Arctic warming can influence tundra ecosystem function with consequences for climate feedbacks, wildlife and human communities. Yet ecological change across the Arctic tundra biome remains poorly quantified due to field measurement limitations and reliance on coarse-resolution satellite data. Here, we assess decadal changes in Arctic tundra greenness using time series from the 30 m resolution Landsat satellites. From 1985 to 2016 tundra greenness increased (greening) at ~37.3% of sampling sites and decreased (browning) at ~4.7% of sampling sites. Greening occurred most often at warm sampling sites with increased summer air temperature, soil temperature, and soil moisture, while browning occurred most often at cold sampling sites that cooled and dried. Tundra greenness was positively correlated with graminoid, shrub, and ecosystem productivity measured at field sites. Our results support the hypothesis that summer warming stimulated plant productivity across much, but not all, of the Arctic tundra biome during recent decades. Satellites provide clear evidence of greening trends in the Arctic, but high-resolution pan-Arctic quantification of these trends is lacking. Here the authors analyse high-resolution Landsat data to show widespread greening in the Arctic, and find that greening trends are linked to summer warming overall but not always locally. Nevertheless, assessing the impacts of climate change on tundra ecosystems throughout the Arctic remains a significant challenge, as recently highlighted by the U.S. National Academy of Sciences5. Multi-decadal field measurements provide the most direct evidence of tundra response to warming, but such studies are scarce across the Arctic, especially in the Canadian and Eurasian Arctic6. Long-term field studies that do exist document recent increases in plant cover, growth, height, and biomass, and a shift towards shrub dominance in some tundra ecosystems9, while other areas show little change in vegetation11, or even warming-induced declines in plant growth13. Diverse ecological responses to warming and the paucity of long-term field measurements underscore the need for effectively using Earth-observing satellites to assess ecological changes that are occurring across one of Earth’s coldest but most rapidly warming biomes.The Arctic tundra biome is rapidly warming14 and aboveground biomass16. Pan-Arctic changes in NDVI since the 1980s have been exclusively assessed with the Advanced Very High-Resolution Radiometers (AVHRR)17. These satellites show increasing NDVI (greening) across large parts of the Arctic, but decreasing NDVI (browning) in several regions . However, the prevalence and spatial patterns of greening and browning differ considerably among AVHRR NDVI data sets18. These discrepancies partially reflect challenges with cross-calibrating sensors flown on 16 separate satellites19. Furthermore, the coarse spatial resolution of AVHRR NDVI data sets  far exceeds the scale of ecological change in heterogeneous tundra landscapes10 and limits the ability to attribute recent trends to potential landscape level drivers . Moreover, the coarse spatial resolution makes it difficult to reconcile trends with field observations11. These issues require caution in analyses based on the AVHRR satellites for pan-Arctic assessment of tundra response to warming and underscore the need for assessments using higher-resolution satellite observations that also extend back to the 1980s.Earth-observing satellites have been used to infer changes in tundra greenness since the 1980s, but pan-Arctic assessments historically relied on coarse spatial resolution satellite data sets that exhibit notable discrepancies through time. The Normalized Difference Vegetation Index (NDVI) provides a metric of tundra greenness that can be derived from satellite observations and is broadly related to tundra plant productivity20. Furthermore, the Landsat satellites provide 30 m resolution observations that more closely match the scale of field measurements and ecological change than AVHRR observations. However, higher spatial resolution means that each location is observed fewer times each growing season, which contributes to the challenge of assessing vegetation phenology, especially since the growing season is often short and cloudy in the Arctic21. The high spatial resolution also increases data volume and thus the Landsat satellites have typically been used for local assessments of tundra greenness24, although recent advances in computing25 and remote sensing26 have enabled regional to continental assessments in the North American Arctic27. Nevertheless, pan-Arctic changes in tundra greenness and their relation to climate, permafrost, and fire have not been assessed using the Landsat satellite series.The high-resolution Landsat satellites offer a promising complement to the AVHRR satellites for assessing pan-Arctic trends in tundra greenness and identifying factors that have driven these changes. The Landsat satellites cover the same period as AVHRR but with fewer satellites, which reduces but does not eliminate challenges with cross-sensor calibrationTo what extent did tundra greenness change during recent decades in the Arctic?How closely did inter-annual variation in tundra greenness track summer temperatures?Were tundra greenness trends linked with climate, permafrost, topography, and/or fire?How closely did satellite observations of tundra greenness relate to temporal and spatial variation in plant productivity measured at field sites?Here, we advance current understanding of recent changes in tundra greenness across the Arctic tundra biome Fig.  using momax) derived from surface reflectance measured by Landsat 5, 7, and 8. We first extract all available summer Landsat data for 50,000 random sampling sites in the Arctic using Google Earth Engine25 and then generate annual time series of NDVImax for each vegetated sampling site. We develop novel approaches to not only further cross-calibrate NDVI among Landsat sensors, but also minimize biases associated with estimating annual NDVImax when few summer measurements were available. Estimates of annual NDVImax are sensitive to sensor radiometric calibration, cross-sensor calibration, and modeling approach, thus we propagate these sources of error and uncertainty in climate and field data sets through the analysis using Monte Carlo simulations (n = 103). After estimating annual NDVImax at each sampling site, we then assess changes in tundra greenness and covariation with summer temperatures from 1985 to 2016 and 2000 to 2016 using rank-based trend tests and correlations in a Monte Carlo uncertainty framework. Moreover, we examine the extent to which tundra greenness trends from 2000 to 2016 were linked with summer temperature, soil moisture, permafrost, topography, land cover, and fire using geospatial data sets and machine learning. Last, we compare Landsat observations of tundra greenness against three metrics of plant productivity at field sites across the Arctic. It is important to note that Landsat observations sufficient for time series analysis were available for ~64% and ~96% of the Arctic domain from 1985 to 2016 and 2000 to 2016, respectively, with particularly improved coverage across the eastern Eurasian Arctic during the more recent period  and 2000 to 2016 . Correlations were weaker, but still positive, when each time series was linearly detrended  computed as the annual sum of mean monthly air temperatures >0 °C (units: °C)5 2.3, 2. °C from 5 2.3, 2. °C from max increased  at 37.3 % of sampling sites [95% Monte Carlo confidence interval] and decreased  at 4.7 % of sites from 1985 to 2016, although exhibited no trend at 58.0 % of sampling sites  at 28.2 % of sampling sites  and negatively correlated (α = 0.10) at 1.0 % of sampling sites  with climate, permafrost, land cover, topography, and fire  estimated from clip harvests, shrub ring-width indices  derived from measurements of annual stem radial growth, and ecosystem gross primary productivity  estimated from measurements by eddy covariance flux towers. We found annual landscape median NDVImax and graminoid ANPP were positively correlated from 1990 to 2017 at a long-term monitoring site on Bylot Island in far northern Canada , willow , and dwarf birch (Betula spp.) in six Arctic countries. The NDVImax–shrub RWI correlations (rs) ranged from −0.12  to 0.84  with a median rs of 0.42  among chronologies . Prior regional studies related positive trends in Landsat NDVI with increasing tundra shrub cover30 and spatial variability in Landsat NDVI with tundra plant aboveground biovolume31 and biomass16. Consequently, we interpret the observed tundra greening as evidence that plant productivity, height, biomass, and potentially shrub dominance increased since the 1980s in large parts of the Arctic in response to recent summer warming. This interpretation of tundra greening is broadly supported by changes in vegetation observed by Inuit communities in northeastern Canada32 and Nenet herders in northwestern Russia33, as well as documented by long-term field surveys8, dendroecology34, and high-resolution remote sensing36 at sites around the Arctic. Nevertheless, attribution of local tundra greening to specific biological changes and environmental drivers remains an important challenge that will require further mapping and modeling of potential drivers at higher spatial and thematic resolution, coupled with field measurements, across the Arctic.We provide here a pan-Arctic assessment of changes in tundra greenness using high-resolution Landsat NDVImax that broadly supports biome-scale changes inferred using coarser-resolution AVHRR (1982 onward at ~8 km resolution) and MODIS (2000 onward at 500 m resolution) data sets, it also highlights inconsistencies among satellites37. For instance, we report a 7.9 :1 ratio of greening to browning from 1985 to 2016. On the other hand, recent analyses of AVHRR (GIMMS3g) data suggested ratios of 29:1 and 14:1 from 1982 to 200818 and 1982 to 201438, respectively, while analysis of MODIS data suggested a ratio of 13:1 from 2000 to 201837. Different time periods, spatial domains, and NDVI metrics hinder direct comparison among these studies, but these initial comparisons suggest that Landsat either detects less greening and/or more browning relative to AVHRR (GIMMS3g) and MODIS.While our Landsat analysis revealed an increase in mean Arctic NDVI37 where micro- and macro-topography shape plant communities that are often interspersed with surface water, snow, and barren ground39. Patches of greening and browning can occur in close proximity on a landscape, leading coarse-resolution observations to integrate divergent trajectories of change37. Consequently, local browning could be obscured by widespread stability or greening on a landscape when viewed at coarse resolution. Similarly, a predominantly stable landscape could be perceived as greening at coarse resolution given enough hotspots of change. The spatial scale of Landsat imagery is much closer to the relevant scales of ecological processes than AVHRR or MODIS and thus potentially captures ecological stability and change in a more realistic way. However, the utility of Landsat is constrained by the lack of observations in parts of the Arctic  prior to 2000 and by the lower frequency of observations acquired each summer, which we partially addressed by modeling annual NDVImax with site-specific information on land surface phenology. Newer satellites  and unmanned aerial vehicles40 can facilitate higher-resolution mapping of tundra properties and could be combined with decades of Landsat observations to better understand recent changes