Dissecting positive selection events and immunological drives during the evolution of adeno-associated virus lineages.

Adeno-associated virus (AAV) serotypes from primates are being developed and clinically used as vectors for human gene therapy. However, the evolutionary mechanism of AAV variants is far from being understood, except that genetic recombination plays an important role. Furthermore, little is known about the interaction between AAV and its natural hosts, human and nonhuman primates. In this study, natural AAV capsid genes were subjected to systemic evolutionary analysis with a focus on selection drives during the diversification of AAV lineages. A number of positively selected sites were identified from these AAV lineages with functional relevance implied by their localization on the AAV structures. The selection drives of the two AAV2 capsid sites were further investigated in a series of biological experiments. These observations did not support the evolution of the site 410 of the AAV2 capsid driven by selection pressure from the human CD4+ T-cell response. However, positive selection on site 548 of the AAV2 capsid was directly related to host humoral immunity because of the profound effects of mutations at this site on the immune evasion of AAV variants from human neutralizing antibodies at both the individual and population levels. Overall, this work provides a novel interpretation of the genetic diversity and evolution of AAV lineages in their natural hosts, which may contribute to their further engineering and application in human gene therapy.

A vaccine targeting the RBD of the S protein of SARS-CoV-2 induces protective immunity.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes a respiratory disease called coronavirus disease 2019 (COVID-19), the spread of which has led to a pandemic. An effective preventive vaccine against this virus is urgently needed. As an essential step during infection, SARS-CoV-2 uses the receptor-binding domain (RBD) of the spike protein to engage with the receptor angiotensin-converting enzyme 2 (ACE2) on host cells1,2. Here we show that a recombinant vaccine that comprises residues 319-545 of the RBD of the spike protein induces a potent functional antibody response in immunized mice, rabbits and non-human primates (Macaca mulatta) as early as 7 or 14 days after the injection of a single vaccine dose. The sera from the immunized animals blocked the binding of the RBD to ACE2, which is expressed on the cell surface, and neutralized infection with a SARS-CoV-2 pseudovirus and live SARS-CoV-2 in vitro. Notably, vaccination also provided protection in non-human primates to an in vivo challenge with SARS-CoV-2. We found increased levels of RBD-specific antibodies in the sera of patients with COVID-19. We show that several immune pathways and CD4 T lymphocytes are involved in the induction of the vaccine antibody response. Our findings highlight the importance of the RBD domain in the design of SARS-CoV-2 vaccines and provide a rationale for the development of a protective vaccine through the induction of antibodies against the RBD domain.

Trait association and prediction through integrative k-mer analysis.

Genome-wide association study (GWAS) with single nucleotide polymorphisms (SNPs) has been widely used to explore genetic controls of phenotypic traits. Alternatively, GWAS can use counts of substrings of length k from longer sequencing reads, k-mers, as genotyping data. Using maize cob and kernel color traits, we demonstrated that k-mer GWAS can effectively identify associated k-mers. Co-expression analysis of kernel color k-mers and genes directly found k-mers from known causal genes. Analyzing complex traits of kernel oil and leaf angle resulted in k-mers from both known and candidate genes. A gene encoding a MADS transcription factor was functionally validated by showing that ectopic expression of the gene led to less upright leaves. Evolution analysis revealed most k-mers positively correlated with kernel oil were strongly selected against in maize populations, while most k-mers for upright leaf angle were positively selected. In addition, genomic prediction of kernel oil, leaf angle, and flowering time using k-mer data resulted in a similarly high prediction accuracy to the standard SNP-based method. Collectively, we showed k-mer GWAS is a powerful approach for identifying trait-associated genetic elements. Further, our results demonstrated the bridging role of k-mers for data integration and functional gene discovery.

A common resequencing-based genetic marker data set for global maize diversity.

