Drosophila Kenny, the homolog of mammalian IKKγ, is a selective autophagy receptor that mediates the degradation of the IκB kinase complex. Selective autophagic degradation of the IκB kinase complex prevents constitutive activation of the immune deficiency pathway in response to commensal microbiota. We show that autophagy-deficient flies have a systemic innate immune response that promotes a hyperplasia phenotype in the midgut. Remarkably, human IKKγ does not interact with mammalian Atg8-family proteins. Using a mathematical model, we suggest mechanisms by which pathogen selection might have driven the loss of LIR motif functionality during evolution. Our results suggest that there may have been an autophagy-related switch during the evolution of the IKKγ proteins in metazoans.Selective autophagy is a catabolic process with which cellular material is specifically targeted for degradation by lysosomes. The function of selective autophagic degradation of self-components in the regulation of innate immunity is still unclear. Here we show that  Drosophila Kenny may be an evolution-selected autophagy receptor for the down-regulation of innate NF-κB activationSelective autophagy describes the selective degradation of cellular components upon stress or nutrient deficiency, but whether it modulates innate immunity is unclear. Here the authors show that  These NF-κB associated pathways regulate the expression of immune and stress response genes aiming at clearing pathogens. However, improper activation of these pathways can cause pathologies such as inflammation and cancer. Thus, to maintain tissue homeostasis and organism survival, the activation of NF-κB associated pathways must be tightly regulated3. Drosophila innate immunity is controlled by two major signaling pathways: the immune deficiency (IMD) and Toll pathways. The activation of either pathway results in the production of antimicrobial peptides (AMPs) that neutralize the bacterial load6.Innate immunity constitutes one of host’s first defense against pathogen invasion and relies on inflammatory responses controlled by the nuclear factor-kappa B (NF-κB) family transcription factors13. Autophagy is subdivided into three types  of which macroautophagy (hereafter referred to as “autophagy”) is the most well-studied. During autophagy there is sequestration of cellular material into double-membrane vesicles called autophagosomes. The autophagosomes eventually fuse with lysosomes where the sequestered cargos are degraded by lysosomal hydrolases14. The sequestration and degradation of cytoplasmic material by autophagy can be selective through the action of specific receptor proteins16. Selective autophagy receptors usually contain a short motif, termed LC3-interacting region (LIR) or Atg8-interacting motif (AIM), that is necessary for their interaction with the autophagosomal membrane protein microtubule-associated protein 1 light chain 3 (Atg8/LC3)18. Atg8/LC3 is required for the proper formation and maturation of autophagosomes, and serves as a central scaffold protein for selective autophagy receptors and cargos19. The implication of autophagy in the degradation of intracellular pathogens has been widely described13. However, the molecular mechanisms that link selective autophagy of cellular self-components and innate immunity remains largely unexplored.Autophagy is an evolutionarily conserved process, by which cells degrade their own cellular material. Autophagy serves as a cellular response to nutrient starvation, and for the removal of aggregated proteins, lipids, damaged organelles, and invading bacteria and virusesDrosophila proteins that interact with Atg8/LC3 proteins, here we show that Kenny, the Drosophila homolog of IKKγ/NEMO, is an Atg8a-interacting protein, and is required for the selective autophagic degradation of the IκB kinase (IKK) complex to prevent the constitutive activation of the IMD pathway by commensal bacteria. We additionally show that mammalian IKKγ/NEMO lacks a functional LIR motif. Finally, we present a mathematical model which explores the evolution of the LIR motif during host interaction with pathogens and commensal microbiota. Overall, our results describe a molecular mechanism of selective autophagy in Drosophila innate immunity, and suggest that pathogen-related selective pressures may have induced the loss of a functional LIR motif in mammalian IKKγ through evolution.Using an in silico approach to identify Drosophila, we screened the Drosophila proteome for the presence of LIR motifs using the iLIR software that we have developed21. We focused on proteins that also contained ubiquitin-binding domains22. One of our hits was the UBAN domain-containing protein Kenny (CG16910) that has a predicted LIR motif at its N terminus  pathway24. We found that in vitro translated Kenny (35S-Myc-Kenny) binds to recombinant GST-Atg8a, the Drosophila homolog of Atg8/LC3  motif-containing proteins in LC3 Fig. . This inLC3 Fig. . This obart Fig. . Atg8-fanny Fig. . In ordeagy Fig. . We alsoAtg8a and Atg7 mutants compared to wild-type flies  and autophagy mutant (Atg1 and Atg13) clonal cells but not in their wild-type neighbors  accumulation of Kenny upon proteasomal inhibition, suggesting that Kenny is predominantly degraded by autophagy  constitutes another major intracellular proteolytic system in eukaryotesAtg8a and Atg7 mutant flies while a single band was observed in wild-type flies  or lamba phosphatase (λPP). In each case, we noticed that the doublet of bands converted into a single band corresponding to the lower molecular weight band 33. Using UAS-mCherry-ird5 transgenic flies, we observed that mCherry-ird5 is diffused in the cytoplasm of fat body cells in fed conditions  ird5 puncta in starved conditions  complex which consists of IKKβ/ird5 and IKKγ/Kenny, and is crucial for nuclear translocation of transcription factor Relish and induction of the expression of antimicrobial peptide (AMP) genes, including ons Fig. . Howeverons Fig. . Using aons Fig. . mCherryons Fig. . These rons Fig. . Interesons Fig. . This obion Fig. . An in vion Fig. 24. In orsed Fig. , the co-sed Fig. . AdditioNAi Fig. , suggestDpt. We observed that there was a significant systemic upregulation of Dpt in Atg8a and Atg7 mutant flies  that can be partially rescued by re-expressing mCherry-Atg8a in the fat body , suggesting that the systemic deregulation of the IMD pathway is predominatly controlled by the fat body. To assess whether the deregulation of the IMD pathway in autophagy-deficient flies is related to the accumulation of Kenny protein, we created Atg8a;kenny double mutant flies. We observed that the absence of kenny expression in autophagy-deficient flies abrogates the constitutive activation of Dpt observed in Atg8a mutant flies  conditions. Interestingly, no upregulation of Dpt was observed in axenic Atg8a and Atg7-depleted flies compared to their conventionally reared siblings , indicating that Kenny-mediated upregulation of AMPs is causative of gut hyperplasia in autophagy mutant flies , a specific marker for mitotic cells. We observed that ies Fig. –h, l. Inies Fig. –k, suggeP < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001), indicating increased retention of ingested food. In addition, we observed that rearing Atg8a mutant flies in axenic conditions increased their median lifespan , mosquito (Aedes aegypti), butterfly (Papilio xuthus), and silk moth (Bombyx mori) suggesting that there is conservation between Diptera and Lepidoptera  A host with neither a LIR motif on IKKγ nor the specific molecular machinery to target IKKγ for autophagic degradation using a ubiquitin tag (host type 1) suffers excess mortality due to the stimulation of the innate immune response by the microbiota. (2) Maintaining a regulatory system capable of appropriately degrading IKKγ using ubiquitination is inherently more costly than regulating IKKγ by LIR induced degradation, due to the proteins required to ubiquitinate IKKγ and the need for appropriate selective autophagy receptors.To explore the possible evolutionary reasons why the LIR motif may have been lost during mammalian evolution, we considered the following biologically plausible effects: (Effect I) If the host can up or downregulate the degradation of IKKγ by ubiquitin tagging, this may afford greater precision in regulating immunopathology during infection, and result in reduced mortality during infection. (Effect II) As in Effect I, but the advantage of being able to precisely regulate the rate of degradation of IKKγ by ubiquitin tagging may be reduced if a LIR motif is present on IKKγ, constantly signaling it for autophagy. (Effect III) If the pathogen encodes a LIR motif, this may enhance the degradation of IKKγ during infection and attenuate the innate immune response. The consequences of this (both of which could be advantageous to the pathogen) may be (IIIa) a slower recovery rate from infection, or (IIIb) reduced host mortality during infection. We assumed that the pathogen is only able to manipulate the host in this way if host IKKγ lacks a LIR motif (host types 1 and 3).Drosophila-like scenario , host type 3 will dominate  complex. Kenny is a component of the immune deficiency (IMD) pathway which is activated following the recognition of bacterial diaminopimelic acid peptidoglycan (DAP-PGN), leading to the assembly of a receptor proximal signaling complex43. Downstream signaling requires the cleavage of the nuclear factor-kappa B (NF-κB)-like immune transcription factor Relish, which translocates to the nucleus and induces the expression of antimicrobial peptide (AMP) genes44. Here we showed that selective autophagic degradation of the IKK complex prevents the systemic immune response that is mediated by Relish and the production of antimicrobial peptides. Autophagy mutants fail to terminate this response and exhibit systemic inflammation that contributed to their premature death. Atg8a and Atg7 mutant flies have been reported to have a short lifespan due to a neurodegeneration phenotype46. Our results indicate that their short lifespan is also mediated by systemic inflammation that affects the function of the gut.Multicellular organisms are exposed to microbes throughout their lives. These exposures can be transient or permanent, with microbes ranging from those considered commensal to pathogenicAtg8a and Atg7 mutant flies.Selective autophagic degradation of Kenny is mediated by its LC3-interacting region (LIR) motif. We have shown the importance of the LIR motif for its degradation using four different technical