was increased at both 12 and 40 hpi (Supplementary Fig. 1).

Comparison with the transcriptome profiles of IM-and IMMimmunized cows

In order to compare the blood transcriptomes among the groups, we first looked for DEGs in the two immunized groups, independently. After DESeq2 analysis, DEGs were identified at 12 and 40 hpi in both groups. The lists showing the top 10 genes with up and down expression in IM and IMM groups are provided in Supplementary Tables 3 and4. The DEG numbers were not different between the groups at 12 hpi (Fig. 3a,c), but it was much lower in the IMM group at 40 hpi (Fig. 3b,d). Venn diagrams are presented in Supplementary Fig. 2 to determine the number of commonly deregulated genes. Interestingly, the number of DEGs was lower in the immunized cows compared to the controls at 12 hpi, and the difference was even larger for the IMM group at 40 hpi, where DEG number represented only 10% of the numbers obtained in the IM and CONT groups, supporting the idea that systemic healing was earlier in this group. Altogether, samples clustered essentially according to the Time in a global heatmap (Fig. 3e), except three samples from IMM cows at 40 hpi that grouped together with the samples collected before inoculation. Bacterial excretion in these cows was the lowest after this time point (<100 bacteria/mL) and their mammary infection was actually cleared after 48 h, and twice faster than other cows. Indeed, expression profiles of these three IMM cows cannot be distinguished between 0 and 40 hpi, indicating that gene expression almost returned to the pre-infection state in these cows. They are further defined as cured cows on the basis of both their lower clinical scores and their gene expression profiles. The top 500 DEGs divided into four modules determined with ToppFun software and were essentially related to immune functions (Fig. 3e). At 12 hpi, activated functions were Defense response to bacterium, and Neutrophil chemotaxis, as shown before for the CONT group. At 40 hpi, Type I interferon signaling and Humoral immune response were significantly activated in the IM group, which showed the slowest recovery rate. The profiles were identical at time 0 hpi among the groups, indicating the absence of any ongoing immune activation or physiological difference among the groups. Plasma cytokine dosage showed that the production of pro-inflammatory cytokines IL-1β, IL-6, and TNFα, but also CCL2 and CCL4 chemokines was significantly lower in the immunized compared to the CONT cows at the acute phase (12 hpi), indicating a lower inflammatory response upon infection in the IM and IMM groups (Supplementary Fig. 3). Furthermore, IFNγ and CXCL10 concentrations were higher in the IM group at 40 hpi, in agreement with the activation of interferon-related pathways in this group during the late phase of the response (Fig. 3e).

To identify the biological functions that correlated with the systemic and mammary scores as presented above in Fig. 1b, f, we used weighted correlation network analysis (WGCNA) on this set of gene expression data. Twelve modules significantly correlated with the mammary and/or systemic scores. Notably, a T helper type 1 cell-related module was activated at 12 hpi in the immunized groups, indicating that immunization protocols through either IM or IMM routes changed the course of the immune response during mammary infection (Supplementary Fig. 4). These results also suggest that immunization promoted a T cell response that was mobilized in blood early during the mastitis course.

Systemic and local routes of E. coli immunization elicit different gene expression profiles Since immunization changed the susceptibility to E. coli mastitis, we investigated DEGs after the first intramuscular and booster injections to decipher the immune-related functions mobilized by each kind of immunization scheme. The comparison of transcriptome data did not reveal any difference among the groups before immunization (I0). Analysis of the primary response at 7 days post-IM injection (I7) indicated that 600 genes were DE in the E. coli/ adjuvant (n = 12 cows) compared to the adjuvant alone condition (n = 6 cows) (Fig. 4a). DEGs were related to immune functions with Secretion, Myeloid leukocyte activation, and Immune effector process (Supplementary Table 5). After the booster injection (B7), through either the IM or the mammary ductal (IMM) routes, gene expression profiles were significantly different among the groups. Interestingly, the number of DEG was greater in the immunized groups compared to the CONT group, with 389 and 416 genes upregulated in the IM and IMM groups, respectively (Fig. 4b). These results suggest that the presence of E. coli antigens and microbe-associated molecular patterns like the lipopolysaccharide (LPS), in addition to the adjuvant which was the same between CONT and IM groups had a strong effect on the secondary response. DEGs in the IM group were related to Interferon alpha/beta signaling and Cytokine signaling in immune system functions, whereas DEGs in the IMM group were related to Neutrophil degranulation and Chemokine signaling pathway functions. Venn diagram indicated that 399 upregulated genes were common between IM and IMM groups. These genes were related to Neutrophil degranulation and Innate immune system functions (Supplementary Table 6). Furthermore, while no module was activated in the CONT group, four modules were commonly upregulated in the immunized groups with Enrichment in monocytes and Immune activation-generic cluster as the main modules. The module TLR and inflammatory signaling was activated only in the IMM group at B7 time point (Fig. 4c). So, the injection of antigens from E. coli culture supernate in the mammary gland lumen stimulated a systemic response which is essentially different from that induced by the IM immunization with killed bacteria in adjuvant in previously sensitized cows.

Blood transcriptome profile in IMM cured cows correlated with early cure

We then paid attention to the comparison between the two IMM subgroups, whose clinical signs and blood transcriptome differed significantly at the late mastitis phase (40 hpi). To define the peculiarities of the cured cows, their blood profile was further scrutinized at all the available dates. A PCA was performed on the top 500 most variable genes from the I0 and B7 data (Fig. 5a). At the B7 time point, PCA showed a significant difference between cured and non-cured cows indicating that the response to immunization of these particular cows was different and correlated with early cure. To further this hypothesis, a modular analysis was performed with the tmod R package. Two modules are common among all animals with Enrichment in monocytes and Immune activation-generic cluster as the main modules. Three modules were further activated in the cured animals and functions of these modules were Enriched in neutrophils, Cell cycle and transcription, and TLR and inflammatory signaling (Fig. 5d). These functions upregulated in the cured subgroup support the idea that the differences of the systemic response are related to neutrophil mobilization, and are probably an indirect consequence and not the true cause of the difference of cure quickness due to immunization.

To look for the genes and immune functions differentially mobilized between cured/not cured IMM cows during the mastitis, a PCA at 12 and 40 hpi was performed with the top 500 most DEGs, showing similar profiles at 12 hpi, but again a marked difference at 40 hpi (Fig. 5b,c). Analysis with DESeq2 identified only 26 genes (21 up and 5 down in the cured group), whereas at 40 hpi, the number of DEGs was 307 genes with 22 genes upregulated in the cured and 285 in the non-cured subgroup, respectively. Biological functions are presented in Supplementary Table 7. Again, the functional differences do not point to a probable cause of early healing in the three cured cows compared to the rest of the group.

In the comparison between IM and IMM groups, the fact that no DEG, nor function, except neutrophil mobilization, lead to a mechanism explaining the better outcome in locally immunized cows, leaves at least two possibilities: (i) these were very transient during the mastitis course and were not identifiable at the chosen time points for analysis or (ii) the functional differences are not present in the blood, because they reside in the mammary tissue in link with the local immunization.