in Arctic tundra.Compared to AVHRR or MODIS, high-resolution Landsat observations may be especially advantageous when assessing vegetation dynamics in heterogeneous tundra landscapes41. On the other hand, our study and prior regional Landsat assessments22 show pronounced greening in northern Quebec, where AVHRR and MODIS suggest modest greening. Greening in this region is likely associated with observed increases in graminoid and shrub cover, particularly of dwarf birch (Betula glandulosa)36. Our Landsat analysis also indicated recent browning along the rugged southwestern coast of Greenland that is consistent with local declines in shrub growth12, but not evident in assessments that used AVHRR or MODIS. The link between recent browning and declining shrub growth is further supported by the positive correlations that we found between annual Landsat NDVImax and stem radial growth of grayleaf willow  and dwarf birch  in this region , our results indicate Arctic plants did not universally benefit from warming in recent decades, highlighting diverse plant community responses to warming likely mediated by a combination of biotic and abiotic factors.We found no trend in tundra greenness at most locations, despite pervasive increases in summer air temperatures. It is possible that indirect drivers of vegetation change, such as permafrost thaw and nutrient release, are accumulating in response to warming of summer air temperatures, or that plants are limited by other environmental constrains. Low soil temperatures, nutrients, and moisture can limit plant response to rising air temperatures13, as well as defoliation from moths (Eurois occulta) and increased browsing by muskoxen (Ovibos moschatus)12. On the other hand, we found that browning from 2000 to 2016 was most probable at sampling sites where summer air temperature, annual soil temperature, or summer soil moisture decreased; however, cooling and drying infrequently occurred in the Arctic during the 2000s. Cooler and drier conditions could suppress tundra plant growth and have contributed, for instance, to recent tundra browning detected by Landsat in parts of the Yablonovy Mountains near Lake Baikal in central Siberia. Other potential drivers of browning in the Arctic include local changes in surface hydrology (wetting or drying) associated with permafrost degradation48, extreme weather events50, and industrial development51. In concurrence with terrestrial biosphere models52, our analysis suggests that warming tended to promote rather than suppress plant productivity and biomass in the Arctic during recent decades, but increasing frequency of permafrost degradation, extreme weather events, pest outbreaks, and industrial development could contribute to future browning49.Our analysis showed that tundra browning occurred at a small percentage (~5%) of sampling sites during recent decades, and athough uncommon, it was widely distributed in the Arctic. Inter-annual variability in tundra greenness and summer air temperatures were negatively correlated at only ~6% of sites that browned, suggesting little direct link between warming and browning on an annual time scale. Our Landsat analysis did detect browning in western Greenland from 2000 to 2016 that aligns with field studies showing recent declines in deciduous shrub growth due to warming-induced drought stress53; however, our results indicate that their contribution is currently quite small at a pan-Arctic extent due to their infrequent occurrence. Examining MODIS satellite observations54 from 2001 to 2016, we found that 1.1% of sampling sites burned over the 16 years period, which suggests a current fire rotation of ~1450 years for the Arctic tundra biome. Regional fire rotation within the biome is strongly governed by summer climate and is considerably shorter (~425 years) in the warmest and driest tundra regions 55. Our analysis further showed that fires recently occurred at ~1.0% of sampling sites that greened and ~2.4% of sampling sites that browned. Tundra fires can emit large amounts of carbon into the atmosphere56 and lead to temporary browning by burning off green plants, while subsequent increases in soil temperature and permafrost active layer depth can stimulate a long-term increase in plant growth and shrub dominance in some but not all cases58. Continued warming will likely increase annual area burned in the tundra biome55; thus, fires could become a more important driver of tundra greening and browning in the Arctic over the twenty-first century.Tundra fires are another contributor to greening and browning in the Arctic60. Moreover, greening can reduce surface albedo as plants grow taller and leafier (positive climate feedback)62 while also affecting soil carbon release from permafrost thaw by altering canopy shading and snow-trapping (mixed climate feedbacks)63. The net climate feedback of these processes is currently uncertain; thus, our findings underscore the importance of future assessments with Earth system models that couple simulations of permafrost, vegetation, and atmospheric dynamics at moderately high spatial resolution.Our analysis contributes to a growing body of evidence showing recent widespread changes in the Arctic environment that can impact climate feedbacks. Rising temperatures are likely stimulating carbon uptake and storage by plants in areas that are greening (negative climate feedback), but also leading to soil carbon loss by thawing permafrost and enhancing microbial decomposition (positive climate feedback)64 and beavers65 recently colonized, or recolonized, increasingly shrubby riparian habitats in tundra ecosystems of northern Alaska and thus appear to be benefiting from recent tundra greening. Conversely, caribou populations in the North American Arctic could be adversely affected if warming stimulates vascular plant growth at the expense of lichens, an important winter forage66. In the western Eurasian Arctic, indigenous herders  manage about two million semi-domesticated reindeer on tundra rangelands. Shrub growth, height, and biomass significantly increased on these rangelands in recent decades, while lichen cover and biomass declined mostly due to trampling during the snow-free period67. Our Landsat analysis showed tundra greening in regions with potential moose, beaver, caribou, and reindeer habitat and demonstrated that variability in tundra greenness was often associated with annual shrub growth in these regions  for 50,000 sampling sites in Arctic tundra71 using 30 m resolution measurements of surface reflectance from the Landsat satellites (Landsat Collection 1)73. We first buffered each site by 50 m (radius) and then used the Google Earth Engine25 Python74 interface to extract all Landsat 5, 7, and 8 surface reflectance measurements acquired June through August from 1984 to 2016. This yielded 507 million multi-band surface reflectance measurements, of which 112 million (28%) were considered to be useable clear-sky measurements based on the CFmask algorithm75 and scene criteria. We then used these clear-sky measurements to estimate annual NDVImax while considering multiple sources of uncertainty. Estimates of annual NDVImax are sensitive to radiometric calibration uncertainty and systematic differences in NDVI among Landsat sensors, as well as to the availability and seasonal timing of measurements. We therefore developed new techniques to further cross-calibrate NDVI among sensors and model annual NDVImax. Furthermore, we characterized uncertainty in our estimates of annual NDVImax using Monte Carlo simulations. These new techniques and the uncertainty analysis are briefly described below and in greater detail in the Supplementary Methods.We developed annual estimates of maximum summer NDVI77. Cross-calibration models have been developed for other biomes77, but not for Arctic tundra. We initially explored cross-calibrating sensors using linear regression, but found non-linearities that led us to develop a novel approach using machine learning algorithms that calibrated Landsat 5/8 to Landsat 7. Specifically, for each sampling site, we (1) identified the years when both sensors  collected imagery, (2) computed 15-day moving median NDVI across the growing season for each sensor using measurements pooled across years, and (3) then randomly selected NDVI from one 15-day period with at least five observations from both sensors. We then used data from 2/3rd of sites to train Random Forest models78 that predicted Landsat 7 NDVI based on Landsat 5/8 NDVI, while withholding data from the other 1/3rd of sites for cross-validation. The models also accounted for potential seasonal and regional differences between sensors by including as covariates the midpoint of each 15-day period (day of year) and the spatial coordinates of each site. We fit the Random Forest models using the ranger package79 in R and evaluation of the models showed they had high predictive capacity (r2 ≈ 0.97) as well as low root mean-squared error and bias . A conceptually similar approach was previously used to examine inter-annual variability in the start and end of the growing season in deciduous forests of eastern North America80. Subsequent assessment showed that our modeled estimates of annual NDVImax were less biased than raw estimates of annual NDVImax when few summer measurements were available . Overall, this process propagated several important sources of error and uncertainty into subsequent analysis, thus allowing us to more rigorously estimate greening and browning trends across the Arctic.Several sources of uncertainty affect estimates of annual NDVImax during recent decades using measurements from sampling sites across the Arctic. We first excluded sampling sites that were barren (mean NDVImax < 0.10) or had short measurement records (<10 years) and then assessed temporal trends in NDVImax for each remaining sampling site  as well as after averaging NDVImax time series across sampling sites in each bioclimatic zone and the Arctic. Justification for the sample size of 50,000 locations is provided in the Supplementary Methods  as greening or a negative NDVImax trend (α = 0.10) as browning. Furthermore, we accounted for how trends were affected by uncertainty in estimates of annual NDVImax by computing every trend using each of the 103 Monte Carlo simulations of annual NDVImax at every sampling site. We computed the median percentage of sites that greened or browned across all simulation and derived 95% confidence intervals using the 2.5th and 97.5th percentile of all simulationsWe assessed temporal trends in annual Landsat NDVIsite n = 1,884 as 28 derived from an ensemble of five global temperature data sets91 re-gridded at 50 km resolution . For each simulation, we generated a stack of annual synthetic SWI rasters built by randomly selecting grid cell values from the five temperature data sets. In other words, each grid cell of a synthetic raster was assigned a value for SWI that was randomly selected from the corresponding grid cell of one of the five temperature data sets. We then used this collection of synthetic SWI rasters to assess temporal trends in SWI as well as correlations between SWI and NDVImax.We assessed recent changes in summer air temperatures across the Arctic, as well as inter-annual covariation between summer temperatures and tundra greenness. Specifically, we characterized annual cumulative summer heat load using the SWI3 Monte Carlo simulations, we evaluated SWI trends from 1985 to 2016 and 2000 to 2016 using non-parametric Mann–Kendall trend tests and Theil–Sen slope estimators  as implemented by the zyp package85 in R86. We assessed SWI trends for each 50 × 50 km2 grid cell and Landsat