approaches: (1) mutation of the LIR motif drastically reduces the co-immunoprecipitation between Kenny and Atg8a when expressed in S2R+ cells, (2) mutation of the Kenny LIR motif abrogates the direct interaction with Atg8a, as demonstrated by GST pull-down experiments. Similarly, Kenny loses its interaction with Atg8a lacking a functional LIR docking site (LDS). (3) Using tandem-tagged Kenny expressed in HeLa cells, we observed that wild-type Kenny is targeted to acidic compartments (autolysosomes) when the cells are under starved conditions, while its LIR mutated counterpart is not, and (4) the ability of GFP-Kenny to localize to autophagosomes and autolysosomes in vivo is compromised when its LIR motif is mutated. Although we were not able to show that autophagic degradation of a genomic Kenny LIR mutant was compromised, we have clearly shown that endogenous Kenny protein was robustly accumulated in 52.Furthermore, we showed that Kenny is accumulated in autophagy mutant flies in its phosphorylated form. Interestingly, Kenny does not appear to be phosphorylated when the proteasome is inhibited. This suggests that phosphorylation of Kenny is important for its function as selective autophagy receptor. Indeed it has been shown that phosphorylation of various autophagy receptors enhances their autophagic degradationAtg8a and Atg7 mutant flies exhibit intestinal hyperplasia that is predominantly controlled by a systemic immune response produced by the fat body. This is in line with a previous study showing that systemic inflammation caused by the fat body leads to intestinal hyperplasia in old flies53. Despite the fact that we observed a very modest upregulation of Diptericin in isolated autophagy mutant guts we have observed nuclear translocation of Relish in the midgut of Atg8a mutant flies. A report by Erturk-Hasdemir et al.33 proposed that the IKK complex is required both for the cleavage and phosphorylation of Relish, the latter being responsible for the efficient upgregulation of AMP genes. There is evidence however that Relish cleavage alone may be enough to upregulate the expression of Diptericin only54. Regulation of the IMD pathway is tissue-specific with its various output molecules being favoured differently from one tissue to another55. Nuclear translocation of Relish in Atg8a mutants may lead to some level of Diptericin expression, however in the absence of infection this may in fact favor the expression of the IMD pathway antagonist Pirk and prevent Relish phosphorylation, which would have an overall negative effect on the local production of AMPs56.We showed that Drosophila under oxidative stress conditions (paraquat feeding)57. However, in this study knockdown of Atg9 or Atg12 was done tissue specifically in the midgut, whereas our results are from full body mutants and without paraquat feeding.It has been reported that Atg9 but not Atg12 was required for JNK-mediated intestinal hyperplasia in 58. Murine cytomegalovirus M45 protein interacts with NEMO, mediating its engulfement by the autophagosome and subsequent degradation in the lysosome35. Analysis of the murine cytomegalovirus M45 protein sequence with iLIR20 shows a predicted functional LIR motif. It has also been shown that p47, an essential factor for Golgi membrane fusion, associates with NEMO upon tumor necrosis factor (TNF) or interleukin-1 stimulation, and inhibits IKK activation. p47 binds to NEMO and mediates its lysosomal degradation59. Interestingly, Shp1, a yeast homolog of p47, binds to ATG8 which is the yeast homolog of LC3 and is required for the elongation of the nascent autophagosome60. Altogether, these results suggest that there was a switch during the evolution of the IKKγ protein in metazoans. Drosophila IKKγ/Kenny can be directly and selectively degraded by autophagy through its functional LIR motif. In contrast, the targeting of mammalian IKKγ for autophagic degradation seems to be mediated either by bacterial effectors (geldanamycin), viral proteins (M45) or through putative selective autophagy receptors (p47).Interestingly we found that the mammalian homolog of Kenny, IKKγ/NEMO, does not interact with any of the mammalian Atg8-family members, so cannot be directly targeted for autophagy by such interactions. There is evidence that IKK degradation may be able to be triggered by proteins expressed by mammalian pathogens. The bacterial effector geldanamycin, which is an inhibitor of Hsp90, causes degradation of the IKK complex by autophagyTo investigate what kinds of interactions between hosts, pathogens, and the microbiota could drive the loss of a LIR motif from IKKγ, we developed a mathematical model in which the mechanisms by which host IKKγ could be selectively targeted for autophagic destruction varied. Hosts of different types competed in the presence of pathogens and a proxy for the microbiota, and we investigated which selective processes caused one or another host type to dominate the population. Our model highlighted two potential mechanisms by which pathogen selection could drive the loss of a LIR motif from IKKγ. The first assumes that hosts which regulate IKKγ using ubiquitin tagging alone have the lowest infection mortality, because they are best able to regulate immunopathology during infection. The second mechanism involves a specific reduction in infection mortality where pathogens encode their own LIR motifs, that only applies in hosts which lack a LIR motif on IKKγ—in other words, pathogen manipulation of the host using LIR motifs, which happens to benefit both host and pathogen by reducing infection mortality.For simplicity, our model pitted the four different host types against one another, starting with each host type at an equal frequency. This showed us which host type was best suited to a particular set of selective pressures, but meant that each simulation started with an evolutionarily unrealistic situation in which equal numbers of hosts with or without the ability to regulate IKKγ using ubiquitin tagging were present. In reality, evolving the ability to regulate IKKγ using ubiquitin tagging must have required a series of steps, including gaining appropriate selective autophagy receptors, before this mechanism to regulate IKKγ could have emerged. It is entirely plausible that pathogen selection was one of the selective pressures which made the evolution of this machinery favorable—but our model does not explicitly capture how the machinery evolved. Considering the severely negative consequences of a complete inability to regulate IKKγ , we can speculate that some alternative means to regulate IKKγ is a prerequisite before the LIR motif can be lost from IKKγ, and thus that the ability to regulate IKKγ using ubiquitin tagging in mammals may have evolved before any loss of the LIR motif.This point is particularly pertinent when considering the second of the two evolutionary scenarios we found that could drive the loss of LIR from IKKγ. In that scenario there is a specific reduction in infection mortality where pathogens encoding their own LIR motifs infect hosts which lack a LIR motif on IKKγ. We considered this an important result to include, by way of showing that pathogens with very particular properties could drive the host to lose the LIR motif from IKKγ; however, reaching that equilibrium point requires that there already exist some hosts in the population for which there is not an excessive cost to losing the LIR motif . Only after such hosts lose their LIR motifs can there be a niche for the pathogen with the necessary properties to invade the population in the first place, and drive the evolutionary switch whereby the entire host population ends up losing the LIR motif from IKKγ.The first of the evolutionary scenarios we proposed has less restrictive conditions. If hosts which regulate IKKγ using ubiquitin tagging alone have the lowest infection mortality, because they are best able to regulate immunopathology during infection, then selection from pathogens with or without LIR motifs can drive a population to an equilibrium where the host loses the LIR motif from IKKγ.Drosophila by degrading Kenny and ird5. We cannot exclude that other components of the IMD pathway are also degraded by autophagy through interaction with Atg8a or other autophagy-related proteins. Further studies are awaited to clarify these questions. Our study highlights the physiological importance of selective autophagy in the innate immune response of metazoans, and demonstrates the plasticity of its participating regulators. We propose that a major switch in the regulation of IKKγ occurred during metazoan evolution, and have shown how this switch could have been driven by host-pathogen coevolution.In conclusion, we have shown that autophagy plays a critical role in the termination of innate immune signaling in response to commensal microbiota in Flies used in experiments were kept at 25 °C and 70% humidity raised on cornmeal based feed.Drosophila stock center: w1118 (#3605), UAS-Atg5-RNAi (#27551), and Cg-GAL4 (#7011). UAS-Atg8a-RNAi (#109654) and UAS-key RNAi (#7723) were obtained from the Vienna Drosophila Ressource Center. The following mutant lines have been used: yw hs-FLP Atg8aKG07569, atg7∆77, and atg7∆14/CyO-GFP46 (gift from G. Juhasz), key123 (gift from D. Ferrandon), yw hs-flp;FRT82 atg13∆8161 and yw hs-flp;FRT80 atg1∆3D62. For the mutant mosaic analysis (FLP/FTR and MARCM mitotic recombination), the following lines were used: yw hs-flp;FRT82 Ubi::GFP and yw hs-flp tub-Gal4 UAS-GFP; FRT80B tub-Gal80 hsc82 y+ (gift from Y. Fan). The clonal analysis using the FLPout system have been performed with the following lines: yw hs-flp Ac > CD2 > GAL4 UAS-GFP (gift from G. Juhasz) and yw hs-flp;UAS-mCherry-Atg8a;Ac > CD2 > GAL4. The transgenic lines UAS-GFP-KennyWT, UAS-GFP-KennyF7A/L10A and UAS-GFP-NEMO have been generated by cloning the cDNA of Kenny or NEMO respectively into the pPGW plasmid (DGRC). The transgenic line UAS-mCherry-ird5 has been generated by cloning the cDNA of ird5 into the pP(mCherry)W plasmid modified from the pPGW by replacing the sequence coding for the GFP by mCherry. The transgenic line UAS-mCherry-GFP-ird5 has been generated by cloning the cDNA of ird5 into the pP(mCherry)GW plasmid modified from the pPGW backbone by adding the sequence coding for the mCherry in 5′ of the GFP. Transgenic flies were generated by P-element-mediated transformation (BestGene Inc). The genotype of the flies used for the experiments displayed in Figs The following fly stocks were obtained from the Bloomington g5-RNAi #7551, and25. L3 