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Therapeutic siRNAs Targeting the JAK/STAT Signalling Pathway in Inflammatory Bowel Diseases

Abstract Background and Aims Inflammatory bowel diseases are highly debilitating conditions that require constant monitoring and life-long medication. Current treatments are focused on systemic administration of immunomodulatory drugs, but they have a broad range of undesirable side-effects. RNA interference is a highly specific endogenous mechanism that regulates the expression of the gene at the transcript level, which can be repurposed using exogenous short interfering RNA [siRNA] to repress expression of the target gene. While siRNA therapeutics can offer an alternative to existing therapies, with a high specificity critical for chronically administrated drugs, evidence of their potency compared to chemical kinase inhibitors used in clinics is still lacking in alleviating an adverse inflammatory response. Methods We provide a framework to select highly specific siRNA, with a focus on two kinases strongly involved in pro-inflammatory diseases, namely JAK1 and JAK3. Using western-blot, real-time quantitative PCR and large-scale analysis, we assessed the specificity profile of these siRNA drugs and compared their efficacy to the most recent and promising kinase inhibitors for Janus kinases [Jakinibs], tofacitinib and filgotinib. Results siRNA drugs can reach higher efficiency and selectivity at lower doses [5 pM vs 1 µM] than Jakinibs. Moreover, JAK silencing lasted up to 11 days, even with 6 h pulse transfection. Conclusions The siRNA-based drugs developed hold the potential to develop more potent therapeutics for chronic inflammatory diseases.


Introduction
The Janus kinase/signal transducers and activators of transcription [JAK/STAT] proteins are key effectors of inflammation that recently have been gaining increasing attention in clinics. This pathway plays a central role in integrating cell signalling cues and coordinating the inflammatory response within immune cells, and it is involved in numerous inflammatory disorders, ranging from chronic inflammatory disorders such as Crohn's disease, ulcerative colitis, polyarthritis rheumatoid or dermatological diseases to the dramatic cytokine burst recently highlighted in COVID-19 patients. [1][2][3] The JAK family is composed of four different protein kinase members, namely JAK1, JAK2, JAK3 and TYK2. They play a major role in the signal transduction pathways triggered by cytokines such as interleukins and interferons. Indeed, receptor activation leads to JAK recruitment and phosphorylation. JAKs can form homo-or heterodimers, which, in turn, activate and phosphorylate STATs, allowing their translocation to the nucleus where they bind to DNA-specific response elements and act as transcription factors. Depending on the ligand, mainly cytokines such as interleukin [IL] and interferon [IFN] but also growth factors such as insulin-like growth factor-1, the composition of JAK dimers leads to several distinct cell responses through various STAT activation cascades. 4,5 Since JAKs are key effectors in inflammation, targeting Janus kinases remains a critical issue in these pathologies. Immense efforts have been dedicated to the development of specific JAK inhibitors [Jakinibs] that target its kinase activity. 6 Among the Jakinibs, two have been particularly studied and evaluated in clinical trials for inflammatory bowel disease: tofacitinib and filgotinib. For instance, tofacitinib has already been approved for the treatment of rheumatoid arthritis, psoriatic arthritis, juvenile idiopathic arthritis and ulcerative colitis, 7,8 while filgotinib, recently approved for rheumatoid arthritis, is under evaluation for both ulcerative colitis and Crohn's disease in the FITZROY cohort. 7,9,10 However, the newly designed drugs are not yet fully satisfactory in terms of specificity, adverse events and side effects such as risks of infection including herpes zoster. [11][12][13] To avoid systemic delivery and enhance selectivity, we designed highly specific JAK1 or JAK3 inhibitors based on short interfering RNA [siRNA], leading to the knockdown of JAK1 and JAK3 expression by targeting their mRNA. RNA interference [RNAi] was highlighted by Craig Mello and Andrew Fire in 1998, and they were awarded the Nobel Prize in Physiology and Medicine in 2006 for their research work. RNAi involves short double-stranded ribonucleic acids that interfere through sequence-specific recognition and pairing with a specific mRNA and leads to its degradation or prevents its translation, inducing the knockdown of the corresponding protein. 14 This mechanism is naturally present in cells and allows the micro-RNA (miRNA) to regulate mRNA and protein expression. It can also be induced by synthetic short hairpin RNA [shRNA] and siRNA. Unlike gene therapy, which is based on modification of the genome within the nucleus, siRNAs act directly in the cell cytoplasm through the RISC multiprotein complex and require neither nuclear import nor integration into the genome. 15 Therefore, siRNA therapies avoid the threat of introducing cancer-leading mutations into the genome of patients. Indeed, due to the benefits of siRNA drugs, patisiran and givlaari were recently approved for the treatment of hereditary transthyretin amyloidosis and acute hepatic porphyria, respectively. 16,17 These studies highlight the therapeutic potential of siRNAs in treating liver diseases. We now wish to extend these innovative solutions to other tissues such as the intestinal tract to cure inflammatory bowel diseases. While RNAi gave some concerns relative to potential off-target effects, 18 we sought to demonstrate that siRNAs possess a better efficiency at a lower dose and specificity profile by providing a side-by-side comparison with the chemical Jakinibs, tofacitinib and filgotinib. In this study, we identified two highly selective JAK1 and JAK3 siRNA sequences. We analysed their effects on human cells [both primary immune cells and cell lines including colonic epithelial cells], providing an exhaustive study of both the efficacy and specificity profile of the sequences relative to the currently available chemical kinase inhibitors.  20 The DSIR includes specific tools and algorithms that help prevent off-target effects such as the following: [i] the guide strand of our siRNAs possesses a perfect match for the mRNA target only; [ii] known proinflammatory-stimulating motif such as toll-like receptor [TLR] motifs are automatically removed during the design; [iii] specific 3′ and 5′ GC and AU content have been applied to favour the guide strand in place of passenger strand processing by the RISC, all criteria being critical for avoiding siRNA off-targeting. 21,22 The DSIR also gives information on putative sequence homology between the seed region of the guide strand and the 3′-UTR mRNA of the whole genome. Indeed, siRNAs have been described for their potential off-target gene regulation through an miRNA-like mechanism. [23][24][25] The greater number of seed sequence homology on the same 3′-UTR, the higher the risk of having miRNA-like effects. During siRNA synthesis, the guide strand has been chemically modified with the addition of a 5′-P to favour guide strand processing by RISC. The siRNA sequences were designed with 21 nucleotides. The seed complement frequencies using seed length were set at 7 nucleotides with a mismatch tolerance of 1. Based on the DSIR algorithm, 20 sequences were selected for targeting human JAK1 [siHJ1D#], while 16 were selected for human JAK3 [siHJ3D#].