sampling site, as well as after averaging SWI among grid cells in each bioclimatic zone and across the Arctic domain  based on environmental characteristics related to climate, permafrost, land cover, fire, and topography 95, and annual maximum active layer thickness96 from 2003 to 2016. The permafrost data sets did not extend before 2003. We included both the linear change over time and model-fit starting value as predictors. Additional predictors included thermokarst vulnerability97, ESA land cover98, MODIS burned area (2001–2016)54, and five topographic predictors  derived from the TanDEM-X 90m Digital Elevation Model99 (© DLR 2020). All together, we included 20 predictor variables in the Random Forests.To explore potential drivers of changes in tundra greenness among sampling sites, we constructed Random Forest models3 Monte Carlo simulations. For every simulation, we classified the NDVImax trend at each sampling site as browning, no trend, greening based on the slope and significance (α = 0.10) of NDVImax change from 2000 to 2016. The frequency of each trend class was highly skewed towards sites with no trend or greening and thus we balanced the sample size among trend class by determining the number of browning sampling sites and then randomly selecting the same number of no trend and greening sampling sites. We then screened highly correlated variables (r > 0.75) by computing pair-wise correlations and removing the variable with highest average absolute correlation. Next, we randomly partitioned the data set into sets for model training (2/3rd) and evaluation (1/3rd), and then repeatedly fit  Random Forest models to optimize out-of-bag classification accuracy by varying the number of variables assessed at each tree node. We selected the Random Forest model with the highest out-of-bag classification accuracy and then re-assessed the classification accuracy using the data withheld for model evaluation , shrub RWIs (unitless), and ecosystem GPP (g C m−2 year−1). For each comparison, we incorporated uncertainty in both remote sensing and field data sets using Monte Carlo simulations. We briefly describe each comparison below and include additional details in the Supplementary Methods.To aid in interpreting Landsat NDVImax and graminoid ANPP from 1990 to 2017 on Bylot Island in northern Canada  in the study site. We developed annual NDVImax time series for four subsites and then assessed the relationship between annual median NDVImax and ANPP from 1990 to 2017 using rs in a Monte Carlo uncertainty framework (n = 103 simulations). Each simulation randomly perturbed both NDVImax and ANPP data sets and utilized data from a random subset (90%) of years. We also explored multi-year and lagged relationships between NDVImax and ANPP.We assessed the temporal correspondence between annual Landsat NDVImax and shrub growth using 22 shrub RWI chronologies from sites in six Arctic countries , willow , and birch (Betula spp.) annual ring width from independent projects67, including measurements previously collated as part of the ShrubHub shrub ring database42. We generated a detrended and standardized median shrub RWI chronology for each shrub genera at a site using the dplR103 package in R. We also developed annual detrended Landsat NDVImax (NDVImax-dt) time series using observations from a 100 m radius area around each sampling location. We then assessed the temporal correspondence between NDVImax-dt and each shrub RWI chronology using Spearman’s correlations in a Monte Carlo uncertainty framework (n = 103 simulations). Each simulation randomly perturbed both NDVImax and shrub RWI data sets and utilized data from a random subset (90%) of years.We assessed the temporal correspondence between annual Landsat NDVImax and ecosystem GPP across 11 eddy covariance flux towers located in Arctic tundra of Greenland, Russia, and the USA 106. Annual ecosystem GPP was estimated at each flux tower by first measuring net ecosystem exchange (NEE) and then partitioning NEE into GPP and ecosystem respiration (Reco) using modeled relationships between Reco and night-time temperatures (NEE = GPP − Reco)107. We acquired annual gap-filled estimates of GPP from FLUXNET and half-hourly gap-filled estimates of GPP from AON that we aggregated to an annual time step (g C m−2 year−1). We generated annual Landsat NDVImax time series for each flux tower using summer observations from a 100 m radius area around each flux tower. We then computed median annual NDVImax and GPP by site and assessed their covariation using rs in a Monte Carlo uncertainty framework (n = 103 simulations). Each simulation randomly perturbed both NDVImax and GPP data sets and utilized data from a random subset (90%) of years.We assessed the spatial correspondence between median annual Landsat NDVI74 and generated maps using ArcGIS , but otherwise handled and visualized data using R86 with a suite of add-on packages. Specifically, we processed geospatial data using raster108, rgdal109, and maptools110. Furthermore, we handled data using data.table111, dplyr112, and tidyr113, and visualized data using lattice114, ggplot2115, and ggpubr116. All package versions are provided in the Reporting summary.We acquired Landsat data using PythonFurther information on experimental design is available in the Supplementary InformationPeer Review FileReporting summary


Retroviruses belong to an important and diverse family of RNA viruses capable of causing neoplastic disease in their hosts. Feline leukaemia virus (FeLV) is a gammaretrovirus that infects domestic and wild cats, causing immunodeficiency, cytopenia and neoplasia in progressively infected cats. The outcome of FeLV infection is influenced by the host immune response; progressively infected cats demonstrate weaker immune responses compared to regressively infected cats. In this study, humoral immune responses were examined in 180 samples collected from 123 domestic cats that had been naturally exposed to FeLV, using a novel ELISA to measure antibodies recognizing the FeLV surface unit (SU) glycoprotein in plasma samples. A correlation was demonstrated between the strength of the humoral immune response to the SU protein and the outcome of exposure. Cats with regressive infection demonstrated higher antibody responses to the SU protein compared to cats belonging to other outcome groups, and samples from cats with regressive infection contained virus neutralising antibodies. These results demonstrate that an ELISA that assesses the humoral response to FeLV SU complements the use of viral diagnostic tests to define the outcome of exposure to FeLV. Together these tests could allow the rapid identification of regressively infected cats that are unlikely to develop FeLV-related disease. FeLV is a single-stranded, positive-sense RNA gammaretrovirus that has global impact, infecting domestic and wild felids worldwide. FeLV can be isolated from the saliva, urine and faeces of viraemic cats and activities such as grooming, fighting and the shared use of food bowls and litter trays facilitates horizontal transmission via the oronasal route ,2,3,4. TAbortive infection has been documented in naïve cats experimentally challenged with a low dose of FeLV as well as in experimentally challenged, FeLV-vaccinated cats ,13. ViraFocal infections are rarely documented in nature but have been reported in up to 10% of experimental infections . Focal iIt is recommended that samples from cats that test positive for p27 antigen using in-clinic point of care tests should undergo confirmatory testing by a diagnostic laboratory ,18. CatsThe antibody response to FeLV infection is directed mainly towards the viral surface unit (SU) glycoprotein (gp70) and p15E , althoug® FIV/FeLV Combo Test  when screened on admission to the Austin Pets Alive! Shelter . These cats remained in the study regardless of subsequent test results. In total, 123 cats were included in this study. Heparinised whole blood samples were received from 66 cats and paired samples of heparinised whole blood, collected at least one month apart, were received from 57 of these cats. The majority of paired samples were collected 6 months apart, as shown in Cats were enrolled in the study if they tested positive for p27 capsid antigen using anticoagulated whole blood on the IDEXX SNAPg for 5 min). Peripheral blood mononuclear cells (PBMC) were then isolated from the remaining fractions by density gradient centrifugation using Ficoll-Paque Plus .Plasma was separated from heparinised whole blood by centrifugation  by testing plasma samples for p27 capsid antigen using the IDEXX FeLV PetChekructions . Quantitructions . In this™, Renfrew, UK) supplemented with 10% foetal bovine serum , 2 mM L-glutamine , 100 U/mL penicillin (Invitrogen™), 100 µg/mL streptomycin (Invitrogen™), 50 µM 2-mercaptoethanol (Sigma-Aldrich) and 100 IU/mL interleukin 2. Culture fluids were sampled on day 14 (D14) and day 21 (D21) and the media were changed on D14. Culture fluids were centrifuged to remove cells and samples of supernatant were stored at −80 °C until required. After 21 days in culture, PBMC were stored at −80 °C until required for DNA extraction and subsequent proviral DNA detection.Following separation from whole blood samples, PBMC were cultured with Concanavalin A  for 21 days in RPMI medium (Gibco® Fast reaction plate (Applied Biosystems®), 5 µL of lysed, diluted sample were added to 15 µL of master mix containing TaqMan Universal PCR Master Mix (Applied Biosystems®), 2 mg/mL bovine serum albumin , 40 U/µL RNasin ribonuclease inhibitor , 0.8 µg/µL MS2 phage RNA , 10 mg/mL calf thymus DNA (Invitrogen™), 20 pmol/µL MS2 phage forward primer (5′-GCC TTT CTC ATT CGT TGT CG-3′), 20 pmol/µL MS2 phage reverse primer (5′-GCT TAT GAT GGA CTC ACC CG-3′) and 5 pmol/µL fluorescent PERT probe (5′(FAM)-TCT TTA GCG AGA CGC TAC CAT GGC TA-(TAMRA)3′). Calf thymus DNA was added to the PERT reaction to remove non-specific activity, a well-recognised method that suppresses endogenous DNA polymerase activity /[positive control A420—negative control A420]) and average A420 values were calculated using Microsoft Excel (version 16.16.15).ELISA plates (Immulon© (version 1.2.1335) was used to determine the distribution of the data using the Shapiro Wilk Normality test, demonstrating that all data were distributed non-parametrically. All other statistical analyses were performed using Prism software . Two tailed Mann–Whitney tests were used to compare unpaired group mean values and the Wilcoxon matched-pairs signed rank test was used to compare paired sample mean values. The Kruskal–Wallis test and Dunn’s multiple comparisons tests were used to compare multiple group means. p values less than 0.05 were regarded as statistically significant.RStudioSamples of culture fluid collected before and after protein purification, the ultrafiltrate waste and PBS wash were analysed by immunoblotting and Coomassie staining, demonstrating the presence of FeLV-A and FeLV-B SU proteins . The SU-® ELISA (data not shown). Immunoblots prepared using virus purified from F422 culture fluids, were cut into strips that were individually probed with cat plasma or control antibody. Three samples  demonstrated the least reactivity against FeLV-A virus; these samples were selected, pooled and used as a negative control in subsequent SU ELISAs.A total of 20 field cat plasma samples (samples 1–20) and two plasma samples collected from FeLV naïve specific pathogen free cats (samples 21 & 22) were examined by immunoblotting . All samn = 36). Firstly, the intra- and inter-assay variation of the FeLV-A SU ELISA were investigated. Positive and negative controls and 30 randomly selected cat