Maize (Zea mays ssp. mays) populations exhibit vast ranges of genetic and phenotypic diversity. As sequencing costs have declined, an increasing number of projects have sought to measure genetic differences between and within maize populations using whole-genome resequencing strategies, identifying millions of segregating single-nucleotide polymorphisms (SNPs) and insertions/deletions (InDels). Unlike older genotyping strategies like microarrays and genotyping by sequencing, resequencing should, in principle, frequently identify and score common genetic variants. However, in practice, different projects frequently employ different analytical pipelines, often employ different reference genome assemblies and consistently filter for minor allele frequency within the study population. This constrains the potential to reuse and remix data on genetic diversity generated from different projects to address new biological questions in new ways. Here, we employ resequencing data from 1276 previously published maize samples and 239 newly resequenced maize samples to generate a single unified marker set of approximately 366 million segregating variants and approximately 46 million high-confidence variants scored across crop wild relatives, landraces as well as tropical and temperate lines from different breeding eras. We demonstrate that the new variant set provides increased power to identify known causal flowering-time genes using previously published trait data sets, as well as the potential to track changes in the frequency of functionally distinct alleles across the global distribution of modern maize.

Non-IL-2-blocking anti-CD25 antibody inhibits tumor growth by depleting Tregs and has synergistic effects with anti-CTLA-4 therapy.

CD25, also known as the interleukin-2 receptor α chain (IL-2Rα), is highly expressed on regulatory T cells (Tregs), but relatively lower on effector T cells (Teffs). This makes it a potential target for Treg depletion, which can be used in tumor immunotherapy. However, marketed anti-CD25 antibodies (Basiliximab and Daclizumab) were originally developed as immunosuppressive drugs to prevent graft rejection, because these antibodies can block IL-2 binding to CD25 on Teffs, which in turn destroys the function of Teffs. Recent studies have shown that non-IL-2-blocking anti-CD25 antibodies have displayed exciting antitumor effects. Here, we screened out a non-IL-2-blocking anti-CD25 monoclonal antibody (mAb) 7B7 by hybridoma technology, and confirmed its antitumor activity via depleting Tregs in a CD25 humanized mouse model. Subsequently, we verified that the humanized 7B7, named as h7B7-15S, has comparable activities to 7B7, and that its Treg depletion is further increased when combined with anti-CTLA-4, leading to enhanced remodeling of the tumor immune microenvironment. Moreover, our findings reveal that the Fab form of h7B7-15S has the ability to deplete Tregs, independent of the Fc region. Taken together, our studies expand the application of anti-CD25 in tumor immunotherapy and provide insight into the underlying mechanism.

Unraveling Prevalence and Effects of Deleterious Mutations in Maize Elite Lines across Decades of Modern Breeding.

Future breeding is likely to involve the detection and removal of deleterious alleles, which are mutations that negatively affect crop fitness. However, little is known about the prevalence of such mutations and their effects on phenotypic traits in the context of modern crop breeding. To address this, we examined the number and frequency of deleterious mutations in 350 elite maize inbred lines developed over the past few decades in China and the United States. Our findings reveal an accumulation of weakly deleterious mutations and a decrease in strongly deleterious mutations, indicating the dominant effects of genetic drift and purifying selection for the two types of mutations, respectively. We also discovered that slightly deleterious mutations, when at lower frequencies, were more likely to be heterozygous in the developed hybrids. This is consistent with complementation as a potential explanation for heterosis. Subsequently, we found that deleterious mutations accounted for more of the variation in phenotypic traits than nondeleterious mutations with matched minor allele frequencies, especially for traits related to leaf angle and flowering time. Moreover, we detected fewer deleterious mutations in the promoter and gene body regions of differentially expressed genes across breeding eras than in nondifferentially expressed genes. Overall, our results provide a comprehensive assessment of the prevalence and impact of deleterious mutations in modern maize breeding and establish a useful baseline for future maize improvement efforts.

Antagonist anti-LIF antibody derived from naive human scFv phage library inhibited tumor growth in mice.