Th17-polarized CD4 T cells predominate in the mammary tissue of IMM quarters and are activated after the challenge with E. coli Local immunization has been previously associated with recruitment and establishment of CD4 and CD8 T cells in the targeted tissues. Furthermore, since no difference could be identified in the blood, we made the hypothesis that the mechanisms responsible for the difference of mastitis outcome may develop in the mammary tissue. To examine the phenotype of the T helper cell compartment in link with the mammary immunization, six cows (3 CONT, 3 IMM) were challenged once again using the same conditions (Fig. 6a). CD4 T cells (CD45+ CD4+ cells) were isolated by fluorescence-acquired cell sorting at 16 hpi from mammary tissue upon collagenase treatment. Four different conditions were examined: one challenged and one healthy control quarters from three CONT and three IMM cows. RNA sequencing of pools of purified (purity > 95%) CD4 T cells showed a transcriptional profile where IL-17-related pathways were significantly overrepresented in cells from the IMM cows. Profiles of DEGs were more related to the immunization than to the challenge status of the mammary quarters, suggesting that resident cells of the Th17 phenotype pre-existed the E. coli mammary challenge. Analysis of biological functions with ToppFun software is shown on a dendrogram presented in Fig. 6b. The top 500 genes were divided into four modules. Module 1 was related to the IMM group and correlated with Neutrophil degranulation and Regulation of BAD phosphorylation. Module 3 correlated with Th17 cell differentiation and CD40L signaling pathway. These modules were expressed in IMM infected Fig. 2 Whole-blood transcriptomic response of control cows during the course of an E. coli mastitis. Differentially expressed genes (DEGs) between times were assessed using Deseq2. a A principal component analysis on the top 500 most differentially expressed genes of control cows at 0, 12, and 40 h post infection (hpi) shows that the expression profile of blood cells changed according to the time during the E. coli mammary infection. A Venn diagram presents the number of DEGs between time points, and the number of genes in common between the two comparisons (between 12 and 0 hpi and between 40 and 0 hpi) (b). Volcano plots were used for determining DEGs in the CONT group between 12 and 0 hpi (c), and 40 and 0 hpi (d). Thresholds were arbitrarily established for a q value < 0.01 and a log2FoldChange > 2 and genes above these thresholds are depicted in red dots. e A Heatmap of the top 500 DEGs during infection in control group is presented using Euclidian distribution analysis. Results of the functional analysis with ToppFun is presented as a dendrogram showing the relationship among biological functions and blocks of commonly regulated genes. The list of DEG identified with DESeq2 analysis was used to evaluate a difference of pathway activation between the CONT and IMM groups upon infection with Ingenuity Pathway Analysis software. The gene network showed that an IL-17-related network was strongly activated in the IMM group during infection (Fig. 6c).

To confirm the overproduction of IL-17A in immunized quarters, cytokine secretion was measured in milk before E. coli inoculation and just before tissue collection at 16 hpi. Despite the low number of animals, IFNγ and IL-17A secretion tend to be higher in the milk of locally immunized cows, while IL-10 secretion was lower (Supplementary Fig. 5), in accordance with the previous report [START_REF] Herry | Local immunization impacts the response of dairy cows to Escherichia coli mastitis[END_REF] . When cytokines were measured in the tissues of infected quarters, IL-17A, and CCL2, a IL-17-induced cytokine were more abundant in IMM cows (Fig. 6d); a difference of IL-1α was also detected (Supplementary Fig. 6). It is worth noting that milk and tissue concentrations were poorly correlated, except for IFNγ cytokine.

Altogether, these data support the idea that the local booster injection promoted the settlement of basal Th17 immunity in mammary tissue, further amplified by the E. coli infection.

DISCUSSION

Our aim was to decipher the immune mechanisms associated with the local route of immunization which revealed more effective at protecting the mammary gland against infection by E. coli. We analyzed blood and milk samples collected on the occasion of our previous challenge study [START_REF] Herry | Local immunization impacts the response of dairy cows to Escherichia coli mastitis[END_REF] . We provide evidence that the two modes of immunization that were evaluated in parallel mobilized a different immune response. The data strongly suggest that the injection of antigens from E coli culture supernate to the local route enabled the establishment in the mammary tissue of Th17polarized CD4 T cells that were undetectable in nonimmunized cows.

We reported previously that the clinical signs of mastitis were less severe in immunized compared to control cows, with faster bacterial clearance and a moderate leukopenia. Nevertheless, there was also a notable difference in response between systemic (IM) and local (IMM) immunization routes, as the inflammatory response persisted longer in the IM group. We suggest now that this was associated with a heightened signaling related to Type I interferon pathway that may be the result of TLR4-Myd88 signaling by LPS released from E. coli bacteria in the infected quarters. It could also be the result of a late influx of T cells that was detected in the milk of the IM group after 40 hpi. These cells were circulating in blood at the time of the mammary challenge and were recruited into the inflamed mammary tissue due to the expression of chemokines and endothelial adhesion molecules. Some may have proliferated locally due to antigen recognition or became activated non-specifically by the local production of cytokines. They may thus have contributed to the increase of the inflammatory response during the late phase of the mastitis, while healing was already ongoing in the CONT and IMM groups.

We chose to evaluate blood cell transcriptome by RNA-seq rather than mammary tissue or milk cell gene expression as previously described by us [START_REF] Bonnefont | Transcriptomic analysis of milk somatic cells in mastitis resistant and susceptible sheep upon challenge with Staphylococcus epidermidis and Staphylococcus aureus[END_REF][START_REF] Bouvier-Muller | Whole blood transcriptome analysis reveals potential competition in metabolic pathways between negative energy balance and response to inflammatory challenge[END_REF] or other groups [START_REF] Sbardella | RNA sequencing differential gene expression analysis of isolated perfused bovine udders experimentally inoculated with Streptococcus agalactiae[END_REF] . Indeed, this approach allowed us to do a kinetics study and to look for mechanisms over time on a tissue whose cell composition is more stable than that of milk during the mastitis course. Indeed, upon infection, recruited cells in milk are essentially neutrophils (>90-95%) as previously shown [START_REF] Herry | Local immunization impacts the response of dairy cows to Escherichia coli mastitis[END_REF] , whereas these represent no more than a third of the milk cells in healthy glands. Furthermore, RNA quality as defined by RNA integrity number (RIN) assessment is better on blood cells than on milk cells, where many leukocytes are dead or apoptotic/dying cells. If neutrophils represent essential terminal effectors against mammary infection, they do not represent the central type of cells induced by immunization. Conversely, CD4 and CD8 T cells are only a minority in the milk of infected glands, as their number in milk at the time of inoculation is very low (often lower than 10 4 cells/mL). Later, they are really difficult to isolate from abundant and highly activated neutrophils upon infection with strongly inflammatory Gram-negative bacteria.

We chose to retain six crucial time points, three during the infection course to decipher the protective mechanisms, with 0 hpi corresponding to our time point of reference, and three during immunization with time I0 corresponding to the time point before the first injection of immunization. In the CONT group, the blood transcriptome during the infection phase was informative about the response to an E. coli infection, with good clustering of the samples according to the Time. Blood leukocyte gene expression changed quickly and massively upon mammary infection with a large number of deregulated genes. This is partly due to changes in the blood leukocyte composition [START_REF] Herry | Local immunization impacts the response of dairy cows to Escherichia coli mastitis[END_REF] but also to changes in cell activation and functions. Indeed, functions related to neutrophils and bacterial recognition are highly activated at the peak of infection, whereas at 40 hpi, these functions are replaced by monocyte/macrophage activation and B cell response. The blood transcriptome actually revealed essential functions that are known to be mobilized or activated during the mastitis course. Blood represents a good compartment to sample to study the immune response to a mammary infection as also shown elsewhere [START_REF] Mitterhuemer | Escherichia coli infection induces distinct local and systemic transcriptome responses in the mammary gland[END_REF] . This may be due to the high blood flow through the lactating mammary gland which is a well perfused, metabolically active organ.