larvae at 4 days after egg laying were dissected.For the generation of mutant clones in the larval fat body using the FLP/FRT (flippase mediated site-specific recombination) and MARCM  mitotic recombination techniques, embryos 0–10 h after egg laying were subjected to heat shock for 1 h at 37 °C. For the FLPout UAS/GAL4 method, the GAL4 expressing cells were generated spontaneously without heat shockEmbryos were collected on apple juice plates for 10 h and dechorionated in 2.7% bleach for 2 min, rinsed in 70% ethanol twice and then in distilled water twice. Embryos were then transferred onto food containing a cocktail of antibiotics. Larvae and adults were reared on autoclaved cornmeal food, enriched with either 0.25 mg/mL ampicillin, tetracycline, streptomycin, and 1 mg/mL kanamycin. Axenic status of the flies was confirmed by CFU on LB agar plates, with one full fly body smear per plate and 16 S PCR . At least 2 flies per stock tube were used to attest the axenic status of the flies prior to experiment.Drosophila Medium  prepared in water supplemented with 5–20 µM Bortezomib or DMSO . Flies were flipped into freshly made food every other day for 5−6 days.Adult flies were transferred within 24 h from hatching onto Nutri-Fly Instant 3VO4, 5 mM Na4P2O7, 50 mM NaF + EDTA-free proteases inhibitors cocktail) using a motorized mortar and pestle. Co-immunoprecipitations were performed on lysates from flies expressing GFP alone or GFP-Kenny along with mCherry-ird5. After a 30 min pre-clear of the lysates  with sepharose-coupled G beads (Sigma), the co-immunoprecipitation was performed for 2 h at 4 °C using an anti-GFP antibody  and fresh sepharose-coupled G-beads. Four consecutive washes with the lysis buffer were performed before suspension of the beads in 60 µL 2X Laemmli loading buffer. For ubiquitination assay, lysates were boiled for 5 min at 95 °C in TNT lysis buffer supplemented with 2% SDS final. Denaturated lysates were then diluted in lysis buffer without SDS to reach a final SDS concentration that does not exceed 0.2% prior to immunoprecipitation overnight at 4 °C. In addition to the usual proteases inhibitor cocktail, the lysis buffer was supplemented with the deubiquitinating enzymes specific inhibitor PR-619. All protein samples (whole fly lysates and co-immunoprecipitation eluates) were boiled for 5 min at 95 °C. Quantity of 10–40 μg of proteins (whole fly lysates) or 20 µL (co-immunoprecipitation eluates) were loaded on acrylamide gels and were transferred onto either nitrocellulose or PVDF membranes .Protein content was extracted from the full fly body in TNT lysis buffer  for 1 h. Primary antibodies diluted in TBST were incubated overnight at 4 °C or for 3 h at room temperature with gentle agitation. HRP-coupled secondary antibodies binding was done at room temperature (RT) for 45 min in 1% BSA or non-fat milk dissolved in TBST and ECL mix incubation for 2 min. All washes were performed for 10 min in TBST at RT.23 , anti-GFP , anti-mCherry , anti-alpha tubulin , anti-ubiquitinated proteins , anti-ubiquitin K48 specific . HRP-coupled secondary antibodies were from ThermoScientific . Following co-immunoprecipitation, Veriblot HRP-coupled IP secondary antibody was used .The following primary antibodies were used: anti-KennyThe uncropped scan of the original films used to assemble the main figures are available in Supplementary Fig. Atg8aKG and Atg7∆14/∆77 adult flies were prepared in modified TNT buffer . Dephosphorylation of 50 μg of total protein was performed in 50 μL reaction volume (top up with ddH2O supplemented with EDTA-free proteases inhibitors cocktail) in the presence of either 10 U/µg proteins of CIP  or 8 U/µg proteins of λPP  supplemented with their respective reaction buffers according to manufacturers’ protocol. As negative control, phosphatase inhibitor cocktail 2  at 1:50 was added to some reactions. All the samples were then incubated at 37 °C for 15 min before dilution in Laemmli buffer and heating for 10 min at 80 °C.Lysates from 23 , anti-Relish . Specificity of the anti-Relish antibody was tested using Relish E20 mutants63 . Primary and secondary antibodies were incubated overnight at 4 °C in PBT. The following primary antibodies were used: anti-Cathepsin-L , anti-GABARAP , anti-pH3 , anti-KennyAtg8a;key1, only heterozygote females for Atg8a were used.RNA extraction was performed with a Life Technologies Ambion PureLinkTM RNA Mini kit according to the manufacturer protocol. Ten full adult flies or 50 isolated guts were used per extract. Males were preferentially used, but due to the lack of male mutants for the double mutant flies Subsequent steps were performed using 1 μg of total RNA. ThermoScientific DNase I was used in order to digest genomic DNA. The ThermoScientific RevertAid Kit was subsequently used to synthesize cDNA. RT-qPCR was performed using the Promega GoTaq qPCR Master Mix ref. A6002). Primer sequences are available in Supplementary Table . Primer Drosophila food at 25 °C in a humidified incubator. Flies were transferred into new tubes every 2–3 days. Dead events were recorded daily. Axenic status of the flies was checked once a week. The statistical analysis has been done with Prism7 software (GraphPad), using a Gehan-Breslow-Wilcoxon test.In order to measure lifespan, males only were collected within 24 h from hatching and cohorts of 20–25 flies were maintained on standard or autoclaved/antibiotics-supplemented Plasmids used in this study are listed in Supplementary Table S2R+ cells  were cultured at 25 °C in Schneider’s Drosophila medium  supplemented with 10% heat-inactivated fetal bovine serum and 1% streptomycin-penicillin . Subconfluent cells were transfected with plasmids using TransIT-2020  following the supplier’s instructions and analyzed 48 or 72 h after transfection. For immunoprecipitation, S2R+ cells were washed 72 h after transfection with ice-cold PBS prior to lysis in lysis buffer , 0.25% Triton X-100(v/v)) supplemented with phosphatase inhibitor mixture set II  and Complete Mini, EDTA-free protease inhibitor mixture . Lysates were incubated with anti-FLAG M2-agarose beads  at 4 °C overnight. Beads were washed five times with 500 µl lysis buffer. Proteins were eluted using 3XFLAG fusion peptide , boiled in SDS-PAGE loading buffer, and subjected to western blotting. The following antibodies were used: mouse anti-FLAG antibody , rabbit polyclonal GFP antibody .Escherichia coli BL21(DE3) and/or SoluBL21 (Amsbio) and GST-fusion proteins were purified on glutathione-Sepharose 4 Fast Flow beads (Amersham Biosciences). GST pull-down assays were performed using in vitro translated 35S-methionine-labelled proteins. L-[35S]-methionine was obtained from PerkinElmer Life Sciences. A volume of 10 µL of the in vitro translation reaction products (0.5 µg of plasmid in a 25 µL reaction volume) were incubated with 1–10 µg of GST-recombinant protein in 200 µL of NETN buffer ) for 1 h at 4 °C, washed six times with 1 ml of NETN buffer, boiled with 2X SDS gel loading buffer, and subjected to SDS-PAGE. Gels were stained with Coomassie Blue and vacuum-dried. 35S-Labeled proteins were detected on a Fujifilm bio-imaging analyzer BAS-5000 (Fuji).GST-fusion proteins were expressed in 2 in a humidified atmosphere. HeLa cells were cultured in 8-well chambered coverslides (Nunc) and transiently transfected with 100 ng of pDest-mCherry-eYFP-Kenny WT or F7A/L10A constructs and 50 ng of pDestMyc-DmAtg8a using TransIT-LT1. Cells were fixed in 4% paraformaldehyde 24 h after transfection. Images were obtained using a Zeiss LSM780 microscope. Quantifications of red only punctae per cell were done using Volocity 6.3 software (PerkinElmer).HeLa (ATCC CCL2) cells were grown in Dulbecco’s modified Eagle’s medium (DMEM). Cultured HeLa cells were maintained at 37 °C with 95% air and 5% COt-test was used. To compare three or more groups, one-way ANOVA with Dunnett’s test correction was used. Statistical significance of fly survival was calculated using a log-rank Mantel-Cox test. The statistical description for the results shown in Figs Statistical analyses were done with Prism6/7 software (GraphPad). For the comparison of two groups, two-tailed 64. Our complete model is described by Eqs. M); a pathogen which does not express the LIR motif (P), and an otherwise identical pathogen variant which does express the LIR motif (Q). We assumed that coinfection between M and either P or Q was possible but that coinfection between P and Q was not possible. Upon recovery from infection with P or Q the host is once again susceptible to reinfection with P or Q. We allowed there to exist four different host genotypes (1–4). Host genotypes 1 and 2 are assumed to lack the ability to target IKKγ for autophagic degradation using a ubiquitin tag; host genotypes 3 and 4 are assumed to possess this ability. IKKγ carries a LIR motif in host genotypes 2 and 4 but not in host genotypes 1 and 3.We combined epidemiological processes and host population genetics in a single dynamical model to allow different host genotypes to compete in the presence of various infectious agents. Epidemiological models incorporating an evolving host have previously been used to address questions of host-pathogen coevolutionM is lifelong. Infection with P or Q has duration 1/σx,i, where σx,i represents the recovery rate from infection with pathogen x for host genotype i. All host genotypes experience a background death rate μ, making the average host lifetime in the absence of infection = 1/μ. The cost of maintaining the necessary molecular machinery to target IKKγ for autophagic degradation using ubiquitin tagging is represented by an extra mortality rate γi, which depends on host genotype i (where genotypes lacking this molecular machinery have γi = 0). Excess mortality due to stimulation of the host immune response by the microbiota is captured by the death rate θi, and excess mortality due to infection of host type i by pathogen x is captured by rate ψxi.Infection with i (Si) is given by Eq. The rate of change of numbers of susceptible hosts of genotype i infected with P only (Pi) is given by Eq. The rate of change of numbers of hosts of genotype i infected with Q only (Qi) is given by Eq. The rate of change of numbers of hosts of genotype i infected with M only (Mi) is given by Eq. The rate of change of numbers of hosts of genotype i infected with M and P (Ki) is given by Eq. The rate of change of numbers of hosts of genotype i infected with M and Q (Li) is given by Eq. The rate of change of numbers of hosts of genotype βx is the transmission parameter for microorganism x, and N is the total size of the host population:The force of infection with each of the three pathogens  was equal to 0.083 per month. The transmission parameter for the proxy for the microbiota (βM) was set at 0.5. The transmission parameter for pathogen variant P (βP) was set at 3.01 and the transmission parameter for pathogen variant Q (βQ) was set at 3, meaning that, all other things being equal, producing a LIR motif is costly to the pathogen and makes it slightly less likely to be transmitted. The cost of maintaining pathways so that IKKγ could be targeted for autophagic degradation by ubiquitin tagging was set at the arbitrary value of 0.0002 per months. This meant that In all simulations shown, the background death rate of the host , in the specific instance where the pathogen expresses a LIR motif and the host does not possess a LIR motif on IKK gamma. For the results shown here, this reduction in infection mortality rate is treated as independent to those of Effects I and II, and is added to them when the different effects are combined together.The authors declare that all data supporting the findings of this study are available within the article and its Supplementary Information files.Supplementary InformationPeer Review File