plasma samples were tested in triplicate for antibodies recognising FeLV-A SU on two different days. A subset of samples submitted to IDEXX Laboratories  for FeLV testing were used to assess the performance of the FeLV-SU assay . Samples with high antibody responses to FeLV-A SU and FeLV-B SU also demonstrated strong reactivity against FeLV-A SU by immunoblotting, as shown in Next, samples submitted to IDEXX Laboratories  for FeLV testing were tested on the FeLV-A SU ELISA, the FeLV-B SU ELISA and by immunoblot analysis against FeLV-A . However, since a positive test for PBMC proviral DNA had been recorded at timepoint 1 (TP1), such cats were determined to have regressive infection and it was considered that the proviral load was below the limit of detection at TP2. Mean plasma p27 capsid antigen concentrations of samples from cats with different exposure outcomes were found to be significantly different . SamplesWhen p27 capsid antigen concentrations were compared over time, it was observed that the concentration of antigen in plasma decreased significantly between the two time points in discordant cats . High anCats with regressive infection showed significantly lower proviral loads compared to cats with progressive infection . Samples® FIV/FeLV Combo Test are highlighted in red, Reverse transcriptase was detected in PBMC culture fluids of discordant cats; cats with regressive infection; and cats with progressive infection a. Uninfe® ELISA neutralisation protocol, as were outlier samples . Outlier samples ‘b’ and ‘d’ also tested positive for RT.PBMC culture fluids (from D14 and D21 of culture) were tested for p27 capsid antigen, The humoral immune responses of cats with different FeLV exposure outcomes were found to differ significantly. Cats with regressive infection showed the highest antibody responses to FeLV-A a and FeLThe average SU antibody responses at TP1 were compared to the average SU antibody responses at TP2 for longitudinal samples a. The deWhen anti-FeLV-A SU antibody responses were compared to anti-FeLV-B SU antibody responses, it was observed that samples that tested positive for VNA clustered together, demonstrating high antibody responses to both SU proteins . VNA titThis study provided a unique opportunity to analyse the antibody responses of cats naturally exposed to FeLV. The SU ELISA is a novel assay that was developed to assess the immune response to the SU proteins of FeLV. Previously, a FeLV SU ELISA was used to measure antibody responses in naturally infected cats, using p45, non-glycosylated gp70 as the antigen . In addiThe terminology for FeLV exposure outcomes has been discussed widely in the literature. However, in order to draw comparisons between cats with different test results, exposure outcome categories were assigned in this study, based on antigenaemia, PBMC virus isolation and the detection of PBMC proviral DNA. The majority of cats tested had progressive infection (68.3%). Cats with progressive infection displayed high proviral loads, were antigenaemic and tested positive following PBMC virus isolation. This is a consistent finding, and it has been suggested that higher proviral loads in cats with progressive infection is associated with a poorer prognosis ,36. From® FIV/FeLV Combo Test and some, but not all, of the positive results from cats with regressive infection could be explained by FIV co-infection. All PBMC culture fluids from uninfected cats tested negative for RT activity, suggesting low level FeLV infection could have been the source of the RT activity detected in the PBMC culture fluids from cats with other exposure outcomes. However, the suspected low level FeLV infection was not confirmed; when culture fluids were tested for p27 capsid antigen, the majority tested negative. The detection of RT activity in the absence of FeLV p27 capsid antigen in these cats could indicate the presence of another retrovirus, such as feline foamy virus (FFV), although this was not confirmed. PBMC culture media were replenished on day 14 of culture after sampling. Therefore, any RT or p27 antigen present in the culture fluid on day 21 of culture represents the continued production of virus from these cultures. PBMC cultures from discordant cats and cats with regressive infection that tested positive for RT at D14 maintained RT production to D21. The four outlier PBMC culture fluid samples that tested positive for p27 capsid antigen on day 14  tested negative on day 21, suggesting that the low level of FeLV infection in these cultures was not sustained beyond day 14.Reverse transcriptase activity was detected in the PBMC culture fluids of discordant cats, cats with regressive infection and cats with progressive infection. The PERT assay detected enzymatically active reverse transcriptase and hence confirmed the presence of any retroviruses in the cultures. All cats were tested for FIV antibodies using IDEXX SNAPUninfected cats tested antigen negative, PBMC proviral DNA negative, PBMC virus isolation negative and had low antibody responses to the SU proteins. It was assumed that the majority of the cats in this outcome group had not been exposed to FeLV, with the possible exception of two outlier cats that exhibited higher antibody responses compared to the rest of the group. Discordant cats also exhibited low antibody responses to the SU proteins. If these cats had focal infection, the level of humoral immunity might depend on the tissues in which the virus was replicating. Cats with regressive infection had the highest mean antibody responses to FeLV-A and FeLV-B SU proteins, correlating with lower proviral loads and potentially better prognoses. Cats with progressive infection showed significantly lower antibody responses to the SU proteins compared to cats with regressive infection, correlating with higher proviral loads and potentially poorer prognoses .The mean SU antibody response was calculated at TP1 and TP2. A statistically significant decrease in mean SU antibody response was observed between the two timepoints in cats with progressive infection. In cats with poor immune responses to infection, viral replication likely proceeds unhindered, leading to FeLV-induced immunosuppression and a decrease in immune function. The majority of the cats with regressive infection maintained high responses between TP1 and TP2. The maintenance of an effective immune response in cats with regressive infection is indicative of viral control, resulting in a good prognosis. Two cats with regressive infection showed evidence of seroconversion from negative to positive between TP1 and TP2; the first sample from each cat displayed low antibody responses to both SU proteins and tested negative for VNA whereas the second sample from each cat had high antibody responses to both SU proteins and contained VNA. This emphasises the importance of sample timing and follow-up sampling . NeverthWhen comparing the proviral loads and SU antibody responses of cats with regressive and progressive infection, these parameters clearly segregated into two separate groups. Cats with regressive infection had higher SU antibody responses (associated with better infection outcomes) and lower proviral loads compared to cats with progressive infection (associated with worse infection outcomes). Cats with discordant results might have had highly suppressed regressive or focal infections, such that they tested PCR negative. The cellular immune response was not measured in this study; however, it is known to play a role in FeLV recovery  and coulThe immune response to FeLV is complex and predicting the outcome of FeLV infection in an infected animal remains challenging, despite the abundance of diagnostic tests available. This study aimed to improve our understanding of the pathogenesis of FeLV infection by investigating how best to interpret diagnostic tests to assess disease progression and determine prognosis. Cats with regressive infection had higher anti-SU antibody responses and VNA titres, as well as lower p27 capsid antigen levels and lower PBMC proviral DNA loads compared to cats with progressive infection. The data generated in this study provides evidence that measuring anti-SU antibody responses as well as the p27 capsid antigen concentration and proviral load might improve FeLV diagnostics, since the viral burden and the immune status can be assessed by the clinician to provide prognostic information. The FeLV SU ELISA described here has been used to demonstrate active immune control in a subset of cats with low levels of p27 capsid antigen  that wer


Primary objective: To determine the efficacy of a candidate antiviral on time to virological cure compared to standard of care within 14 days of randomisationSecondary objectives:• To determine the safety of the antiviral• To determine the clinical benefit of the antiviral over placebo according to the WHO 7-point ordinal scale• To determine the clinical benefit of the antiviral over placebo on time to resolution of clinical symptoms• To determine the effect of the antiviral over placebo on biomarkers of inflammation and immune activationThis is a multi-centre, triple-blind, randomised placebo controlled phase II, 2-arm trial with parallel-group design with allocation ratio 1:1.Inclusion Criteria:• Provision of informed consent by the participant• Age ≥18 years• Confirmed SARS-CoV-2 by nucleic acid testing in the past 5 days• COVID-19 related symptom initiation within 5 days• Female patients of childbearing potential must have a negative pregnancy test at Screening. Female patients of childbearing potential and fertile male patients who are sexually active with a female of childbearing potential must use highly effective methods of contraception throughout the study and for 1 week following the last dose of study treatment.Exclusion criteria:• Known allergy to the study medication• Is on another clinical trial investigating an antiviral treatment for COVID-19• Pregnancy• Patients with severe hepatic dysfunction equivalent to Grade C in the Child-Pugh classification• Patients with renal impairment requiring dialysis• Is deemed by the Investigator to be ineligible for any reasonParticipants will be recruited from, and the study visits will take place at Alfred Hospital, Monash Health, Austin Health in Victoria, Australia for hospitalised participants as well as recruitment in the community in participants homes for eligible people not requiring hospitalisation.The first candidate antiviral is favipiravirArm 1: Favipiravir 1800 mg favipiravir BD on Day 1 followed by 800 mg BD favipiravir for the next 13 days.Arm 2: PlaceboPrimary outcome: Time to virological cure as defined by 2 successive throat (or combined nose/throat) swabs negative for SARS-CoV-2 by nucleic acid testing during the 14 days after enrolment.Randomisation performed at the Alfred Hospital Clinical Trials Pharmacy using computer generated block-randomisation lists with 6 participants per block. Within each block half of the participants will be randomised to the candidate antiviral and the other half to placebo. Randomisation is stratified by study site, with participants enrolled in the community considered as a study site.Study participants, study investigators and the study statistician will be blinded to treatment allocation.The study aims to recruit 190 people (95/arm) with the first candidate antiviral favipiravirProtocol version 2.0 Dated 31-Jul-2020. Recruitment will take place between July 2020 and December 2020.NCT04445467clinicaltrials.gov First posted 24-Jun-2020The full protocol is attached as an additional file, accessible from the Trials website (Additional file  Additional file 1. Full Study Protocol.


