Development of a reliable cell-based reporter gene assay to measure the bioactivity of anti-HER2 therapeutic antibodies.

Human epidermal growth factor receptor 2 (HER2) is a key player in the pathogenesis and progression of breast cancer and is currently a primary target for breast cancer immunotherapy. Bioactivity determination is necessary to guarantee the safety and efficacy of therapeutic antibodies targeting HER2. Nevertheless, currently available bioassays for measuring the bioactivity of anti-HER2 mAbs are either not representative or have high variability. Here, we established a reliable reporter gene assay (RGA) based on T47D-SRE-Luc cell line that expresses endogenous HER2 and luciferase controlled by serum response element (SRE) to measure the bioactivity of anti-HER2 antibodies. Neuregulin-1 (NRG-1) can lead to the heterodimerization of HER2 on the cell membrane and induce the expression of downstream SRE-controlled luciferase, while pertuzumab can dose-dependently reverse the reaction, resulting in a good dose-response curve reflecting the activity of the antibody. After optimizing the relevant assay parameters, the established RGA was fully validated based on ICH-Q2 (R1), which demonstrated that the method had excellent specificity, accuracy, precision, linearity, and stability. In summary, this robust and innovative bioactivity determination assay can be applied in the development and screening, release control, biosimilar assessment and stability studies of anti-HER2 mAbs.

Improving product quality and productivity of an antibody-based biotherapeutic using inverted frustoconical shaking bioreactors.

The Chinese hamster ovarian (CHO) cells serve as a common choice in biopharmaceutical production, traditionally cultivated in stirred tank bioreactors (STRs). Nevertheless, the pursuit of improved protein quality and production output for commercial purposes demand exploration into new bioreactor types. In this context, inverted frustoconical shaking bioreactors (IFSB) present unique physical properties distinct from STRs. This study aims to compare the production processes of an antibody-based biotherapeutic in both bioreactor types, to enhance production flexibility. The findings indicate that, when compared to STRs, IFSB demonstrates the capability to produce an antibody-based biotherapeutic with either comparable or enhanced bioprocess performance and product quality. IFSB reduces shear damage to cells, enhances viable cell density (VCD), and improves cell state at a 5-L scale. Consequently, this leads to increased protein expression (3.70 g/L vs 2.56 g/L) and improved protein quality, as evidenced by a reduction in acidic variants from 27.0% to 21.5%. Scaling up the culture utilizing the Froude constant and superficial gas velocity ensures stable operation, effective mixing, and gas transfer. The IFSB maintains a high VCD and cell viability at both 50-L and 500-L scales. Product expression levels range from 3.0 to 3.6 g/L, accompanied by an improved acidic variants attribute of 20.6%-22.7%. The IFSB exhibits superior productivity and product quality, underscoring its potential for incorporation into the manufacturing process for antibody-based biotherapeutics. These results establish the foundation for IFSB to become a viable option in producing antibody-based biotherapeutics for clinical and manufacturing applications.

Highly drug/target-tolerant neutralizing antibody (NAb) assay development through target-based drug depletion and drug-based NAb extraction for an anti-EGFR therapeutic monoclonal antibody.

The reduction of immunogenicity is fundamental for the development of biobetter Erbitux, given that the development of an immune response reduces treatment efficacy and may lead to potential side effects. One of the requirements for the clinical research of a Erbitux biobetter candidate (CMAB009) is to develop a neutralizing antibody (NAb) assay, and sufficient drug and target tolerance for the assay is necessary. Here, we describe the development of a competitive ligand binding (CLB) assay for CMAB009 with high drug and target tolerance through target-based drug depletion and drug-based NAb extraction, the integrated experimental strategy was implemented to simultaneously mitigate drug interference and enhance target tolerance. Following troubleshooting and optimization, the NAb assay was validated for clinical sample analysis with the sensitivity of 92 ng/mL, drug tolerance of 70 µg/mL and target tolerance of 798 ng/mL. The innovative drug depletion and NAb extraction achieved though the combination of drug and target beads would enable the development of reliable NAb assays for many other therapeutics that overcome drug and its target interference for more precise and sensitive NAb assessment.

Successful treatment of 1 patient with chlorine-induced ARDS using awake self-prone positioning and nasal high-flow oxygen: A case report.