Strained Ge QW pMOSFETs achieve the significantly improved effective hole mobility μeff as compared with the relaxed Si and Ge control devices. At an inversion charge density of Qinv of 2 × 1012 cm−2, Ge QW pMOSFETs on SOI exhibit a 104% μeff enhancement over relaxed Ge control transistors. It is also demonstrated that μeff of Ge pMOSFETs on SOI can be further boosted by applying an external uniaxial compressive strain.Well-behaved Ge quantum well (QW) p-channel metal-oxide-semiconductor field-effect transistors (pMOSFETs) were fabricated on silicon-on-insulator (SOI) substrate. By optimizing the growth conditions, ultrathin fully strained Ge film was directly epitaxially grown on SOI at about 450 °C using ultra-high vacuum chemical vapor deposition. In situ Si Germanium (Ge) has been attracting tremendous research interests for future pMOSFET applications due to it possesses the higher hole mobility over Si. Theoretical and experimental results proved that in order for Ge channel transistors to have significantly improved mobility and driving current over their Si and SiGe channel competitors, compressive strain is essential –3. A greIn this paper, ultrathin strained Ge QW pMOSFETs on SOI are realized and characterized. Devices achieve the superior hole mobility to the relaxed Si and Ge control transistors. Electrical performance of Ge QW transistors is further improved by applying the external uniaxial compressive strain being parallel to channel direction.4 in a H2 carrier gas is 20 sccm, the pressure in growth chamber is about 10−4 Pa, and the growth duration is 5 min. Figure Ge channel was epitaxially grown on lightly doped p-type SOI wafer using ultra-high vacuum chemical vapor deposition tool. Before loading into the growth chamber, the top Si layer was thinned down to about 7 nm using dry oxidation followed by the dilute HF etching. It is well known that, as Ge is epitaxially grown on Si, Ge islands tend to form in Stranski-Krastanow mode due to the 4.2% lattice mismatch between Ge and Si. The formation of Ge islands could be prevented by decreasing the growth temperature; nevertheless, the misfit dislocations are preferred to appear in Ge layer to release the strain energy. To achieve the continuous and defect-free epitaxial Ge film, we carried out the growth of Ge (the growth of Ge) on ultrathin SOI with different substrate temperatures. During the growth, the flow rate of GeH2H6 passivation at 350 °C for 15 min. Gate stack comprising hafnium dioxide (HfO2) by atomic layer deposition and metal gate by physical vapor deposition was formed. After the gate patterning and etching, BF2+ was implanted into drain and source regions at an energy of 10 keV and a dose of 1 × 1015 cm−2. Self-aligned metallic source/drain (S/D) was formed by depositing 10 nm nickel followed by a thermal anneal (RTA) at ~450 °C for 30 s. During the Ni germanosilicide, the p-type dopants in S/D regions were partially activated by the dopant segregation. The residual Ni was removed by the concentrated H2SO4 cleaning. Using the similar process, Si and Ge control pMOSFETs on bulk substrates were also fabricated.The key process steps for fabricating ultrathin fully strained Ge QW pMOSFETs are shown in Fig. 2 dielectric layer are 3.7 and 4.0 nm, respectively. Excellent interface quality and a uniform SiO2/Si interfacial layer (IL) are observed.Figure σ along the channel direction is calculated by σ = E∙y∙t/[2a∙(L/2 − 2a/3)], where E is the Young’s Modulus, y is the sample vertical displacement, t is the total thickness of the sample, L is the distance between the two supports, and a is the distance between the support and load  direction is 138 GPa , where W is the channel width, Qinv is the inversion charge density in Ge QW channel, and ΔRtot/ΔLG is the slope of the Rtotal as a function of LG plots as shown in Fig. Qinv in the channel was calculated by integrating the measured Cinv versus VGS curves. Figure μeff as function of Qinv characteristics of the Ge QW pMOSFETs on SOI with and without a −250 MPa uniaxial stress. Comparison of μeff for the Ge QW pMOSFETs with Si and Ge control on bulk wafers is also presented. Ge QW pMOSFETs under uniaxial compressive strain achieve a peak μeff of 897 cm2V−1s−1. At the low Qinv, Ge QW pMOSFETs under uniaxial compressive strain demonstrate an 18 times higher μeff as compared with Si control devices. At the Qinv of 2 × 1012 cm−2, Ge QW pMOSFETs without external uniaxial strain demonstrate a 104% μeff enhancement of over the relaxed Ge control device. This is attributed to the fact that the biaxial compressive strain in ultrathin Ge channel leads to the splitting of valence bands, and top valence band populated by carrier has a reduced effective mass than relaxed Ge [μeff enhancement in comparison with the transistors without external strain. It is speculated that the external uniaxial compressive strain further decreases the effective hole mobility, leading to the additional mobility enhancement, along transport direction in strained Ge channel.laxed Ge , 15. It μeff over Si and Ge control pMOSFETs. Ge QW pMOSFETs on SOI obtain a 104% improvement in μeff in comparison with the relaxed Ge control transistors at a fixed Qinv of 4 × 1012 cm−2. Ge QW pMOSFETs on SOI under an external uniaxial compressive stain achieve a further μeff enhancement, contributing to the reduced Rch and the improved drive current over the transistors without external uniaxial compressive stain.High-mobility strained Ge QW pMOSFETs with high channel crystallinity on SOI platform are realized. Devices exhibit good transfer and output characteristics and a significantly improved


The European badger is recognised as a wildlife reservoir for bovine tuberculosis (bTB); the control of which is complex, costly and controversial. Despite the importance of badgers in bTB and the well-documented role for macrophages as anti-mycobacterial effector cells, badger macrophage (bdMφ) responses remain uncharacterised. Here, we demonstrate that bdMφ fail to produce nitric oxide (NO) or upregulate inducible nitric oxide synthase (iNOS) mRNA following Toll-like receptor (TLR) agonist treatment. BdMφ also failed to make NO after stimulation with recombinant badger interferon gamma (bdIFNγ) or a combination of bdIFNγ and lipopolysaccharide. Exposure of bdMφ to TLR agonists and/or bdIFNγ resulted in upregulated cytokine  mRNA levels indicating that these critical pathways were otherwise intact. Although stimulation with most TLR agonists resulted in strong cytokine mRNA responses, weaker responses were evident after exposure to TLR9 agonists, potentially due to very low expression of TLR9 in bdMφ. Both NO and TLR9 are important elements of innate immunity to mycobacteria, and these features of bdMφ biology would impair their capacity to resist bTB infection. These findings have significant implications for the development of bTB management strategies, and support the use of vaccination to reduce bTB infection in badgers. Meles meles) are implicated as a major wildlife reservoir for bovine tuberculosis (bTB), the control of which poses substantial practical and political challenges1279European badgers  responses and mice deficient in inducible nitric oxide synthase (iNOS) are highly susceptible to TB16Listeria monocytogenes (LM). To test whether this lack of NO phenotype was badger-specific or found in another Mustelid, we examined macrophages derived from ferret peripheral blood monocytes . A putative badger iNOS gene fragment was obtained by PCR, cloned and the sequence verified. This sequence was used to develop a QRTPCR, which revealed that the iNOS mRNA signal was very low in untreated and TLR agonist-treated Mφ or those treated with heat-killed bacterial preparations (<1000 copies/1.1 million copies of GAPDH) . In compIFNγ initiates a TLR-independent pathway of NO production, which enhances LPS-induced NO productionUsing a combination of RTPCR, 5′ RACE and genomic sequencing, we identified an in-frame coding sequence for an iNOS isoform with high homology to iNOS transcripts in other species . InteresM. bovis BCG induced upregulation of IL1β mRNA to levels comparable with those induced by exposure to heat-killed TB  which induced a dose dependent upregulation of IL1β  killing mechanism with bovine MφM. tuberculosis in Mφ from other mammals41Exposure of bdMφ to a wide range of TLR agonists led to upregulation of cytokine mRNA  in a similar manner to that seen with other mammals. However, TLR activation and/or exposure to IFNγ did not result in the induction of nitric oxide (NO) or an effective upregulation of iNOS mRNA. Production of NO is a feature of the immune response of many vertebrates following treatment with TLR agonists alone or in combination with cytokines, including IFNγ28293031323338M. tuberculosis and bTB respectively suggesting that variation in the NO response is field relevant434412Polymorphisms in the human and bovine iNOS genes have also been linked to increased rates of In addition to a lack of NO, we demonstrate that bdMφ express only low levels of TLR9 and exhibit low responses to TLR9 agonists. Both TLR2 and TLR9 knockout mice have enhanced susceptibility to mycobacterial infections4950It is important to reiterate that bdMφ upregulate a full range of cytokines via