Type I interferons secreted by plasmacytoid dendritic cells (pDCs) play a crucial role in the pathogenesis of systemic lupus erythematosus by driving the formation of autoantibodies against nuclear debris. Inherited mutations causing activation of the Type I interferon pathway result in a phenotype of systemic autoimmunity which resembles some of the manifestations of lupus. Patients with lupus have increased expression of interferon-stimulated genes in the peripheral blood mononuclear cells which is abrogated following immunosuppressive treatment. Recent therapeutic approaches have involved monoclonal antibodies directly targeting interferon alpha  or the use of interferon alpha kinoid to stimulate endogenous production of anti-interferon antibodies in lupus. Other drugs used in lupus such as hydroxychloroquine and bortezomib also reduce circulating levels of type I interferons. Newer therapeutic strategies being investigated in preclinical models of lupus that reduce the production of Type I interferons include dihydroartemisinin, Bruton’s tyrosine kinase antagonists, Bcl-2 antagonists and sphingosine-1 phosphate agonists. Mycobacterium tuberculosis. Type III interferons include interferon lambda .1Interferons, originally identified as inhibitors of viral replication, are cytokines produced by immune cells. There are three families of interferons viz. Type I, Type II and Type III interferons. Type I interferons, which are predominantly involved in immune responses against viral infections, include interferon alpha (IFN-α), interferon beta (IFN-β), IFN-kappa, IFN-epsilon and IFN-omega. Type II interferon (IFN-gamma) is predominantly involved in immune responses against intracellular bacteria such as 2 Cross-reactivity between antibodies to EBV nuclear antigen 1 (EBNA1) and anti-Ro 60 antibodies (which are often found early in the course of lupus) suggests a role for EBV in driving the initiation of lupus.3 One of the mechanisms linking viral infections and lupus is that both of these are associated with higher circulating levels of Type I interferons. Indeed, Type I interferon plays a critical role in the pathogenesis of pristane-induced lupus, which is a robust animal model of this disease. Disease phenotype is ameliorated in this lupus model by knocking out receptors for Type I interferon.4 Genes involved in regulation of interferon signaling such as IRF 5, IRF 7, IFIH 1 and STAT 4 have been identified as risk factors for SLE across different populations.5 A group of inherited diseases characterized by high circulating levels of Type I interferons  have been recently identified. These involve defects in genes encoding for proteins involved in nucleic acid  clearance such as TREX 1, RNASEH 2A and RNASEH 2B. These result in a multisystem autoimmune disease (Aicardi Goutierre syndrome) with cutaneous and neurologic involvement.6 Mutations in TREX 1 also predispose to lupus.6 In humans, Type I IFN is mostly produced by plasmacytoid dendritic cells (pDCs).Evidences in literature suggest that viral infections such as Epstein-Barr virus (EBV) may have a role in driving pathogenesis of systemic lupus erythematosus (SLE), which is a multisystem autoimmune disease associated with significant morbidity and higher risk of mortality, most commonly affecting young females. Patients with childhood-onset SLE have higher titres of antibodies to EBV compared to healthy controls.8 . The critical nature of this mechanism in the pathogenesis of lupus is emphasized by the fact that scavenging nucleic acid debris has emerged as the newest therapeutic modality being investigated in animal models of lupus.9Apoptotic defects have been identified as a key mechanism in the generation of autoantibodies in lupus. Normally, apoptotic debris is cleared before it could attract the attention of the immune system. In lupus, apoptotic debris containing nuclear material  persists due to defects in clearance due to complement deficiencies, defective enzymes responsible for cleaving nucleic acids (such as DNAse and RNAse) and defects in Fcγ receptors. These are now taken up by antigen presenting cells, which in turn present them to B and T lymphocytes, ultimately resulting in formation of plasma cells that produce antibodies to these nuclear antigens . These autoantibodies further bind to existing apoptotic debris, forming antigen-antibody complexes which now act on Toll-like receptors on the surface and well as intracellularly inside endosomes of the pDCs . In turn, via IRF5, IRF7 and other downstream mediators, this results in stimulation of interferon-stimulated genes (ISGs) which result in increased secretion of Type I IFN. Type I IFN further acts to promote autoimmunity mediated by B and T lymphocytes, including production of autoantibodies10, highlighting the importance of pDCs and Type I IFN in the pathogenesis of lupus. Systemic lupus erythematosus is associated with increased risk of incident cardiovascular events.11 Type I IFNs have been shown to promote endothelial dysfunction, plaque instability and promote plaque rupture in murine models of lupus,12 hence may be contributory towards the observed increase in cardiovascular events in patients with lupus.Early depletion of pDCs in another murine model of lupus resulted in significant amelioration of the phenotype of lupus which was associated with reduced transcription of genes induced by IFN-α and IFN-β13 The United States Food and Drug Administration (USFDA) is the major regulatory authority for drugs in the United States of America and is considered a reference for therapeutics worldwide. Very few therapies are approved by the USFDA for the treatment of lupus including hydroxychloroquine and belimumab .14 Hydroxychloroquine acts to decrease interferon signature in the peripheral blood lymphocytes of patients with lupus.15 Of late, direct targeting of Type I IFN in lupus has been the focus of numerous pre-clinical and clinical studies in lupus over the past few years. Considering the spate of recent trials on molecules targeting this pathway, we decided to review the recent literature (over the past five years) on interferon-targeted therapies in SLE.Increased expression of genes expressed downstream to interferon signaling has been detected in lupus as well as in other autoimmune diseases such as polymyositis, dermatomyositis, rheumatoid arthritis and systemic sclerosis.16 The database MEDLINE/PubMed was searched using terms “Type I interferons” and “lupus” on the 25th of October 2016 for articles published over the past five years. 361 items were retrieved and screened to identify those describing the targeting of Type I interferons in experimental models or human subjects with lupus. Using the same search terms, an additional search on Scopus on the 15th of November 2016 retrieved 503 articles, and a search on Cochrane Central Register of Controlled Trials retrieved 18 results. These new searches identified no new published clinical trials from those already identified on the search on MEDLINE/PubMed. Published original articles were included; case reports and review articles were excluded. We did not include conference abstracts that had not been already published as original articles.The search strategy advocated by Gasparyan et al. was adhered to.17 analyzed 30 patients with pediatric lupus (pSLE) and compared them with nine healthy controls and 12 patients with juvenile idiopathic arthritis (JIA). Peripheral blood mononuclear cells (PBMC) from these subjects were isolated and ribonucleic acid (RNA) from these cells was extracted and subjected to microarray analysis to identify gene expression. They identified 26 such Type I IFN-induced genes in patients with pSLE compared with healthy controls and those with JIA. Eight of these Type I IFN-induced genes correlated with disease activity in patients with pSLE assessed by the SLE disease activity index (SLEDAI). Treatment with high dose intravenous methylprednisolone pulses for three days in three patients with active lupus extinguished this Type I IFN signature. Baechler et al18 further identified genes upregulated by interferons by treating PBMC from healthy individuals with interferons in vitro and assessing the induced genetic signature by microarray analysis of extracted RNA from these cells. The expression of these interferon-stimulated genes when quantified electronically constituted the interferon signature, which they further studied in 48 patients with lupus compared with 42 healthy controls. The interferon signature was significantly higher overall in patients with lupus compared with healthy controls. Approximately three-fourths of their patients with lupus had this interferon signature, and these patients had higher proportion of renal, neurological or hematological involvement compared with those without.In order to understand the targeting of Type I IFN in lupus, it is important to understand the interferon signature metric. It was demonstrated that patients with active lupus have a higher expression of genes whose expression is induced by Type I IFN. Bennett et al.20 identified three genes  amongst 128 genes upregulated in response to Type I interferons. The quantitative expression of these three genes  strongly correlated with the quantitative expression of the 128 aforementioned genes taken together (Spearman’s rho > 0.96). Hence, the ISM generated using these three genes was a surrogate for gene expression induced by Type I interferons. Furthermore, on multivariate analysis, they identified that shorter disease duration, positivity for anti-double stranded DNA antibodies and antibodies to extractable nuclear antigens, serum levels of B cell activating factor (BAFF) and lower CD4+ lymphocyte counts associated with the ISM. ISM may be derived using as many number of genes as is feasible in a particular study and not just these three genes. This ISM has been used to identify those patients with high interferon-inducible gene expression who may be candidates for therapy with drugs directly targeting Type I IFNs, as we shall subsequently discuss. It is important to note that the different types of interferon signature metric described in different studies vary from each other. Hence, before embarking on a study intending to utilize the ISM, one must run a microarray of interferon induced genes in the population of interest and attempt to derive an ISM based on as many genes as are feasible to be studied repeatedly depending on the available resources, for that particular population and that particular study. Table 1 summarizes the different genes used in derivation of the ISM in the different studies assessing the utility of interferon-targeted therapies in lupus.Quantitatively analyzing a large number of genes is cumbersome and costly. It was possible that a smaller number of genes may accurately reflect the overall burden of genes induced by Type I interferons. Hence, Kennedy