Immunized cows had less significant changes in their blood transcriptome than the controls, in accordance with the systemic changes that were smaller in these cows, as shown in previous reports indicating that immunization with the E. coli J5 bacterin diminished clinical signs and associated symptoms like milk production drop, a parameter that is well correlated with the severity of E. coli mastitis [START_REF] Burvenich | Severity of E. coli mastitis is mainly determined by cow factors[END_REF] . Indeed, the number of DEGs at 12 hpi compared to the 0 hpi time point is smaller in the immunized groups compared to the control group with 44% and 57% fewer DEGs for the IM and IMM groups, respectively. On the other hand, at 40 hpi, this decrease is only seen in the IMM group with a 90% decrease of DEGs, confirming the shorter duration of the mastitis in the IMM group in accordance with recorded systemic and mammary clinical signs. This is confirmed by the lower production of inflammatory cytokines (IL-1β, IL-6, and TNFα) and chemokines (CCL2 and CCL4). In addition, high production of IFNy was detected at 40 hpi in the IM group that corresponded to the probable activation of Th1-related CD4 or CD8 T cells that were primed by systemic immunization. This production was lower in IMM cows, maybe because they received only one injection of vaccine in adjuvant or because antigens from E. coli culture supernate in the mammary lumen were not able to expand or differentiate the cells to the same magnitude and/or phenotype as did the systemic immunization. As to the IL-17 cytokine, no difference of secretion could be detected in the plasma at any time point [START_REF] Herry | Local immunization impacts the response of dairy cows to Escherichia coli mastitis[END_REF] .

To look for the pathways correlating with the systemic and/or mammary scores, WGCNA was implemented. It enabled the identification of two different modules that were associated with lymphocytic functions in the IM group and granulocyte degranulation in the IMM group. The indication that T cell activation was weaker in the IMM group compared to the IM group was interesting and led us to investigate CD4 T cells in the mammary tissue, and not only the blood cell compartment.

We provide evidence that IL-17-producing CD4 T cells are enriched in the tissue of bacterial antigen-exposed mammary glands. The antigen repertoire of these cells is not known and whether they are specific to E. coli antigens remains to be determined as previously shown for CD8 T cells in another tissue [START_REF] Mcmaster | Pulmonary antigen encounter regulates the establishment of tissue-resident CD8 memory T cells in the lung airways and parenchyma[END_REF] . The role of IL-17 cytokine in immune responses against extracellular bacteria is now well established [START_REF] Matsuzaki | Interleukin-17 as an effector molecule of innate and acquired immunity against infections[END_REF] . This cytokine is known to participate in the local defence by inducing the production of CXCL8 by epithelial and/or stromal cells during infection by fungi and extracellular bacteria, which in turn induces neutrophil influx and antibacterial peptide secretion at the infection site [START_REF] Mcaleer | Mechanisms controlling Th17 cytokine expression and host defense[END_REF] . Its role in bovine mastitis has been highlighted in several reports. In a mastitis model with E. coli infection in mice, the IL-17 response in the mammary gland is instrumental in E. coli clearance and reduction of mammary inflammation [START_REF] Porcherie | IL-17A is an important effector of the immune response of the mammary gland to Escherichia coli infection[END_REF] . In dairy cows, despite reports of IL-17 detection in mastitis cases [START_REF] Blum | Comparison of the immune responses associated with experimental bovine mastitis caused by different strains of Escherichia coli[END_REF][START_REF] Roussel | Investigating the contribution of IL-17A and IL-17F to the host response during Escherichia coli mastitis[END_REF] , clear evidence was still lacking about its role in reducing mastitis severity.

IL-17-expressing CD4 T cells may be activated by antigenpresenting cells locally or IL-17 expression follows the secretion of IL-23 and activation of T mammary-resident memory (TRM) cells. IL-17 cytokine by acting on epithelial and stromal cells induces CXCL8 secretion and recruitment of neutrophils as shown by the upregulation of this pathway in the modular analysis. Unfortunately, by measuring cell recruitment in milk every 4 h, we were unable to show an influx difference among the groups. The number of cells in milk may not be completely correlated with precocity or intensity of the recruitment in the tissue since neutrophils need to cross the epithelial barrier to reach the mammary lumen and be identifiable in milk. Furthermore, neutrophil bactericidal activity may differ among conditions depending on the cytokine/chemokine pattern related to the type 3 activity they have encountered. Again we were not able to show any difference of opsonophagocytosis and killing activity among the groups using an in vitro assay [START_REF] Herry | Local immunization impacts the response of dairy cows to Escherichia coli mastitis[END_REF] , suggesting that the antibody response is not central to the mechanisms of bacterial clearance and mastitis healing. Unfortunately, the production of antibacterial peptides was not measured in the secretion of infected quarters to look for other mechanisms related to type 3 immunity.

What determines the phenotype of TRM cells in the mammary gland is another important question to resolve. For priming immunization, we used an adjuvant that is known to promote the development of cellular response in cattle, with a Th1/Th17 profile as previously shown using a model antigen like ovalbumin [START_REF] Rainard | Antigen-specific mammary inflammation depends on the production of IL-17A and IFN-γ by bovine CD4+ T lymphocytes[END_REF] . When we evaluated IFNγ and IL-17 production using a wholeblood assay with heat-killed bacteria in the same cows, cytokine production in IM and IMM cows was well above that of CONT cows. Although a high degree of variability was seen from cow to cow, IFNγ and IL-17 production increased after the booster injection in the IM group, suggesting that immunization induced circulating CD4 T lymphocytes producing IL-17A and/or IFNγ, contrary to what was seen in the IMM group. Intra-ductal injection of concentrated E. coli culture supernate at this step helped to recruit T cells within the tissue. Pro-inflammatory cytokine response induced locally by E coli-derived LPS may have increased homing and/or local proliferation of T cells, notably of IL-17expressing CD4 T cells, as previously described in the lungs [START_REF] Strutt | IL-15 supports the generation of protective lung-resident memory CD4 T cells[END_REF] or the intestine [START_REF] Bergsbaken | Local inflammatory cues regulate differentiation and persistence of CD8+ tissue-resident memory T cells[END_REF] for CD4 and CD8 TRM cells, respectively. Mechanisms of T cell homing within the mammary tissue are probably determinant and merits to be further examined in order to define the best modalities to settle locally more efficient T cell immunity and to improve the immunization protocol efficacy above the results of our proof of concept study.

When studying the response to immunization, blood transcriptome analysis allowed us to identify the presence of differential genes in IM and IMM, while very few DEGs were identified in the CONT group (Fig. 4b). At B7, activation of functions related to monocytes, neutrophils, and T cells were present in the immunized groups. Also, the modules related to inflammatory response and interferon pathway were activated in the IM group, indicating a stronger systemic inflammatory response in the group that received two vaccine injections in adjuvant. PCA showed that the difference of response was already present at B7 time point with upregulated functions remembering what was seen at time 40 hpi, like activation of neutrophil-related functions and TLR signaling. In contrast, profiles were indistinguishable at the peak of infection (12 hpi). This suggests that the cured group of IMM cows had a response at the time of the booster injection that is predictive of more rapid healing upon infection. A genetic control of the response to immunization cannot be excluded at this point to explain this difference.

To summarize we provided some evidence that the two routes of immunization and two different forms of E. coli antigens gave different results of immune activation and protective immunity against mammary E. coli infection (Fig. 7). The local route of immunization provided some mechanisms of E. coli mastitis control above what was observed in adjuvant-only receiving cows. Systemic immunization was helpful to generate a circulating pool of antigen-specific activated/memory T cells that may be mobilized after mammary infection. However, this recruitment occurred later in the course of mastitis and does not seem to be as protective as was the local immunization. Conversely, the antigenic booster enabled the local establishment of TRM cells that are poised to respond from the beginning of the infection. We provide a proof of principle that mammary type 3 immunity may be instrumental in protection against mastitis. Eliciting type 3 immunity in the mammary gland is likely to help the development of new commercial vaccines.