TLR-dependent pathways, including TNFα, IL12 and IL1β53549Our study makes two important contributions; firstly, it identifies molecular mechanisms that are likely to contribute to the susceptibility of badgers to infection with bTB: specifically, no NO and low TLR9-responses. Secondly, our study identifies TLR pathways that could be exploited to improve the design of adjuvants to enhance vaccine efficacy in badgers (e.g. supplementing with non-TLR9 agonists). In the absence of a suitable vaccine for cattle, vaccination of badgers represents a proven, sustainable and humane approach to the control of this disease in the field. Our study also highlights the importance and potential benefits of studying immune function in wildlife species threatened by zoonotic disease.Badgers were blood sampled under sedation with ketamine hydrochloride (Zoetis) as part of an on-going study of their socio-ecology at Wytham Woods, Oxford, United Kingdom5 cells/well on 96 well plates and incubated for 24 hours prior to removal of non-adherent cells by washing with medium. Following a further 24 hours incubation cells were either mock-treated or treated with defined TLR agonists, microbial lysates  and centrifuged  to isolate plasma. Peripheral blood mononuclear cells were isolated following established proceduresRNA was extracted from cells frozen in RLT buffer (Qiagen) using either single columns or 96 well format columns (both Qiagen) following the manufacturer’s spin protocol. For QRT-PCR, the input RNA amount was normalised for cDNA reactions across a sample set and cDNA was generated (Life Techologies).Primers were designed based upon regions of target genes conserved in multiple mammalian genomes. Appropriate primer sets were used to amplify fragments of badger cytokine or TLR genes from PBMC cDNA using Myfi PCR mix (Bioline), according to manufacturer’s instructions. Products were cloned into pGEM TEasy (Promega) and sequenced using plasmid targeting (M13) or gene specific primers with BigDye chemistry (Life Technologies), according to manufacturer’s instructions. Sequences were analysed using Bioedit10 of the plasmid dilution series present on each plate were calculated. The slopes of the respective gene of interest (GOI) and GAPDH dilution series were used to calculate GOI Ct values and adjusted for differences in input total RNA as follows: corrected Ct value = Ct + (Nt−Ct’) 3 S/S’, where Ct is the GOI Ct value, Nt is the median GAPDH Ct for all samples within an experiment, Ct’ is the GAPDH value of the individual sample, S is the GOI slope, and S’ is the GAPDH slope(ab), where “y” is the y intercept, “a” represents the slope of the dilution series and “b” is the corrected 40-Ct of the sample.Intron spanning SYBR green QRT-PCR assays were designed using badger gene sequences . Assays The published sequenceE. coli DNA .Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Understanding how the structure of gene dependency network changes between two patient-specific groups is an important task for genomic research. Although many computational approaches have been proposed to undertake this task, most of them estimate correlation networks from group-specific gene expression data independently without considering the common structure shared between different groups. In addition, with the development of high-throughput technologies, we can collect gene expression profiles of same patients from multiple platforms. Therefore, inferring differential networks by considering cross-platform gene expression profiles will improve the reliability of network inference. We introduce a two dimensional joint graphical lasso (TDJGL) model to simultaneously estimate group-specific gene dependency networks from gene expression profiles collected from different platforms and infer differential networks. TDJGL can borrow strength across different patient groups and data platforms to improve the accuracy of estimated networks. Simulation studies demonstrate that TDJGL provides more accurate estimates of gene networks and differential networks than previous competing approaches. We apply TDJGL to the PI3K/AKT/mTOR pathway in ovarian tumors to build differential networks associated with platinum resistance. The hub genes of our inferred differential networks are significantly enriched with known platinum resistance-related genes and include potential platinum resistance-related genes. Complex biological processes often require the precise regulation and interaction of thousands of genes and their products4578910111213141516The accumulation of gene expression profiles from microarrays paves the way for inferring gene networks using computational methods182122Dependencies within gene networks often undergo changes between two groups (e.g. of patients) that represent different stress conditions, tissues, and/or disease states1024252427283Advances in biotechnology allow biomedical researchers to collect a wide variety of gene expression measurements for the same patients from different platformsIn statistics, researchers have proposed several joint graphical lasso (JGL) models to simultaneously estimate multiple related networks using gene expression profiles with observations belongs to distinct groups25332122https://github.com/Zhangxf-ccnu/TDJGL.To address the above problems, we propose a two dimensional joint graphical lasso (TDJGL) model to simultaneously infer gene dependency networks corresponding to different patient groups based on gene expression data collected from different platforms . Our modn observations that are independently drawn from a multivariate normal distribution N, where Θ = Σ−1 denotes the precision matrix and Σ denotes the covariance matrix. According to the theory of Gaussian graphical models, conditional dependencies among the variables can be directly read from Θ = [θij]. In particular, the partial correlation between genes i and j can be computed as ith and jth genes are conditionally independent if and only if θij = 0.Graphical models can encode the conditional dependencies among a set of genes using a graph, where nodes represents genes and edges connect conditionally dependent pairs of genes. A pair of genes are conditionally independent given all the other genes if and only if there is no edge between themWe can estimate Θ via maximum likelihood. However, when the number of genes is larger than the number of observations, this approach fails since the sample covariance matrix is singular. To deal with this problem, graphical lasso, which maximize a penalized log-likelihood, has been proposed2122S is the sample covariance matrix, λ is a nonnegative tuning parameter, ||Θ||1 denotes the sum of the absolute values of the elements of Θ, det(⋅) is the determinant of a matrix and tr(⋅) is the trace of a matrix. The solution to problem (1) serves as a sparse estimate of precision matrix and can be directly used to infer conditional dependencies among genes.where In this study, we focus on exploring the changes of gene dependency networks between two different patient groups, based on data sets collected from different platforms. Suppose we have collected group-level sample information regarding whether a patient belongs  to group 1 or 2 and gene expression profiles of these samples from multiple microarray platforms . Our goaIn this section, we propose a two dimensional joint graphical lasso (TDJGL) model to infer gene networks, which jointly estimates multiple graphical models corresponding to distinct but related platform types and patient groups. We refer to our model as TDJGL since it characterizes the gene expression profiles from two aspects: platform types and patient groups .K data sets K platforms. Here Xkc is a nc × p matrix consisting of measurements for p genes, which are common to all 2K data sets, from the k-th platform on nc patients in the c-th group. Furthermore, we assume that the n1 + n2 observations are independent, and that the nc observations within each data set are from the same Gaussian distribution: kc is the precision matrix. We seek to estimate the 2K precision matrices K platforms and the 2 patient groups given the 2K gene expression data sets. We shall index elements of precision matrix by using i = 1, …, p and j = 1, …, p, index platform types by using k = 1, …, K and index patient groups by using c = 1, 2.We assume that there are 2Skc = (1/nc)(Xkc)TXkc be the sample covariance matrix for the k-th platform type and the c-th patient group. Without loss of generality, here we assume that the observations within each data set are centered. For the sake of convenience, we denotes Let Here we assume that the measurements of the same samples from different platforms are independent for simplicity.K data sets correspond to gene expression measurements collected from distinct but related platform types and patient groups, the 2K precision matrices may be similar with each other or share some common structures. Therefore, we can combine the 2K data sets to estimate the 2K precision matrices jointly, rather than estimate them separately.Minimizing Following the joint graphical lasso modelsp, and P({Θ}) is a penalty function.where K data sets. For each platform, the difference between patient group-specific precision matrices should be sparse. Based on this restriction, we can identify individual edges that are shared or differ across the two patient groups. To incorporate the similarity between different platforms, the sparse structure of differential networks should be preserved across all the K platforms. In particular, we develop the following penalty function:Motivated by the property that the number of links in a biological network is far less than that of a full connected network, we require the resulting precision matrices to be sparse. Since the gene expression profiles are collected using similar platforms from related patients, the sparse structure should be preserved across the 2λ1 and λ2 are non-negative tuning parameters. The first term applies a group bridge penaltyi, j) element across all 2K precision matrices where for each pair of genes , we treat the 2K parameters K data sets26i, j) element across all the K differential networks where for each pair of genes , the differences of precision matrices between patient groups across different platforms, K differential networks. Note that here we can also use the group lasso penaltyλ1 and λ2 controls the sparsity of resulting gene networks and differential networks, which require tuning. We present our parameter selection strategy at the end of this section.where