RATIONALE: Accidents involving chlorinated compounds in the context of cleaning are not uncommon. However, improving the treatment success rate for acute respiratory distress syndrome (ARDS) patients caused by chlorine gas presents significant challenges. PATIENT CONCERNS: A 28-year-old female was admitted to the intensive care unit after accidental inhalation of chlorine gas resulting in ARDS. DIAGNOSES: The patient was diagnosed with ARDS attributed to chlorine gas exposure. INTERVENTIONS: The intervention involved utilizing a combination of awake self-prone positioning (ASPP) and high-flow nasal oxygen therapy for treatment. OUTCOMES: After continuous ASPP and high-flow nasal oxygen therapy, the patient quickly recovered and was transferred out of the intensive care unit on the 6th day without any adverse events. LESSONS: ASPP combined with high-flow nasal oxygen therapy can improve patients' hypoxemia, prevent the need for intubation, avoid rapid deterioration of the condition, reduce treatment complexity, and lower mortality rate.

Utilizing transcriptomics and metabolomics to unravel key genes and metabolites of maize seedlings in response to drought stress.

BACKGROUND: Drought stress can substantially restrict maize growth and productivity, and global warming and an increasing frequency of extreme weather events are likely to result in more yield losses in the future. Therefore, unraveling the molecular mechanism underlying the response to drought stress is essential for breeding drought-resilient crops. RESULTS: In this study, we subjected the 3-leaf-period plants of two maize inbred lines, a drought-tolerant line (si287) and a drought-sensitive line (X178), to drought stress for seven days while growing in a chamber. Subsequently, we measured physiological traits and analyzed transcriptomic and metabolic profiles of two inbred lines. Our KEGG analysis of genes and metabolites revealed significant differences in pathways related to glycolysis/gluconeogenesis, flavonoid biosynthesis, starch and sucrose metabolism, and biosynthesis of amino acids. Additionally, our joint analysis identified proline, tryptophan and phenylalanine are crucial amino acids for maize response to drought stress. Furthermore, we concentrated on tryptophan (Trp), which was found to enhance tolerance via IAA-ABA signaling, as well as SA and nicotinamide adenine dinucleotide (NAD) consequent reactive oxygen species (ROS) scavenging. We identified three hub genes in tryptophan biosynthesis, indole-3-acetaldehyde oxidase (ZmAO1, 542,228), catalase 1 (ZmCAT1, 542,369), and flavin-containing monooxygenase 6 (ZmYUC6, 103,629,142), High expression of these genes plays a significant role in regulating drought tolerance. Two metabolites related to tryptophan biosynthesis, quinolinic acid, and kynurenine improved maize tolerance to drought stress by scavenging reactive oxygen species. CONCLUSIONS: This study illuminates the mechanisms underlying the response of maize seedlings to drought stress. Especially, it identifies novel candidate genes and metabolites, enriching our understanding of the role of tryptophan in drought stress. The identification of distinct resistance mechanisms in maize inbred lines will facilitate the exploration of maize germplasm and the breeding of drought-resilient hybrids.

Ceramide induces pyroptosis through TXNIP/NLRP3/GSDMD pathway in HUVECs.

BACKGROUND: Pyroptosis of endothelial cells is a new cause of endothelial dysfunction in multiple diseases. Ceramide acts as a potential bioactive mediator of inflammation and increases vascular endothelial permeability in many diseases, whether it can aggravate vascular endothelial injury by inducing cell pyroptosis remains unknown. This study was established to explore the effects of C8-ceramide (C8-Cer) on human umbilical vein vascular endothelial cells (HUVECs) and its possible underlying mechanism. METHODS: HUVECs were exposed to various concentrations of C8-Cer for 12 h, 24 h, 48 h. The cell survival rate was measured using the cell counting kit-8 assay. Western blotting and Real-time polymerase chain reaction (RT-PCR) were used to detect the pyroptosis-releated protein and mRNA expressions, respectively. Caspase-1 activity assay was used to detect caspase-1 activity. Hoechst 33342/propidium iodide double staining and flow cytometry were adopted to measure positive staining of cells. Lactate dehydrogenase release assay and enzyme-linked immunosorbent assay were adopted to measure leakage of cellular contents. FITC method was used to detect the permeability of endothelial cells. ROS fluorescence intensity were detected by flow cytometry. RESULTS: The viability of HUVECs decreased gradually with the increase in ceramide concentration and time. Ceramide upregulated the expression of thioredoxin interacting protein (TXNIP), NLRP3, GSDMD, GSDMD-NT, caspase-1 and Casp1 p20 at the protein and mRNA level in a dose-dependent manner. It also enhanced the PI uptake in HUVECs and upregulated caspase-1 activity. Moreover, it promoted the release of lactate dehydrogenase, interleukin-1ß, and interleukin-18. Meanwhile, we found that ceramide led to increased vascular permeability. The inhibitor of NLRP3 inflammasome assembly, MCC950, was able to disrupt the aforementioned positive loop, thus alleviating vascular endothelial cell damage. Interestingly, inhibition of TXNIP either chemically using verapamil or genetically using small interfering RNA (siRNA) can effectively inhibit ceramide-induced pyroptosis and improved cell permeability. In addition, ceramide stimulated reactive oxygen species (ROS) generation. The pretreatment of antioxidant N-acetylcysteine (NAC), ROS scavenger, blocked the expression of pyroptosis markers induced by C8-cer in HUVECs. CONCLUSION: The current study demonstrated that C8-Cer could aggravate vascular endothelial cell damage and increased cell permeability by inducing cell pyroptosis. The results documented that the ROS-dependent TXNIP/NLRP3/GSDMD signalling pathway plays an essential role in the ceramide-induced pyroptosis in HUVECs.