METHODS

Animals and ethical statement

All experimental procedures and clinical and laboratory records were previously described in Herry et al. [START_REF] Herry | Local immunization impacts the response of dairy cows to Escherichia coli mastitis[END_REF] . All procedures involving animals were approved by the Ethics Committee of Val de Loire (France), DGRI's agreement APAFIS#813-2015061109103810v2. Animal studies were compliant with all applicable provisions established by the European directive 2010/63/UE. All methods were applied by approved staff members in accordance with the relevant standard operating procedures approved by the above-mentioned Ethics Committee. All animals used in this study were handled in strict accordance with good clinical practice and all efforts were made to minimize suffering. In brief, 18 Holstein-Frisian heifers in total were synchronized and inseminated at 15 month-old on average. During the last trimester of the gestation, 12 heifers were immunized using IM injections with heat-killed E. coli P4 emulsified in Montanide ISA 61VG® (Seppic, France) oil adjuvant, as recommended by the manufacturer. Two months later, half of the heifers (IM group, n = 6) got a second injection as a booster. The others received 50 µg of protein concentrate prepared from a E. coli P4 supernate in two quarters (IMM group, n = 6). Cows of the control group (CONT, n = 6) were injected intramuscularly with adjuvant only twice at the same dates. Cows were then challenged on average at 49 days in milk in one healthy quarter by infusion of a E. coli P4 bacterial suspension (10 3 bacteria). Forty days after the first challenge, a new mammary infusion with E. coli P4 was performed using the same conditions as before, in order to collect tissue samples and to assess the local response. Six previously challenged cows (3 CONT, 3 IMM) were inoculated in one healthy (SCC < 50, 000 cells/ mL), and not previously challenged quarter. Clinical data were not collected. SCCs were assessed and E. coli bacteria were re-isolated in order to check that inoculation was effective at inducing a mastitis. Cattle were euthanized 16 h after challenge and just before tissue harvest.

CD4 T cell isolation

Mammary tissue specimens from challenged and control quarters were harvested in the parenchyma region. Mononuclear cells were isolated from 2 g of mammary tissue. Briefly, mammary gland homogenates were incubated with collagenase type IV and DNAse in RPMI for 30 min at 37 °C under agitation. Cells were recovered through Percoll gradient centrifugation and stained with PE-labeled CD45 (CC1, Bio-Rad) and FITC-labeled CD4 (CC8, Bio-Rad) mAbs, before cell sorting using a MoFlo Astrios EQ (Beckman Coulter, Brea, CA, USA).

RNA extraction

Blood samples were collected in EDTA tubes from the jugular vein at several time points, put on ice, and immediately treated with lysis buffer before storage at -80 °C. Total RNA was extracted from these samples with Nucleospin® RNA Blood kit (Macherey-Nagel). The amount of RNA was determined by measuring 260 nm absorbance using a NanoDrop ND-1000 (NanoDrop Technologies, USA). RNA quality was checked by measuring RIN with a Bioanalyzer (Agilent Technologies, USA). Measured values ranged between 7.8 and 9.3. RNA from isolated CD4 T cells was extracted using Nucleopin® XS kit (Macherey-Nagel).

Library preparation RNA-seq was performed at the GeT-PlaGe core facility, INRA Toulouse. RNA-seq libraries have been prepared according to Illumina's protocols using the Illumina TruSeq Stranded mRNA sample prep kit to analyze mRNA. Briefly, mRNA was selected using poly-T beads. Then, RNA was fragmented, and double stranded cDNA were generated before adaptors were ligated. Eleven cycles of PCR were applied to amplify libraries. Library quality was assessed using a Fragment Analyzer and libraries were quantified by QPCR using the Kapa Library Quantification Kit. RNA-seq experiments were performed on an Illumina HiSeq3000 using a paired-end read length of 2 × 150 pb with the Illumina HiSeq3000 sequencing kits.

RNA-seq data processing and analysis

Read quality was assessed with FastQC software. Reads were aligned in comparison to the bovine reference genome (UMD3.1.87) with STAR software [START_REF] Dobin | STAR: ultrafast universal RNA-seq aligner[END_REF] . Count table was generated with Cufflinks/RSEM [START_REF] Trapnell | Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation[END_REF][START_REF] Li | RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome[END_REF] . Identification of differentially expressed genes (DEGs) was performed with DESeq2 package within the R environment (R Core Team (2013)) following standard normalization procedures. False discovery rate was estimated with Benjamini and Hochberg correction. Expression changes were considered significant when both q value < 0.01 and a AbsLog2Fold-Change > 1, except when otherwise indicated. DEGs were determined with group comparison at each time point. For control group analysis, the design matrix was defined by design = ~time. For group comparison analysis, the design matrix was calculated by design = ~time + group + time/group.

Determination of cytokine production

Mammary tissue was weighed to normalization before grounding using ceramic beads (Bio-Rad) in a mixture containing Triton X-100 and a antiproteases cocktail (Sigma). Plasma, milk, and mammary tissue concentrations for 15 cytokines (IL-1α, IL-1β, IL-1RA, IL-2, IL-4, IL-6, IL-17a, IFNγ, CCL-2, CCL-3, CCL-4, CXCL8, CXCL10, and TNFα) were determined using a Custom bovine cytokines MilliPlex xMAP assay (Merck Millipore). Data were recorded on a MAGPIX flow cytometer using Xponent software (Luminex).

Modular analysis

To identify immune functions activated at each time point of the transcriptome analysis, a modular analysis was performed with tmod package using normalized count table and modules created by Chaussabel et al. [START_REF] Chaussabel | A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus[END_REF] and Li et al. [START_REF] Li | Molecular signatures of antibody responses derived from a systems biology study of five human vaccines[END_REF] as previously described.

Weighted correlation network analysis (WGCNA)

WGCNA was used to identify co-expressed genes associated with mammary and systemic clinical scores. The WGCNA R package was used for analysis [START_REF] Langfelder | WGCNA: an R package for weighted correlation network analysis[END_REF] . The minimum module size was set to 30, with a merge cut height of 0.25. The soft-thresholding power as a function of the scale-free topology index was defined at six. In this construction, all pairwise comparisons were conducted between control, IM, and IMM groups. Modules with correlation (r ≥ 0.5) and p value (≤0.05) were extracted for further inquisition. WGCNA module annotation was carried out with ToppFun [START_REF] Chen | ToppGene Suite for gene list enrichment analysis and candidate gene prioritization[END_REF] . A hypergeometric test with Bonferroni correction was applied to select statistically significant terms.

Data visualization

PCA and volcanoplot were prepared with DESeq2 rlog-normalized RNAseq data only. For PCA, the top 500 most variable genes were used. The EnhancedVolcano R package was used for volcanoplot [START_REF] Blighe | Publication-ready volcano plots with enhanced colouring and labeling[END_REF] and the pcaExplorer [START_REF] Marini | pcaExplorer: an R/Bioconductor package for interacting with RNA-seq principal components[END_REF] was used for PCA. The pheatmap package was used for heatmap visualization [START_REF] Kolde | pheatmap: Pretty Heatmaps[END_REF] . The clustering was based on Euclidean distance. Boxplox graphs were drawn using tmod R package [START_REF] Chaussabel | A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus[END_REF][START_REF] Li | Molecular signatures of antibody responses derived from a systems biology study of five human vaccines[END_REF] . To visualize gene expression and networks, Ingenuity Pathways Analysis (IPA, Qiagen) solfware was used to create network and to examine genes functions. Gene set enrichment analysis was represented in a dendrogram created with the IDEP R script [START_REF] Ge | iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data[END_REF] . The network analysis was carried out with IPA Software (Qiagen). Barplot and dot plot representation were performed with GraphPad Prism version 6.01 for Windows (GraphPad Software, La Jolla California USA).