K platforms.Unlike previously developed joint graphical lasso models252634t, the penalty function (4) can be approximated asWe use an iterative approach based on local linear approximation36K individual optimization problems:where ωij and ψij in (5) are applied to all the K platforms, therefore, our model can encourage a shared pattern of network structures across all platforms. Problem (5) can be solved efficiently by using an alternating direction method of multipliers (ADMM)k = 1, …, K and c = 1, 2.Initialize k = 1, …, K by solving problem (5).Update Repeat Step 2 until convergence is achieved.Problem (5) is similar to the fused graphical lasso problemnc ≥ p, we can use (Skc + δIp)−1 as an initial estimate, where Ip is the identity matrix and δ is chosen to be a small constant to guarantee Skc + δIp is positive definite. Here we set δ = 10−3. When nc < p, this method does not perform well. In this case, we can use the solution of (5) with ωij = 1/2 and ψij = 1/2, because in high dimensional case, a reasonable estimate can be obtained by using a fused graphical lasso model. Our algorithm requires specification of a convergence criterion. Here we declare convergence whenSince the penalty function (4) is nonconvex, our algorithm only guarantees to find a local solution. Therefore, the initial value is important to yield an appropriate estimatekc at the tth iteration.where K precision matrices. Conditional dependencies among genes can be directly inferred from the nonzero elements of the estimated precision matrices. That is, genes i and j are connected in the network for k-th platform type and c-th patient group if and only if K differential networks for different platforms by comparing partial correlations between the two patient groups. For the k-th platform type and c-th patient group, the partial correlation between genes i and j can be computed as k-th platform type, we construct differential score between genes i and j as k-th differential network is determined by i, j) is presented in the k-th differential network if and only if K differential networks can be considered as common structures shared by different platforms.Through the above algorithm, we obtain the estimates, λ1 controls the sparsity of the final gene networks. Larger values of λ1 tend to yield sparser networks and smaller values of λ1 yield dense networks. The tuning parameter λ2 controls the sparsity of the resulting differential networks. When λ2 is larger, more elements of λ1 and λ2 is critical. A number of approaches such as Akaike information criterion, Bayesian information criterion and cross-validation have been used in previous studies. Here we determine the regularization parameters in a data-driven way via stability selection40For TDJGL, the tuning parameter In this section, we present the results of simulation experiments that demonstrate the empirical performance of TDJGL.K = 3 platform types and 2 patient groups. We generate 6 gene networks  corresponding to the 3 platform types and the 2 patient groups, each of which contains a common set of p genes. For each platform type, we choose τ  of edges as differential edges between the two patient groups. A larger τ represents a larger difference between the two patient groups. The structures of gene networks and differential networks are preserved across the 3 platform types. We generate the Erdös-Rényi, scale-free, and community networks following the settings of Mohan et al.et al.In this simulation study, we consider p = 100, and n ∈ {50, 100, 200}:τ of edges as differential edges.We generate an Erdös-Rényi network for which each edge is presented with probability 0.02k = 1, …, K, we repeat Steps 3–5 to generate data sets for each platform type.For p × p symmetric matrix Ak1 with zeros on elements not corresponding to network edges, and values from Unif on elements corresponding to network edges. We duplicate Ak1 into Ak2. Then, we set the elements of Ak2 corresponding to differential edges to be zeros or change their signs (at random). This results in τ of edge values that are different between the two patient groups.We create a d = min(λmin(Ak1), λmin(Ak2)), where λmin(⋅) denotes the smallest eigenvalue of the matrix. To ensure positive definiteness, we set Θk1 = Ak1 + (0.1 + |d|)Ip and Θk2 = Ak2 + (0.1 + |d|)Ip.We let n independent observations each from a N−1) distribution and a N−1) distribution, and use them as gene expression data sets Xk1 and Xk2.We generate Data generated for Erdös-Rényi network: We generate the data as follows, for mlinks = 2 and seed = 1 to generate a scale-free network with p = 100 genesData generated for scale-free network: The data are generated as Erdös-Rényi network, expect that the network generation process in Step 1 is modified: Instead of generating an Erdös-Rényi network, we use the SFNG function in Matlab with parameters Ak1 and Ak2, the  and  submatrices of Ak1 and Ak2 are set equal to zero. That is, the non-zero elements of Ak1 and Ak2 are concentrated in the top and bottom 60 × 60 submatricesData generated for community network: We generate data as Erdös-Rényi network, expect for one modification in Step 3: After obtaining et al.We use several metrics to evaluate algorithm performance. We are interested in quantifying (1) recovery of edges, (2) detection of differential edges, and (3) error in estimation of precision matrices. Details are presented in τ = 10%) over 100 random generations of the data. Each row corresponds to a sample size and each column corresponds to a performance metric. Within each plot, each colored line corresponds to the results obtained using a fixed value of the tuning parameter λ2 (for TDJGL and FGL) or ω2 (for GGL), as the tuning parameter λ1 (for TDJGL and FGL) or ω1 (for GGL) is varied. Note that GL corresponds to FGL with λ2 = 0 or GGL with ω2 = 0. We observe that TDJGL outperforms the three compared methods for a suitable range of the parameters λ2 and ω2. For a fixed number of false positive edges, TDJGL identifies more true positive edges; for a fixed number of false positive differential edges, TDJGL identifies a greater number of true positive differential edges; and for a fixed number of edges estimated, TDJGL has a lower squared error. Unlike FGL which only exploits similarity between the two patient groups and GGL which only borrows strength across different platform types, TDJGL is capable of making full use of the characteristics shared by different platform types and different patient groups. FGL and GGL have similar performance when we focus on identifying edges and estimating precision matrices. However, FGL dominates GGL when it is used to identify differential edges, since it shrinks the difference between edge values corresponding to two different patient groups. GL perform worst among the four methods, since it estimates each network separately. The simulation results for the Erdos-Renyi and community networks (with τ = 10%) are displayed in τ = 20%, 50%) in In this section, we apply TDJGL to analyze gene expression data of ovarian cancer and present the corresponding results.Ovarian cancer is the most common cause of death from gynaecological cancers, and overall survival has not improved significantly for several decades42https://github.com/Zhangxf-ccnu/TDJGL.We use a criterion that is used in refs To make the computation less intensive, we take a pathway-based analysis. We present our analysis of genes that overlap with the PI3K/AKT/mTOR pathway. The PI3K/AKT/mTOR pathway is frequently mutated or altered in ovarian cancerλ1 and λ2 from a total of 20 possible values equally spaced in log scale between 0.25 and 0.025. According to the StARS model selection approach . The estimated group-specific networks and differential networks are provided in We apply TDJGL to gene expression data from the three platforms with respect to platinum-sensitive tumors and platinum-resistant tumors. To avoid disparate level of sparsity between the two patient groups, we weight each patient group equally instead of by sample size in Equation (3) approach , we set We observe that most of edges identified by TDJGL are common to both patient groups and there are only a few differential edges for all the three platforms . This miA hub gene within a network is important for the control of the underlying networket al.et al.Besides well-known genes  associated with platinum (or drug) resistance, the other hub genes  may be potential platinum resistance-related genes. FGFR1 is receptor tyrosine kinase which plays an essential role in the regulation of embryonic development, cell proliferation, differentiation and migration. Amplification of FGFR1 has been reported frequently in ovarian cancer, and is associated with poor survival4951We present the additional comparison of TDJGL with other graphical models GL with appWe have proposed TDJGL, a method for inferring patient group-specific gene networks and identifying differential networks between two patient-specific groups from gene expression data collected from different platforms. TDJGL jointly estimates multiple conditional dependence networks corresponding to different but related patient groups and platform types. It borrows strength across different data sets through a joint sparsity penalty function. TDJGL outperforms several competing algorithms over a range of simulated data sets. We apply TDJGL to TCGA ovarian cancer gene expression data from three platforms to identify differential networks associated with platinum resistance. In the PI3K/AKT/mTOR pathway, the set of hub genes in the estimated differential networks is significantly enriched with drug resistance-related genes and cancer-related genes. The hub genes  which have not been reported in previous literature might be potential platinum resistance-related genes in ovarian cancer.In previous studies, joint graphical lasso models have been proposed to estimate multiple gene networks from observations belonging to different patient-specific groups. However, these studies only focus on gene expression data from single platform. Advances in high-throughput technologies allow us to collect gene expression measurements on a common set of samples from multiple platforms. TDJGL infer gene networks for different patient groups by integrating gene expression profiles collected from different platforms. Unlike previous joint graphical lasso models which can only borrow strength from one aspect , TDJGL is a new extension to borrow information from two aspects .p × p matrix in the ADMM iteration, where p is the number of genes .