et al.We could identify two phase I studies and one phase II study on sifalimumab, one phase I and one phase II study on rontalizumab and a single phase I/II study on interferon-alpha kinoid in lupus.21 50 patients with SLE receiving a single dose of intravenous sifalimumab  were compared with 17 patients treated with placebo and found to have similar adverse effect profile, including no increased risk of infections. Those receiving sifalimumab had improvement of SLEDAI scores when compared with worsening of SLEDAI scores in those receiving placebo. Those patients with a high interferon signature metric had reduction of the same following treatment with sifalimumab; thus, the reduction of IFN-α/β induced proteins in the skin biopsy of a patient with SLE was also observed after administration of the drug. In another phase I study,22 121 patients with active lupus were treated with intravenous sifalimumab at varying doses between 0.3, 1, 3 and 10 mg/kg 2 weekly for 14 doses, with 40 patients receiving placebo. No increase in adverse effects was observed in those patients receiving sifalimumab. When adjusted for use of steroids to treat flares, patients receiving sifalimumab had a significant improvement in mean scores of Safety of Estrogens in Lupus Erythematosus National Assessment SLEDAI (SELENA-SLEDAI) compared to those receiving placebo. Sifalimumab was more effective than placebo in normalizing complement levels. A phase IIb trial of sifalimumab in SLE was recently reported.23 In this study, 324 patients with active lupus were treated with intravenous sifalimumab at doses of 200 mg, 600 mg or 1200 mg  compared with 107 patients receiving placebo. Higher proportion of patients receiving sifalimumab at all doses individually attained the primary end-point of the SLE Responder Index (SRI) when compared with placebo. Those with significant cutaneous involvement had better improvement in the cutaneous lupus erythematosus area and severity index (CLASI) compared to placebo; better improvement of fatigue scores was also noted. Patients receiving sifalimumab had lesser disease flares, better physician global assessment scores, greater improvement in SLEDAI 2000 (SLEDAI-2K) scores and response when assessed using the British Isles Lupus Assessment Group (BILAG)-based Composite Lupus Assessment (BICLA) criteria compared to placebo. In this study, ISM was assessed using four genes. Those with a high baseline ISM had significant improvement in lupus disease activity scores compared to placebo. However, the small number of patients with low ISM meant that comparisons between ISMhigh and ISMlow subgroups were not feasible. The adverse effect profile was similar in patients in both the groups.Sifalimumab is a fully human monoclonal IgG1κ antibody against interferon alpha. In a phase I study,24 rontalizumab was studied in 48 patients with SLE compared with 12 others receiving placebo. The doses used with gradual escalation under careful monitoring were 0.3mg/kg intravenously (iv), 1 mg/kg iv and subcutaneous (sc), 3 mg/kg iv and sc and 10 mg/kg iv. There were no significant differences in the incidence of mild, moderate and serious adverse events (including infections) in between patients receiving rontalizumab or placebo. Notably, one of the patients receiving rontalizumab developed acute myeloid leukemia. Regarding interferon signature, the ISM for this particular study was calculated using expression of seven IFN-inducible genes. A dose-dependent reduction in the ISM score as observed after administration of the first as well as subsequent doses of rontalizumab, with reduction in circulating levels of proteins known to be induced by interferons such as monocyte chemoattractant protein 1 (MCP1). No significant reduction in circulating autoantibodies was noted following treatment with rontalizumab. Based on the promise shown by the phase I study, a phase II clinical trial of rontalizumab in SLE  was conducted.25 In this study, 159 patients with lupus were randomized to receive rontalizumab  and compared with 79 placebo-treated patients. All patients on placebo, 79 on iv rontalizumab and 77 on sc rontalizumab completed follow-up till 24 weeks. The study did not meet the pre-specified end point, i.e., similar proportion of patients receiving rontalizumab and placebo attained response as defined by the BILAG responder index or the secondary end point of SRI, irrespective of whether they were ISMhigh or ISMlow. Paradoxically, secondary analysis revealed that those patients having a low ISM had decreased number of flares on rontalizumab, and more of them were able to attain doses of prednisolone ≤ 10 mg/day compared to placebo. However, this was not seen in those with a high ISM. No significant difference was observed in adverse effects in between the groups receiving rontalizumab and placebo. The SRI response rates with sifalimumab and rontalizumab are comparable with those achieved with belimumab.27Rontalizumab is another humanized IgG1 monoclonal antibody which neutralizes all subtypes of IFN-α. In a phase I dose ranging study,29 This approach was replicated in humans in a phase I/II dose escalation placebo-controlled study of 28 patients with lupus.30 Such antibodies to IFN-α generated endogenously by immunizing with IFN-K administered intramuscularly at doses of 30 μg, 60 μg, 120 μg or 240 μg administered at day 0, day 7, day 28 and day 84 , wherein patients with lupus are immunized with IFN-α conjugated with keyhole limpet hemocyanin to enhance its antigenicity. The ability of this approach to generate neutralizing antibodies that ameliorated the phenotype of lupus was initially shown in NZB/W F1 mice, which serve as a murine model of lupus.31 Another monoclonal antibody, AIA22, neutralizes multiple IFN-α subtypes and binds to a novel epitope on IFN-α which binds to the Type II IFN receptor.32 Its utility in lupus is being further investigated. Anifrolumab is another monoclonal antibody that targets interferon receptor Type I that is being currently investigated in lupus.33MEDI-546 is an antibody to type I IFN-α receptor that has been tried in patients with systemic sclerosis and has potential for investigation in patients with lupus.34 Bruton’s tyrosine kinase (Btk) is an enzyme involved in signal transduction in multiple immune cells. A recent paper35 demonstrated that inhibition of Btk by RN486 in purified human pDCs in-vitro resulted in abrogation of inflammatory signals downstream of TLR9 (including levels of IFN-α production), but not those following TLR7 stimulation. Blood dendritic cell antigen 2 (BDCA2) is an inhibitory receptor on human pDCs, which inhibits signaling via TLR7 and TLR9. A monoclonal antibody stimulating BDCA236 was shown in-vitro in human pDCs derived from healthy controls as well as patients with lupus and in cynomolgus monkeys to inhibit production of Type I IFNs. This may be another potential method of reducing pathogenic Type I IFN secretion. Sphingosine-1 phosphate receptor (S1PR) is expressed on the surface of different immune cells including pDCs. Activation of S1PR ameliorates Type I IFN production resulting from stimulation of TLR7 or TLR9 in human pDCs,37 and serves as another potential target to reduce the secretion of Type I IFNs. Inhibitory oligonucleotides whose guanine moiety has been modified have been found to inhibit production of Type I IFNs by human pDCs as well as pDCs from MRL/lpr mice model of lupus which were stimulated via TLR7 and TLR9 agonists,38 identifying another potential therapeutic route in lupus.Hydroxychloroquine (HCQ) is considered as one of the most important drugs in the management of lupus. Mechanistically, HCQ acts by interfering with endosomal interaction of nucleic acids (derived from apoptotic debris and immune complexes) with TLRs. This interrupts the loop driving type I interferon synthesis, which was responsible for creating a permissive environment for the production of autoantibodies. Indeed, treatment with HCQ has been shown to reduce levels of circulating Type I IFN.39 of 12 patients with refractory lupus who received between one to four cycles of intravenous bortezomib (at a dose of 1.3 mg/m2) revealed not only a depletion of plasma cells  but also reduced the expression of Siglec-1 on monocytes, which is expressed following exposure to Type I IFNs.Bortezomib is emerging as a promising agent for the treatment of refractory lupus, and acts by inhibiting the proteasome. A recent study40Bcl-2 is an anti-apoptotic factor that plays a role in persistence of pathogenic pDCs, which are the source of Type I IFN in lupus. Studies in pDCs derived from NZB/W F1 mica as well as from humans  reveal that exposure to Bcl-2 antagonists ABT-737 and ABT-199 reduces survival of these pDCs. This is another potential avenue to ameliorate the Type I IFN signature in lupus.41Figure 1 summarizes the sites of action of these various therapies that have been discussed.Lipopolysaccharide (LPS) is a ligand for TLR4 and this further leads on to Type I IFN expression. Dihydroartemisinin, a commonly used anti-malarial drug, has been found to reduce secretion of IFN-α and IFN-β induced by LPS from splenocytes cultured from MRL/lpr mice.25 than those with a high ISM. Hence, the use of ISM as a biomarker to decide the use of interferon-targeted therapies needs further exploration.Type I IFNs play a crucial role in driving the process of autoantibody generation in lupus. Patients with lupus have been found to exhibit an interferon signature metric that reflects activation of genes following stimulation by Type I IFNs. Although one would expect that interferon-targeted therapies should be more useful in those patients having a high interferon signature, the exact role of ISM in determining whether a patient will benefit with IFN-targeted therapies in unclear. For example, patients with a low ISM showed better response to rontalizumabMonoclonal antibodies directly inhibiting Type I IFN , blocking of interferon receptor (anifrolumab) and stimulation of endogenous production of antibodies to Type I IFN by interferon alpha kinoid are promising strategies in the management of lupus and other autoimmune diseases. These drugs may be effective for inducing remission in difficult-to-treat lupus. There is inadequate literature to answer whether or not they may be useful for maintenance of remission. In addition, hydroxychloroquine and bortezomib also reduce the circulating levels of Type I IFN in lupus. Numerous other agents such as Bcl-2 antagonists, Btk inhibitors and dihydroartemisinin have shown promise in reducing levels of circulating Type I IFN in laboratory and animal models of lupus. IFN-blockade may be of value in those patients who are refractory to conventional induction regimens, however, their role in renal and neuropsychiatric lupus, which are two subtypes of lupus associated with higher morbidity, requires further exploration.