Statistical methods

All data are presented as the mean value ± SD for n = 6 cows per experimental group, with individual values showed as a dot. Statistical analyses were performed using R software by applying Kruskal-Wallis, Mann-Whitney, or repeated measures Wilcoxon tests. The statistical significance was considered with a threshold p value below 0.05, except otherwise indicated.

Fig. 1

 1 Fig. 1 Clinical, hematological, and mammary response of the cows according to their immunization group at three informative time points of the E. coli mastitis course. a Experimental scheme with immunization and experimental challenge phases. Systemic (b) and mammary (f) clinical scores were established as composite values of several signs recorded by observers blind to the treatment. Milk production expressed as the relative percentage of milk produced at each time compared to the average production before inoculation (e). Milk bacteriology (g) expressed as the log 10 value of colony-forming units per mL of milk, white blood cells (c) and neutrophils (d) counts are presented at the same times. Each dot corresponds to an individual cow with colors indicating the treatment group and the box plot showing the mean and standard deviation at each time point. Letter indicate a difference (Kruskal-wallis, p < 0.05) between groups (a: CONT vs IM; b: CONT vs IMM; c: IM vs IMM).

Fig. 3

 3 Fig.3Whole-blood transcriptomic analysis of the response of immunized cows during the E. coli mastitis course. DEGs at two time points (12 and 40 hpi) in comparison with 0 hpi were identified with Deseq2. Volcano plots comparing gene expression differences between 0 and 12 hpi (a, c), and at 0 and 40 hpi (b, d) are presented for the intramuscular (IM) and the intramammary (IMM) group, respectively. Red dots correspond to genes with a q value < 0.01 and a Abslog2FoldChange > 2. e Heatmap of the top 500 DEG in control and immunized groups during the course of infection is presented. Functional analysis was performed with ToppFun and a dendrogram shows the relationship among biological functions of commonly deregulated genes according to the mammary and systemic scores, the time, and the treatment group. MS mammary score, SS systemic clinical score. Red arrows indicate IMM cows with 0 and 40 hpi profiles that were undistinguishable (further designed as cured cows).

Fig. 4

 4 Fig.4Whole-blood transcriptomic response to immunization in control and immunized groups. a A principal component analysis (PCA) on the top 500 most DEG of CONT (black) and immunized IM + IMM (orange) cows after the first injection (I7) shows that the expression profile of blood cells changes upon systemic immunization. b Venn diagram of identified genes in control, intramuscular and intramammary groups at 7 days after booster injection (B7) compared to I0 (before immunization). c A modular analysis on B7 gene expression shows the significant difference (p value < 10 -5 ) of upregulated biological functions between CONT, IM, and IMM groups.

Fig. 5

 5 Fig. 5 Response upon the mammary booster injection and correlation with the outcome of the E. coli mammary challenge. a A principal component analysis of gene expression profiles (top 500 most variable genes) for the intramammary-immunized IMM cows at B7. b At 12 h post infection and c and at 40 h post infection; d a modular analysis at B7 shows the difference of upregulated biological functions between cured and not cured cows (p value above 10 -4 ).

Fig. 6

 6 Fig. 6 Gene expression profile of CD4 T cells isolated from the mammary tissue of control and challenged quarters from locally immunized and control cows. a Experimental scheme with immunization and challenge phases. DEGs were assessed between four conditions corresponding to challenged and control quarters in CONT and IMM cows. b A heatmap showing the top 500 DEGs according to the conditions is presented. Functional analysis performed with ToppGene is presented as a dendrogram of functions in each block of correlated genes (p value < 0.05). c Gene network of the DEGs in CD4 T cells between the infected IMM and infected CONT conditions is presented and shows the relationship with IL-17 cytokine. d Tissue IL-17A and CCL2 production in non-infected and challenged quarters of CONT and IMM cows was determined on a pre-defined amount of tissue homogenate with a Milliplex assay at 16 hpi. Individual values are shown for n = 3 cows per group.

Fig. 7

 7 Fig. 7 Local mammary immunization establishes resident memory CD4 T cells in the mammary tissue on the contrary to the intramuscular route of immunization. The schematic describes our current view of the beneficial effect of intramammary immunization and compares the two routes of immunization. Systemic injection of killed bacteria in adjuvant produces a pool of Th1/Th17 circulating CD4 T cells. Injection of E. coli culture supernate antigens via the teat ductal route enables settlement of CD4 TRM cells in the mammary tissue that are poised to express IL-17 and mobilize a protective response through neutrophil recruitment early during the course of an E. coli mastitis.

npj Vaccines (2020) 108 Published in partnership with the Sealy Institute for Vaccine Sciences 1234567890():,;

Published in partnership with the Sealy Institute for Vaccine Sciences npj Vaccines (2020) 108

npj Vaccines (2020) 108Published in partnership with the Sealy Institute for Vaccine Sciences

ACKNOWLEDGEMENTS

This work was essentially supported by a grant from Agence Nationale de la Recherche (ANR) ANIHWA in the project KOlimastIR (ANR-13-ANWA-0003). N.C. received a fellowship from Apis-Gene (MASTIM project) and Occitanie region (Transcriptovac project).

DATA AVAILABILITY

Sequence data that support the findings of this study have been deposited in Gene Expression Omnibus (GEO) with the accession number GSE159283 and GSE159286.

AUTHOR CONTRIBUTIONS

COMPETING INTERESTS

The authors declare no competing interest.

ADDITIONAL INFORMATION

Supplementary information is available for this paper at https://doi.org/10.1038/ s41541-020-00258-4.

Correspondence and requests for materials should be addressed to G.F.

Reprints and permission information is available at http://www.nature.com/ reprints Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Mohamad Nazar

Ali Ibrahim

Zoubir Khatir

Nicolas Degrenne

Zeina Al Masry

Z Al-Masry

Wire-bond Contact Degradation Modelling for Remaining Useful Lifetime Prognosis of IGBT Power Modules

come   L'archive ouverte pluridisciplinaire

Introduction

The reliability of power electronic devices is an important issue for most power electronic systems. Electric components such as IGBTs are subject to high environmental and functional stresses (ambient temperature, vibrations…). All these factors have a strong impact on device reliability and lifetime.

Prediction of the Remaining Useful Lifetime (RUL) with a high confidence level is needed to assure reliability. It is important economically to have predictive maintenance for such devices than having preventive maintenance performed at fixed time intervals. One way to have a prediction approach is to have a model that describes the degradation in the power device. Two main kinds of models are presented in the literature for prognostic: the damage accumulation models and the condition based prediction models. The damage accumulation models can estimate the lifetime of components operating under given usage conditions. The effect of past usage is explicitly modeled by summing up the life consumption due to each stress undergone by the component in operation [START_REF] Manson | thermal stress law-cycle fatigue[END_REF][START_REF] Yang | Physics-offailure lifetime prediction models for wire bond interconnects in power electronic modules[END_REF]. Research is ongoing to improve these types of models, for example, N. Dornic [START_REF] Dornic | Stress-Based Model for Lifetime Estimation of Bond Wire Contacts Using Power Cycling Tests and Finite-Element Modeling[END_REF] gives the dependence of the 𝑁 𝑓 (number of cycles until failure) with the stress duration 𝑡 𝑜𝑛 and junction temperature swing amplitudes 𝛥𝑇 𝑗 . On the other hand, condition-based prediction models are based on the measurement and extrapolation of a precursor to failure. It assesses the behavior of individual units not only on their usage history but also on expected future load profiles. In other words, it utilizes a degradation model that predicts the future state based on input that describes the current system state and the expected load levels on the system. These component-specific models are generally adaptable and can be developed based on the physics of the system like [START_REF] Degrenne | Diagnostics and Prognostics of Wire-Bonded Power Semi-Conductor Modules subject to DC Power Cycling with Physically-Inspired Models and Particle Filter[END_REF][START_REF] Hu | Cost-Effective Prognostics of IGBT Bond Wires with Consideration of Temperature Swing[END_REF], or can be learned from run to failure data using data-driven methods like [START_REF] Sreenuch | Probabilistic Monte-Carlo Method for Modelling and Prediction of Electronics Component Life[END_REF][START_REF] Alghassi | Prognostics and Health Management of Power Electronics[END_REF]. Sreenuch in [START_REF] Sreenuch | Probabilistic Monte-Carlo Method for Modelling and Prediction of Electronics Component Life[END_REF] used a probabilistic Monte-Carlo method for modeling and online prediction of the remaining useful life (RUL) in an electronic component. His study is based on the 𝑉 𝐶𝐸 data collected from the electronic component, also Zhen Liu in [START_REF] Liu | Remaining useful life estimation of insulated gate biploar transistors (IGBTS) based on a novel volterra K-nearest neighbor optimally pruned extreme learning machine (VKOPP) model using degradation data[END_REF] used the Machine learning method to predict RUL for IGBT semiconductors using degradation data.