Immunogenicity can have devastating consequences on the safety and efficacy of therapeutic proteins. Therefore, evaluating and mitigating the risk of product immunogenicity is critical for the development these products. This study, showed that Betaseron and Extavia, which are reported to be more immunogenic among IFNβ products in clinical usage, contain residual innate immune response modulating impurities (IIRMIs) capable of activating NF-κB and induced expression of inflammatory mediators. These IIRMIs were undetectable in Rebif or Avonex. The stimulatory effect was attributed solely to IIRMIs because it was evident in murine cells lacking the interferon receptor (IFNAR). The IIRMIs in Betaseron and Extavia triggered NF-κB activation in HEK-293 cells bearing TLR2 and TLR4 in MyD88 dependent manner. Importantly, the IIRMIs in Betaseron induced up-regulation of IL-6, IL-1β, and ccl5 in the skin of IFNAR knock out mice following subcutaneous administration. This indicates that trace level IIRMIs in Betaseron could contribute to the higher immunogenicity rates seen in clinics. Together these data suggest that cell based assays can reveal subtle but clinically relevant differences in IIRMIs following manufacturing changes or between products with the same active ingredients but different manufacturing processes. Appreciating these differences may inform immunogenicity risk assessments. Many factors have been reported that contribute to immunogenicity risk of therapeutic proteins7. Although for most products the downstream purification steps are designed to minimize impurities, some residual impurities can be present in the final drug product in trace levels that can be hard to detect and quantify. In recent years our group and others have shown that trace levels of process, host cell derived impurities, and contaminants introduced unintentionally during the manufacturing process can activate the receptors of innate immune cells increasing the risk of developing anti-drug antibodies (ADA)11. Some of those impurities, particularly those derived from adventitious microbial agents or resulting from cellular stress can be recognized by germline-encoded pattern recognition receptors (PRRs) that are expressed on barrier endothelia, antigen presenting cells (dendritic cells and macrophages) and various immune cells12. A growing number of PRR have been identified including Toll-like receptors (TLRs), C-type lectin receptors (CLRs) expressed predominantly on the cell surface, as well as NOD-like receptors 13. Activation of PRRs by IIRMI can lead to transcriptional regulation of distinct genes and nuclear translocation of transcription factors depending on the type of receptors or cells14. This in turn, can lead to recruitment of immune cells to the site of the inoculation, activation and maturation of antigen presenting cells, inhibition of regulatory T cells, or direct activation of B cells, ultimately resulting in increased immunogenicity risk17.Over the past decades, development of protein-based therapeutics has transformed the treatment paradigm and outcome of many chronic and serious diseases. Despite significant advances in recombinant protein engineering, a high incidence of unwanted immunogenicity is still encountered in clinical settings, resulting in important challenges to safety and efficacy19. In vivo, studies in non-human primates showed that the subcutaneous inoculation of minute amounts of PRRAgs induced the local expression of immune and inflammation related genes as well as increased ADA when these PRRAgs were co-injected with a therapeutic protein22. This indicated that very low levels of PRRAgs in a therapeutic product can increase the risk of immunogenicity.While the adjuvant effect of PRR is well known, recent studies from our group showed that minute amounts of purified PRR agonists (PRRAg) can activate immune cells locally and systemically as evidenced by increased levels of immune related cytokines such as IFNγ, TNFα and IL-625. Despite the broad spectrum of IIRMIs that could be present in therapeutic proteins and peptides, current testing strategies are often limited to the use of the LAL test to measure endotoxin, a PCR test to detect host cell DNA, and ELISA based tests for host cell proteins. To broaden the spectrum of IIRMI tested in therapeutic proteins we recently established a test that uses human and mouse macrophage cell lines rich in PRRs and showed it can detect low levels of purified PRRAgs26. In the current study, we test the capacity of this platform to detect differences in IIRMIs among commercial therapeutic products produced on different manufacturing platforms using recombinant human interferon beta (IFNβ) as a model.Recent advances in product characterization have enabled sponsors and regulatory agencies to establish whether therapeutic proteins are highly similar, however there remain concerns that differences in the manufacturing processes can lead to subtle differences in impurities that could impact on the product’s immunogenicity risk27. RhIFNβ-1b products are produced in E. coli and are non-glycosylated , Avonex (IFNβ-1a), Betaseron (IFNβ-1b) and Extavia (IFNβ-1b) . The prefilled syringe Avonex® dosage form contain 30 μg IFNβ-1a. All IFNβ products were stored and handled according to label instructions and used before expiry. All calculations for test product concentration were based on international units (IU) as labeled. The maximum concentration used in our in vitro assays was equivalent to 1/8th, 1/12th or 1/6th of the clinical dose for Betaseron/Extavia, Rebif and Avonex respectively. LPS from Salmonella Minnesota Re 595 was purchased from Calbiochem . Pam3CSK4, FSL-1, were purchased from InvivoGen  and used at the concentration indicated in each individual figure.Lots of IFNβ products were obtained from Betaseron , Extavia , Avonex , and Rebif . The presentation of Betaseron and Extavia products was as lyophilized powder containing 0.3 mg IFNβ-1b (9.6 MIU). The products were reconstituted on the day of use per manufacturer’s instructions using 1.2 mL of the supplied diluent. The Rebif® dosage form is 0.5-mL aqueous-solution formulation in a prefilled syringe containing 44-μg IFN-β-1a (24 MIU). Each lyophilized vial of AvonexMurine RAW-Blue, HEK-BLUE-hTLR2 and HEK-BLUE-hTLR4 cells carrying a SEAP reporter construct were purchased from InvivoGen . Cells were grown in DMEM supplemented with 10% FCS, 2 mM L-glutamine, 100 µg/mL Normocin in the presence of selection antibiotic and passaged when 70% confluence was reached per manufacturer’s recommendation. Cells were scraped and resuspended in test media as suggested by manufacturer for testing .Cells were plated at the density indicated on each figure legend in flat bottom 96-well plates in a 100 µL final volume and a serial dilution of respective products or TLR ligands were prepared and added at 100 µL volume as indicated in figure. In some experiments, Polymyxin B (InvivoGen) was used at the final concentration of 25 µg/mL in order to block endotoxin activity. In some experiments, IFNβ products were treated with proteinase K for 1 h at 37 °C and inactivated at 65 °C for 10 minutes to degrade protein impurities. After 24 h of stimulation, supernatants were collected and NF-κB activation was determined using detection medium QUANTI-Blue prepared according to manufacturer recommendations .−/− (IFNAR KO) and C57BL/6-MyD88−/− (MyD88 KO) mice used in this study were bred as homozygous breeding pairs (>20 generations). Mice were housed in microisolator cages with a 12-hour day/night cycle and given food and water ad libitum in the specific pathogen-free, AAALAC accredited animal facility of the U.S. Food and Drug Administration’s Division of Veterinary Medicine . All animals and experimental protocols were reviewed and approved by the Food and Drug Administration Institutional Animal Care and Use Committee (FDA-IACUC) and performed accordingly.C57BL/6 (B6), C57BL/6-IFNAR6 cell/mL concentration. 500 µL of cell suspension were plated on 24 well plate and 500 µL of drug product or TLR ligand (positive control) was added at the concentration indicated on the figure legend.Mice were housed in microisolator cages with a 12-hour day/night cycle and given food and water ad libitum in the specific pathogen-free, AAALAC accredited animal facility of the U.S. Food and Drug Administration’s Division of Veterinary Medicine . All animals and experimental protocols were reviewed and approved by the Food and Drug Administration Institutional Animal Care and Use Committee (FDA-IACUC). All experimental animal protocols were performed according to the ethical principles of FDA-IACUC. 8–10 weeks old wild type C57B6, IFNARKO or MyD88 KO were sacrificed and spleens were removed. Spleens were mashed using the rubber end of plunger from 2 mL syringe. The single cell suspension was passed through 70-µm cell strainer. Splenocytes were suspended at 6 × 10−/− (IFNAR KO) mice were shaved one day prior to start of the experiment. Mice were injected with 1 × 106 IU of Betaseron or Rebif in 50 µL subcutaneously (SC) using a 30 gauge needle. Control mice (n = 4) received 0.9% normal saline solution . Skin biopsies were obtained from the site of injection under aseptic conditions 5 hours post-injection of IFNβ using disposable 4 mm biopsy punches . The tissues were immediately submerged in TRIzol reagent , and stored at −80 °C until processing. To homogenize the tissues, skin biopsies were thawed and mixed with one millimeter-diameter Zirconia Beads . The samples were shredded by glass-bead friction using the Precellys 24, Cryolys system  using the following settings: 6800 rpm; number of runs 2; run time 30 s; pause time 30 s. Homogenates were transferred into sterile 2 mL eppendorf tubes and total RNA was isolated as described below.C57BL/6-IFNAR−ΔΔCt.Cytokine mRNA measurement following stimulation of cells and skin was performed by qRT-PCR. Total RNA was prepared from cells lysate using TRIzol  as per manufacturer instructions. Contaminating genomic DNA was removed with TurboDNase . Subsequently RNA (1 μg/mL) was reverse transcribed into cDNA using high capacity cDNA Reverse Transcription Kit  as per manufacturer recommendation. The qRT-PCR reactions were conducted in 1X Universal master mix  with 1/20 volume cDNA/reaction for individual gene expression assays, in a Viia7 Real-time PCR system . Custom-made human specific TaqMan® Micro Fluidic Cards containing panels of 96 gene expression assays were loaded with a sample volume of cDNA solution equivalent to 250 ng of the original RNA, mixed with 2x Universal Master Mix  The Ct values obtained for each gene were directly normalized to housekeeping values (GAPDH or 18S) in order to obtain ΔCt. Change in the expression levels of mRNA in stimulated cultures were indicated as fold increase over media treated cells using 2The endotoxin content in IFNβ products were determined by using the endpoint or kinetic chromogenic LAL Kinetic-QCL assays according the manufacturers instruction .36.Protein aggregates were made by mechanical stress by stirring 1.2 mL for Betaseron or Rebif using a Teflon stirrer in a glass vial for 20 hours at Room temperature . The formation and size of protein aggregates was confirmed by visual observation, by measuring optical density at wavelength 200–500 nm with a spectrophotometer, and by micro-flow imaging (MFI) using a MFI500 from Proteinsimple . Oxidation of IFNβ-1a (Avonex without HSA) was performed by overnight treatment with 5 mM ascorbic acid, 5 mM H2O2 and 5 µM Cu (II) at 25 °C. Oxidants were removed by spin column filtration  applied twice. Aliquots of the original IFNβ-1a before oxidation and the oxidized IFNβ-1a were evaluated side-by-side using SDS-PAGE followed by silver staining . An increase in total carbonylation in the oxidized IFNβ-1a was confirmed by the carbonyl ELISAP value < 0.05 was considered significant. For multiple comparisons the stars in the graphs correspond to the post-test comparison with *p < 0.05, **p < 0.01, and ***p < 0.001.Statistical significance was determined using “t” test or ANOVA with post-test multiple comparisons using GraphPad Prism 7.0. In all cases, a E. coli than the IFNβ-1a produced in CHO cells and this has been generally ascribed to minor differences in sequence and presence of aggregates  in products is focused on endotoxin and nucleic acids, however, depending on the cell substrate, adventitious agents and manufacturing process employed, other IIRMIs could be present. Most human cell lines bearing PRRs also have receptors for interferons and thus could be activated by the IFNβ products masking the presence of impurities. To avoid this we selected RAW-Blue cells, a murine cell line that does not express receptors for human IFN. We recently showed that RAW-Blue cells expressing a secreted embryonic alkaline phosphatase (SEAP) reporter construct inducible by NF-κB could be used to detect trace levels of different IIRMI in therapeutic productsE. coli-derived products have IIRMIs that induce an inflammatory response independent of the IFN- β activity.IFNs are known to modulate the expression of many interferon inducible genes. Despite the low homology between human and murine IFNs, all four IFNβ products showed similar modest increases in the activation of interferon-β inducible genes such as Ccl2, Csf-1, Cxcl10, Cxcl11, Socs1, Socs2 and Stat1 in the murine RAW-Blue cells suggesting a low level of activation of the murine IFN receptor. To establish that the signal detected using the RAW-Blue cells was due to IIRMIs and not derived from differences in the active product ingredient (API), splenocytes from IFNAR knockout mice were stimulated with the four different IFNβ products for 24 h. As shown in Fig. Toll like receptors are thought to be the main family of PRR that sense the presence of microbial and stress related signals. All of the TLR, except for TLR3, signal their activation using adaptor protein MyD88. To determine whether the potential IIRMIs in Betaseron and Extavia were signaling through TLRs we tested the gene expression induced by the four IFNβ products using splenocytes from MyD88 KO mice. Splenocytes from C57BL/6 wild type mice showed increased expression of nos2 when incubated with Extavia and Betaseron but not Rebif or Avonex. On the other hand, Extavia and Betaseron failed to induce nos2 Fig.  in splenE. coli, Intron A  did not induce NF-κB activation . As shown in Fig. ion Fig. . This sulls Fig. . Togethe38. The presence of endotoxin in therapeutic products is often tested using the LAL test. Betaseron and Extavia activated TLR4 bearing cells suggesting they contained trace levels of endotoxin, however none the products showed evidence of endotoxin when tested using the LAL test , a cyclic, cationic peptide antibiotic that neutralizes endotoxin. Addition of polymyxin B resulted in partial reduction of NF-κB activation by Betaseron and Extavia compared to untreated controls on RAW-Blue cells . To further explore the discordance between the data obtained with the RAW-Blue cells and the LAL assay we spiked the products with known quantities of endotoxin and tested the samples using the LAL test. As shown in Supplementary Figure TLR4 is known to be the receptor for endotoxin as well as for mannan, heat shock proteins, glycoinositolphospholipids and envelope proteins , while TLR2 is known to respond to peptidoglycans from gram positive bacteria, as well as other microbial antigens such as porin, MALP-2, lipophosphoglycan, and zymosanig. Fig. . To fur39 had shown that contamination with endotoxin could result in the activation of TLR2 we next determined whether treatment with Polymixin B reduced the activation of HEK-BLUE TLR2 transfected cells. As shown in Fig. The studies above had shown that the addition of polymyxin B resulted in only a partial reduction in NF-κB activation in the RAW-Blue cells. Since previous studies40. In order to explore whether differences in the levels of aggregates between products was responsible for the above observations Betaseron and Rebif were aggregated by stirring . The expression of nos2, il-1β, ccl5, and ptpgs2, was used as markers of inflammation. As above, Rebif induced very low levels of mRNA for pro-inflammatory genes as compared to Betaseron in mouse derived RAW-Blue cells (p < 0.05). Spiking Rebif with endotoxin or Pam3CSK4 Fig. . Similarin vitro induction of innate immune related genes suggests that there are impurities in the product that could activate immune cells but does not necessarily imply that they would be sufficient to induce a response in vivo that could foster a change in the local milieu where the product is administered. To explore whether the impurities could lead to local immune activation we administered 1 × 106 IU of Betaseron and Rebif subcutaneously to IFNAR knock mice and collected the local skin after 6 hours. Analysis of local gene expression showed an increase in local expression of IL-6, IL-1β and ccl5 mRNA in the skin of IFNAR knock out mice that received Betaseron, but not in the ones that received Rebif as compared with mice that received saline. This suggests that the levels of impurities in the product, while low, could foster a local inflammatory response that could facilitate antigen uptake by antigen presenting cells and product immunogenicity  such as residual host DNA, host cell proteins, endotoxins and other microbial fragments, which may activate the local immune system where the product is administered, acting as inadvertent adjuvants. The range of impurities that could be present in the final drug product are likely to be largely dependent on the manufacturing, purification and fill-finish processes including the cell substrate platform and thus, changes in the manufacturing process can impact on the range and levels of IIRMI. Detecting the presence of such IIRMI is necessary for controlling the immunogenicity risk of therapeutic proteins and peptides. In these studies we sought to investigate whether a recently developed cell based assay could be used to detect impurities in commercial therapeutic proteins. Our studies showed that the method exclusively detected low levels of IIRMIs in Betaseron and Extavia that were not evident using the LAL test. IIRMIs in Betaseron and Extavia included low levels of endotoxin as well as at least one other protein IIRMI that activated TLR2 and they induced the expression of pro-inflammatory genes in a MyD88 dependent manner. Lastly, we showed that the levels of impurities that were present in the product were sufficient to induce local expression of cytokines in the skin of mice when injected subcutaneously suggesting that these IIRMI could contribute to an inflammatory milieu and activation of innate immune response in the space where the product is inoculated. Together these finding raise the possibility that the difference in IIRMI could be associated with the increased immunogenicity of the IFNβ-1b products and support the use of IIRMI characterization in the assessment of immunogenicity risk.Immunogenicity risk cannot be predicted from protein structure alone as it is influenced by a myriad of often interacting parameters such as preexisting tolerance, immunological status and HLA type, underlying disease and presence of product aggregates26 and this was particularly important since IIRMI can be complex and trigger multiple innate immune receptors. In this study the differences in IIRMI were evident when using the RAW-Blue cells but not when using the human MM6 cell line , the response of the murine RAW-Blue cell line to the IFNβ was very modest, allowing the differences in IIRMI to surface. Similarly, in primary cells derived from IFNAR KO mice, which lack the capacity to respond to IFN, the effect of the IIRMI was clearly evident. This suggests that, the selection of cell lines to assess IIRMI should take in consideration the mechanism of action of the product, particularly when the API is immunomodulatory.The assay system used to detect IIRMIs is based on the premise that macrophage cell lines bear numerous PRR and are able to detect a variety of impurities that could modify the milieu where the product is deposited. In a previous study we suggested that the combined use of multiple cell lines of mouse and human origin provided a broader set of highly sensitive PRR43, however several conditions are known to reduce endotoxin recovery including the presence of surfactants, deoxycholate, or other formulation components that may alter endotoxin structure45. As shown in Supplementary Figure 48.Our studies showing the activation of HEK293 bearing TLR4 and the corresponding partial reduction when the product was treated with Polymixin B strongly suggests that the product contains trace levels of endotoxin despite the negative finding using the LAL test. The LAL test is broadly used for testing endotoxin in therapeutic productsE. coli induced the transcription of inflammatory genes as observed when Intron A was used.Further investigation will be needed to assess whether the albumin in IFNβs can mask the presence of endotoxin in therapeutic proteins. Importantly, and not all products produced in Inoculation of IFNAR KO mice allowed us to establish that the IIRMI in a single dose of Betaseron but not Rebif resulted in induction of inflammatory genes IL-6, IL-1β and ccl5 in the skin. This is line with data from primate studies showing that trace levels of IIRMI activated the innate immune system in the skin milieu. However, our data didn’t show whether the expression of inflammatory genes at the injection site will translate into a clinically significant increase in immunogenicity and cannot be used to make quantitative predictions in humans.49. Nabs appear usually within two years of treatment and can cross react with other IFNβ products. Indeed subjects are often asked to wait 2–3 months when switching between products to allow for the immunoglobulin response to subside51. The immunogenicity of rhuIFNβ-1b products has been ascribed to multiple factors including the absence of glycosylation, the N terminal methionine, a substitution of a cystein for a serine in position 1752 and aggregates. Of note, the rhuIFNβ-1a products, which are more homologous to the endogenous IFN, are also immunogenic albeit to a lesser degree32. Several studies suggested that the difference was possibly due to the difference in aggregate and particle content between the products40. Betaseron-rhIFNβ-1b has been shown to contain a higher percentage of large, soluble protein aggregates whereas size-exclusion chromatography indicates less than 2% aggregates in Avonex-rhIFNβ-1a54. Further, dissociation of rhIFNβ-1b aggregates using high hydrostatic pressure considerably reduced immunogenicity in transgenic, immune tolerant mice supporting a role for product aggregates55. Recently, it was suggested that aggregates could activate innate immune cells partly by triggering TLR2 and TLR4 receptors56. Interestingly, in our model, submitting Betaseron and Rebif to physical or chemical destabilizing and aggregating conditions did not modify the readout of innate immune activation induced in the reporter cell line. This, together with the reduction in NF-κB activation in Polymyxin B treated product further suggests that the immune activation detected by the RAW-Blue as well as TLR2 and TLR4 transfected cell lines cells is due to IRMIs and not secondary to the presence of aggregates.Recombinant huIFNβ has been indicated for the treatment of relapsing and remitting multiple sclerosis (MS) since 1993, and reports regarding its immunogenicity and the reduction in efficacy that follows have been published since it was licensedOver the last 15 years the recognition of the risk posed by product immunogenicity to safety and efficacy of therapeutic proteins and the inability to predict what products will induce clinically relevant immune responses has resulted in the requirement of extensive clinical trials during clinical development or as a post approval commitment. Recent improvements in analytical chemistry and product characterization are enabling new regulatory paths for peptides and proteins that could require fewer (351(k), 505(b)(2)) or no clinical studies (505(j); however concerns over potential immunogenicity remain. This is partly due to uncertainties regarding possible differences in product and process related impurities between products. At this time differences in techniques and lack of common controls hinder the determination of absolute levels of IIRMI that could be considered as increasing the risk of product immunogenicity. However the expectation is that technical progress, improved characterization and the correlation of the product attributes with clinical outcome will allow us eventually to make better predictions regarding the immunogenicity risk of product impurities. These studies demonstrate a possible approach to test protein products for the presence of IIRMI that may be used to inform an assessment of immunogenicity risk. The consequent reduction in residual uncertainty may facilitate review of these products and assist in the determination of the extent of clinical trials needed.Supplementary information