Contemporary control  groups were maintained at the Kennedy Space Center, prior to sacrifice and similar tissue collection at the NASA Ames Research Center. Ovarian tissues were collected and processed for RNA and steroid analyses at initial carcass thaw. Vaginal wall tissue collected from twice frozen/thawed carcasses was fixed for estrous cycle stage determinations. The proportion of animals in each phase of the estrous cycle  did not appreciably differ between baseline, vivarium, and flight mice, while habitat control mice exhibited greater numbers in diestrus. Ovarian tissue steroid concentrations indicated no differences in estradiol across groups, while progesterone levels were lower (p < 0.05) in habitat and flight compared to baseline females. Genes involved in ovarian steroidogenic function were not differentially expressed across groups. As ovarian estrogen can dramatically impact multiple non-reproductive tissues, these data support vaginal wall estrous cycle classification of all female mice flown in space. Additionally, since females exposed to long-term spaceflight were observed at different estrous cycle stages, this indicates females are likely undergoing ovarian cyclicity and may yet be fertile.Ovarian steroids dramatically impact normal homeostatic and metabolic processes of most tissues within the body, including muscle, bone, neural, immune, cardiovascular, and reproductive systems. Determining the effects of spaceflight on the ovary and estrous cycle is, therefore, critical to our understanding of all spaceflight experiments using female mice. Adult female mice ( Many, if not all, normal homeostatic processes occurring within mammals, both male and female, are dramatically impacted by gonadal hormones. Indeed, the reproductive system is often considered one of the best markers of overall health and fitness of an animal2. However, research is limited regarding the effects of spaceflight on the reproductive system and its consequences on normal physiology4.Spaceflight microgravity is known to impact numerous physiological systems. These include cardiovascular changes, decreased bone mineral density, loss of muscle tone, adipogenesis, insulin resistance, vision and vestibular disturbances, altered fluid and electrolyte balance, and altered kidney function to name a few5. Moreover, ovarian steroidogenic output can be dramatically impacted by stress as well as other internal homeostatic processes6. In cycling rodents, the antral follicle is the primary steroidogenic tissue within the ovary, unless mating occurs at which time the corpus luteum becomes the major producer of progesterone. The mural granulosa cells and the vascularized theca cells within the antral ovarian follicles are responsible for the enzymatic conversion of cholesterol to its ultimate endpoint product estradiol. In addition to steroidogenesis, these follicular cells also carry out the critical missions of nurturing the enclosed growing oocyte, as well as facilitating the process of ovulation, or expulsion of oocyte, and subsequent formation of the ephemeral corpus luteum. Cyclic and temporal changes in expression of steroidogenic pathway proteins and other critically important ovarian genes known to play critical roles in ovulation, oocyte development and corpus luteum development and function are well known and may be evaluated to provide information regarding ovarian function. Understanding these molecular changes is vital for informing female reproductive health during and after spaceflight.The ovarian steroid hormones, estrogen and progesterone, play a significant role in regulating a myriad of physiological functions, and are subject to cyclic changes based on the hypothalamic/pituitary regulation of ovarian folliculogenesis7. Similarly, pregnant female rats flown on Cosmos 1514 (1982), NASA-NIH Rodent (R)1 (STS-66 in 1994), and NASA-NIH R2 (STS-70 in 1995) were of short duration (4.5–11 days) and had live animal return10. Notably, in the pregnant rats no effects of spaceflight on healthy and atretic ovarian antral follicle populations, fetal wastage in utero, plasma concentrations of progesterone and luteinizing hormone (LH) or pituitary content of follicle stimulating hormone (FSH) were noted8. Spaceflight, however, significantly increased plasma concentrations of FSH and decreased pituitary content of LH analyzed postpartum . In these prior studies, it is not possible to separate the effects of microgravity from that of reentry with the associated hypergravity, turbulence, and the likely ensuing stress responses linked to these events. Moreover, these studies while evaluating maintenance of pregnancy, provide no information regarding the fertility of these animals and their ability to exhibit normal estrous cycles and ovarian function. The current investigation provides insights into the endocrine status and ovarian cyclicity of female mice following an extended period of exposure to microgravity in the absence of reentry effects. Addressing the effects of extended duration spaceflight on endocrine status are significant and of high impact to long-term female astronaut health before and after flight, interpretation of spaceflight findings derived from female mice, and for long-term plans for multigenerational mammalian studies in spaceflight.To date, studies of ovarian function and fertility in mice exposed to microgravity during NASA Space Shuttle flights were of short duration (<12 days) and, importantly, all included the effects of live reentry prior to collection of tissues and analysis of animal behaviorn = 2 or 3/stage). Flight group mice presented at estrus (n = 6) and metestrus  in every animal, and overall averaged around 5.71 ± 0.12 compared to 4.08 ± 0.11 for RNAlater preserved ovarian tissue  and flight mice (p = 0.013) in comparison to baseline control mice  in mice housed in the habitat maintained on the ground (HC) when compared to baseline and vivarium controls but were not different from flight animals that were housed in the habitat on the ISS , scavenger receptor class B type 1 (Scarb1), luteinizing hormone receptor (Lhcgr) as well as oocyte-specific proteins, growth differentiation factor 9 (Gdf9), zona pellucida glycoprotein 3 (Zp3) showed no differences across the treatment groups  expression was elevated (p ≤ 0.05) ~4-fold compared to all other stages , P450 cholesterol side chain cleavage (Cyp11a1), 3β-hydroxysteroid dehydrogenase (Hsd3b1), Cyp19a1, and liver receptor homolog-1 (Nr5a2) expression did not change appreciably across experimental conditions. Expression of Cyp17a1 under baseline and vivarium control conditions were significantly decreased (p < 0.05) compared to habitat control conditions (p < 0.05). Ovarian Cyp17a1 levels in flight animals in estrus were not notably different from estrus stage animals in the other experimental conditions . Typically, under conditions of exogenous treatments/stress, mice may exhibit constant vaginal estrus or an extended or constant diestrus20, but not metestrus. In contrast to mice, rats exhibit a greater synchrony when group housed, making it difficult to extrapolate finding across these two species21.Rodent Research-1 (RR-1), a Validation mission launched aboard SpaceX’s fourth cargo resupply mission, was the first to transport rodents to the International Space Station (ISS) in an unmanned commercial vehicle. Lasting 37 days in microgravity, RR-1 was, at the time, the longest space rodent study conducted by NASA. In-flight studies of RR1 mouse behavior and post-flight physiology revealed the Validation mice showed no overt physiological signs of chronic stress or compromised health or welfare23.While measuring true estrous cyclicity in space is logistically daunting due to the intensive daily monitoring required, we posit that the analysis of the vaginal wall provides a robust methodology that can indicate the estrous stage, and thus the endocrine status of the animal at the moment of sacrifice. Indeed the morphology of these twice frozen and thawed vaginal wall tissues rivaled that of freshly isolated and fixed tissues from contemporary animals collected in our laboratory (data not shown) and in those laboratories who pioneered the use of vaginal wall histology as a mechanism to track cyclicity27. In contrast, we did observe that flight and habitat control animals had lower overall P4 levels in ovarian tissue. One might predict this as these two groups had greater numbers of mice in metestrus and diestrus, in total 4 and 5 for flight and habitat groups, respectively. During both of these stages of estrous cycle the ovary has reduced steroidogenic activity28. This was in contrast to the vivarium and baseline control groups, which both had 8 animals each in proestrus and estrus in total, stages associated with increased ovarian follicular steroidogenesis. We recognize that it would have been ideal to examine the interaction of estrous cycle stage on ovarian steroidogenesis as well as ovarian gene expression, but due to the insufficient numbers of animals at individual stages, we were unable to do so. It is worth noting the experimental design for RR1 as a hardware demonstration flight determined how tissues were preserved as well as group sizes and thus were beyond our control. Whole ovarian gene expression analyses were also dictated by the RR1 design. This type of analysis is highly confounded as whole ovarian tissue contains an abundance of stromal tissue and smaller follicles, which predominate over the few selected follicles that become highly steroidogenic. Thus, when whole ovarian RNA is used, it was not surprising to see no major differences in ovarian gene expression across the treatment groups or even during different stages of the cycle. Future studies need to take into consideration this limitation, and collect and fix tissues for analysis, so they can be properly analyzed using in situ hybridization/RNAscope and immunohistochemistry. Cyp17a1, the enzyme located in the theca cell compartment of the ovarian follicle and responsible for androgen production was elevated in the habitat control animals. This difference was however, manifested primarily in two of the three HC animals in