ALBERT

Description: Abstract 2998 Introduction: GvHD remains the most deadly complication of HSCT despite current prevention strategies. To address the unmet need for better GvHD control, we have created a non-human primate (NHP) model with which to rigorously test mechanism and efficacy of novel therapeutics. In this study, we determined whether a novel combination of mTOR inhibition (with sirolimus) and CD28:CD80/86 costimulation blockade (with belatacept) could control GvHD. Here we show for the first time that these two agents combine synergistically to prevent both the clinical and immunologic manifestations of primate aGvHD. Methods: Rhesus macaque recipients were irradiated (9.6 Gy in 2 fractions at 7cGy/min), and then transplanted with G-CSF-mobilized PBSC from a haplo-identical donor (1–5×108 TNC/kg). Recipients were treated with either sirolimus alone (n = 4, troughs targeted at 5–10 ng/mL), belatacept alone (receiving weekly doses of 20 mg/kg), or combination therapy. Clinical GvHD was monitored using our previously described NHP grading scale (Miller et al., Blood 2010), and multiparameter flow cytometric analysis was performed. Results: Untreated controls (n = 5) developed rapid, severe histopathologically-proven aGvHD and succumbed rapidly (MST = 7 days). Recipients treated with either sirolimus or belatacept alone were partially protected from the clinical manifestations of GvHD. Sirolimus-treated recipients (n = 6) developed predominantly GI disease (with diarrhea but no elevation of bilirubin) and had an MST of 14 days (Figure 1). Recipients treated with belatacept alone (n = 3) developed primarily liver aGvHD (bilirubin rapidly rising to 6–30 × normal with histologically-confirmed lymphocytic infiltration) and an MST of 11 days. In striking contrast, recipients treated with combined sirolimus + belatacept (n = 5) demonstrated neither uncontrolled diarrhea nor hyperbilirubinemia at the timed terminal analysis (1 month post-transplant). We employed multiparameter flow cytometry to determine the immunologic consequences of sirolimus and belatacept on T cell proliferation (using Ki-67 expression) and cytotoxity (using granzyme B expression). We found that the clinical synergy observed with combined therapy was recapitulated immunologically. Thus, while untreated aGvHD was associated with rampant CD8+ proliferation (with 83 +/− 14% Ki-67+ CD8+ vs 4.7 +/− 0.6% pre-transplant), sirolimus or belatacept as monotherapy both partially controlled proliferation (35 +/− 3% and 65 +/− 23% Ki-67+ CD8+ with sirolimus or belatacept, respectively). Combined sirolimus + belatacept dramatically reduced proliferation (to 8 +/− 3%, favorably comparing with 13% Ki-67+ CD8+ T cells using standard Calcineurin Inhibitor/Methotrexate (CNI/MTX) prophylaxis). Sirolimus and belatacept both also partially controlled GvHD-related T cell cytotoxicity. Thus, while untreated aGvHD was associated with excessive granzyme B expression in CD8+ T cells (82 +/− 2% granzyme Bvery high CD8+ cells vs 0.3 +/− 0.2% pre-transplant) sirolimus or belatacept monotherapy also partially controlled cytotoxicity (8 +/− 1% and 35 +/− 1% granzyme Bvery high with sirolimus or belatacept, respectively). Combination therapy dramatically reduced the proportion of these cells, to 1.5 +/− 0.8 % granzyme Bvery high, favorably comparing with 4% granzyme Bvery high using CNI/MTX. The ability of sirolimus, belatacept, or the combination to control Ki-67 and Granzyme B expression closely correlated with survival (Figure 2A, B) supporting a pathogenic role for these highly proliferative and cytotoxic cells in aGvHD pathology. Moreover, significant co-expression of granzyme B in the Ki-67+ cells was observed (Figure 2C) suggesting that dual-positive Ki-67/Granzyme B cells may mark a pathogenic population, amenable to tracking in the peripheral blood. Implications: These results reveal a previously undiscovered synergy between sirolimus and belatacept in the control of primate aGvHD, and provide support for future clinical investigation of this novel prevention strategy. They also identify CD8+/Ki-67+/Granzyme Bvery high dual-positive T cells as a potentially sensitive biomarker of GvHD pathogenesis, amenable to monitoring in either the blood or in GvHD target organs. Disclosures: No relevant conflicts of interest to declare.