The remainder of the paper is organized as follows. Section 2 defines different types of degradations that occur in IGBT devices due to aging. Section 3 presents the analytical model used for RUL prediction. It describes the evolution of the dominant degradation that takes place in the studied module. It is based on the theory of contact resistance and on the spreading resistance in a thin metallization film. In section 4, an online prognostics model methodology is presented to predict the RUL. The steps for building the model are illustrated. This model is tested on experimental results with different stress conditions.

Aging behaviors and Degradations

During the aging of power electronic devices, different types of degradations can take place as shown in Fig. 1.

Fig.1: Possible degradation location in IGBT power module

The degradation types are: wire-bond degradation that usually leads to heel cracks, wire-bond lift-off due to crack propagation in the wire-bond interconnection with the metallization, top metallization degradation due to grains recrystallization, die-attach degradation that leads to cracks at the die bottom connection, cracks in the metallization ceramic interface, cracks in the ceramic itself, and substrate attach that leads to solder cracks.

In the IGBT module, wire-bond lift-off is a dominant degradation (see Fig. 2a[9]). For this reason, the focus in this paper is related to the crack propagation leading to the lift-off. (Fig. 2b) presents crack propagation in the wire-bond interconnection with the metallization.. A way to observe the 𝑉 𝐶𝐸 evolution with degradation due to thermal fatigue is using accelerated aging tests like power cycling. Fig. 3 shows experimental data for 4 IGBT modules of the same type aged under the same stress conditions. As data from the field are not easy to obtain, these types of accelerated results are used hereafter for model testing and validation. The only degradation observed in these aging tests is the lift-off due to crack propagation. Hence, it is assumed that the voltage variation in the wire-bond contact is the only responsible of the voltage variation observed between the collector and the emitter terminals.

In Fig. 3, the experimental accelerated conditions used are from power pulses with 3s heating time and 6s cooling time with power value that leads to 110°C temperature variation, from 35°C to 145°C. This figure gives the 𝑉 𝐶𝐸 evolution during power cycling of the 4 devices under tests (DUTs). The lifetime of the devices can be divided into two main zones. In zone 1, the voltage increase is due to the crack propagation in the wire-bond interconnection with the metallization. At the end of this zone, more than 80% of the lifetime is passed. Zone 2 presents the increase in voltage due to the lift-off process. The focus of this paper is related to the degradation in zone 1.

Contact Resistance Physical Model

In this section, an analytical model is built that estimates the increase of the crack length at the wire-bond contact from the increase in the 𝑉 𝐶𝐸 . This model is then used to predict the RUL, assuming a mean speed linear crack propagation. In comparison to literature, a study done by Degrenne and Mollov [START_REF] Degrenne | Diagnostics and Prognostics of Wire-Bonded Power Semi-Conductor Modules subject to DC Power Cycling with Physically-Inspired Models and Particle Filter[END_REF] presents a physical model that relates the crack propagation to on-state voltage. Using a specific algorithm, this model is used to predict the RUL with an accuracy of less than ±10%. This model assumed that crack propagation is linear. However, Hu in [START_REF] Hu | Cost-Effective Prognostics of IGBT Bond Wires with Consideration of Temperature Swing[END_REF] built a physical model that relates the crack propagation to the voltage using the Finite Element Method and taking into consideration nonlinear crack propagation. This model took into account also the effects of temperature variation and metallization degradation on the on-state voltage increase.

The model in this paper combines two different models. The first one describes the constriction resistance at the bond contact due to the crack. The second one gives the spreading resistance in the metallization due to the current lines flowing from the die, collected by the metallization pad and going through the bonding wire. The spreading of the current lines is also affected by the crack length. The model is simplified in order to consider a disc as a surface contact between a cylindrical bond-wire and the metallization as depicted in Fig. 4. Based on Holm's relation [START_REF] Holm | Electric Contacts[END_REF], the constriction resistance is given by:

Constriction Resistance in the wire-bond part

𝑅 𝐶 = 𝜌 4𝑎 (1)
where 𝜌 (resistivity of the material) and 𝑎 (radius of the contact area). This constriction resistance is due to the current constricted to pass through the contact area (see Fig. 4).

According to Eq.1, 𝑅 𝐶 is dependent on the radius of the area still in contact. Since crack propagates during the aging, the contact area decreases with the radius 𝑎 that leads to an increase of 𝑅 𝐶 .

Metallisation's Resistance Model

The resistance of the metallization is due to the lateral current flowing vertically from the die (see Fig. 5). If the bond-wire contact radius is 𝑎 and the radius of the considered metallization area is 𝑏, then the equivalent resistance of metallization, "seen" between 𝑟 = 𝑎 and 𝑟 = 𝑏 is given by eq.2, see appendix.

𝑅 𝑚𝑒𝑡. = 𝜌 2𝜋𝑡 ( 𝑏 2 𝑏 2 -𝑎 2 𝑙𝑛 𝑏 𝑎 - 1 2 ) ( 2
)
Where t is the metallization thickness and 𝜌 its resistivity. The effect of crack propagation on the metallization resistance is very important to be modeled. It is shown later that the metallization resistance variation is high in comparison to the constriction resistance.

This resistance in Eq.2 covers the resistance of the metallization form 𝑎 to 𝑏. This relation doesn't include the resistance increase in the metallization due to resistivity increase and/or structure change (recrystallization and grain grooving). Nevertheless, the resistance of metallization increases with aging due to power cycling as mentioned in [START_REF] Zhao | A study on the effect of microstructure evolution of the aluminum metallization layer on its electrical performance during power cycling[END_REF]. The literature reports that the metallization resistance can increase by a factor 2 to more than 10 depending on stress conditions [START_REF] Zhao | A study on the effect of microstructure evolution of the aluminum metallization layer on its electrical performance during power cycling[END_REF][START_REF] Arab | Experimental investigations of trench field stop IGBT under repetitive short-circuits operations[END_REF][START_REF] Pommier | Effect of die metallization layer ageing in the case of power semiconductor devices[END_REF]. Based on that, a factor "w" is multiplied to the resistance of Eq.2 to form Eq.3. No additional measurements were done to fix "w", however, it is assumed to be constant and equal to 2 based on the study of [START_REF] Zhao | A study on the effect of microstructure evolution of the aluminum metallization layer on its electrical performance during power cycling[END_REF] that carries almost the same conditions of power cycling tests used in this study.