estrus.Ovarian tissue steroid levels are also not commonly measured, as most investigators use serum levels of estrogen and progesterone as a measure of ovarian steroidogenic activity. Again, in animals exposed to microgravity, the ability to collect serum at the time of sacrifice or longitudinally over time is severely restricted logistically. We, therefore, analyzed whole ovarian tissue levels of estrogen and progesterone. We detected no striking difference in tissue estrogen concentrations across the four treatment groups, and it should be noted that serum estrogen levels in rodents also do not exhibit wide variations across the cycle, often ranging from ND to 8–50 pg/ml in total29. This may be due to a time of collection aspect with respect to spleen vs ovary following thawing of the carcass, or just to tissue differences in RNA susceptibility to the freeze/thaw. We note that freshly isolated ovarian tissues that were snap-frozen in LN and then simultaneously processed with the RR1 samples yielded RIN values >9.Safeguarding RNA tissue quality is acutely dependent on tissue storage methods, which ideally would minimize RNase activity throughout the process. Tissue collection and preservation during spaceflight presents a unique challenge in the analysis of these valuable specimens. Our current analyses of the right and left ovaries processed in RNALater or snap-frozen in liquid nitrogen (LN) indicated that LN preserved tissues always had greater RIN values when compared to those of RNALater. Previous studies utilizing RR1 RNAlater preserved spleen tissues showed higher RIN values than those observed here30. Determining the effects of spaceflight on estrous cyclicity is thus critical to making accurate interpretations of experimental endpoints for all spaceflight experiments using female mice.In conclusion, our analyses of ovarian tissues and vaginal wall tissues of mice exposed to extended periods of microgravity and sacrificed in space indicate that these female mice were exhibiting estrous cyclic activity. These studies point positively to the ability of these animals to either continue or regain estrous cycle activity during extended microgravity exposure. We posit that vaginal wall estrous cycle activity be collected and provided to investigators to include as covariate on all female mice used in future space missions, much the same as weight and age. The current studies also provide critical logistical information regarding tissue collection, particularly for organs such as the ovary and uterus, which are composed of highly dynamic tissues , we recommend that they be preserved primarily in fixatives or in a manner where tissue/cell specific aspects can be determined afterward. Lastly, the ability of these females to exhibit estrous cycle activity provides a tractable mammalian model system to test whether fertility is actually impacted by microgravity. Reproductive cycles characterized by alterations in hormone levels can exert significant effects on experimental outcomes, including emotion and the response to environmental stressorsn = 10/group): baseline control, vivarium control, habitat control, and flight. Flight mice were launched on SpaceX-4 (Rodent Research 1) on September 21, 2014, as part of NASA’s Validation study. Details of RR1 mission timeline  as well as mouse spaceflight hardware details are available31. Briefly, vivarium control mice were maintained in standard mouse cages (5 mice/cage) for the duration of spaceflight at Kennedy Space Center. Habitat control mice were housed in a Rodent Research Transporter and Habitat Hardware on Earth for durations consistent with flight times at the same density of mice as those in flight (n = 10/habitat). The habitat control was placed in the ISS Environmental Simulator at KSC on a 4-day delay to mimic flight temperature, CO2, and humidity conditions on the ISS for the duration of spaceflight. Baseline control mice were euthanized one day after launch and then partially dissected prior to freezing of the animal carcasses mimicking the procedures to be conducted on flight and for all other animals at the conclusion of experiment. Flight mice were exposed to microgravity for a total of 37 days (33 days on ISS and 4 days in the Dragon Capsule). Mice were euthanized by injection of Euthasol followed by cervical dislocation and immediately fast frozen intact (n = 8) or partially dissected (n = 2) prior to carcasses being frozen. Mice carcasses were frozen using pre-chilled Ice Bricks prior to transfer to the Minus Eighty Degree Laboratory Freezer (MELFI) aboard the ISS. All ground mice were processed similarly. Flight mice carcasses returned to Earth February 2015 aboard the SpaceX CRS-5 and were then maintained along with the control mice frozen at the Biospecimen Sharing Program (BSP) at the Ames Research Center until dissection11. All animal procedures performed were approved by the Institutional Animal Care and Use Committees (IACUC) for flight at the NASA Ames Research Center (ARC) and the Kennedy Space Center (KSC) and the methods were carried out in accordance with relevant guidelines and regulations11.Forty 12-week old C57BI/6J female mice  were selected based on similar body weights for 4 experimental groups  were collected from all carcasses at about 35–45 min after removal from the −80 °C freezer. One ovary was placed into RNAlater  and the other was flash-frozen in liquid nitrogen (LN). Similarly, the right and left uterine horns were placed in RNAlater or snap-frozen in LN; uterine tissues are part of an independent study by another investigator and will not be further described here. Tissues preserved in RNAlater were kept at 4 °C for 2 days then frozen and stored at −80 °C, tissues snap-frozen in liquid nitrogen were also stored at −80 °C. Following primary tissue dissections in April of 2015, the remaining carcass was refrozen and stored at −80 °C. In March of 2016, carcasses were thawed again for a secondary tissue dissection. At this time the combined cervix and vaginal wall were dissected free of the pelvic cavity, and these tissues were fixed in 4% paraformaldehyde in phosphate-buffered saline. Following overnight fixation these tissues were transferred to 70% EtOH, in preparation for shipping to the University of Kansas Medical Center.Ovaries tissues were transferred directly from RNAlater to tubes containing 1 ml of Trizol (Sigma) or 1 ml of Trizol was added directly to ovarian tissue snap-frozen in liquid nitrogen. Tissues were briefly dissociated on ice  in a PowerGen 700 homogenizer (Fisher Scientific). RNA was isolated from Trizol as per manufacturer protocol. To facilitate precipitation 1 μL of glycogen  was added to aqueous fraction and the resulting pellet was then resuspended in 15 μL of RNase free water (ThermoFisher 18064022). An aliquot (1 μL) of this resuspended RNA was used to determine RNA concentration and RNA integrity number (RIN) values using the Agilent RNA 6000 Pico kit and Agilent 2100 Bioanalyzer (Agilent Technologies). Total RNA (400 ng) from each sample was reverse transcribed using SuperScript II Reverse Transcriptase (Invitrogen/ThermoFisher) with random hexamer primers (IDT).Hsd3b1), P450 cholesterol side chain cleavage (Cyp11a1), 17α-hydroxylase (Cyp17a1), P450 aromatase (Cyp19a1), steroidogenic acute regulatory protein (Star), liver receptor homolog-1 (Nr5a2), estrogen receptor 1 (Esr1), estrogen receptor 2 (Esr2), scavenger receptor class B member 1 (Scarb1), luteinizing hormone receptor (Lhcgr), zona pellucida glycoprotein 3 (Zp3), and growth differentiation factor 9 (Gdf9). Primers were designed using Primer Express 3.0 (Applied Biosystems) and purchased from IDT . qPCR on Star, and 18 S rRNA levels were determined using the TaqMan Universal PCR Master Mix (Applied Biosystems) protocol and Applied Biosystems  probes and primers. Samples were run in triplicate, and the ΔΔCt method was used to calculate the relative expression between the samples after normalization with 18S levels. All SYBR Green reactions were evaluated for the presence of a single dissociation curve, to confirm the amplification of a single transcript and lack of primer dimers.Quantitative PCR (qPCR) was performed using a 1:5 dilution of cDNA on an Applied Biosystems HT7900 sequence detector. Genes interrogated included 3β-hydroxysteroid dehydrogenase . Tissue sections were then deparaffinized, rehydrated, and stained with hematoxylin and eosin prior to histological examination. Slides were randomized and sample  labels were blinded prior to examination by three independent reviewers for estrous cycle staging . Non-determinable samples were the result of tissues not containing sufficient vaginal wall tissue to yield reliable results. Following independent reviews, those samples (~10%) without a consensus stage call were reevaluated by the reviewers and a consensus stage agreed upon.g for 5 min. Approximately 150 μL of supernatant was collected. 100 μL of undiluted supernatant was shipped to the Ligand Assay and Analysis Core (UVA School of Medicine) on dry ice and the rest were stored as 50 μL aliquots at −80 °C. Estradiol (E2) and progesterone (P4) were determined by commercial ELISAs . E2 assay characteristics were as follows: sensitivity = 3 pg/ml; intra-assay coefficient of variation (CV) = 7.5%; inter-assay CV = 10.1%. For P4, sensitivity = 0.15 ng/ml; intra-assay CV = 6.5%; inter-assay CV = 10.3%.The steroid content (estrogen/progesterone) content of the ovary, as determined by RIA, was used as a surrogate for serum analyses for establishing the functional ovarian status of the RR1 females at the time of their sacrifice. Flash frozen ovaries were halved and homogenized using a sonicator in 200 μL of PBS. Homogenate was spun down at 10,000 × F-test was observed. All statistical analyses and graphs for the RIA were performed using GraphPad Prism v6 .One-way ANOVA was used to determine differences amongst treatment groups (flight status) or estrous cycle stage. Tukey’s mean separation tests were used to determine differences between mean once a significant Further information on experimental design is available in the Supplementary Figures and TablesReporting Summary Checklist