Allyl methyl trisulfide protected against LPS-induced acute lung injury in mice via inhibition of the NF-κB and MAPK pathways.

Allyl methyl trisulfide (AMTS) is one major lipid-soluble organosulfur compound of garlic. Previous studies have reported the potential therapeutic effect of garlic on acute lung injury (ALI) or its severe condition acute respiratory distress syndrome (ARDS), but the specific substances that exert the regulatory effects are still unclear. In this study, we investigate the protective effects of AMTS on lipopolysaccharide (LPS)-induced ALI mice and explored the underlying mechanisms. In vivo experiments, ICR mice were pretreated with 25-100 mg/kg AMTS for 7 days and followed by intratracheal instillation of LPS (1.5 mg/kg). The results showed that AMTS significantly attenuated LPS-induced deterioration of lung pathology, demonstrated by ameliorative edema and protein leakage, and improved pulmonary histopathological morphology. Meanwhile, the expression of inflammatory mediators and the infiltration of inflammation-regulation cells induced by LPS were also inhibited. In vitro experiments also revealed that AMTS could alleviate inflammation response and inhibit the exaggeration of macrophage M1 polarization in LPS-induced RAW264.7 cells. Mechanistically, we identified that AMTS treatment could attenuate the LPS-induced elevation of protein expression of p-IκBα, nuclear NF-κB-p65, COX2, iNOS, p-P38, p-ERK1/2, and p-JNK. Collectively, these data suggest that AMTS could attenuate LPS-induced ALI and the molecular mechanisms should be related to the suppression of the NF-κB and MAPKs pathways.

Vegetation, water infiltration, and soil carbon response to Adaptive Multi-Paddock and Conventional grazing in Southeastern USA ranches.

We examine Adaptive Multi-Paddock (AMP) grazed with short grazing events and planned recovery periods and paired ranches using Conventional Continuous Grazing (CG) at low stock density on vegetation, water infiltration, and soil carbon across SE USA. Increased vegetation standing biomass and plant species dominance-diversity were measured in AMP grazed ranches. Invasive perennial plant species richness and abundance increased with AMP grazing in the south, while in the north they increased on CG grazed ranches. Percent bare ground was significantly greater in CG at the Alabama and Mississippi sites, no different at the Kentucky and mid-Alabama sites, and greater on AMP at the Tennessee pair. On average, surface water infiltration was higher on AMP than paired CG ranches. Averaged over all locations, soil organic carbon stocks to a depth of 1 m were over 13% greater on AMP than CG ranches, and standing crop biomass was >300% higher on AMP ranches. AMP grazing supported substantially higher livestock stocking levels while providing significant improvements in vegetation, soil carbon, and water infiltration functions. AMP grazing also significantly increased available forage nutrition for key constituents, and increased soil carbon to provide significant resource and economic benefits for improving ecological health, resilience, and durability of the family ranch.

PDEGEM: Modeling non-uniform read distribution in RNA-Seq data.