𝑅 = 𝑤𝜌 2𝜋𝑡 ( 𝑏 2 𝑏 2 -𝑎 2 𝑙𝑛 ( 𝑏 𝑎 ) - 1 2 ) ( 3
)
It is necessary to be mentioned that the wire-bond contact shape in IGBT is ellipsoidal. However, Holms mentioned in [START_REF] Holm | Electric Contacts[END_REF] that for ellipse contact shape the difference can be neglected if the multiplication of the ellipse's major and minor semi-axis is equal to 𝑎 2 (which is our situation). Moreover, the dimensions of the metallization are not circular; however, in this study "b" is calculated experimentally by fitting the whole experimental curve to the model. Besides, a parameter sensitivity study for "b" is done and it shows that changing b in an acceptable range between 1.3 mm and 2 mm has a very small effect in changing the predicted curves.

Proposed model

By combining two models described in 3.1 and 3.2, Eq.4 is built that describes the effect of crack propagation (contact area decrease) on the resistance between wire-bond and metallization. Fig. 6: Effect of contact area radius "a" change in Eq. 1 & 9 on the resistance

𝑅(𝑎) = 𝜌 2 ( 1 2𝑎 + 𝑤 𝜋𝑡 ( 𝑏 2 𝑏 2 -𝑎 2 𝑙𝑛 𝑏 𝑎 - 1 2
))

Fig. 6 shows the effect of the radius change of the bond contact on both resistances due to crack propagation. It is observed that with a decrease in the radius, the two resistances increase. It can be observed that the metallization resistance remains very high compared to that of constriction except when the contact is reduced to a very small radius. The sensitivity of resistance to radius change increases with radius decrease, and it increases in high slope at the last few micrometers.

The values of parameters of Eq.1 & 3 used in this study based on the structure are 𝑏 = 2 𝑚𝑚, radius initial value 𝑎 0 = 332 𝜇𝑚, 𝑡 = 4𝜇𝑚, and 𝜌 = 3.8 * 10 -8 Ω𝑚 This model is used in the prognostics methodology to predict the remaining voltage variation at each measurement. Besides, as mentioned before, the variation of voltage calculated from Eq.3 equals the variation of collector-emitter voltage (𝛥𝑉 𝐶𝐸 ). The approach is more detailed in the next section.

Prognostics Approach

The prognostics approach for RUL estimation is based on Eq.4. At the current time, the 𝛥𝑉 𝐶𝐸 /𝑉 𝐶𝐸 value is measured (mth measurement), (see Fig. 7). This is used to calculate the current contact area radius 𝑎 𝑚 using Eqs.5-8 and compare it to the initial radius 𝑎 0 . If a difference is detected, this means that a crack has occurred or progressed. The initial contact area radius 𝑎 0 , as well as the successive contact radius 𝑎 𝑖 (i=1 to m) are used to compute the rate of crack propagation (𝐾 𝑚 ), between the mth and the (m-1)th measurements, using Eq.9. For prognostic purpose, a mean value 𝐾 ̅ of the successive 𝐾 𝑚 is used to predict the evolution of the contact area radius with the assumption of linear crack propagation using Eq.10.

The detailed process is as follows, at first, relative variation in 𝑉 𝐶𝐸 is calculated, applying Ohm's law:

𝛥𝑉 𝐶𝐸 𝑉 𝐶𝐸,𝑖𝑛𝑖 = 𝛥𝑅(𝑎) 15𝑊 * 𝐼 𝑉 𝐶𝐸,𝑖𝑛𝑖 (5)
where 𝑉 𝐶𝐸,𝑖𝑛𝑖 is the initial collector-emitter voltage and 𝐼 is the direct current used. 𝛥𝑅(𝑎) 15𝑊 is the resistance variation of 15 parallel wire-bonds connected to the metallization pad. Eleven of them are assumed degrading and four are not. This degradation distribution assumption is due to a study done that is related to the structural wire bonding on the device used, which leads the current to take less resistive paths and make degradation effect on 11 wire-bonds rather being on all of them. Which could give the possibility to assume degradation in 11 wire-bonds. Also it is assumed that the rate of degradation for the 11 damaged wire-bonds is the same.

𝑅(𝑎 0 ) 15𝑊 and 𝑅(𝑎 𝑚 ) 15𝑊 represents respectively the initial resistance of the 15 wire-bonds and the resistance of the 15 wirebonds at the 𝑚 𝑡ℎ measurement, with:

𝑅(𝑎 0 ) 15𝑊 = 𝑅(𝑎 0 ) 15 (7) 𝑅(𝑎 𝑚 ) 15𝑊 = 𝑅(𝑎 𝑚 ) * 𝑅(𝑎 0 ) 4𝑅(𝑎 𝑚 ) + 11𝑅(𝑎 0 ) (8)
The resistances 𝑅(𝑎 0 ) and 𝑅(𝑎 𝑚 ) are calculated from Δ𝑉 𝐶𝐸 using Eq.5-8, the parameter 𝑎 𝑚 is calculated after inverting Eq.4 and then used to compute 𝐾 𝑚 using Eq.9

𝐾 𝑚 = 𝑎 𝑚 -𝑎 𝑚-1 𝑛 𝑚 -𝑛 𝑚-1 (9)
Using the average value (𝐾 ̅ ) of the 𝐾 𝑚 values (𝐾 1 , . . . , 𝐾 𝑚 ), in Eq.10 gives the predicted values of radius contact 𝑎 𝑛 until the crack propagation completes.

𝑎 𝑛 = 𝐾 ̅ 𝑛 + 𝑎 0 [START_REF] Holm | Electric Contacts[END_REF] where 𝐾 ̅ is the average crack propagation rate. "n" represents the number of cycles for the prediction process at each planned measurement m.

By introducing the predicted values of 𝑎 𝑛 in Eqs.5-8, the values of 𝛥𝑉 𝐶𝐸 /𝑉 𝐶𝐸,𝑖𝑛𝑖 are calculated.

Which means that at each new measurement m, a prediction curve that predicts 𝛥𝑉 𝐶𝐸 /𝑉 𝐶𝐸,𝑖𝑛𝑖 is formed. This prediction curve stops when the contact area radius "𝑎 𝑛 " reaches zero, that means lift-off.

For instance, for m=1, 𝛥𝑉 𝐶𝐸 /𝑉 𝐶𝐸,𝑖𝑛𝑖 is measured at 8 kilocycles and is equal to 0.16% as shown in Fig. 8. This allows to calculate the area contact radius 𝑎 1 . Then, by having 𝑎 0 and 𝑎 1 , the first crack speed rate 𝐾 1 is calculated and assumed as constant for predicting the remaining radiuses until "a" reaches zero The predicted values of radiuses are reinserted in Eqs.5-8 to calculate the predicted evolution of the curve 𝛥𝑉 𝐶𝐸 /𝑉 𝐶𝐸,𝑖𝑛𝑖 . The first prediction can be observed in Fig. 8. By repeating the previous process for several measurements and plotting the predicted 𝛥𝑉 𝐶𝐸 /𝑉 𝐶𝐸,𝑖𝑛𝑖 , Fig. 9 could be observed. As it can be observed in Fig. 9, the prediction was done to estimate the remaining 𝛥𝑉 𝐶𝐸 /𝑉 𝐶𝐸,𝑖𝑛𝑖 curve at each step of measurement. The conventional failure criteria assumption on the collector-emitter voltage indicator is when the 𝛥𝑉 𝐶𝐸 /𝑉 𝐶𝐸,𝑖𝑛𝑖 reaches a 5% increase. However, it is important to note that this threshold is quickly achieved in the last part of aging, which is wire-bond lift-off (zone 2 Fig. 3). In Fig. 9, the experimental data represented by the dashed black curve is used to validate the predictions. The first and third predictions (at 8 kcycles and 16 kcycles) are less acceptable than the rest of the curves. This is due to the primary construction of the degradation propagation rate. However, this rate is then tuned after each new measurement due to computing the mean of crack propagation rates, and as observed for the remaining six predictions, the estimated 𝛥𝑉 𝐶𝐸 /𝑉 𝐶𝐸,𝑖𝑛𝑖 is close to the experimental observation and can be relied on. Fig. 11 and Fig. 12 present the predicted RUL and Real RUL change with the number of cycles for the two different test conditions applied in this study. They start where the first prediction curve took place, except for Fig. 12 the curve starts from 32 k.cycles due to the errors observed in the experimental data at the first 30 k.cycles. The values of predicted RULs could be also verified in Fig. 9 and Fig. 11 where the RUL in this study presents the wire-bond's lift-off.