Description: Abstract 1008 Regulatory T cells (Tregs) have been shown to be potent inhibitors of autoimmunity, and to be capable of suppressing alloimmune responses that occur during both allograft rejection and graft-versus host disease. However, they have yet to gain widespread use clinically, due in part to the fact that it remains extremely costly and difficult to produce them in sufficient numbers and with sufficient suppressive capacity to significantly impact the alloimmune response. Here we have used our established non-human primate model to demonstrate that significant Treg expansion (up to 600-fold in 21 days) can be maintained, and suppressive capacity enhanced by exposing Treg cultures to a short burst of sirolimus at the end of the culture period. Using a highly sensitive and specific in vitro CFSE-MLR assay we show that Tregs significantly inhibit allo-proliferation of multiple T cell subpopulations including both CD4+ and CD8+ T cells (3.2 and 2.7-fold inhibition of proliferation, respectively), as well as their CD28+CD95+ and CD28-CD95+ subpopulations (2.2 and 2.1 and 1.9 and 2.7-fold inhibition of CD4+ and CD8+ subpopulation proliferation, respectively). Tregs were able to combine in vitro with the newly FDA-approved CTLA4-Ig analog belatacept to enhance the inhibition of alloproliferation that occurred with either agent alone (4.8-fold inhibition of CD8 T cell proliferation with Tregs + belatacept, compared to 3.0-fold or 1.9-fold inhibition of CD8 T cell proliferation with Tregs or belatacept alone, respectively). Importantly, we have found that the suppressive activity of ex-vivo expanded Tregs could be further enhanced by pulsing with sirolimus. Thus, while long-term culture of Tregs in the presence of sirolimus (1–1000 nM) profoundly inhibited Treg expansion (50–800 fold inhibition of expansion when cultured in the presence of 1–1000 nM sirolimus), a 48 hour pulse of sirolimus (100 nM) on days 20–21 of culture completely preserved Treg yields while doubling their suppressive function against CD8 proliferation when compared to unpulsed Tregs, p

Description: Abstract 1888 Introduction: There is a critical unmet need to devise effective strategies to prevent GvHD. However, the best combinatorial therapies remain undetermined, and the identification of new targeted approaches to GvHD prevention remains a challenge. To address this, we have developed a genome-wide approach to studying GvHD, using whole-transcriptome analysis of pathogenic T cells in a clinically-relevant non-human primate (NHP) model. Using computational approaches, we have identified, for the first time, the transcriptional networks that drive primate GvHD, and that lead to its partial control with sirolimus. Methods: CD3+/CD20- T cells were purified flow cytometrically from 4 cohorts: (1) Healthy Controls (“HC” n = 15); (2) Recipients of an autologous HSCT (“Auto” n = 3); (3) Haplo-identical allogeneic HSCT recipients without GvHD prophylaxis, who developed histopathologically confirmed severe aGvHD (“GvHD” n = 4); and (4) Allo-HSCT recipients who received sirolimus alone, and were partially protected from aGvHD (“Sirolimus” n = 4). Purification of T cells after allo-HSCT occurred 1–2 weeks post-transplant. RNA was purified (Qiagen), and rhesus macaque-specific Affymetrix Gene Arrays were performed. Computation: Gene array signals were processed and normalized using the Robust Multichip Averaging Method and ComBat. Principal Component Analysis (PCA) was applied to summarize modes of gene array variance. Importantly, PCA revealed that variation was primarily determined by the experimental cohort (Figure 1). This result was critical, and confirmed that transcriptomics could be applied to identify genes and pathways controlling GvHD. Differentially expressed genes (“DE”, fold change 〉 2) were defined between cohorts, yielding unique and overlapping gene signatures. We found that 775 annotated genes were DE between GvHD and HC and 286 were DE between Sirolimus and HC (Figure 2A, B). Importantly, a subset of the GvHD and Sirolimus DE gene sets were overlapping, indicating incomplete control of T cell activation with sirolimus (Figure 2B), and identifying pathways that could be targeted in combination with sirolimus for improved GvHD control. To further define genes by their individual expression profiles using an unbiased approach, we applied Class Neighbor Analysis (GenePattern, Figure 3A). Finally, using Ingenuity Pathway Analysis (IPA) we characterized gene signatures according to molecular pathways (using right-tailed Fisher's Exact test and FDR correction, Figure 3B). Results: T cells from animals with severe aGvHD demonstrated transcriptional signs of rampant proliferation and cytotoxicity as well as potentially counter-regulatory cell death pathways. IPA identified highly statistically significant upregulation of Cell Cycle and Cellular Movement networks (Figure 3B, p〈 0.001) as well as Cell Trafficking and Inflammatory Response Networks (Figure 3B, p 〈 0.001). These networks contained some expected genes and some surprises. Thus, as previously documented, GvHD was associated with upregulation of JAK and IFN signaling (p 〈 0.001). Unexpectedly, GvHD was also associated with upregulation of the Sonic Hedgehog and Aurora Kinase A Pathways (p 〈 0.01). Both of these represent targetable pathways for which novel therapeutics are currently available. Sirolimus resulted in significantly different gene expression patterns compared to uncontrolled GvHD. This included partial downregulation of the proliferation marker Ki-67 and the cytotoxicity gene, Granzyme B. However, there were many genes, pathways and networks that were shared between the Sirolimus and GvHD cohorts. These prominently included upregulation of the FOXM1 and IRF8 transcription factors, involved in cell cycle progression (p

Description: © The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Jiang, L.-Q., Pierrot, D., Wanninkhof, R., Feely, R. A., Tilbrook, B., Alin, S., Barbero, L., Byrne, R. H., Carter, B. R., Dickson, A. G., Gattuso, J.-P., Greeley, D., Hoppema, M., Humphreys, M. P., Karstensen, J., Lange, N., Lauvset, S. K., Lewis, E. R., Olsen, A., Pérez, F. F., Sabine, C., Sharp, J. D., Tanhua, T., Trull, T. W., Velo, A., Allegra, A. J., Barker, P., Burger, E., Cai, W-J., Chen, C-T. A., Cross, J., Garcia, H., Hernandez-Ayon J. M., Hu, X., Kozyr, A., Langdon, C., Lee., K, Salisbury, J., Wang, Z. A., & Xue, L. Best practice data standards for discrete chemical oceanographic observations. Frontiers in Marine Science, 8, (2022): 705638, https://doi.org/10.3389/fmars.2021.705638.

