Transcribing RNA polymerase II (RNAPII) is decorated by a plethora of post-translational modifications that mark different stages of transcription. One important modification is RNAPII ubiquitylation, which occurs in response to numerous different stimuli that cause RNAPII stalling, such as DNA damaging agents, RNAPII inhibitors, or depletion of the nucleotide pool. Stalled RNAPII triggers a so-called “last resort pathway”, which involves RNAPII poly-ubiquitylation and proteasome-mediated degradation. Different approaches have been described to study RNAPII poly-ubiquitylation and degradation, each method with its own advantages and caveats. Here, we describe optimised strategies for detecting ubiquitylated RNAPII and studying its degradation, but these protocols are suitable for studying other ubiquitylated proteins as well. A number of elongation-stimulating factors have evolved to help RNAPII overcome these transcription-impeding events, yet resumption of transcription may not always be possible in vivo. Subsequently, poly-ubiquitylated RNAPII is subject to proteasomal degradation The RNAPII elongation complex is remarkably stable, which is crucial to ensure processivity of transcription under normal conditions. However, this great stability comes at a cost: just a single stalled RNAPII complex can block transcription of an entire gene. To avoid potentially detrimental consequences of persistently stalled/arrested RNAPII, cells have evolved several mechanisms to solve this problem. For example, damage-stalled RNAPII can act as a DNA lesion sensor, triggering transcription-coupled nucleotide excision repair (TC-NER) and removal of the lesion, which would allow resumption of transcription Intriguingly, proteasome subunits have been found to associate with several yeast genes, particularly gene ends, in a transcription-dependent manner. Moreover, inhibition of proteasome proteolytic activity resulted in transcription readthrough beyond the transcription termination site in vitroIn the context of transcription arrest, experiments in yeast suggested that RNAPII phosphorylated at serine 2 (S2) in the heptapeptide repeats of the C-terminal domain (CTD) is subject to poly-ubiquitylation and degradation, while phosphorylation of CTD S5 inhibited RNAPII poly-ubiquitylation in vitroin vivoin vivo results only in partial loss of ubiquitylation, or in lack of RPB1 ubiquitylation only at specific stages of the response – for example, depletion of NEDD4 abrogates RPB1 poly-ubiquitylation but only at early timepoints after UV-irradiation in human cells RNAPII is composed of 12 subunits; however, only RPB1, the largest and catalytic subunit, is subject to RNAPII arrest-induced degradation in yeast In order to help address these questions, robust and reliable methods to study ubiquitylation are required. Here, we discuss some of these techniques and provide detailed protocols for tracking human and yeast RPB1 ubiquitylation and degradation. We also briefly address future perspectives, such as mapping individual ubiquitylation sites and generating suitable cell model systems. Importantly, although the approaches described were designed specifically to investigate RNAPII ubiquitylation, they can easily be modified to investigate ubiquitylation of other proteins as well.2As mentioned above, many different kinds of stimuli can cause ubiquitylation and degradation of RNAPII largest subunit, RPB1. Among those cues, UV-irradiation is probably the best studied. Below, we outline the equipment, protocols and considerations needed for UV-irradiating human and yeast cells. We also provide a brief overview of other protocols to trigger RPB1 ubiquitylation and degradation.2.1Any device capable of emitting a regulated dose of UV light can potentially be used to irradiate mammalian or yeast cells grown in culture. UV light of any wavelength  can thus induce the formation of DNA lesions (cyclobutane pyrimidine dimers (CPDs) and (6-4)-photoproducts (6–4)PPs) 2) typically used to irradiate human cells, and that they give disconcertingly non-uniform irradiation across the exposed surface . Whether using a UV-crosslinker or a UV box, the UV meter probe can be inserted in the irradiation chamber to measure the actual UV dose given. Due to the spatial variability in the actual given doses by many UV sources, the experimental setup needs to be carefully tested by measuring the UV dose across the surface of the irradiation device to determine not only the uniformity of irradiation but also the maximum size of the surface that can be accurately and uniformly irradiated e.g. see B.2 energy may take 2–3 s in a UV crosslinker, and ∼10 s in a UV box with greater distance between the UV lamp and the sample. In the context of RPB1 ubiquitylation and degradation, we are not aware of any differences in results whether cells receive a similar total dose over a shorter or longer period of time.Different UV-irradiation devices will achieve a given UV dose in different amounts of time – for example, total emission of 20 J/m2.2•2 with crosslinkers).Warm up the UV-irradiation device before use . The experimenter should aim to work fast and avoid leaving the cells without medium for more than a few minutes. The dishes should be transferred from the tissue culture hood to the UV-irradiation device with the dish lid kept on, to maintain sterility. The lid should be taken off just before irradiation, inside the UV crosslinker chamber. The lid should be kept facing down on a clean, sterilised surface. If the samples will be used for assaying transcription, we recommend replenishing each sample of cells with the medium they were grown in before irradiation. Adding fresh medium can cause a “boost” in transcription and potentially skew the results 2.46–2 × 107 cells/ml) at the time of UV-irradiation. 25 μg/ml cycloheximide may be added 5 min before starting the experiment (see below). The cells should be washed and resuspended in PBS or saline for UV-irradiation. The original media should be saved as it will be re-added to the yeast cells after treatment. We typically start with a 200 ml culture per condition. The culture is split in two, with one half irradiated and the other half saved as the negative control. The cells are concentrated 5 times for the irradiation such that 100 ml of culture is resuspended in 20 ml of PBS/Saline, and irradiated in a pyrex dish with mixing of the sample during irradiation, if possible. The UVC dose we give is usually 300 J/m2 (over approximately 30 s), as measured with the UV meter. Immediately after the samples have been irradiated they are collected by centrifugation and resuspended in their original media for recovery. At this point, it is important to keep the recovery cultures in a shaking incubator in the absence of light, to ensure that activation of photolyase is prevented Yeast cells should be in early log phase (5 × 102.5•The meter should be able to measure the wavelength of the given UV-irradiation device.•The meter should be able to measure cumulative UV exposure (over a period of time).•The probe of the meter should have a cable of appropriate length to be able to fit into the corresponding irradiation device’s chamber.Most UV-induced phenomena are dose dependent (see below). Considering that some UV devices are inconsistent in providing a given dose, it is of utmost importance to independently monitor the emitted dose of irradiation with a UV radiation meter in every experiment. While there are many UV radiation meters on the market, it is important for it to fulfil the following criteria:We have successfully used UV radiation meter VLX-3W with probe SX254, with UVC crosslinkers and a UVC box.2.6•Device not warmed up properly. Warm up the device as recommended in “Notes before starting”, or try warming up for a longer period of time.•Old bulbs. Replace bulbs.•If neither of the above helps, the device may not be suitable for irradiation of live cells.•For multi-well plates (24-well to 384-well) alternative irradiation devices may need to be built to achieve consistent exposure across samples. We have successfully constructed and used UV conveyor belts that operate by having a single UVC light source in the centre of the device’s chamber, and a moving conveyor belt that will carry the sample through the chamber with constant speed Measuring low actual doses, or very variable actual doses between samples. Potential causes:2.7Apart from UV-irradiation, other agents are also known to induce RPB1 ubiquitylation and degradation. The common feature is that they induce transcription stress either by creating transcription-blocking DNA damage , by blocking RNAPII activity  or by depleting the NTP pool (e.g. 6-AU). The commonly used dose ranges for these agents are listed in For practical reasons, it may be easier to use one of these chemicals rather than UV-irradiation to induce RPB1 ubiquitylation and degradation. For example, 4-NQO is typically used in yeast when performing time-course experiments, as UV-irradiation of yeast requires a significant amount of time to carry out, and might not be possible to perform for multiple different strains or conditions in a time-course set-up. We tested 4-NQO treatment in human cells and found that similarly to yeast, it induces RPB1 poly-ubiquitylation and degradation, but at much lower doses than those typically used for yeast C. While 33.1i) RPB1 is a very large protein so it is difficult to resolve and visualize these “extra” bands, ii) only a small fraction of RPB1 molecules are ubiquitylated at any given time, even after high doses of UVC-irradiation.Mono-, oligo- or poly-ubiquitylation of proteins can often be detected by simple Western blot analysis as additional, slower-migrating bands and smears, for example for the well-known DNA damage-induced mono-ubiquitylation of histone H2AX In both mammalian and yeast cells, a faint band can sometimes be seen above the phosphorylated RPB1 band in UV-irradiated samples  tagged ubiquitin and then probe for RPB1. This approach is reliable, but sometimes impractical as it requires the generation of cell lines which express tagged ubiquitin. Below we describe the principles of the methods we regularly use to easily and reliably detect ubiquitylation of RNAPII. Importantly, these approaches can actually be used to detect any ubiquitylated protein for which a specific antibody is available.3.2The budding yeast Dsk2 protein contains an UBA domain, which binds ubiquitin with relatively high affinity Generation of GST-MultiDsk resin has already been described in detail 3.33.3.1•pGEX3-Dsk2 plasmid •One Shot BL21(DE3) or One Shot BL21(DE3) Star competent bacterial cells, and all the necessary equipment for bacterial transformation, growth and induction.•PBSA buffer, protease inhibitors, Triton X-100, DTT.•Glutathione sepharose beads, 50% packed bead volume solution.•Sodium azide solution.•50 ml falcon tubes, 200 ml glass beakers.•Tip probe sonicator .•Centrifuge.•For specific details of the reagents and equipment please refer to 3.3.2•Day 1: Transform One Shot BL21(DE3) or One Shot BL21(DE3) Star competent bacterial cells with pGEX3-Dsk2 plasmid according to the manufacturer’s instructions, and plate cells on ampicillin selection plates. Keep at 37 °C overnight.•Day 2: pick a single colony and inoculate into 20 ml of LB containing 100 μg/ml ampicillin (LBamp) and shake at 37 °C at 200 rpm overnight. This is the pre-inoculum culture.•Day 3:oamp with 5 ml of the pre-inoculum in 2L Erlenmeyer flask. Shake at 37 °C at 200 rpm.Inoculate 300 ml of LBo600).Regularly take 1 ml aliquots to track bacterial growth by measuring absorbance at 600 nm , and then induce the culture with 1 mM  of IPTG.When the ODoShake at 30 °C at 200 rpm for 4 h.oTake a 1 ml aliquot as the “induced” sample.oAliquot the culture into 50 ml falcon tubes (or other appropriate vessels) and centrifuge at 4,500 rpm for 10 min to pellet bacteria. Also pellet the “non-induced” and “induced” samples by centrifugation.oRemove the supernatant and snap-freeze the pellets in liquid nitrogen. Store at −80 °C.•Day 4:oDefrost the pellets from the 300 ml culture  quickly at room temperature then transfer to ice.oTo each falcon tube (pellet derived from 50 ml culture), add 15 ml of cold PBSA containing protease inhibitors and resuspend the pellet completely by careful pipetting, or vortexing. Avoid denaturing proteins, often signified by bubbles in the mixture. Combine all 6 samples  into one 200 ml glass beaker.oSonicate with a tip probe sonicator  at 33% output, with 15 s ON, 30 s OFF pulses, for a total ON pulse duration of 10 min. Keep the sample on ice at all times. If a different sonicator is used, the conditions for cell lysis will have to be optimised.oAdd Triton-X100 to a final concentration of 0.5%, mix gently.oIncubate on ice for 30 min.oTransfer the sonicated lysates to appropriate vessels and centrifuge at 12,000 rpm for 10 min at 4 °C to remove debris. Alternatively, 4,500 rpm for 30 min in falcon tubes can be used.oTransfer the supernatant to new 50 ml falcon tubes on ice . Also take a 1 ml aliquot as the “lysate” sample. Successful expression of the GST-Dsk2 protein may be tested by gel electrophoresis and staining with Coomassie Blue before proceeding.oResuspend the glutathione sepharose beads well and take 3 ml of suspension into a fresh tube (this corresponds to 1.5 ml packed bead volume). Spin at 500 g for 5 min at 4 °C and remove supernatant carefully. Wash once with cold PBSA, and then resuspend in 3.3 ml cold PBSA.oFor binding of GST-Dsk2 to the beads, add 1 ml of well-resuspended glutathione sepharose bead solution from the previous step to each 30 ml of cleared lysate. Also add DTT to a final concentration of 2 mM. Rotate gently in the cold room for at least 4 h or overnight.•Day 5:oSpin binding reactions at 500 g for 5 min at 4 °C. Remove and save the supernatant as the unbound fraction.oWash the beads twice with ice-cold PBSA, 0.1% Triton X-100, containing protease inhibitors. During one of the washes, combine the pellets from the 3 falcon tubes into one.owithout Triton X-100, but containing protease inhibitors.Wash once more with PBSA oAdd 30 ml of PBSA containing protease inhibitors and 0.02% sodium azide to the prepared Dsk2 beads and store at 4 °C. In our hands, the beads can be stored for long periods of time (months) without losing efficiency.•Day 6:oTo check the efficiency of the Dsk2 bead preparation, pipet 100 μl of “non-induced”, 100 μl of “induced” and 100 μl of “lysate” into Eppendorf tubes. Also pipet 250 μl of prepared Dsk2 bead suspension (12.5 μl packed beads) into a tube, spin at 500 g for 5 min at 4 °C, and remove the supernatant. Add a corresponding amount of Laemmli buffer containing DTT or β-mercaptoethanol to each sample including the pelleted Dsk2 beads, and boil at 96–98 °C for 5 min. Spin at max speed for 5 min to remove debris or beads. Transfer supernatants to new tubes.oRun the samples on a polyacrylamide gel  and stain the gel with Coomassie or similar. GST-Dsk2 protein should run at ∼65 kDa, and some free GST band may also sometimes be seen at ∼28 kDa.3.4600 reaches 0.6. Since the MultiDsk protein is found in inclusion bodies, we use a different method of lysis and purification as described below.•Day 4:oDefrost the pellet from the 300 ml culture quickly at room temperature then transfer to ice. Add 20 pellet volumes of STE buffer  containing lysozyme at 0.1 mg/ml and resuspend the pellet completely by careful pipetting, or vortexing. After 15 min on ice, add sarcosyl (N-lauryl sarcosine) to a final concentration of 1.5% in order to denature the protein.oSonicate with a tip probe sonicator  at 20% output, with 15 s ON, 30 s OFF pulses, for 4 cycles. Keep the sample on ice at all times. If a different sonicator is used, the conditions for cell lysis will have to be optimised.oCentrifuge at 10,000 g for 5 min to remove insoluble material. Save the supernatant and take an aliquot for analysis.oAdd Triton X-100 to the supernatant at a final concentration of 3%. Triton X-100 masks the sarcosyl by forming mixed micelles allowing the protein to refold.oAdd this lysate to glutathione beads, pre-equilibrated in STE buffer. Use approximately 1 ml resin per 1 ml bacterial pellet starting material. Add DTT to 2 mM. Rotate gently in the cold room for at least 4 h or overnight.•Day 5:oSpin bead-binding reactions at 500 g for 5 min at 4 °C. Remove and save the supernatant as the unbound fraction.oWash the beads twice with ice-cold STE containing 500 mM NaCl and 0.1% Triton X-100, followed by a wash in the same buffer at 50 mM NaCl.oWash twice with PBSA. Then add 30 ml PBSA.oThe beads can be stored in PBSA, 0.02% sodium azide at 4 °C. In our hands, the beads can be stored for long periods of time (months) without losing efficiency.•Day 6:oCheck the efficiency of MultiDsk bead preparation, as for Dsk2 beads. GST-MultiDsk protein should run at ∼60 kDa, and some free GST band may also sometimes be seen at ∼28 kDa.MultiDsk plasmid (pGST-MD) is propagated and MultiDsk protein expressed as described for GST-Dsk2 in 3.53.5.1•Seed at least one 10 cm dish per condition. Apply treatments as required for your model system . At the time of collection, the cells should ideally be 60–80% confluent.•Note: it is not necessary to use proteasomal inhibitors to observe RPB1 poly-ubiquitylation in human cells, if the optimal experimental timepoints are chosen (see osen see .•Aspirate the media and add the appropriate amount of PBS (e.g. 1–2 ml for a 10 cm or 15 cm dish).•Immediately scrape cells off and transfer to a tube.•Spin at 1300 rpm for 3 min, remove supernatant. The pellets can be flash-frozen in liquid nitrogen at this point and stored at −80 °C.•Add the appropriate volume of TENT buffer  containing protease inhibitors, phosphatase inhibitors and 2 mM of N-ethylmaleimide  to the pellet. We typically use 1 ml of TENT buffer for a pellet from a 10–15 cm dish. Note that the NEM stock solution (200 mM in ethanol) should be made up fresh every time.•Leave on ice for 10 min.•Sonicate in a 4 °C water bath sonicator (Bioruptor) at high power, with 30 s ON and 30 s OFF pulses, for a total duration of 7 min, or by using a single sonication pulse (10 s at 20% output) with the tip-probe sonicator (Branson). Alternative sonicators can be used but the conditions would need to be optimised.•Transfer to Eppendorf tubes and centrifuge at maximum speed  for 5 min to remove debris.•Transfer the supernatant (lysate) to a new tube. Quantify the protein concentration using a Bradford assay or similar.3.5.2•8 yeast cells by centrifugation. At the time of collection, the cells should ideally be in early log phase (5 × 106–2 × 107 cells/ml).Harvest approximately 3 × 10•Note: MG132 is not added to yeast cell media as yeast cells are impermeable to it, but it is used later in the lysis buffer to prevent protein degradation in the lysates (see below).•Wash once in PBS before flash-freezing the pellets in liquid nitrogen.•Thaw pellets and resuspend in 800 μl buffer .•Add roughly 500 μl 0.5 mm diameter glass beads (BioSpec Products) and disrupt the cells using a FastPrep-24 cell homogenizer (MP Biosystems), using 6 rounds of beating at an intensity of 5.5, for 30 s each. Incubate samples on ice for 1 min between disruptions, to reduce heating of the sample with an additional 5-minute ice incubation after 3 rounds. If a different method of homogenization is used, the conditions for cell lysis will have to be optimised.•Clarify the extracts at 20,000 g for 10 min. Save supernatant and repeat centrifugation until the lysate is clear.•Determine the protein concentrations of the extracts using the Bio-Rad protein assay or similar.3.63.6.1•Day 1:oWe typically use 0.5 ml of GST-Dsk2 (or MultiDsk) bead suspension  prepared as described in oKeep both beads and protein samples on ice at all times.oFor each cell lysate sample, pipet 1 mg of total protein into 2 ml safe-lock Eppendorf tube and slowly adjust all samples to the same volume with TENT buffer containing protease inhibitors, phosphatase inhibitors and 2 mM freshly made NEM. Typically, the final sample volume should be between 700 μl and 1 ml.oNote: it is possible to use less than 1 mg of protein per pulldown – we have tested amounts down to 300 μg. In this case, the volume of Dsk2 beads and the total reaction volume should be scaled accordingly.oPrewash the beads in bulk. Spin beads at 500 g for 5 min at 4 °C, remove supernatant and wash once with TENT buffer containing protease inhibitors, phosphatase inhibitors and 2 mM NEM see .oNote: sepharose beads are very easily disturbed when removing the supernatant. It is therefore better to leave some supernatant behind than to accidentally remove some of the beads from the pellet.oGently resuspend beads in a smaller volume of TENT buffer containing protease inhibitors, phosphatase inhibitors and 2 mM NEM . Avoid making bubbles.oAliquot the same volume  of well-resuspended Dsk2 bead slurry to each sample.oRotate on a turning wheel/rotator (low to moderate speed) in the cold room for several hours to overnight.•Day 2:oSpin the samples at 500 g for 5 min at 4 °C, remove supernatant and save as “unbound” fraction. Wash the beads carefully twice with 1 ml of TENT buffer containing protease inhibitors, phosphatase inhibitors and 2 mM NEM.oWash the beads carefully once with 1 ml of PBS containing protease inhibitors, phosphatase inhibitors and 2 mM NEM. Spin at 500 g for 5 min at 4 °C and remove as much supernatant as possible. Re-spin a few times if necessary. At this point, any remaining liquid may also be removed with a fine pipet-tip.oTo each bead sample, add 40 μl of Laemmli buffer containing DTT or β-mercaptoethanol, mix by brief vortexing, and boil at 96–98 °C for 5 min.oSpin the samples and save supernatant which now contains the enriched, ubiquitylated proteins (“bound” fraction). Samples can be run on SDS gels at this point or can be frozen.oNote: it is possible to elute bound proteins from the Dsk2/MultiDsk beads by the addition of 50 mM Phosphate buffer pH 8.1, 300 mM KCl, 10% glycerol, 1xPIs, 0.05% Triton X-100, 15 mM Glutathione, pH adjusted to >8.1 oNote: “unbound” and “bound” fractions can be run on the same gel, and a Western blot with anti-ubiquitin antibodies can be used to determine the efficiency of the pulldown .•To resolve Dsk2-enriched, ubiquitylated RPB1, we typically use a 3–8% Tris-Acetate gel, and continue electrophoresis until the 75–100 kDa marker runs out. Alternatively, 4–15% TGX gels can be used, again until the 75–100 kDa marker runs out.•We use wet protein transfer (500 mA for 1 h), in a transfer buffer without SDS or alcohols, and nitrocellulose membranes with 0.45 μm pores. Other transfer conditions may be suitable but have to be optimised by the experimenter.•Upon transfer, membranes should be stained with Ponceau S and image-scanned for future reference. To block membranes, incubate in PBS + 0.05% Tween (PBST) with 5% skimmed milk powder for 1 h at room temperature.•Note: 4H8 is the most commonly used primary antibody for detection of ubiquitylated RPB1, as it recognises most forms of RPB1  with high affinity . Other RBP1 antibodies can be used as well, but they will generally give lower signal intensity. In addition to RPB1, a housekeeping protein such as human Vinculin (∼120 kDa), or yeast Tub1 (50 kDa) or Pgk1 (45 kDa) should be probed against as well to control for loading in the inputs.•From this step, standard Western blot procedures should be applied. For 4H8, we use a 1:10,000 primary antibody dilution (stock concentration 1 mg/ml).3.8•Timing – when to look for RNAPII ubiquitylation after a stimulus? In the case of UV-irradiation, RPB1 poly-ubiquitylation can be detected as early as 10 min after irradiation. The poly-ubiquitylated smear peaks around 30 min after UV, and is present up to a few hours after UV-irradiation  whereas at later timepoints after UV, little or no change is observed between NEDD4-depleted and wild type cells •Use of proteasome inhibitors. If optimal timepoints are chosen (see previous point), the use of proteasome inhibitors such as MG132 is not required to observe human RPB1 poly-ubiquitylation. However, proteasome inhibitors can of course be used, if the experimental question requires so . In this case, we typically use a 1–3 h pre-incubation with 5 μM MG132. It is important not to add a very concentrated MG132 stock solution directly to cell culture media as precipitates will form – we typically dilute the original 50 mM MG132 stock (in DMSO) 1:500 (in warm media) and add the diluted stock to cells. In yeast, MG132 is used only in the protein extraction buffer.•Pulldown efficiency. When performing a Dsk2/MultiDsk pulldown, especially for the first time, it is recommended to run the input, unbound and bound fractions on protein gels and probe with an anti-ubiquitin antibody. This will show whether similar amount of input material was used, and reveal how efficient the pulldown is under the given conditions. The amount of beads and lysate can be varied to find optimal pulldown conditions . Upon pulldown, bound proteins together with Dsk2/MultiDsk itself can be eluted with glutathione 3.9•No signal in the pulldown fractions or variable signal between pulldown samples where it is not expected due to biology. Possible reasons:oLow pulldown efficiency – was the efficiency checked with anti-ubiquitin antibody?oAntibody not working in Western blot – is the expected band(s) detected in the input samples?oDeubiquitylation of proteins in the extract – has freshly-made NEM been added to lysis buffer and pulldown reactions?4Background: RPB1 degradation can be assessed by Western blot analysis, with some specific considerations outlined below.4.1Either whole cell extracts or chromatin fractions can be used to assess RPB1 degradation in mammalian cells. Most extraction protocols will be suitable as long as protease inhibitors, phosphatase inhibitors and freshly made NEM are added.7 cells are pelleted and resuspended in 100 μl of 100 mM NaOH for 5 min at room temperature. Cells are pelleted and the pellet resuspended directly in 50 μl of 1.5X Laemmli loading buffer and boiled for 5 min, before placing on ice. Samples are re-heated and spun at 14,000 g for 1 min before loading on SDS-PAGE gels . Using this method, it is not possible to measure the protein concentration and so it is important to ensure that an equal number of cells is processed per condition, and to employ a non-degraded control protein.For yeast cells, the protocol for preparing lysates for Dsk2-pulldown can be used. Alternatively, it may be preferable to use a quicker lysis method that requires significantly less cells, such as alkaline quick whole cell extract preparation •10–50 μg of total protein should be loaded. It is important that equal amounts of protein are added to compare the different samples.•To resolve unphosphorylated and phosphorylated RPB1, 3–8% Tris-Acetate gels should be used, and electrophoresis continued until the 75–100 kDa marker position runs out. Alternatively, 4–15% TGX gels can also be used as long as the 75–100 kDa marker protein runs out of the gel.•We use wet protein transfer (500 mA for 1 h), transfer buffer without SDS or alcohols, and nitrocellulose membranes with 0.45 μm pores. Other transfer conditions may be suitable but have to be tested by the experimenter.•Upon transfer, membranes should be stained with Ponceau S and scanned for future reference. To block membranes, incubate in PBS + 0.05% Tween (PBST) with 5% skimmed milk powder for 1 h at room temperature.•From this step, standard Western blot procedures should be applied.•The choice of antibody for detecting RPB1 degradation is very important. We recommend using phospho-unbiased antibodies, such as those raised against RPB1 N-terminus , when analysing RPB1 degradation. This is because stimuli such as UV-irradiation can cause RPB1 phosphorylation changes as well as degradation. Thus, interpreting changes of abundance of only the phosphorylated forms of RPB1 might be difficult. . Importantly, two opposing forces will affect the total amount of phosphorylated RPB1 (IIo form) observed: continued CTD phosphorylation will increase its levels, while degradation will decrease it. Using phosphorylation-independent antibodies, such as N-20, D8L4Y or ARNA-3, raised against RPB1 N-terminus, will therefore provide an objective image of total RPB1 levels, influenced by degradation  to detect Rpb1 degradation. Addition of cycloheximide (25 μg/ml) to yeast cells immediately before UV treatment largely alleviates this problem. We have successfully observed Rpb1 degradation both in the absence •Replicates – As with any experiment, we recommend performing the analysis in at least three independent, biological replicates. If expected/observed differences between the samples are moderate, image analysis (using programs such as ImageJ) can be used to quantify these differences. Loading controls such as Vinculin (human cells), and Tub1 or Pgk1 (yeast) can be used to normalise the data.4.3•No RPB1 degradation in samples where it is expected. Possible reasons:oUnequal loading. Ensure that equal amounts are loaded based on the control protein.oInsufficient dose of the stimulus – try increasing the dose of the stimulus see  and testoOld reagents – some agents, such as cisplatin, need to be made fresh every time, otherwise they are hydrolysed or lose activity in solution.oFor both of the above, it is useful to also blot for markers of the given stimulus (e.g. DNA damage markers such as γH2AX for cisplatin or UV) to check that the treatment has worked.•Additional bands or background in Western blot apart from IIo and IIa. Possible reasons:oNon-specific degradation of proteins – have protease inhibitors been added to the lysis buffer? Has the sample been kept cold at all times? Have the samples been boiled in Laemmli buffer for too long (more than 7–8 min)?oPrimary/secondary antibody background – try using different antibodies.•Phosphorylated RPB1 band (IIo) appears too weak. Possible reasons:oNon-specific dephosphorylation of proteins – has phosphatase inhibitor been added to the lysis buffer?55.1Apart from targeting proteins for degradation, ubiquitylation can also act as a signalling module, in the form of mono-ubiquitylation or ubiquitin chains with linkage other than K48 While it is possible to couple Dsk2/MultiDsk pulldown with mass spectrometry, if Dsk2/MultiDsk are first crosslinked to the beads see , this apBuilding on the above, we have established a few modifications to this procedure in human cells. Briefly, we employed a SILAC-based quantitative proteomics approach. Cells are grown in media containing heavy- or light-isotope-containing amino acids to achieve light/heavy labelling of proteins Cells are then pelleted and lysed by addition of 9 cell-pellet volumes of 9 M urea solution (9 M urea in 20 mM Hepes pH 8.0) and sonicated to facilitate extraction of chromatin-bound proteins. Optionally, 100 U/ml of benzonase may be added to the lysate prior to sonication to facilitate the process. Lysates are then cleared by centrifugation, and equal amounts of protein from each sample  is subjected to LysC and tryptic digests, as specified by the Ubiquitin Remnant Motif (K-ε-GG) Kit manufacturer. After C18-reverse phase purification using Sep Pak cartridges, ubiquitylated peptides are enriched using K-ε-GG antibody conjugated to protein A agarose beads. 40 μl of antibody-coupled beads are used per 30 mg of input sample. Enriched peptides are eluted with 0.15% TFA, lyophilized, and subjected to SCX (strong cationic exchange) or basic reverse phase fractionation For the diGly IP protocol in yeast cells please refer to a study by Espenshade and colleagues We envisage that mapping individual sites of ubiquitylation on RNAPII using methods outlined above will be crucial for understanding the molecular mechanisms that mediate RNAPII degradation under different conditions.5.2The RNAPII complex harbours numerous post-translational modifications (PTMs), the most famous being CTD phosphorylation. These modifications are a platform for regulating RNAPII behaviour at different stages of transcription. Even for the most well studied RNAPII PTMs, such as phosphorylation of Serine 5 or Serine 2 within the CTD hepta-peptad repeats, novel roles are constantly emerging  gene. For studying long-term effects of a given RNAPII PTM, alternative “switchover” systems may be developed by targeting the endogenous RNAPII loci with siRNA/shRNA or CRISPR-mediated knock-outs, while at the same time expressing PTM-mutant forms from stably integrated sites in the genome. Finally, it may be possible to target the endogenous RNAPII loci with CRISPR-mediated knock-in point mutations in the desired PTM sites when these mutations are not lethal.When studying PTMs, the first approach is often to mutate the modification acceptor site on the target protein. To prevent ubiquitylation of a specific lysine residue, lysine (K) is mutated to either the conservative arginine (R) or, less optimally, to alanine (A). While K → R/K → A mutant proteins can be tagged, overexpressed and purified for 6Affinity resins based on ubiquitin binding domains such as Dsk2 or MultiDsk provide a robust and easy method to enrich endogenous ubiquitylated proteins in their native state. Importantly, these strategies are suitable not only for analysis of RNAPII ubiquitylation, but also of any other ubiquitylated protein, and can be coupled either to Western blot or other analyses, such as further purification or mass spectrometry. While Dsk2 protein alone has some preference towards K48-linked ubiquitin chains, MultiDsk binds different kinds of ubiquitin chains equally well. Nonetheless, both resins successfully enrich poly-ubiquitylated RNAPII species, although Dsk2 resin is preferable for this purpose as it does not enrich the mono-ubiquitylated form to an extent where it affects the visualization of the often weaker RPB1 poly-ubiquitylation signal.RNAPII ubiquitylation triggered by transcription stress, such as that caused by bulky DNA lesions, targets RNAPII for degradation by the proteasome. The kinetics and peak time of RNAPII degradation will depend on the type and intensity of the stress stimulus . Some of these agents also induce changes in RNAPII phosphorylation patterns, thus using the phosphorylation-unbiased antibodies for detection of total RNAPII levels is recommended.At present, it is unclear which exact sites on RPB1 are modified by ubiquitylation, and by what type of ubiquitin chains, and how such modification affects RNAPII function. As multiple E3 ubiquitin ligase pathways have been implicated in targeting RNAPII, this question becomes increasingly important. Advanced strategies for profiling individual ubiquitylation sites in the proteome and generating suitable model systems to study those, will be crucial to understand how the “ubiquitin code” regulates the behaviour and fate of RNAPII.


Therapeutic proteins can induce immune responses that affect their safety and efficacy. Product aggregates and innate immune response modulating impurities (IIRMI) are risk factors of product immunogenicity. In this study, we use Intravenous Immunoglobulin (IVIG), Avastin, and Human Serum Albumin (HSA) to explore whether increased aggregates activate innate immune cells or modify the response to IIRMI. We show that increased aggregates (shaken or stirred) in IVIG and Avastin, but not HSA, induced activation of MAPKs  and transcription of immune-related genes including IL8, IL6, IL1β, CSF1, CCL2, CCL7, CCL3, CCL24, CXCL2, IRAK1, EGR2, CEBPβ, PPARg and TNFSF15 in human PBMC. The immunomodulatory effect was primarily mediated by FcγR, but not by TLR. Interestingly, increased aggregates in IVIG or Avastin magnified innate immune responses to TLR2/4 agonists, but diminished responses to TLR3/9 agonists. This study shows that IIRMI and aggregates can modify the activity of immune cells potentially modifying the milieu where the products are delivered highlighting the complex interplay of different impurities on product immunogenicity risk. Further, we show that aggregates could modify the sensitivity of PBMC-based assays designed to detect IIRMI. Understanding and managing immunogenicity risk is a critical component of product development and regulation. Occasionally, the immune response can elicit cross-reactive antibodies that neutralize low-expression non-redundant endogenous proteins in the host leading to deficiency syndromes as observed in patients that developed episodes of pure red cell aplasia or thrombocytopenia secondary to the development of antibodies to Eprex® and megakaryocyte growth and development factor (MGDF), respectively3. Thus, understanding and managing immunogenicity risk is a critical component of product development and regulation.Therapeutic proteins and peptides, whether recombinant, synthetic, or naturally derived have the potential to induce an immune response in the host that impacts on the safety and efficacy of the product. Product immunogenicity is not necessarily tied to adverse effects but can neutralize the product’s activity or result in changes in product pharmacokinetic or pharmacodynamics profiles, ultimately affecting the product’s safety or efficacy and depriving patients of important therapies6. This is supported by clinical data showing reduced immunogenicity in products that changed their manufacturing process and reduced their aggregate content7. For example, historically patients receiving human growth factor had a high incidence of persistent ADA (50–70%), which was reduced to <10% following a reduction in the aggregate content in the product7. Similarly, human gamma-globulin displayed increased immunogenicity as compared to the aggregate-free version9. More recently, product denaturation and aggregation were linked to the emergence of neutralizing antibodies in a clinical trial for erythropoietin10. These clinical observations are supported by several studies in mice showing that aggregated proteins are more immunogenic than monomers6. Further, aggregates may facilitate breaks in tolerance as suggested by studies where treatments with aggregated human IFNα induced anti-drug antibodies (ADA) in transgenic mice expressing human IFNα11. Several mechanisms by which aggregates mediate increased immunogenicity have been proposed, including modification of the pharmacokinetics and tissue distribution of the product, direct crosslinking of B cell receptors, increased uptake of particulate matter by antigen presenting cells (APC), and shifting intracellular trafficking and processing13. However, the mechanism by which aggregates foster immunogenicity are still not well understood16. This is partly because the effects of aggregates on immunogenicity may differ depending on their composition, morphology, size and charge; which in turn are heavily dependent on the characteristics of the monomer and the conditions under which the aggregates are formed21.Currently most therapeutic proteins have low levels of aggregates, however these can form during manufacturing, shipping, and storage. Protein aggregation has been identified as a key risk factor in protein immunogenicity assessments24. It is known that protein aggregates can induce local inflammatory responses when deposited on tissues, as reported in patients with Alzheimer’s, where deposits of Beta-amyloid causes local inflammatory responses, tissue damage and an influx of immune cells25. This immune activation is thought to be partly linked to the activation of innate immune receptors on immune cells and the local release of pro-inflammatory cytokines26. Among therapeutic proteins, aggregates of monoclonal antibodies were shown to crosslink Fcγ receptors (FcγR) on the surface of B cells, dendritic cells, macrophages and neutrophils embedded in the tissues with greater affinity than monomers27 and trigger the activation of innate effector cells. This enhances antigen presentation and maturation of dendritic cells (DCs)28. Further, recent studies suggest that aggregates of monoclonal antibodies (mAbs) induce an inflammatory response by PBMC that is not evident when monomers are used. Indeed, blocking of FcγR I, FcγR II and FcγRIII reduced the interaction between aggregated mAbs and FcγRs reducing the pro-inflammatory effect induced by antibody aggregates29. Lastly, recent reports suggest that Human PBMC, human monocytes and THP-1 cells could interact with Toll-like receptor (TLR) 2 and TLR4 to produce cytokines and chemokines when stimulated in-vitro with aggregated proteins30.One possible mechanism by which aggregation increases immunogenicity risk involves the induction of local inflammation and activation of innate immune cells embedded in the tissues where the product is administered31. Their ligands include various bacterial cell wall components such as lipopolysaccharide (LPS), peptidoglycan (PGN) and lipopeptides, as well as flagellin, bacterial DNA and viral RNA. Also, a variety of intracellular proteins such as heat shock proteins as well as protein fragments from the extracellular matrix can trigger them. These receptors initiate signaling cascades leading to the activation of transcription factors, such as AP1, NFκB and interferon regulatory factors (IRFs). In recent years, our group and others have suggested that trace levels of process or host cell derived impurities, or contaminants introduced unintentionally during the manufacturing process can activate PRR increasing the risk of product immunogenicity37. Further, several studies suggest that multiple innate immune response modulating impurities (IIRMI) can act on different receptors or cell types synergizing their effect on local inflammation and systemic immune activation, making IIRMI an important risk factor for immunogenicity40. In this study, we characterize the pro-inflammatory activity of protein aggregates and determine whether they synergize with different IIRMI to increase the immunogenicity risk of therapeutic proteins. We show that aggregates of IVIG, which is a complex mixture of human immunoglobulins, or Avastin, a monoclonal antibody (mAb) targeting VEGF-A, can modulate the response to IIRMI magnifying the induction of IL8, IL1β, and IL6 in response to TLR2 or TLR4 agonists. Interestingly, the same aggregates reduced the CXCL10, ISG15 and MX1 response to TLR9 (CpG) or TLR3 (poly I:C) agonists. The effect of the aggregates of antibodies was not universal as aggregates of HSA induced minimal innate immune activation and did not modulate the pro-inflammatory activity of endotoxin. Our study results highlight the interplay between product-related risk factors and the complexity of predicting immunogenicity risk.Toll-like Receptors are one of several families of germline-encoded pattern recognition receptors (PRRs) expressed on sentinel cells such as macrophages and dendritic cells that recognize structurally conserved molecules derived from microbesRecombinant Human serum albumin (HSA) therapeutic grade , IVIG  and Avastin  were used in these studies. Purified PRRagonists (PRRAgs) including lipopolysaccharide , Pam3Cys4, , and poly I:C, (TLR3) were obtained from InvivoGen . TLR9 agonists CpG ODN D-35, (5′-ggTGCATCGATGCAGGGGgg-3′) and CpG ODN B2006 (TCGTCGTTTTGTCGTTTTGCTGTT) were synthesized at the FDA core facility .−/− mice were housed in sterile microisolator cages under 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 . This study was carried out in strict accordance with the recommendations in the Public Health Service Policy on Humane care and Use of Laboratory Animals. All protocols involving animals were approved by the Animal Care and Use Committee at US-FDA.C57BL/6 (WT) and MyD886cells/well) obtained from the NIH blood bank were cultured in 24 well plates using RPMI , Penicillin-Streptomycin (50 μg/mL) 10 mM HEPES, B-mercaptoethanol (1 µl /mL) and stimulated with aggregated or unaggregated protein (80 μg/mL as described in30). In some studies, various concentrations of purified PRRagonists (PRRAgs) including lipopolysaccharide , Pam3Cys4, , CpG ODN and poly I:C, (TLR3) were added for the time intervals indicated in the individual figure legends.Unidentified PBMC , and MyD88−/− mice. BMDMs were cultured in DMEM supplemented with 20% (vol/vol) LADMAC (ATCC) conditioned medium as source of macrophages colony stimulating factor-1, 10%FBS, 2 mM glutamate, penicillin and streptomycin (50 μg/mL) for six days. Cells were washed and stimulated with aggregated or unaggregated IVIG either in the presence or absence of LPS (10 ng/mL) for 3 h in the absence of conditioned medium.Bone marrow derived macrophages (BMDM) were obtained from WT C57BL/6, FcγRRecombinant protein HSA, IVIG and Avastin were diluted to 1 mg/mL in sodium citrate buffer  and used either as 1) unaggregated protein (room temperature for 20 h), 2) shaken protein (450 rpm for 20 h at 25 °C), or 3) stirred protein . Aggregated and unaggregated proteins were kept refrigerated at 4 °C until further use and were stable in solution for up to 30 days. All preparations tested negative by LAL for endotoxin.The optical density of aggregated and unaggregated HSA, IVIG and Avastin was measured using the Agilent Carry 100 UV-VIS spectrophotometer .Aggregated and unaggregated proteins (10 µg) were loaded on Bio-Rad Mini-PROTEAN TGX, 4–20% gels  in the presence or absence of reducing agent. For reducing condition aggregated or unaggregated protein were treated with Laemmli lysis buffer with a reducing agent  at 100 °C for 5 minutes. Electrophoresis was carried out and gels were stained with Coomassie Brilliant Blue and photographed.A FlowCAM PV-100 bench top system  fitted with an FC100 flow cell, a 10X objective, and collimator, and a 0.5 mL syringe. The gain and flash duration were set such that the average intensity means of the image was consistently between 180 and 200. Initial calibration was carried out by flushing five times with 1 mL water. The aggregated and unaggregated IVIG, Avastin and HSA samples were analyzed at a flow rate of 0.145 mL/min without dilution. Particles ranging in size (2 μm–2 mm)  were counted and normalized by dividing the number of particles by the total volume imaged for each sample to obtain the particle concentration (#/mL). In addition to the samples, buffer solutions were also analyzed by FlowCAM. The data is shown after subtraction of particles from sample buffer.The number and size of particles in aggregated and unaggregated IVIG, Avastin and HSA were measured using the MFI 5200 system from Proteinsimple  equipped with a 100-micron flow cell and MFI view system software, (version 2-R2.6.1.20.1915). The system was cleaned with purified distilled water at maximum flow rate with the plush mode. Flow cell cleanliness was visibly checked confirmed visually before running the samples. The samples were analyzed at a flow rate of 0.17 mL/mL and fixed camera. Prior to use, the MFI performance was calibrated using 5 μm particles/mL  and NIST traceable size standard 3000 particles, . The samples were diluted 1000X in sodium citrate buffer. For each read 0.9 mL of product was prepared. The purge volume was 0.2 mL and analyzed sample volume was 0.6 mL. The MFI was set to capture 20,000 images/sample. Samples were measured in triplicate. The data is shown after subtraction of particles from sample buffer as background.HEK293 cells that co-express stably individual human TLR 2,4,5, or 9 and an NF-κB/AP-1-inducible secreted embryonic alkaline phosphatase (SEAP) reporter gene  were cultured in DMEM  and supplemented with Blasticidin and Zeocin as per manufacturer’s instructions. The cells were plated at InvivoGen’s recommended density in 100 µL in flat bottom 96-well plates and stimulated with 100 µL of aggregated or unaggregated protein (80 µg/mL) or TLR ligands for 24 h. Supernatants were collected and NFkB activation was measured using detection medium with quanti-blue preparation per the manufacture’s protocol. Briefly, 150 µL of quanti-blue were added per well in a 96 well flat bottom with 50 µL of supernatant from the stimulated samples with unaggregated product, aggregates or TLR ligands (positive controls). After 2 h incubation at 37 °C, the SEAP levels were determined colorimetrically at 620 nm by spectroscopy.6cells) were plated per well in 24 well plate in the presence of FcγR cocktail containing of F(ab)2 anti- FcγRI, F(ab)2 anti- FcγR II , F(ab)2 anti- FcγR III  (each 10 µg/mL) and isotype control (10 µg/mL) , incubated for an hour at 37 °C before stimulation with aggregated or unaggregated protein (80 µg/mL) for 24 h. mRNA of CCL2, CCL7, PPARG, EGR2, SPP1 and IL10 were measured by real-time PCR and protein in 24 h supernatants by using a Luminex ProcartaPlex® Immunoassay Kit (eBioscience) as per the manufacture’s recommendations.Human PBMC  as specified by the manufacturer. RNA was quantified by spectrophotometric analysis using the Nanodrop . The cDNA was prepared from 1 μg of total RNA using high capacity cDNA Reverse Transcription Kit  as per the manufacturer’s recommendation. Real-time PCR using TaqMan probes  was conducted using the Viia7 Real-time PCR system. The change in the relative expression levels of mRNA was calculated by normalizing against house-keeping genes and are expressed as fold increase over media treated cells using the 210) and fold change (log2) relative to unstimulated (M) cells. Highly significant targets are displayed on the top of the graph and highly differentially expressed genes fall to either side. Green point colors and horizontal lines indicate the various false discovery rates. The differences in expression for individual genes were tested by ANOVA (Graph pad Prism 7.0). The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.The NanoString nCounter Human Immunology V2 Panel  was used in these studies per manufacturer’s instructions. Briefly, probes were hybridized to 100 ng of total RNA for 19 h at 65 °C, after which excess capture and reporter probes were removed, and transcript-specific ternary complexes were immobilized on a streptavidin-coated cartridge. The code set contains a 3′ biotinylated capture probe and a 5′reporter probe tagged with a fluorescent barcode, two sequence-specific probes for each of 594 transcripts. All solution manipulations were carried out using the NanoString preparation station robotic fluids handling platform. Data collection was carried out with the nCounter Digital Analyzer to count individual fluorescent barcodes and quantify target RNA molecules present in each sample. Normalization was performed based on a standard curve constructed using the spike in exogenous control samples. Background hybridization signal was determined using the spike in negative controls provided. mRNAs with counts lower than the mean background +2 standard deviations were considered to be below the limits of detection. The nSolver (v.2.5) user interface (Nanostring) was used to operate the nCounter Advanced analysis module, which employs the R statistical software. The global gene expression data is presented in volcano plots, which show each target’s p value  with protease inhibitor cocktail (Roche), and boiled for 5 min with Laemmle’s lysis buffer for SDS-PAGE. Twenty µg of total protein were run on a Bio-Rad Mini-PROTEAN TGX, 4–20% gradient gel in Tris/glycine/SDS buffer  , and then electrotransferred onto Immobilon-P transfer membranes  at 100 V for 2 h (4 °C). After blocking for 1 h in TBS-T  containing 5% nonfat milk, membranes were washed 3 times in TBS-T and probed for 20 h at 4 °C with anti-pp38, anti-pERK, anti-pJNK, anti-pAKT, anti-pSTAT1 (701), anti-IRF7 and anti-β-actin  per manufacturer’s instructions. Following washing in TBS-T, membranes were incubated with secondary HRP-conjugated, anti-rabbit IgG or anti-mouse IgG from Cell Signaling  for 1 h at room temperature.The bands were detected using ECL plus reagents  and quantitated with ImageJ software.Cytokine were measured in 24 h supernatants using a Luminex ProcartaPlex® Immunoassay Kit from the eBioscience as per the manufacturer’s recommendations.P < 0.05.Statistical comparisons were assessed using a corrected multiple T test or one-way or two-way ANOVA followed by Bonferroni’s multiple comparisons test as appropriate. Statistical analyses were performed with Graph Pad Prism 7.0 Statistical Software . Synergy: a regression modelling cytokine production as a function of TLR ligand concentration, product aggregation, and their interaction was used to assess whether product aggregation and TLR ligand concentration affect cytokine production and whether such contribution was synergistic. A significant interaction between the two main effects would denote a synergistic association. This regression was fit using the PROC GLM procedure in SAS®, version 9.4. Statistical significance was defined as 42. Recent reports suggest that the increased immunogenicity could be mediated in part by aggregates inducing pro-inflammatory cytokines and chemokines43. To better understand the immunomodulatory effect of aggregated proteins, we induced the formation of aggregates in intravenous immunoglobulin (IVIG) by shaking or stirring the product for 20 h at room temperature as previously described30. IVIG is comprised primarily of polyclonal gamma globulins (IgGs) isolated and pooled from the plasma of healthy donors and is currently used to treat several autoimmune diseases44. The increased level of aggregates was evidenced by augmented absorbance at wave length ranges between 320–400 nm using light scattering analysis46  also suggest the activation of macrophages. In addition, the IVIG aggregates increased the expression of pro-inflammatory signals including IL8, IL1α and IL1β, as well as markers linked to innate immune activation via TLR such as serine/threonine kinases intermediate molecule IRAK1 and transcription factor CEBPB, TNFSF15 and HAMP50. Of note however, the increase in IL1RN, CSF1, Galectin 3, PPARG, EGR2, and reduction of CXCR2 also suggest an anti-inflammatory or regulatory macrophage component to the response51.To obtain a more complete characterization of the effects of aggregates on the immune response, we next stimulated PBMC with unperturbed or aggregated IVIG for 24 hours and screened gene expression using a Nanostring Immunology panel of 594 genes linked to the immune response. Results showed that unperturbed IVIG did not induce a significant increase in gene expression, whereas the same cells stimulated with IVIG containing increased aggregates modified the expression of over 100 genes Fig. 48,49. InInterestingly, although the expression of most genes was similarly modified by shaken and stirred products, PBMC stimulated with stirred IVIG had relatively higher transcripts for PPARG, CD276/B7, IL-1RN, CSF1, and EGR2, hepcidine (HAMP) and lower CXCL2, IL-8, IL-6 and IL-1b Figs  and 2B. 52. Previous studies suggested that aggregated immunoglobulins cross-link FcγR on the surface of monocytes more efficiently than monomeric ones eliciting an inflammatory response29 and blocking of FcγR was shown to reduce the secretion of pro-inflammatory cytokines by human PBMC and THP-1 cells stimulated with aggregated mAbs30. To determine whether FcγR mediates the increase in cytokine levels induced by aggregated IVIG, we stimulated BMDM from C57BL6 or FcγR−/− mice. As shown in Fig. −/− mice suggesting that in mice FcγR mediate the response to aggregates. Several reports have suggested that the role of FcγR may differ in mice and human, therefore we next exposed PBMC to a cocktail containing neutralizing concentrations of F(ab)2 anti- FcγR 1, F(ab)2 anti-FcγR II, F(ab)2 anti- FcγR III prior to stimulating the cells with unaggregated or aggregated IVIG. As shown in Fig. IVIG is used in the treatment of several pro-inflammatory diseases and the underlying mechanism is thought to be linked to the blocking of FcγR on the surface of B cells and monocytesin vitro by directly stimulating TLR2 and TLR4 in human monocytes or PBMC53. These receptors activate cells via NF-κB leading to the production of pro-inflammatory IL1β, IL6 and IL-8. We reasoned that if aggregates were activating PBMC primarily via TLR4, then they would induce a similar pattern of gene expression as the canonical TLR4 ligand, LPS. Therefore, we first compared the response of PBMC from 6 blood donors cultured in the presence of very low levels of endotoxin (100 pg/mL), unperturbed, or aggregated IVIG  reporter gene. Previous studies had shown HEK293 cells expressing TLR2 to be activated by soluble aggregates of amyloid peptides54. As shown in Fig. −/−), a universal adaptor protein required for signaling by almost all TLR except TLR3. As shown in Fig. −/− mice had impaired response to LPS, there were no differences in mRNA levels of IL6, IL10, IL1β and IL12p40 in BMDM from MyD88−/− after stimulation with IVIG aggregated protein. Together, this indicates that IVIG aggregates do not require TLRs or Myd88 to induce a pro-inflammatory response.Recent studies suggest that aggregates can activate innate immune responses VIG Fig. . IVIG agIn vitro and in vivo studies with mouse and primate models have shown that trace levels of innate immune response modulating impurities (IIRMI) can induce the expression of pro-inflammatory cytokines such as IL1β, IL6, and IL856. Such an increase in inflammatory cytokines is associated with enhanced antigen presentation and was shown to correlate with increased risk of immunogenicity for therapeutic proteins in vivo57. Since both IIRMI and aggregates could be present in a therapeutic protein preparation, we next explored whether the pro-inflammatory effect of aggregated IVIG would modify the response to trace levels of impurities. As expected, incubating PBMC with increasing concentration of LPS (1 pg to 100 pg/mL) induced IL6, IL1β and IL8 mRNA expression in a dose dependent manner ; thus CXCL10, ISG15 and MX1 were used to monitor their activity. As shown in Fig. Biologics can contain nucleic acids derived from the host cell or adventitious agents that are present during manufacture. Multiple receptors have been described in the endosomes and cytoplasm that recognize nucleic acids and can foster an immune response60. To determine whether the pro-inflammatory effect of aggregates and the magnification of the effect of IIRMI was restricted to IVIG we produced aggregates of Avastin, a humanized recombinant monoclonal mAb that binds VEGF-A. As shown in Fig. IVIG is a complex biologic product comprised primarily of immunoglobulins of different specificities and previous studies had suggested that it can downregulate TLR responses, although the mechanism is unknownTo explore whether the immunomodulatory effect of aggregates extended to other therapeutic proteins, we studied the effects of aggregated HSA. Human serum albumin is frequently used in the formulation of biologics. The aggregates formed by HSA were smaller and fewer than those formed under the same conditions by IVIG or Avastin Fig.  and fail61. Separately these different types of impurities have been shown to act as unintended adjuvants that can activate immune cells directly, mediate immune cell recruitment, enhance antigen uptake and processing, and foster adaptive immune responses. In these studies, we sought to characterize better the pro-inflammatory response induced by aggregates and determine whether their presence could modify the response to trace levels of IIRMI, thus increasing the immunogenicity risk of a therapeutic protein. Our results show that increased aggregate content in IVIG or Avastin leads to the induction of a complex innate immune response characterized by increased activation of p38, JNK and ERK, and consequent IL1β, IL6 and IL8 suggesting a pro-inflammatory response that could increase the immunogenicity risk. The innate immune activation induced by immunogloblin aggregates is partially dependent on FcγRs but not does not appear to require TLR2 or TLR4 receptors unlike what had been previously suggested43 since the absence of MyD88 did not modify mRNA expression in response to the aggregates. Further, we show that the presence of IVIG aggregates magnified the expression of pro-inflammatory cytokines in response to TLR2 and TLR4 agonists, whereas they reduced the response to nucleic acid impurities that stimulate a type I IFN responses, indicating that the interplay between aggregates and IIRMI is complex. Together these data suggest that protein aggregates and some IIRMI can synergize to activate pro-inflammatory responses and increase the immunogenicity risk for therapeutic products and underscores the complexity of assessing immunogenicity risk.Immunogenicity risk cannot be predicted from protein structure alone as it is influenced by a myriad of often interacting parameters such as product structure, preexisting tolerance, immunological status, HLA type, and underlying disease. Two product-related factors thought to be critical in determining whether a product will be immunogenic are the presence of product aggregates and IIRMI such as residual host DNA, host cell proteins, endotoxins and other microbial fragments62, HSA induced minimal innate immune activation in PBMC and did not modulate the pro-inflammatory activity of endotoxin or CpG ODN. This shows that the underlying structure of the aggregated product plays a critical role in its ability to activate innate immune responses. This is consistent with previous studies showing increased binding of aggregated IgA or IgG to FcγR as compared to monomers64 as well as studies in neutrophils showing that crosslinking of FcγRs with aggregated IgG leads to p38, ERK and JNK phosphorylation, activation of NFkB and subsequent induction of proinflammatory cytokines65. Surprisingly, while FcγR were absolutely required for activating mouse BMDM, blocking FcγRs in PBMC with a combination of anti-FcγRs monoclonal antibodies  only partially reduced the transcription of IL10 and TNFα, and not that of CCL3, CCL7, or IL1β. This could be due to achieving only partial neutralization of the receptors with the concentrations used, or to biological differences between human and mouse cells. Of note, as shown in Fig. 29, the available neutralizing antibodies to FcγR can stimulate PBMC leading to increased IL1β, IL10 and TNFα levels even in the absence of aggregated monoclonal antibody29. This suggests that these results must be interpreted cautiously as the pro-inflammatory response to the cocktail may have masked a reduction in response to the FcγRs mediated activation by the IVIG aggregates. Additional studies will need to address whether different isotypes of aggregated Ig or Fcγ fusion proteins preferentially bind FcγR resulting in various downstream signaling and activation.The formation of product aggregates can take many forms depending on the structure of the product and the conditions that facilitate the aggregation. In this study, we have singled out aggregates formed by physical stress (stirring or shaking conditions). While these conditions are thought to represent conditions that model stress during distribution and storage of the product, the concentration of particles used are beyond what would be typically found in commercial products and accounts for a worst-case scenario used to explore any potential impact of such aggregates in a physiologically relevant system. In our studies, increased levels of antibody aggregation led to the induction of pro-inflammatory genes. In contrast, despite forming aggregates of similar sizes and binding FcRN30. Surprisingly, in our studies HEK-293 expressing individual TLRs failed to be activated by the aggregates. Further, BMDC from MyD88 KO mice induced similar cytokine levels as those from the parental strain suggesting that TLR responses are not required for the response of aggregated IVIG  and activator protein 1 (AP-1). In contrast, FcγR lead to recruitment of tyrosine kinase Syk leading to increased calcium flux and NADPH activation. Although we did not see an increase in TLR expression in the presence of aggregates, we did see increased levels of IRAK1, CEBPB, TNFSF15 and HAMP which could indicate increased signaling. Further, TLR and FcγR can be found in the same lipid rafts and TLR4 was shown to activate Syk directly in neutrophils and macrophages67, and both FcγR and TLRs were shown to downregulate miRNAs that regulate the production of pro-inflammatory cytokines IL-6 and TNFa68. Therefore, the possibility that crosslinking of FcγR by immunoglobulin aggregates may augment TLR2/4 mediated activation is not unexpected. In contrast, the signaling that leads to a reduced response to TLR9 and TLR3 agonists in the presence of IVIG aggregates is less clear. However, previous studies have shown that NFkB driven maturation of pDC may led to reduced type I IFN production, and a recent study suggests that immune complexes may downregulate TLR9 via FcγRIIb69. Lastly, although these studies explored the role of FcγR and some TLRs, there are multiple other receptors that may be involved in the pro-inflammatory response observed in vitro. Understanding these interactions will require further studies, but the observation clearly illustrates the complexity of signals that could be elicited by product and process related impurities that stimulate multiple receptors and underscores the difficulty of predicting immunogenicity risk.The crosstalk between TLRs and FcγR has been previously described, although the mechanisms underlying it are still unclear30. Our studies confirm their findings, but provide a more detailed gene expression analysis of the in vitro response that suggests that small structural differences in the aggregates formed by different stresses can result in different patterns of innate immune activation. Indeed, as shown in Figs 72. Importantly, in these studies we used PBMC as the platform to explore the response to aggregates. While PBMC are frequently used to assess for innate immune modulators, the frequency of neutrophils, and particularly macrophages and dendritic cells is very low, suggesting that the immunomodulatory effects in the tissues, where there is greater frequency of FcγR and PRR rich cells may be more pronounced. Detailed studies linking the physicochemical properties of the aggregates induced by different stressors to the kinetics of the pro-inflammatory response may be helpful in fully understanding the response to aggregates.Previous studies had identified IL6, IL8 and IL1β as biomarkers of innate immune response activation by aggregates73. Since it is not possible to predict the complete array of possible impurities that could be present in a product, we have previously suggested using cells bearing multiple PRRs to determine whether the sum of impurities present in a product could trigger an inflammatory response that would increase the likelihood of an immune response to inform the immunogenicity risk55. The results presented herein suggest that the presence of aggregates may magnify the response to some impurities while dampening the response to others. When used together with TLR2 or TLR4 agonists, the enhancement was not mediated by increased TLR expression and did not seem to modify the minimal concentration of PRRAgs capable of inducing a detectable response in PBMC; however, the expression level of proinflammatory cytokines and chemokines was significantly increased as compared to that of cells exposed to the same PRRAgs in the presence of the unaggregated product. In contrast, in the presence of aggregates PBMCs required higher levels of TLR3 or TLR9 agonists to become activated, which could make cell-based assay to detect IIRMI less sensitive to these types of impurities. Interestingly the reduction was evident for genes linked to the activation of IRF7, IRF3, and STAT1, but not for the expression of IL6 or IL8, which is mediated via NFkB activation suggesting that the aggregates specifically reduced the IRF-IFN path38. Importantly, since different stresses stimulate distinct mechanisms of aggregation and result in different types of aggregates, from soluble oligomers to high molecular weight particles, it will be important to understand what type and level of aggregates can be tolerated when using cell-based assays to assess IIRMI. Future identification of biomarkers of innate immune activation that can differentiate between stimulation by aggregates or IIRMI will help to evaluate and mitigate the risk posed by innate immune activation.Although all proteins, self and foreign, bear antigenic sites to which an immune response can theoretically be directed, the actual development of an immune response depends on numerous host and product-related factors. One of the critical risk elements for immunogenicity is the presence of impurities of bacterial or host cell origin that can act as adjuvants to the product. These impurities are recognized by several families of PRR that are engaged in surveillance of the extracellular and intracellular space for the presence of microbial products or endogenously derived danger signals. Each of these receptors is triggered by unique microbial signatures but evokes mostly overlapping responses that are primarily channeled through the activation of NFkB and AP1, and result in the production of pro-inflammatory cytokines  and chemokines . Numerous studies have shown that multiple ligands can activate PRR. Indeed, over 20 different ligands have been identified for TLR4Supplementary information


Heterogeneous treatment effects (HTEs), or systematic differences in treatment effectiveness among participants with different observable features, may be important when applying trial results to clinical practice. Current methods suffer from a potential for false detection of HTEs due to imbalances in covariates between candidate subgroups.N = 200 phase II oncology trial where there were either no HTEs (1A) or two HTE subgroups (1B) and an N = 6000 phase III cardiovascular disease trial where there were either no HTEs (2A) or four HTE subgroups (2B). Additionally, we considered an N = 6000 phase III cardiovascular disease trial where there was no average treatment effect but there were four HTE subgroups (2C).We introduce a new method, matching plus classification and regression trees (mCART), that yields balance in covariates in identified HTE subgroups. We compared mCART to a classical method (logistic regression [LR] with backwards covariate selection using the Akaike information criterion ) and two machine-learning approaches increasingly applied to HTE detection (random forest [RF] and gradient RF) in simulations with a binary outcome with known HTE subgroups. We considered an In simulations 1A and 2A (no HTEs), mCART did not identify any HTE subgroups, whereas LR found 2 and 448, RF 5 and 2, and gradient RF 5 and 24, respectively . In simulation 1B, mCART failed to identify the two true HTE subgroups whereas LR found 4, RF 6, and gradient RF 10 . In simulations 2B and 2C, mCART captured the four true HTE subgroups, whereas the other methods found only false positives.All HTE subgroups identified by mCART had acceptable treated vs. control covariate balance with absolute standardized differences less than 0.2, whereas the absolute standardized differences for the other methods typically exceeded 0.2. The imbalance in covariates in identified subgroups for LR, RF, and gradient RF indicates the false HTE detection may have been due to confounding.Covariate imbalances may be producing false positives in subgroup analyses. mCART could be a useful tool to help prevent the false discovery of HTE subgroups in secondary analyses of randomized trial data. Precision medicine aims to direct the right medication to the right patient at the right time . A requiHeterogeneous treatment effects (HTEs)—or systematic differences in treatment effects among participants with different observable features—are increasingly identified through post-trial data analyses , 3. BecaHTE estimation approaches attempt to predict individual-level treatment effects and identify subgroups of participants with above average or lower average benefit from a treatment. In this paper, we focus on identifying subgroups with HTE, rather than individual-level treatment effects. A major challenge in HTE subgroup analysis is that, even if the data come from a randomized trial, subgroups that are identified may be imbalanced on important clinical characteristics . For exaThe consequences of falsely identifying a subgroup can be dire: a life-saving treatment could be withheld or an ineffective treatment prescribed that increases the chances of a serious adverse event. One potential strategy to reduce the likelihood of false HTE detection is to reduce the observed imbalance between treatment and control groups when applying methods to detect HTE .Here, we propose a method for detecting and potentially avoiding imbalance in observed characteristics in trial data when constructing HTE models for precision medicine applications. We specifically combine matching  with classification and regression trees , and demonstrate the virtues of this approach compared to popular alternatives currently implemented in the post-hoc trial analysis literature.https://github.com/joerigdon/HTE.We simulated multiple trials in which the true HTEs were known, to compare the rates of finding true and false positive subgroups from: (i) the matching with classification and regression trees approach (mCART), (ii) a logistic regression (LR) model with interaction terms between participant covariates and the treatment arm, (iii) the common machine-learning approach of RF analysis, and (iv) the newer machine-learning approach specifically designed for detecting HTEs, of gradient forest analysis . These Y1, or when randomized to placebo, Y0. In practice, only one of Y1 or Y0 is revealed by the trial  = b, and let V ~ Multinom be shorthand for V is drawn from a multinomial distribution where V can take on the values  with corresponding probabilities . For setting (1), the oncology trial, we simulated n = 200 records of the following six baseline covariates: age ~ N, disease stage = 4 ~ B(0.98), disease site =  ~ Multinom, previous treatment =  ~ Multinom, Eastern Cooperative Oncology Group (ECOG) score ~ Bern(0.55), and disease-free interval >12 months from adjuvant to recurrence ~ Bern(0.35).Let δ =  ~ Multinom for all N = 200 individuals in the trial, independent of covariates such that the average treatment effect was approximately –0.2. For individuals with δ = −1, Y1 and Y0 immediately follow as 0 and 1, respectively, and for individuals with δ = 1, Y1 and Y0 immediately follow as 1 and 0, respectively. For individuals with δ = 0, Y1 and Y0 were set to 1, so that our sample means were approximately Y1 = 0.5 and Y0 = 0.7.We simulated the treatment effects in two ways for the oncology trial. In simulation 1A, we considered a setting where there were no HTE subgroups, i.e., δ ~ Multinom, such that the average treatment effect (ATE) was approximately −0.4 (Y1 = 0.4 and Y0 = 0.8), and for women aged >65, δ ~ Multinom, such that the ATE was approximately 0 (Y1 = 0.6 and Y0 = 0.6), and such that the overall ATE was still approximately −0.2.In simulation 1B, we considered a setting where there were HTE subgroups in the oncology trial in two groups: women over 65 years old versus women 65 years and younger, each constituting about half of the sample population of the trial. In particular, for women aged ≤65, Y0) to an event rate of 5.2% in the intensive target group (Y1). Such a study design would require n = 3000 per group , Pr[δi = 0], and Pr[δi = 1] for i = 1, …, n. Within the terminal nodes identified in step 7, we can estimate the ATE in the original data set using methods for inference on a risk difference.We apply the model estimated in step 7 to our To identify HTE subgroups that are balanced on the covariates, we propose a novel algorithm using rank-based Mahalanobis distance matrix matching followed by classification trees for inference on the HTEs. Henceforth, we term this method matching plus classification and regression trees (mCART):We compared our method to three strategies commonly applied or proposed for identifying HTEs: LR, RF, and gradient RF. Unless otherwise specified, default settings for parameters in RF or gradient RF were used.p1, and a probability of the outcome for the control group, p0, were estimated for each of the 6000 individuals. Treatment effects were estimated for each individual as p1 – p0. Estimated treatment effects were partitioned into subgroups using one classification and regression tree (via the R package ‘party’)  for outliers . Additiohttps://github.com/joerigdon/HTE. In simulations 1A, 1B, 2A, 2B, and 2C, one hypothetical trial was generated by assigning half of the simulated participants to the treatment group (Z = 1) and half to the control group (Z = 0), and the outcome for each individual was calculated as Y = Z × Y1 + (1 – Z) × Y0. For all methods, after subgroup identification, HTEs were computed within identified subgroups along with treated minus control absolute standardized differences (ASDs) for every covariate. Subgroups were deemed to have acceptable balance if all ASDs were below 0.2, a cutoff point chosen because it is of the same order as small effect sizes [δ’s in the subgroup).Simulations were performed in R , using tct sizes . Bias waY) to be attributed to control or treatment exposure without concerns due to confounding arising from baseline covariates. The outcomes were: 46/100 individuals in the combination group experienced the event, while 70/100 individuals in the single agent arm experienced the event. The risk difference was The characteristics of the simulated trial population for simulation 1A are displayed in Table In subgroup analyses of the data shown in Table Table 7 in the appendix shows the data at baseline for simulation 1B. Data are balanced across the single agent and combination groups with the minor exception of previous treatment equals none , whereas LR found 4, RF 6, and gradient RF also 10 . Fig. 4 in the appendix is a plot of the maximum ASD within subgroup versus the bias for each of the methods.Table Simulation 2A contained no subgroups with HTEs. In subgroup analyses, mCART did not find any subgroups, whereas LR found 448, RF 2, and gradient RF also 24 . Fig. 5 in the appendix is a plot of the maximum ASD within subgroup versus the bias for each of the methods.Table 8 in the appendix shows the data at baseline for simulation 2B. Data are balanced across the treatment and placebo groups with all ASDs< 0.2. The trial is a success as the treatment group (160/3000) is shown to have a lower event rate than the control group (203/3000) with a risk difference of −0.014 .Simulation 2B contained four subgroups with HTEs as outlined in ‘Methods’: individuals taking aspirin with eGFR ≤ 72, individuals taking aspirin with eGFR > 72, individuals not taking aspirin with eGFR ≤ 72, and individuals not taking aspirin with eGFR > 72. In the subgroup analyses, mCART found four subgroups  is shown to have the same event rate as the control group (190 / 3000) with a risk difference of 0.001 .In the subgroup analyses, mCART found four subgroups , whereas LR found 442, RF 3, and gradient RF 46 . The four subgroups found by mCART approximately equaled the four true subgroups: individuals taking aspirin with eGFR ≤ 72.057 (versus 72), individuals taking aspirin with eGFR > 72.057 (versus 72), individuals not taking aspirin with eGFR ≤ 72.796 (versus 72), and individuals not taking aspirin with eGFR > 72.796 (versus 72). Fig. 7 in the appendix is a plot of the maximum ASD within subgroup versus the bias for each of the methods. Notably, all the subgroups discovered by mCART had maximum ASDs < 0.2 and bias never exceeding 0.008.Precision medicine requires the detection of HTEs from randomized trial data to provide personalized effect estimates—that is, to determine if a particular patient is likely to experience benefits, no effects, or harms from therapy. This contextualizes the ATE in a trial for the individual patient.Here, we found that while common standard regression and alternative machine-learning methods can identify HTE subgroups, they may also yield imbalances between the study arms within identified subgroups, such that differences in outcomes are falsely attributed to differences in treatment effect, but are in fact due to imbalances in covariates. We strongly recommend that researchers report the balance between study arms in identified subgroups to reduce the risk of false HTE reporting. Guidelines already recommend that studies estimating causal effects have a detailed discussion of the covariate balance of the groups under discussion , 24.We also tested the method of matching followed by CART analysis and found it may reduce the imbalance in observable covariates and thereby prevent false HTE detection. The method yielded subgroups with a balance in observable characteristics, suggesting that differences in outcomes in identified subgroups were attributable to treatment or unobserved covariates. The method also produced an interpretable decision tree that may be more transparent to clinicians than alternative machine learning methods.A limitation of our study is that we considered a handful of simple data-generating processes that we believe to be representative of trials hypothetically seen in clinical practice, with the intention of demonstrating a situation where an imbalance in subgroups can cause confounding. We do not know how often this will occur in practice, as this requires a further systematic review of the literature. Importantly, a second limitation is that real data could have unmeasured confounders that we cannot control for.N = 200 oncology trial).In future work, we hope to study how the choice of different matching algorithms impacts the performance of the mCART algorithm. Other avenues of future research include applying mCART to non-binary outcomes , considering methods of inference for smaller sample sizes within identified subgroups , and fumCART could be an interpretable and rigorous tool for identifying HTE subgroups after the conclusion of a clinical trial, and may help identify subgroups balanced on potential prognostic baseline variables that also differ in treatment effects. Perhaps most importantly, mCART may help prevent the wasteful false discovery of HTE subgroups in secondary analyses of randomized trials.