BACKGROUND: RNA-Seq is a powerful new technology to comprehensively analyze the transcriptome of any given cells. An important task in RNA-Seq data analysis is quantifying the expression levels of all transcripts. Although many methods have been introduced and much progress has been made, a satisfactory solution remains be elusive. RESULTS: In this article, we borrow the idea from the Positional Dependent Nearest Neighborhood (PDNN) model, originally developed for analyzing microarray data, to model the non-uniformity of read distribution in RNA-seq data. We propose a robust nonlinear regression model named PDEGEM, a Positional Dependent Energy Guided Expression Model to estimate the abundance of transcripts. Using real data, we find that the PDEGEM fits the data better than mseq in all three real datasets we tested. We also find that the expression measure obtained using PDEGEM showed higher correlation with that obtained from alterative assays for quantifying gene and isoform expressions. CONCLUSIONS: Based on these results, we believe that our PDEGEM can improve the accuracy in modeling and estimating the transcript abundance and isoform expression in RNA-Seq data. Additionally, although the stacking energy and positional weight of the PDEGEM are relatively related to sequencing platforms and species, they share some common trends, which indicates that the PDEGEM could partly reflect the mechanism of DNA binding between the template strain and the new synthesized read.

A Gene Expression Profiling of Early Rice Stamen Development that Reveals Inhibition of Photosynthetic Genes by OsMADS58.

Stamen is a unique plant organ wherein germ cells or microsporocytes that commit to meiosis are initiated from somatic cells during its early developmental process. While genes determining stamen identity are known according to the ABC model of floral development, little information is available on how these genes affect germ cell initiation. By using the Affymetrix GeneChip Rice Genome Array to assess 51 279 transcripts, we established a dynamic gene expression profile (GEP) of the early developmental process of rice (Oryza sativa) stamen. Systematic analysis of the GEP data revealed novel expression patterns of some developmentally important genes including meiosis-, tapetum-, and phytohormone-related genes. Following the finding that a substantial amount of nuclear genes encoding photosynthetic proteins are expressed at the low levels in early rice stamen, through the ChIP-seq analysis we found that a C-class MADS box protein, OsMADS58, binds many nuclear-encoded genes participated in photosystem and light reactions and the expression levels of most of them are increased when expression of OsMADS58 is downregulated in the osmads58 mutant. Furthermore, more pro-chloroplasts are observed and increased signals of reactive oxygen species are detected in the osmads58 mutant anthers. These findings implicate a novel link between stamen identity determination and hypoxia status establishment.

Multimodel simulations of forest harvesting effects on long­term productivity and CN cycling in aspen forests.

The effects of forest management on soil carbon (C) and nitrogen (N) dynamics vary by harvest type and species. We simulated long-term effects of bole-only harvesting of aspen (Populus tremuloides) on stand productivity and interaction of CN cycles with a multiple model approach. Five models, Biome-BGC, CENTURY, FORECAST, LANDIS-II with Century-based soil dynamics, and PnET-CN, were run for 350 yr with seven harvesting events on nutrient-poor, sandy soils representing northwestern Wisconsin, United States. Twenty CN state and flux variables were summarized from the models' outputs and statistically analyzed using ordination and variance analysis methods. The multiple models' averages suggest that bole-only harvest would not significantly affect long-term site productivity of aspen, though declines in soil organic matter and soil N were significant. Along with direct N removal by harvesting, extensive leaching after harvesting before canopy closure was another major cause of N depletion. These five models were notably different in output values of the 20 variables examined, although there were some similarities for certain variables. PnET-CN produced unique results for every variable, and CENTURY showed fewer outliers and similar temporal patterns to the mean of all models. In general, we demonstrated that when there are no site-specific data for fine-scale calibration and evaluation of a single model, the multiple model approach may be a more robust approach for long-term simulations. In addition, multimodeling may also improve the calibration and evaluation of an individual model.

Adjustment method for microarray data generated using two-cycle RNA labeling protocol.

BACKGROUND: Microarray technology is widely utilized for monitoring the expression changes of thousands of genes simultaneously. However, the requirement of relatively large amount of RNA for labeling and hybridization makes it difficult to perform microarray experiments with limited biological materials, thus leads to the development of many methods for preparing and amplifying mRNA. It is addressed that amplification methods usually bring bias, which may strongly hamper the following interpretation of the results. A big challenge is how to correct for the bias before further analysis. RESULTS: In this article, we observed the bias in rice gene expression microarray data generated with the Affymetrix one-cycle, two-cycle RNA labeling protocols, followed by validation with Real Time PCR. Based on these data, we proposed a statistical framework to model the processes of mRNA two-cycle linear amplification, and established a linear model for probe level correction. Maximum Likelihood Estimation (MLE) was applied to perform robust estimation of the Retaining Rate for each probe. After bias correction, some known pre-processing methods, such as PDNN, could be combined to finish preprocessing. Then, we evaluated our model and the results suggest that our model can effectively increase the quality of the microarray raw data: (i) Decrease the Coefficient of Variation for PM intensities of probe sets; (ii) Distinguish the microarray samples of five stages for rice stamen development more clearly; (iii) Improve the correlation coefficients among stamen microarray samples. We also discussed the necessity of model adjustment by comparing with another simple adjustment method. CONCLUSION: We conclude that the adjustment model is necessary and could effectively increase the quality of estimation for gene expression from the microarray raw data.