As shown in Fig. 12, the predicted RUL from 13 to 18 kilocycles is overestimating the real RUL. The prediction curves are not very reliable. However, after 18 kcycles the RUL is being underestimated and near to the real RUL curve. In both figures, the predicted RUL underestimates the failure which could mean that no additional costs the company will pay to failure consequences.

One way to assess the model robustness is by applying it to different stress conditions. For that, the physical model (including its parameters) is also applied to data aged in different conditions.

The test conditions used in Fig. 13 are for an aging test under 𝛥𝑇 = 90°𝐶 and 𝑡 𝑜𝑛 = 3𝑠𝑒𝑐. As expected, these tests last much more than tests under 𝛥𝑇 = 110°𝐶. The lifetime for tests at 𝛥𝑇 = 90°𝐶 reaches around 100 kcycles whereas the lifetime of the tests at 𝛥𝑇 = 110°𝐶 reaches around 35 kcycles. As observed in Fig. 13 the first and second curves are far from the experimental curve. This is due to high variations in data measurements as observed at first 30 kcycles. Then, the prediction curves start to trend upward as the experimental curve.

Conclusion

In this study, a physics-based analytical model is built to relate crack propagation to 𝑉 𝐶𝐸 . Since 𝑉 𝐶𝐸 is monitored in realtime operation, then the crack length at each measurement can be estimated. Then a crack rate of propagation is computed and used to predict the crack lengths until the crack is completed and lift-off is achieved, assuming a linear crack propagation. These predicted crack lengths give the predicted collector-emitter voltage variation using the model built. The rate of crack propagation is averaged at each new measurement and used for the next prediction. The predicted 𝑉 𝐶𝐸 identifies the end of zone 1, which forms 80% of the total lifetime of the IGBT semiconductor device. The importance of this work lies under the online prediction ability of the important part of the lifetime in the IGBT; it could be improved to predict the whole life.

Fig. 2 :

 2 Fig.2: a) wire-bond lift-off[START_REF] Ciappa | Selected failure mechanisms of modern power modules[END_REF], b) crack propagation in the wire-bond interconnection with the metallization[START_REF] Degrenne | Diagnostics and Prognostics of Wire-Bonded Power Semi-Conductor Modules subject to DC Power Cycling with Physically-Inspired Models and Particle Filter[END_REF]

Fig. 3 :

 3 Fig.3: Collector-Emitter voltage variation, and its zones due to types of degradation

Fig. 4 :

 4 Fig.4: Model geometry with the constriction and the spreading areas.

Fig. 5 :

 5 Fig.5: Current flow in the metallization

Fig. 7 :

 7 Fig.7: Vce measurements instants 𝛥𝑅(𝑎) 15𝑊 = 𝑅(𝑎 𝑚 ) 15𝑊 -𝑅(𝑎 0 ) 15𝑊

Fig. 8 :

 8 Fig.8: Voltage variation percentage of the first prediction at 8 k.cycles compared to Experimental data

Fig. 9 :

 9 Fig.9: Prediction of the relative increase in 𝑉 𝐶𝐸 for IGBT device under the conditions 𝛥𝑇 = 110°𝐶 & 𝑡𝑜𝑛 = 3𝑠𝑒𝑐 Fig.9 can show the predicted curves for 𝛥𝑉 𝐶𝐸 /𝑉 𝐶𝐸,𝑖𝑛𝑖 at several numbers of cycles. The experimental results in Fig.9 refer to a device aging under 𝛥𝑇 = 110°𝐶 and 𝑡 𝑜𝑛 = 3 𝑠𝑒𝑐. The experimental data used are filtered with an average moving filter on 3 consecutive points.As it can be observed in Fig.9, the prediction was done to estimate the remaining 𝛥𝑉 𝐶𝐸 /𝑉 𝐶𝐸,𝑖𝑛𝑖 curve at each step of measurement. The conventional failure criteria assumption on the collector-emitter voltage indicator is when the 𝛥𝑉 𝐶𝐸 /𝑉 𝐶𝐸,𝑖𝑛𝑖 reaches a 5% increase. However, it is important to note that this threshold is quickly achieved in the last part of aging, which is wire-bond lift-off (zone 2 Fig.3). In Fig.9, the experimental data represented by the dashed black curve is used to validate the predictions. The first and third predictions (at 8 kcycles and 16 kcycles) are less acceptable than the rest of the curves. This is due to the primary construction of the degradation propagation rate. However, this rate is then tuned after each new measurement due to computing the mean of crack propagation rates, and as observed for the remaining six predictions, the estimated 𝛥𝑉 𝐶𝐸 /𝑉 𝐶𝐸,𝑖𝑛𝑖 is close to the experimental observation and can be relied on.

Fig. 10 :

 10 Fig.10: Contact area radius measured (𝑎 𝑚 ) decrease with the number of cycles for the aging tests done under 𝛥𝑇 = 110°𝐶 𝑎𝑛𝑑 𝑡 𝑜𝑛 = 3𝑠𝑒𝑐 At each measurement done a new contact area radius is computed. Fig.10 presents the radius decrease variation with cycles. It is shown that the variation of radius 𝑎 with cycles is almost linear. This verifies the assumption of linearity used in crack propagation.

Fig. 11 :Fig. 12 :

 1112 Fig.11: Predict RUL change when applied on aging tests done under the test condition 𝛥𝑇 = 110°𝐶 and 𝑡 𝑜𝑛 = 3𝑠𝑒𝑐

Fig. 13 :

 13 Fig.13: Prediction of the relative increase in 𝑉 𝐶𝐸 for IGBT device under the conditions of 𝛥𝑇 = 90°𝐶 & 𝑡𝑜𝑛 = 3𝑠𝑒𝑐

Appendix

The metallization resistance is formulated considering Fig. 14.

Considering 𝐽 0 as the vertical current density in the die, 𝐼 1 the total current from die under the contact (𝐼 1 = 𝐽 0 𝜋𝑎 2 ) and 𝐼 2 the total current outside the contact (𝐼 2 = 𝐽 0 𝜋(𝑏 2 -𝑎 2 )), 𝑎 is the radius of the contact area, and 𝑏 is the outer radius of the metallization. The whole current is 𝐼 = 𝐼 1 + 𝐼 2 . One can observe that the current in the metallization 𝑖(𝑟) is negative in the radial oriented axis but the current variation 𝑑𝑖/𝑑𝑟 at radius r is positive and given by:

Eq.11 is integrated from 𝑟 to 𝑏 knowing that 𝑖(𝑏) = 0. The following equation is formed:

Where 𝑖(𝑟) is the lateral current in the metallization, the negative sign indicates that it is flowing in the reverse direction of the radial oriented axis. Let 𝑗(𝑟) be the lateral current density in the metallization at 𝑟:

where 𝜌 is the electrical resistivity of the material, and 𝑡 is the thickness of the metallization. By using Eqs.12 & 13 the Eq.14 is obtained:

Thus, after integration from 𝑎 to 𝑏 :

The equivalent resistance of metallization, seen between 𝑟 = 𝑎 and 𝑟 = 𝑏, can be written: