RelB proximity proteomics and CRISPR screening define chromatin regulators of noncanonical NF-κB control of HIV latency and reactivation

Reviewer #1 (Public review):

Summary:

Bussey-Sutton, Gray, Wu et al. combine RelB proximity-labeling proteomics (BioID) with a custom, BioID-informed HIV-CRISPR screen to map the chromatin/transcriptional network that links non-canonical NF-κB (RelB/p52) activation by the SMAC mimetic AZD5582 to HIV-1 latency reversal. They identify a broad basal RelB interactome (chromatin remodelers, RNA-binding proteins, transcription machinery) that is partially reshaped, rather than replaced, by AZD5582 treatment. A targeted CRISPR screen against the BioID hit list then functionally separates these interactors into pro-reactivation factors (KAT7/HBO1, NSD2, SIN3A) and restrictive factors (p300, CHD4, USP7, UHRF1, DNMT1).

Pharmacological inhibition of the two acetyltransferase hits (KAT7i and p300i) validates opposing phenotypes predicted by the screen, and a comparative TNF-α (canonical NF-κB) screen argues that at least the p300 and, more selectively, the KAT7 pharmacology are biased toward the ncNF-κB axis. The dataset is a useful resource, and the BioID, CRISPR, pharmacology pipeline is a nice template for functionally triaging proximity-proteomics hits rather than treating them as a static interactome.

Strengths:

This study takes an integrative approach in which proximity proteomics is used to define candidate RelB-associated regulators, a purpose-built targeted CRISPR library tests these candidates directly for a functional phenotype, and pharmacological inhibition cross-validates the two acetyltransferase hits with an orthogonal, catalytic-activity-specific perturbation. This funneling from unbiased proteomic discovery to targeted functional testing to mechanistic pharmacology is an efficient and logical pipeline for converting a large interactome into a smaller set of high-confidence, functionally validated regulators.

The comparison of basal versus AZD5582-stimulated RelB interactomes is conceptually useful, as it shows that RelB engages a largely pre-existing chromatin and transcriptional regulatory scaffold upon ncNF-κB activation, rather than recruiting an entirely new complex. This is a finding with potential implications for non-canonical NF-κB signaling beyond the HIV latency context specifically.

The inclusion of a parallel HIV-CRISPR screen using TNF-α to activate canonical NF-κB signaling is a valuable control, allowing the authors to distinguish regulators that are broadly required for NF-κB-dependent HIV expression from those more selectively linked to AZD5582-driven ncNF-κB signaling. This comparison strengthens the authors' claims of pathway specificity for hits such as KAT7 and p300.

Finally, pharmacological validation of the top acetyltransferase hits was performed across two independent latency models (J-Lat 10.6 and N6), which helps address, at least in part, the concern that phenotypes are specific to a single clonal integration site. Together with the deposited proteomics and screen datasets, this provides a genuinely useful resource for the field.

Weaknesses:

(1) BioID inherently reports both direct and indirect (proximal) interactions, and no individual hits are validated by an orthogonal method (e.g., co-IP, PLA). As a transcription factor, RelB would be expected to reside near transcriptional/chromatin machinery, so this enrichment alone isn't surprising, making it hard to distinguish functionally relevant RelB complexes from bystander proteins in the same active chromatin neighborhood. At least a subset of top hits should be validated independently.

(2) BioID interactome was defined in a single clonal cell line, limiting generalizability across latent cell types. The RelB BioID proteomics, which forms the entire basis for the downstream targeted CRISPR library generation, was performed in one engineered Jurkat N6 clone. Both RelA/RelB pathway dependency and the baseline expression of the chromatin regulators identified as RelB interactors (e.g., DNMT1, UHRF1, SWI/SNF subunits, NSD2) can vary considerably across cell lines, latency clones, and primary CD4+ T cells. Since the CRISPR screen, and thus all functional hits in the paper, is entirely constrained by this single-cell-line interactome, any RelB interactor that is absent or differently regulated elsewhere would never have been tested, regardless of its true biological importance. To support claims of a broadly relevant RelB/ncNF-κB regulatory network, the authors should at least partially validate the basal and AZD5582-induced interactome (e.g., by co-IP/western for key hits) in a second cell line or primary CD4+ T cells, to distinguish clone-specific interactions from generalizable ones.

(3) Is KAT7/HBO1's contribution to reactivation a direct interaction/synergy with AZD5582, or an independent parallel requirement? The data show that KAT7/HBO1 knockout and catalytic inhibition (WM-3835) both reduce AZD5582-induced reactivation, and that KAT7 was detected as AZD5582-enriched in the RelB BioID. However, it remains unclear from the current data whether KAT7/HBO1 acts as a direct downstream effector in the ncNF-κB/RelB signaling axis (i.e., is recruited by or acts together with RelB/p52 at the LTR specifically upon AZD5582 stimulation), or whether it represents a more general/parallel chromatin requirement for HIV reactivation that is simply necessary regardless of the activating stimulus (and happens to also associate with RelB). Distinguishing these possibilities matters for interpretation: the former would support a model in which AZD5582 and KAT7 act synergistically through the same pathway (i.e., acetylation is mechanistically coupled to ncNF-κB activation), whereas the latter would suggest KAT7-mediated acetylation and RelB/p52 signaling are two independent, simultaneously required mechanisms that converge on the same locus without being mechanistically linked. The comparative TNF-α data (Figure 3C), where the genetic screen shows a shared dependency but the pharmacological inhibitor does not phenocopy this for TNF-α, adds to this ambiguity rather than resolving it. Clarifying whether KAT7/HBO1 functions upstream, downstream, or in parallel with RelB/p52 (e.g., via RelB ChIP after KAT7 knockdown, or KAT7 recruitment kinetics relative to RelB/p52 at the LTR) would strengthen the mechanistic claims in this section.

(4) A major limitation of the study is that screen results were validated in only two cell line models, and neither is a primary cell system. Different latency models are known to have distinct dominant transcriptional blocks, not just different genetic backgrounds. J-Lat clones, U1, ACH2, and other transformed lines differ substantially in why the provirus is silent; some are more Tat/P-TEFb-limited, some more chromatin/Polycomb-restricted, some more dependent on transcription factor availability (NF-κB, NFAT, AP-1). A regulator that is rate-limiting in J-Lat 10.6 specifically (a single integration site, clonally selected for a particular chromatin environment around the LTR) may therefore be irrelevant, or could even act in the opposite direction, in a model where the block is elsewhere.

(5) HIV latency and LRA responsiveness are well known to be strongly integration-site and clone-dependent. Pharmacological validation of CRISPR screen hits extends to N6, but the primary screen hits themselves are not confirmed in a second genetic background. This is an important caveat that should be discussed explicitly, and ideally at least the top hits (KAT7, NSD2, SIN3A, p300, CHD4) should be individually knocked out (not just inhibited) in a second model to confirm the genetic phenotype generalizability, not just the pharmacology.

(6) More importantly, no validation was performed in primary CD4+ T cells or ex vivo resting CD4+ T cells from people with HIV, which remain the field's benchmark for confirming that a candidate regulator is relevant to authentic latency rather than to a transformed cell line. Basal chromatin state, NF-κB pathway, and expression of the chromatin regulators identified here (e.g., DNMT1, UHRF1, SWI/SNF subunits) can differ substantially between transformed lines and primary resting T cells. Given this, the current claims of a broadly applicable "chromatin regulator" network should be tempered, and at least the top hits (e.g., KAT7/HBO1, NSD2, SIN3A, p300) should ideally be tested in a primary cell model before being generalized as regulators of HIV latency and reactivation more broadly.

(7) The finding that p300 restricts AZD5582-driven reactivation is framed as consistent with Horvath & Sadowski (ref 36), but p300/CBP is also a well-established Tat/LTR coactivator in many contexts (Kiernan et al., Marzio et al). The Discussion would benefit from a slightly more developed treatment of how these findings can be reconciled rather than citing a single concordant paper.

Reviewer #2 (Public review):

Summary:

Bussey-Sutton et al. investigate the RelB interactome in the presence and absence of the SMAC mimetic AZD5582. They combined RelB BioID with CRISPR screening and highlighted multiple potential transcriptional regulators that may contribute to the maintenance or reversal of HIV latency. However, there are several technical limitations and gaps in validation in the current version of the work.

Strengths:

The overall topic is of clear interest; the RelB interactome and CRISPR screens provide interesting hits.

Weaknesses:

From a technical standpoint, RelB is expressed using a doxycycline-inducible system, yet the fusion protein (Figure S1A) appears to be expressed at high levels even in the absence of doxycycline (and does not seem to respond to doxycycline supplementation). As this is the key model system used in the paper to identify RelB interactors, this limitation should be discussed and potentially addressed.

It would also be important to include some orthogonal validation (e.g., co-immunoprecipitation) of top novel RelB interactors identified by BioID.

In Figure 2C, two non-targeting controls appear among the most highly enriched hits. This raises the question that other top hits might reflect nonspecific effects or assay noise. Consistent with this concern, the effects observed following siRNA-mediated depletion of the top CRISPR-screen candidates in Figure 2D and Figure 3 appear relatively modest.

More broadly, the absence of validation in primary-cell models is a further limitation, as the chromatin environment and transcriptional regulation of HIV latency in the two cell-line models used here are likely to differ substantially from those in primary CD4+ T cells.

Minor point:

Endogenous RelB remains present in the RelB-BioID cells and could compete with the miniTurbo-RelB fusion protein for physiological binding partners. Moreover, the effect of two sources of constitutive RelB expression (see comment above) on the physiologic RelB interactome is not obvious. Controlling for this is not trivial, but this potential confounding factor should be acknowledged (and further reinforces the need for orthogonal targeted validations of interactors in cells with endogenous RelB).