siRNA transfection
For siRNA transfection, lipofectamine RNAiMAX [Invitrogen cat. #13778-150] and Opti-MEM medium [Gibco cat. #11058-021] were used. CACO-2 cells were reverse transfected using 10 5 cells per well seeded on a 12-well plate, except for transcriptomic analysis, where 10 6 CACO-2 cells were seeded in 10-cm-diameter dishes. For T47D and PC3 cells, 7 × 10 4 to 8 × 10 4 cells were seeded per well on a 12-well plate 24 h before the transfection. The transfection was performed using 0.8 µL [CACO-2] or 1 µL [PC3 and T47D] of lipofectamine RNAiMAX per well and mixed with the siRNA at different concentrations, according to the manufacturer's instructions. For primary immune cells, the transfection was performed using lipofectamine RNAiMAX by means of a 6-h pulse in Opti-MEM media, and the media were then changed into complete media. Cells were harvested 48 h after the transfection, unless otherwise specified, for downstream experiments.

Apoptosis analysis
Cell apoptosis was monitored by a Becton Dickinson LSR2 flow cytometer and Diva 6.0 software using the CellEvent Caspase-3/7 green detection reagent [Thermo Fischer Scientific, cat. #C10423], according to the manufacturer's instructions. Briefly, CellEvent reagent was added at 2 µM 72 h after cell transfection. Cells were then harvested and fixed in 4% paraformaldehyde (PFA) in PBS before fluorescence-activated cell sorting (FACS) analysis. The siRNA inducing cell apoptosis [Qiagen; Hs Cell-Death control siRNA] was used as a positive control.

Cell proliferation analysis
Cell proliferation was analysed using flow cytometry following EdU incorporation during the S phase with the Click-It EdU Alexa Fluor 647 Flow cytometry assay [Thermo Fischer Scientific, cat. #C10419], as per the manufacturer's instructions. Briefly, a total of 5 µM EdU was added to the culture medium on the 3rd day of transfection, for 5 h, before harvesting the cells and proceeding to cell fixation in 4% PFA and FACS analysis. An siRNA targeting the kinesin-related molecular motor EG5 [Qiagen Hs siKIFF11_6] was used as a positive control, with its transfection inducing mitotic arrest and then cell apoptosis.

Metabolism analysis
ATP levels reflecting cell metabolism correlated with cell proliferation and viability were analysed using the ViaLight Plus Cell Proliferation and Cytotoxicity Bioassay kit [Lonza, #LT07-121], according to the manufacturer's instructions. Briefly, 3000 CACO-2 cells were seeded into 96-well plates. Three days after the transfection, 25 µL per well of Cell Lysis Reagent was added to the cells, then the ATP level was quantified using 50 µL AMR PLUS reagent with a GloMax luminometer. The Hs Cell-Death control siRNA was used as a positive control of cell transfection.

High content screening
PC3 clones overexpressing JAK3 fused to GFP were seeded into 96 black µClear cell culture well plates in antibiotic-free RPMI medium containing 10% fetal bovine serum. After 24 h of culture, cells were transfected with different siRNA sequences targeting JAK3 using the lipofectamine RNAiMAX transfection agent, according to the manufacturer's instructions. The non-targeting siRNA siAllStars [AllStars negative control siRNA, referred to as siMock] and an siRNA targeting the GFP [siGFP-22 from Qiagen] gene were used as negative and positive controls, respectively. After 48 h of transfection, cells were fixed with 2% PFA for 15 min, and cell nuclei were then stained using 5 µM Hoechst 33342 for 10 min. After two washes with PBS, the wells were filled with 100 µL PBS containing 50% glycerol. Plates were stored at 4°C in the dark before analysis. GFP quantification was performed at a single-cell level using a CellInsight NXT automated microscope using 10× objectives and HCS Studio. The siRNA sequences were tested in triplicate at concentrations of 10 and 1 nM. Cell fluorescence levels were extracted after cell segmentations on ten different fields per well, and data were analysed at the cellular level using the R software.

Toll-like receptor [TLR] activation monitoring
The stimulation of TLR receptors [TLR1/2/3/4/5/9] was monitored using THP1-XBlue-MD2-CD14 cells [Invivogen cat. #thpxmdcdsp], a human monocytic cell line engineered to stably express NF-κB and the AP-1-inducible secreted embryonic alkaline phosphatase [SEAP] reporter gene. This cell line stably expresses all TLRs and a reporter gene allowing TLR activation through absorbance of the culture medium. As controls, 10 7 cells were incubated with 1 ng/ mL Pam3CSK4, 10 7 cells/mL HKLM, 10 µg/mL poly[I:C], 10 ng/ mL LPS-EK, 10  . GAPDH was used as the reference gene. To conduct RNASeq analysis, RNA contents of CACO-2 cells transfected for 2 days with 100 pM of siRNA using lipofectamine RNAiMAX or incubated for 2 days with 1 µM tofacitinib or 5 µM filgotinib were analysed by the IPMC platform. Negative controls included cells cultivated in a cell medium, in a cell medium with DMSO, exposed to lipofectamine RNAiMAX, or transfected with a mock siRNA. RNASeq data sets were deposited in the ArrayExpress database [fgsubs #491636].

Proteomic analysis-SOMAmer
Protein lysates of CACO-2 cells transfected for 3 days with 10 nM siRNA using lipofectamine RNAiMAX were analysed by the SomaLogic company. The negative control was lipofectamine RNAiMAX-treated cells. Briefly, using their SomaScan Proteomics platform based on aptamers, 1305 different proteins were quantified. Statistical analysis of statistically and differentially expressed proteins [DEPs] was performed by the AltraBio Company. The first filtering was performed as per the following parameters: adjusted < 0.05, fold-change > ×1.23, or < /1.23. This allowed the comparison of 484 remaining proteins that are considered as differentially expressed between siRNA and lipofectamine RNAiMAX conditions.

Statistical analyses
Statistical significance was assessed using the paired Student's t-test. The exact p-value are indicated in figure legends.

Ethical statement
Human blood samples from healthy de-identified donors were obtained from EFS [French national blood service] as part of an authorized protocol [CODECOH DC-2015-2502]. Donors gave signed consent for their blood to be used in this exploratory study.

Design and screening of siRNAs targeting JAK1 and JAK3
First, we designed siRNA sequences with a high specificity to target human JAK1 and JAK3 genes. To that end, we started from the mRNA sequence of both genes and used the siRNA designer software [DSIR] to identify putative siRNA sequences with strong efficacy and absent immunostimulatory motifs. 20 To select the most efficient siRNA sequences among those identified, we performed cell transfection experiments to analyse knockdown efficiencies at both mRNA and protein levels, using RT-qPCR, western blotting, flow cytometry and high-content fluorescence microscopy quantification. We used three cell lines expressing detectable levels of endogenous JAK1 protein: the colon cell line CACO-2, the prostate cell line PC3 and the breast cell line T47D [Supplementary Figure 3A]. However, as these cell lines did not express endogenous JAK3 to a sufficient level to enable knockdown analysis, we engineered PC3 cells to express a JAK3-GFP fusion protein with a high stability level [Supplementary Figure 3B]. The same approaches were used for JAK1 to quantify knockdown efficiency. To filter the top sequences targeting JAK1 that trigger the highest knockdown efficiency with the lowest off-target effects, we first classified the sequences using in silico analysis and information provided by the DSIR, which allowed us to filter the top ten sequences. For instance, siHJ1D4 was selected as it corresponded to the highest corrected score [98.7] despite a putative high 3′ untranslated region [UTR] recognition score [putative miR-like effects] [≥3 hits score], while siHJ1D5/6/9/19/20/33/37/81 were selected on the high ≥3 hits score, and siHJ1D24 was selected on a low corrected score [87.5] [Supplementary Table 1]. The best ten sequences for JAK1 were filtered based on their gene-silencing activity with the help of westernblotting and RT-qPCR after siRNA transfection [Supplementary Table 2]. To obtained the top two siRNAs targeting JAK1, we combined the data pertaining to gene-silencing efficiency, the putative 3′UTR hits, and the data obtained from the in silico analysis of potential targets of both sense and antisense siRNA sequences using the NCBI blast online platform, a strategy published recently by Mroweh and colleagues 27 [Supplementary Table 3]. Based on this strategy, we further focused on siHJ1D8 and siHJ1D2 sequences in order to target JAK1 and siHJ3D41 and siHMJ3D1 to then target JAK3 [Supplementary Tables 4-6].

Efficiency of siRNAs targeting JAK1 and JAK3
We then assessed the specificity of the siRNAs selected against other JAK family members. We first transfected mock or JAK1 and JAK3 targeting siRNA at 10 nM final concentration in PC3-JAK3-GFP engineered cells before analysing for the protein expression levels of JAK1/2/3 or TYK2 using western blotting to ensure the specificity of our sequences against the JAK protein family. We showed that siHJ1D8 and siHJ1D2 decreased JAK1 protein expression by 96% [ Figure 1A], while siHJ3D41 and siHMJ3D1 were able to decrease JAK3 protein expression with a knockdown efficiency of 95% [ Figure 1B]. None of these four siRNAs decreased the expression level of JAK2 protein [ Figure 1C], whereas a very slight decrease of TYK2 protein expression was observed with siHJ1D8 and siHJ1D2, with knockdown efficiencies of  Figure 1D]. These results were confirmed in PC3 wild-type cells [Supplementary Figure 3C]. To ensure the physiological relevance of our JAK-targeting siRNA sequences in inflammatory disease, we analysed protein expression after siRNA transfection in primary macrophages that were derived from PBMCs. The level of JAK1 protein expression in unpolarized primary macrophages allowed us to study JAK1 downregulation. Both siHJ1D8 and siHJ1D2 were able to efficiently knockdown JAK1 in these cells [Supplementary Figure 3D]. As the level of JAK3 expression was extremely low in these unpolarized cells, we analysed siJAK3 transfection efficiency in pro-inflammatory macrophages, polarized through LPS stimulation. This strategy enabled the detection of JAK3 protein knockdown in human primary immune cells [Supplementary Figure 3E]. The four siRNA-designed sequences can knockdown JAK1 or JAK3 protein expression in primary human macrophages, thereby opening the way for new avenues for the treatment of inflammation-related diseases. As gene silencing was already effective at 10 nM, we further decreased the siRNA concentration to investigate the IC50. To that end, we used a range of siRNA concentrations of 0.5-12.5 pM. Endogenous JAK1 expression was monitored at both protein and mRNA levels, while JAK3 was only monitored at mRNA levels due to the weak expression in PC3 cells, close to the protein detection limit. Strikingly, we observed a 50% reduction of JAK1 protein expression using both siHJ1D8 and siHJ1D2 at 2.5 pM [ Figure 1E and F]. This result was also observed at the mRNA level for both sequences [ Figure 1G and H], thus highlighting the high efficiency and good correlation between mRNA and protein detection for JAK1. Regarding JAK3, we observed a 50% reduction in mRNA level at 5 pM using both siHJ3D41 and siHMJ3D1 sequences [ Figure 1I and J]. Together, our data demonstrate that we identified highly efficient JAK1and JAK3-targeting siRNAs.

Specificity of siRNA targeting JAK1 and JAK3
To verify that these siRNAs were highly specific, we analysed potential off-target effects in silico of both sense and antisense siRNA sequences using the NCBI blast online platform. As expected, we did not find any perfect match between the siRNA sequences and the human transcriptome. However, partial homologies were found, as described in Table 3.
We then monitored the mRNA expression using RT-qPCR following siRNA transfection to analyse whether these partial sequence homologies could trigger an alteration in gene expression. Furthermore, although we observed very efficient gene silencing at 10 pM [ Figure 1], we choose to work at 10 nM so as to further increase the likelihood of observing potential off-target effects. We decided to analyse genes that displayed partial sequence homology sequences [i] with the siRNA seed sequences, and/or [ii] with at least 13 matches, and [iii] with antisense or both antisense and sense strands. Unfortunately, we could not detect the expression of the  [F] Proliferation analysis in CACO-2 cells, following transfection using NPBWR2, ADAMTSL2 and DNAH9 genes at the mRNA level, either in PC3 or in T47D cell lines. We analysed the effect of the four siRNAs' transfection in T47D cells while JAK1 or JAK3 expression levels were monitored as a positive control to ensure transfection efficacy. Among the six genes tested, only OAS1 showed minor modulation after siHJ1D8 transfection in T47D cells [ Figure 2A]. These results were confirmed in PC3 cells, except for ZDHHC23 and OAS1 whose expressions were almost undetectable [Supplementary Figure 4A]. After siHJ1D2 transfection, we did not observe any change in expression for the three genes tested in T47D cells [ Figure  2B]. Transfection of siHMJ3D1 did not alter the expression of the four genes tested in T47D cells [ Figure 2C] nor confirmed the results for one of these candidates in PC3 cells [Supplementary Figure  4B]. Among the three genes tested after siHJ3D41 transfection, a decrease of 85% in ZSWIM4 mRNA expression level was found on both T47D and PC3 cell lines [ Figure 2D and Supplementary Figure 4C]. Moreover, siHJ3D41 transfection in PC3 cells led to a decrease in expression of another gene, LRCH2 [85%], which was not statistically significant [Supplementary Figure 4C]. In summary, by analysing the different putative off-target effects from the siRNA sequences, we found that siHJ3D41 and siHJ1D8 have more confirmed off-target effects than siHJ1D2 and siHMJ3D1. Therefore, siHJ1D2 and siHMJ3D1 were identified as the most specific siRNAs.

Phenotypic consequences of the JAKs depletion
As the activation of the immune response has been described as a potential off-target effect induced after siRNA transfection, we questioned whether the four sequences were able to activate the innate immune signalling through Toll-like receptors [TLRs]. 28 We used the NFkB reporter cell line THP1-XBlue-MD2-CD14 [further referred to as THP1-XBlue cells], derived from the human monocytic THP1 cells. THP1-XBlue cells were exposed to siRNAs at a final concentration of 10 nM. Positive controls such as Pam3CSK4, HKLM, LPS, FLA-ST or CpG were used to confirm TLR activation of TLR1/2, TLR2, TLR4, TLR5 and TLR9, respectively. Neither JAK1-nor JAK3-targeting siRNA exhibited increased absorbance, indicating that these sequences do not activate the TLR response in immune cells [ Figure 2E].
The main phenotypic outcomes were analysed in CACO-2 cells following the transfection of JAK1 or JAK3 targeting siRNA, including cell proliferation, apoptotic cell death and cell metabolism. In addition, we used tofacitinib and filgotinib for a direct comparison of the phenotypic effect induced by both siRNA and chemical inhibitors that are used in clinics. The four siRNA sequences had no impact on cell proliferation, apoptosis or metabolism, while filgotinib at 100 µM strongly decreased cell proliferation and ATP levels, and increased apoptosis [ Figure 2F-H]. We concluded that the four selected siRNAs had no phenotypic effect on CACO-2 cells, suggesting that, besides their inhibitory efficiency, those JAKtargeting siRNAs have minimal impact on the main cell functions, a prerequisite for drug candidates.

Comparative analysis of JAK-targeting siRNAs and effect of chemical inhibitors on JAK/STAT signalling
To analyse the clinical potential of siRNAs to regulate JAK/STAT signalling relative to chemical Jakinibs, filgotinib and tofacitinib, we used western blotting to monitor the activation levels of STAT proteins, the molecular effectors' downstream JAK kinases, using specific phospho-STAT antibodies. We quantified the levels of proinflammatory pathways in response to five well-known signalling transduction pathways triggered by IL22, IFNγ, IFNβ, IL4 and IL9 cytokines after siRNA transfection or incubation with Jakinibs. To ensure that siRNAs and both JAK chemical inhibitors were at the right concentrations to block the JAK/STAT signalling cascade, preliminary experiments were conducted in CACO-2 cells, showing that the IL22 signalling pathway was completely blocked using siRNA at 0. We showed that siHJ1D8 and siHJ1D2 transfection abolished IL22-induced STAT3 activation, IFNγ-induced STAT1 activation, IFNβ-induced STAT2 and STAT1 activation, as well as IL4induced STAT3 and STAT1 activation in CACO-2 cells [ Figure  3A-D]. To confirm these results obtained in CACO-2 cells, the same experiment was conducted in T47D and/or PC3 cells using IL22, IFNγ or IFNβ stimulation [Supplementary Figure 6A-C]. As expected, because JAK3 was not involved in these pathways, JAK3-targeting siHJ3D41 and siHMJ3D1 had no effect on IL22, IFNγ or IFNβ signalling, indicating perfect siRNA selectivity. Of note, tofacitinib and filgotinib also exerted an inhibitory effect but to a lesser extent for the latter. PC3 cells have also been stimulated by IL4 to analyse whether the other IL4 receptor isoform [IL4Rα and IL4Rγ c ] was involved in signal transduction through JAK1 and JAK3 in this cell line. [29][30][31] However, we observed that JAK3 was not involved in the IL4 response in CACO-2, PC3 nor T47D cells [respectively Figure 3D, and Supplementary Figure  6D and E]. To examine the efficacy of JAK3-targeting siRNA on JAK/STAT signalling, PC3 and T47D cells were stimulated by IL9. siHJ3D41 was the only one to exhibit a decrease in STAT1 phosphorylation [Supplementary Figure 6F and G]. Overall, using our siRNA approach, we showed that JAK1 seems to be the most important signalling hub for JAK/STAT-mediated signal transduction in these cell lines in the context of IL22, IFNγ, IFNβ and IL4 stimulation, but IL9 signalling seems to involve both JAK1 and JAK3. In addition, we demonstrated that the JAK1-and JAK3targeting siRNAs at 10 nM have a highly selective effect on JAK/ STAT signalling and a similar, or an even superior, effect on IL22 and IFNγ, to a dose of 1 µM of tofacitinib or 5 µM of filgotinib.

JAK-targeting siRNAs do not lead to off-target effects at transcriptomic or proteomic levels
To verify the specificity of our JAK-targeting siRNAs, we performed unbiased broad-scale studies to analyse the potential off-target effects at both transcriptomic and proteomic levels. First, we conducted an RNASeq experiment to assess the transcriptomic profile of JAK-targeting siRNA-transfected cells and compared them to those of tofacitinib-or filgotinib-exposed cells in the absence of  Figure 7G and H, respectively]. The siMock vs siHJ1D8 and siHJ1D2 conditions showed nine and 28 DEGs, respectively [ Figure 4A and B, respectively], with two in common between them [one of which is actually JAK1- Figure 4C]. The siMock vs siHJ3D41 and siHMJ3D1 conditions showed five and 130 DEGs, respectively [ Figure 4D and E, respectively], but no common genes [ Figure 4F]. DMSO vs tofacitinib and filgotinib showed two and 27 DEGs, respectively [ Figure 4G and H, respectively]. Second, we analysed changes in protein expression using the SomaScan Proteomics platform developed by the SomaLogic Company. This proteomic   approach allowed us to analyse the transfection effect of our siRNA sequences on the expression level of 1305 different proteins. To ensure the good quality of our samples, we verified JAK1 knockdown by western blotting, thereby confirming their quality in terms of transfection efficiency, with a strong knockdown of JAK1 induced by our siRNAs siHJ1D8 and siHJ1D2 [Supplementary Figure 7I]. The analysis of DEPs between JAK-targeting siRNAs and negative control showed 19, 39, 11 and 124 proteins that were differently expressed in siHJ1D8, siHJ1D2, siHJ3D41 and siHMJ3D1 conditions, respectively, with a fold change > 1.23 and a p-value < 0.05 [Supplementary Figure 8]. Together, these results highlighted a specificity profile of siHJ1D8, siHJ1D2 and siHJ3D41 compatible with further clinical applications. However, siHMJ3D1 presents a higher risk of off-target effects at both proteomic and transcriptomic levels. Of note, our data also demonstrated that the profile of transcriptomic changes induced by tofacitinib showed a better specificity for this drug than filgotinib.

Long-lasting effect of JAK-targeting siRNAs
To evaluate the clinical potential of using JAK-targeting siRNAs, we then analysed the kinetics of siRNA efficiency. We first assayed the minimal time for siRNA transfection by performing a 3-9 h transfection pulse. The data revealed a knockdown efficiency for both JAK1 and JAK3 after only 3 h of transfection at the mRNA level [respectively Supplementary Figure 9A and B], while at the protein level, their expression level was downregulated after 7 h [Supplementary Figure 9C]. The difference between protein and mRNA downregulation may be due to JAK proteins' half-life. 32 To assess the long-lasting effect of JAK-targeting siRNA transfection, we transfected CACO-2 cells and analysed JAK expression for 1-7 days. We confirmed that the siMock transfection did not affect JAK1 at the protein level [Supplementary Figure 9D]. The analysis of JAK1 and JAK3 at the transcriptomic level showed a complete silencing from Day 2 to Day 7 [respectively Figure 5A and B], which was confirmed at the protein level for JAK1 from Day 1 to Day 7 [ Figure 5C and D]. Furthermore, we performed pulse transfection analysis over time, to ensure the long-term efficiency did not result from the siRNA remaining in the cell media. We first assayed 1, 3 and 6 h pulse transfection and analysed JAK expression 2 days post-transfection in order to choose the best pulse transfection timing. The data showed that complete JAK1 protein downregulation and STAT phosphorylation were observed with a 6 h pulse transfection [Supplementary Figure 9E]. We performed a 6 h transfection pulse of JAK1-targeting siRNA and monitored subsequent JAK1 expression for 14 days. Strikingly JAK1 downregulation was effective for 11 days [ Figure 5E and F].
Our results thus showed a very interesting long-lasting efficiency of JAK downregulation even after a short pulse of siRNA transfection, further highlighting the clinical potential of RNAi to modulate the activity of JAK proteins.

Discussion
In the present study, we provided a framework using the DSIR program to design several siRNA sequences targeting JAK1 or JAK3. We selected the two sequences displaying the lowest off-target effects while showing the highest efficiency at a concentration as low as 5 pM. As off-target effects are concentration-dependent, the possibility of using siRNAs at a very low concentration demonstrates the high potential of our siRNAs as drug candidates for therapeutic use. 24,33 The slight decrease of TYK2 expression under JAK1-targeting siRNA transfection should be further evaluated for its presence in the majority of JAK1 membrane receptor complexes. Moreover, TYK2 is also a target gene of Jakinibs in inflammatory bowel disease that may be another therapeutic agent for Crohn's disease. 34 Unbiased in silico and genome-wide omics approaches allowed us to ensure the specificity of the designed siRNAs. These approaches allowed us to propose three siRNA sequences with fewer than 40 off-target genes, at lower than the two-fold level that was a threshold assessed as acceptable by Birmingham and colleagues. 33 Combined with a phenotypic study of the in cellulo effect of our sequences, our data supported the potential for further clinical testing of three of the four selected sequences. Moreover, we provided a comparative analysis between JAK-targeting siRNAs and chemical inhibitors tofacitinib or filgotinib, with respect to their activity to modulate JAK/STAT signalling. Interestingly, we showed at least a ten-fold difference in efficacy between tofacitinib and filgotinib, which also corresponds to the doses administered to patients in clinical trials [respectively 10-20 mg daily vs 200 mg daily]. 35,36 These Jakinibs chemical inhibitors were shown to exhibit dramatic side-effects, including infections, and haematological and cardiovascular effects mainly due to unspecific JAK targeting that could be avoided using JAK1-or JAK3-specific siRNAs. 11,37 For instance, in 2019, Salguero-Aranda and colleagues developed a STAT6-targeting siRNA for treatment of cancer. Their optimal siRNA concentrations to knockdown STAT6 at the protein level [at least 60% inhibition] in vitro were 100 nM in HT-29 colorectal cancer cells and ZR-75-1 breast cancer cells, which are much higher compared to the 10 pM concentration of the JAK-targeting siRNAs described in this study. Moreover, they showed an effect lasting 7 days posttransfection, 38 while we were able to observe a knockdown of JAK1 at the protein level even 11 days after a 6-h pulse. This long-term efficacy at ultra-low doses would make it possible to consider a protocol for patients with low constraints, thus improving their quality of life.
Until now, siRNA therapeutics have been poorly investigated in the context of inflammatory bowel diseases. A chemically modified antisense oligonucleotide targeting SMAD7, an intracellular protein that blocks TGFβ signalling, has been investigated in clinical trials for patients with Crohn's disease. 39 TGFβ is a cytokine which negatively regulates inflammation, and its defective activity can lead to the development of colitis. Interestingly, SMAD7 inhibition restores TGFβ signaling and activity, thus leading to decreased production of inflammatory cytokines. In this study, the antisense oligonucleotide is embedded as 'naked' in an external tablet coating made of pH-sensitive polymers used for oral delivery purposes. Unfortunately, the subsequent phase III trial was terminated early because of a lack of efficacy. 40 In animal models of experimental colitis, other targets have been considered for inhibition through siRNAs, such as TNFα 41,42 or CD98, 43 leading to slight improvements in body weight recovery after colitis induction. In these studies, the siRNA was delivered through different polymer particles, using either polyethylenimine [PEI] 42,43 or synthetic polymers smartly designed to be sensitive to environmental factors. 41 These studies suffer from several limitations with regard to future product development in the field using siRNA therapeutics: [i] their targets do not lead to a broader inhibition of inflammation, in contrast to JAK inhibition that interferes with several signalling cascades of pro-inflammatory mediators, and [ii] their delivery systems are either complicated and costly to consider for scaled up production or present some toxicity issues. In this context, the design of siRNA therapeutics leading to a broad inhibition of mucosal inflammation is still highly anticipated and associated with challenges with regard to local delivery. For instance, lipid nanoparticles are widely used in vivo to deliver RNA, and some products are even already on the market, such as Onpattro or COVID-19 vaccines. 16,44,45 However, the delivery challenge to reach some immune cells in the lamina propria of intestines is especially high as the carrier has to overcome several biological obstacles, such as stability in the colonic lumen and its harsh environment, transport across the mucus layer and the underlying epithelial barrier and finally transfection across the plasma membrane of the target cells. No carrier has previously been available on the market or in R&D with high maturity level that fulfils all the aforementioned criteria. Lipid nanoparticles are interesting, but Ball and colleagues have demonstrated that intestinal fluids could trigger their aggregation, thus hampering their potency. 46 Moreover, these lipid particles cannot easily diffuse through the mucus layer. Intensive research is still needed to design and validate the most appropriate carrier for the local delivery of RNA in the inflamed gut, with good control on their toxicity and feasibility for scaled up production at reasonable costs. Indeed, a recent review by Saw and Song, 47 highlighting the potential of siRNA therapeutics, underlined the benefits of siRNAs as compared to small-molecule drugs, including specificity, potency, high selectivity with low toxicity, and stable serum stability. The authors raised the issue that it is the route of administration such as electroporation, local injection or topical application [mucosa, oral or rectal] that remains the biggest challenge of siRNA therapeutics, not the development of potent and safe siRNA. 47 Addressing delivery issues would lead towards a targeted medicine at several levels-molecular through the RNAi and cellular and tissular through specific vectorization.
Future work should emphasize testing the in vivo stability of our JAK-targeting siRNAs, as well as their in vivo efficiency, either in human 3D organoids or in biopsies rather than animal models, as the sequence specificity of our siRNA-designed sequences restricted their use in human cellular models. In addition, many cases reported that animal models with mice or rodents often do not accurately recapitulate human inflammatory diseases. 48 In this regard, the recent development of siRNA transfection in 3D should improve the preclinical testing of therapeutic siRNAs. 49 Developing new therapeutics highly specific to either JAK1 or JAK3 could be greatly beneficial for patients with cancer and immune-related diseases, or even infections. Indeed, JAK1 and JAK3 have been widely described as potential therapeutic targets for several types of cancer, including gliomas, 50,51 head and neck cancers and oesophageal squamous cell carcinoma, 52,53,54,55 breast cancer, 56,57 multiple myeloma, 58,59 colon cancer 60,61 and pancreatic cancer, 62-64 as well as for developing cancer immunotherapies. 65 JAK3 has been particularly highlighted as a therapeutic target in immune cell-related cancers, including B-lineage acute lymphoblastic leukaemia 66 and natural killer/T-cell lymphoma, 67 and also for improving allotransplantation. 68,69 Both JAK1 and JAK3 selective downregulation could also be of great benefit for inflammatory bowel disease patients, 70 and autoimmune and inflammatory disease-related patients. 6,71 Targeting the JAK/ STAT pathway would also benefit patients with diseases related to JAK1 or JAK3 overactivation, including COVID-19 patients. Indeed, the use of Jakinibs has been recently suggested to treat Covid-19 patients, for instance ruxolitinib for the treatment COVID-19-related cytokine storm, 72 or barcitinib, which also displayed promising outcomes even in elderly patients. 73 In conclusion, we have provided a detailed analysis of both the specificity profile and efficacy of new JAK1-and JAK3-targeting siRNAs. We hope our work will pave the way for novel therapeutics exploiting RNAi in inflammatory diseases.

Funding
This research was funded by the European Union's Horizon 2020 research and innovation programme H2020 'NEWDEAL' [grant agreement No. 720905]. F.C., A.N. and A.D. were supported by a fellowship from the H2020 NEWDEAL project. This publication reflects only the authors' views, and the Commission is not responsible for any use that may be made of the information it contains. This project received funding from CEA and GRAL, a programme from the Chemistry Biology Health [CBH] Graduate School of University Grenoble Alpes [ANR-17-EURE-0003].