Description: Effective data management plays a key role in oceanographic research as cruise-based data, collected from different laboratories and expeditions, are commonly compiled to investigate regional to global oceanographic processes. Here we describe new and updated best practice data standards for discrete chemical oceanographic observations, specifically those dealing with column header abbreviations, quality control flags, missing value indicators, and standardized calculation of certain properties. These data standards have been developed with the goals of improving the current practices of the scientific community and promoting their international usage. These guidelines are intended to standardize data files for data sharing and submission into permanent archives. They will facilitate future quality control and synthesis efforts and lead to better data interpretation. In turn, this will promote research in ocean biogeochemistry, such as studies of carbon cycling and ocean acidification, on regional to global scales. These best practice standards are not mandatory. Agencies, institutes, universities, or research vessels can continue using different data standards if it is important for them to maintain historical consistency. However, it is hoped that they will be adopted as widely as possible to facilitate consistency and to achieve the goals stated above.

Description: Funding for L-QJ and AK was from NOAA Ocean Acidification Program (OAP, Project ID: 21047) and NOAA National Centers for Environmental Information (NCEI) through NOAA grant NA19NES4320002 [Cooperative Institute for Satellite Earth System Studies (CISESS)] at the University of Maryland/ESSIC. BT was in part supported by the Australia’s Integrated Marine Observing System (IMOS), enabled through the National Collaborative Research Infrastructure Strategy (NCRIS). AD was supported in part by the United States National Science Foundation. AV and FP were supported by BOCATS2 Project (PID2019-104279GB-C21/AEI/10.13039/501100011033) funded by the Spanish Research Agency and contributing to WATER:iOS CSIC interdisciplinary thematic platform. MH was partly funded by the European Union’s Horizon 2020 Research and Innovation Program under grant agreement N°821001 (SO-CHIC).

Description: The Global Ocean Data Analysis Project (GLODAP) is a synthesis effort providing regular compilations of surface-to-bottom ocean biogeochemical bottle data, with an emphasis on seawater inorganic carbon chemistry and related variables determined through chemical analysis of seawater samples. GLODAPv2.2022 is an update of the previous version, GLODAPv2.2021 (Lauvset et al., 2021). The major changes are as follows: data from 96 new cruises were added, data coverage was extended until 2021, and for the first time we performed secondary quality control on all sulphur hexafluoride (SF6) data. In addition, a number of changes were made to data included in GLODAPv2.2021. These changes affect specifically the SF6 data, which are now subjected to secondary quality control, and carbon data measured onboard the RV Knorr in the Indian Ocean in 1994–1995 which are now adjusted using CRM measurements made at the time. GLODAPv2.2022 includes measurements from almost 1.4 million water samples from the global oceans collected on 1085 cruises. The data for the now 13 GLODAP core variables (salinity, oxygen, nitrate, silicate, phosphate, dissolved inorganic carbon, total alkalinity, pH, CFC-11, CFC-12, CFC-113, CCl4, and SF6) have undergone extensive quality control with a focus on systematic evaluation of bias. The data are available in two formats: (i) as submitted by the data originator but converted to World Ocean Circulation Experiment (WOCE) exchange format and (ii) as a merged data product with adjustments applied to minimize bias. For the present annual update, adjustments for the 96 new cruises were derived by comparing those data with the data from the 989 quality controlled cruises in the GLODAPv2.2021 data product using crossover analysis. SF6 data from all cruises were evaluated by comparison with CFC-12 data measured on the same cruises. For nutrients and ocean carbon dioxide (CO2) chemistry comparisons to estimates based on empirical algorithms provided additional context for adjustment decisions. The adjustments that we applied are intended to remove potential biases from errors related to measurement, calibration, and data handling practices without removing known or likely time trends or variations in the variables evaluated. The compiled and adjusted data product is believed to be consistent to better than 0.005 in salinity, 1 % in oxygen, 2 % in nitrate, 2 % in silicate, 2 % in phosphate, 4 μmol kg-1 in dissolved inorganic carbon, 4 μmol kg-1 in total alkalinity, 0.01–0.02 in pH (depending on region), and 5 % in the halogenated transient tracers. The other variables included in the compilation, such as isotopic tracers and discrete CO2 fugacity (fCO2), were not subjected to bias comparison or adjustments.