Projecting climate change effects on forest net primary productivity in subtropical Louisiana, USA.

This study projected responses of forest net primary productivity (NPP) to three climate change scenarios at a resolution of 5 km x 5 km across the state of Louisiana, USA. In addition, we assessed uncertainties associated with the NPP projection at the grid and state levels. Climate data of the scenarios were derived from Community Climate System Model outputs. Changes in annual NPP between 2000 and 2050 were projected with the forest ecosystem model PnET-II. Results showed that forest productivity would increase under climate change scenarios A1B and A2, but with scenario B1, it would peak during 2011-2020 and then decline. The projected average NPP under B1 over the years from 2000 to 2050 was significantly different from those under A1B and A2. Forest NPP appeared to be primarily a function of temperature, not precipitation. Uncertainties of the NPP projection were due to large spatial resolution of the climate variables. Overall, this study suggested that in order to project effects of climate change on forest ecosystem at regional level, modeling uncertainties could be reduced by increasing the spatial resolution of the climate projections.

Comparison of remote sensing change detection techniques for assessing hurricane damage to forests.

This study compared performance of four change detection algorithms with six vegetation indices derived from pre- and post-Katrina Landsat Thematic Mapper (TM) imagery and a composite of the TM bands 4, 5, and 3 in order to select an optimal remote sensing technique for identifying forestlands disturbed by Hurricane Katrina. The algorithms included univariate image differencing (UID), selective principal component analysis (PCA), change vector analysis (CVA), and postclassification comparison (PCC). The indices consisted of near-infrared to red ratios, normalized difference vegetation index, Tasseled Cap index of greenness, brightness, and wetness (TCW), and soil-adjusted vegetation index. In addition to the satellite imagery, the "ground truth" data of forest damage were also collected through field investigation and interpretation of post-Katrina aerial photos. Disturbed forests were identified by classifying the composite and the continuous change imagery with the supervised classification method. Results showed that the change detection techniques exerted apparent influence on detection results with an overall accuracy varying between 51% and 86% and a kappa statistics ranging from 0.02 to 0.72. Detected areas of disturbed forestlands were noticeable in two groups: 180,832-264,617 and 85,861-124,205 ha. The landscape of disturbed forests also displayed two unique patterns, depending upon the area group. The PCC algorithm along with the composite image contributed the highest accuracy and lowest error (0.5%) in estimating areas of disturbed forestlands. Both UID and CVA performed similarly, but caution should be taken when using selective PCA in detecting hurricane disturbance to forests. Among the six indices, TCW outperformed the other indices owing to its maximum sensitivity to forest modification. This study suggested that compared with the detection algorithms, proper selection of vegetation indices was more critical for obtaining satisfactory results.

Hurricane Katrina-induced forest damage in relation to ecological factors at landscape scale.

Forest stand stability to strong winds such as hurricanes has been found to be associated with a number of forest, soil and topography factors. In this study, through applying geographic information system (GIS) and logit regression, we assessed effects of forest characteristics and site conditions on pattern, severity and probability of Hurricane Katrina disturbance to forests in the Lower Pearl River Valley, USA. The factors included forest type, forest coverage, stand density, soil great group, elevation, slope, aspect, and stream buffer zone. Results showed that Hurricane Katrina damaged 60% of the total forested land in the region. The distribution and intensity of the hurricane disturbance varied across the landscape, with the bottomland hardwood forests on river floodplains most severely affected. All these factors had a variety of effects on vulnerability of the forests to the hurricane disturbance and thereby spatial patterns of the disturbance. Soil groups and stand factors including forest types, forest coverage and stand density contributed to 85% of accuracy in modeling the probability of the hurricane disturbance to forests in this region. Besides assessment of Katrina's damage, this study elucidates the great usefulness of remote sensing and GIS techniques combined with statistics modeling in assessment of large-scale risks of hurricane damage to coastal forests.