Reviewer #3 (Public review):

The study is built on the premise that the SMAC mimetic AZD5582 can reverse HIV latency via the RelB-mediated non-canonical NF-kB pathway activation; however, its molecular regulators remain undefined. The authors use an inducible BioID RelB reporter system in Jurkat N6 cells containing an integrated HIV-1 provirus and identify both the basal and the AZD5582-activated chromatin and transcriptional regulatory network of the non-canonical NF-kB pathway. To further explore the role of the identified regulators, an HIV-CRISPR latency screening approach was used, and based on enrichment or depletion of guide RNAs in the supernatant, genes that inhibited or activated virus reactivation, respectively, were identified. Genes like LRPPRC (RNA-binding protein), KAT7 (acetyltransferase), NSD2 (H3L36 methyltransferase), SIN3A (part of histone deacetylase complex), and others were identified to be essential for HIV reactivation. Nearly half of the identified factors were found to be negative regulators of reactivation, notably CBP/p300 (acetyltransferase), CHD4, USP7, UHRF1, and DNMT1. The roles of KAT7 and p300 were validated through the use of specific inhibitors. To narrow down regulators that are specific for nc NF-kB HIV reactivation vs NF-kB dependent HIV expression, a parallel CRISPR screen was performed with TNF-alpha, which reconfirmed the role of RelB, KAT7, and p300 in AZD5582-mediated HIV reactivation.

Overall, this is a solid and well-designed study that combines complementary approaches to deliver a valuable resource.

The study's strength lies in integrating two high-throughput approaches of RelB proximity labelling and targeted CSIPR screening to identify molecular regulators of the HIV reactivation pathway. A parallel CSIPR screen with TNF-alpha provides key insights into pathway-specific vs shared regulators of latency reversal.

The limitation of the study, as indicated by the authors, is that the assays are based only on HIV output. Additionally, some of the identified interactors may be indirect or context-dependent and may not represent all HIV reservoirs in vivo.

Author response:

We greatly appreciate the editors and reviewers for their time and careful evaluation of our manuscript. It was clear that the reviewers valued our study that integrated RelB proximity proteomics with targeted HIV-CRISPR screening to identify chromatin and transcriptional regulators of AZD5582-mediated HIV reactivation. We appreciate all of the constructive comments and agree that the manuscript can be further strengthened by clarifying several technical points, correcting text and figure issues, and more clearly defining the scope of the study.

A recurring theme of the reviews is the need to better define which RelB-associated factors are functionally meaningful regulators of HIV reactivation. We agree with the reviewers’ point that BioID/miniTurbo proximity labeling does not by itself establish direct physical interaction. That said, the method provides an in vivo cell measurement of protein proximity and can capture core complex interactions as well as transient, weak, or chromatin-associated interactions. Notably, such transient interactions are often lost during cell lysis and extraction, as well as co-IP. In the revised manuscript, we will clarify this distinction and emphasize that the BioID dataset identifies RelB-proximal candidate regulators rather than exclusively direct binding partners. We will also consider orthogonal validation of key hits using either co-IP or an orthogonal in cell proximity assay like PLA, which would preserve the cellular context of RelB-associated regulatory complexes.

The reviewers also raised a fair concern regarding physiological relevance and the use of transformed latency models. We agree that primary cell validation would be an important next step for determining how broadly these findings apply to HIV regulation. At the same time, the present study was intended to be an initial resource of RelB-associated factors that contribute to HIV transcription in tractable latency models. Nonetheless, we will temper any language that implies broad generalization across all latency contexts and further discuss the limitations of clonal and transformed latency models. Additionally, and as the reviewers suggested, we will also test whether the KAT7/HBO1 and p300 inhibitor phenotypes extend to a primary CD4+ T-cell latency model.

We also appreciate comments regarding the RelB-miniTurbo system; notably concerns with the basal expression of the fusion protein and presence of endogenous RelB. We will revise the manuscript to better describe these limitations and discuss how they may affect interpretation of the BioID data. In particular, we will emphasize that the dataset should be viewed as a resource for candidate RelB-associated regulatory factors that require functional and orthogonal follow-up.

Several reviewers identified figure and text issues that will be addressed in the revised manuscript. Specifically, we will correct the Figure 1 legend and reference errors, the duplicated sentence, and other typos mentioned. We will also clarify the number of biological replicates and statistical thresholds used for the BioID comparisons, improve the Supplementary Table 1 annotation, and expand the discussion of p300/CBP to better reconcile our findings with prior literature.

Overall, the revised manuscript will more clearly separate what this study establishes from what remains to be tested. Our goal for revision will be to enhance the value of the study while more clearly highlighting the limitations and what this resource provides for future directions.