BACKGROUND: Leukemia inhibitory factor (LIF) is a multifunctional member of the IL-6 cytokine family that activates downstream signaling pathways by binding to the heterodimer consisting of LIFR and gp130 on the cell surface. Previous research has shown that LIF is highly expressed in various tumor tissues (e.g. pancreatic cancer, breast cancer, prostate cancer, and colorectal cancer) and promotes cancer cell proliferation, migration, invasion, and differentiation. Moreover, the overexpression of LIF correlates with poor clinicopathological characteristics. Therefore, we hypothesized that LIF could be a promising target for the treatment of cancer. In this work, we developed the antagonist antibody 1G11 against LIF and investigated its anti-tumor mechanism and its therapeutic efficacy in mouse models. RESULTS: A series of single-chain variable fragments (scFvs) targeting LIF were screened from a naive human scFv phage library. These scFvs were reconstructed in complete IgG form and produced by the mammalian transient expression system. Among the antibodies, 1G11 exhibited the excellent binding activity to human, cynomolgus monkey and mouse LIF. Functional analysis demonstrated 1G11 could block LIF binding to LIFR and inhibit the intracellular STAT3 phosphorylation signal. Interestingly, 1G11 did not block LIF binding to gp130, another LIF receptor that is involved in forming the receptor complex together with LIFR. In vivo, intraperitoneal administration of 1G11 inhibited tumor growth in CT26 and MC38 models of colorectal cancer. IHC analysis demonstrated that p-STAT3 and Ki67 were decreased in tumor tissue, while c-caspase 3 was increased. Furthermore, 1G11 treatment improves CD3+, CD4 + and CD8 + T cell infiltration in tumor tissue. CONCLUSIONS: We developed antagonist antibodies targeting LIF/LIFR signaling pathway from a naive human scFv phage library. Antagonist anti-LIF antibody exerts antitumor effects by specifically reducing p-STAT3. Further studies revealed that anti-LIF antibody 1G11 increased immune cell infiltration in tumor tissues.

Dissecting the colocalized GWAS and eQTLs with mediation analysis for high-dimensional exposures and confounders.

To leverage the advancements in genome-wide association studies (GWAS) and quantitative trait loci (QTL) mapping for traits and molecular phenotypes to gain mechanistic understanding of the genetic regulation, biological researchers often investigate the expression QTLs (eQTLs) that colocalize with QTL or GWAS peaks. Our research is inspired by 2 such studies. One aims to identify the causal single nucleotide polymorphisms that are responsible for the phenotypic variation and whose effects can be explained by their impacts at the transcriptomic level in maize. The other study in mouse focuses on uncovering the cis-driver genes that induce phenotypic changes by regulating trans-regulated genes. Both studies can be formulated as mediation problems with potentially high-dimensional exposures, confounders, and mediators that seek to estimate the overall indirect effect (IE) for each exposure. In this paper, we propose MedDiC, a novel procedure to estimate the overall IE based on difference-in-coefficients approach. Our simulation studies find that MedDiC offers valid inference for the IE with higher power, shorter confidence intervals, and faster computing time than competing methods. We apply MedDiC to the 2 aforementioned motivating datasets and find that MedDiC yields reproducible outputs across the analysis of closely related traits, with results supported by external biological evidence. The code and additional information are available on our GitHub page (https://github.com/QiZhangStat/MedDiC).

An electronic patient-reported outcome symptom monitor: the Chinese experience with rapid development of a ready-to-go symptom monitor.

BACKGROUND: Monitoring symptoms is crucial for the early detection of disease progression and timely intervention, which is essential for reducing severe cases and mortality rates in rapidly spreading pandemics, such as COVID-19. Therefore, during infectious disease pandemics, the rapid development of real-time symptom monitoring platforms is essential. This study aimed to explore the urgent development process of an electronic system for patient-reported outcome monitoring in emergency situations. METHODS: The development of the electronic patient-reported outcome COVID-19 symptom monitoring platform (ePRO-CoV-SM) included the following steps: (1) modifying an electronic patient-reported outcome symptom-reporting platform to assess patients with COVID-19 and validating its feasibility and sensitivity for longitudinal symptom measurement; (2) updating the system to accommodate the newly emerged severe acute respiratory syndrome coronavirus 2 BA.2.2 variant; and (3) applying it in real-world settings. Literature review, expert consultation, and subject-group discussions were used to develop symptom items. Response rate and missing item rate were used as validation indicators for ePRO-CoV-SM. RESULTS: The ePRO-CoV-SM (2.0) consists of a core set of symptom items, a WeChat mini program, an online project design backend, a management and communication front, and a database. During the 2020 verification, the response rate of ePRO symptom monitoring reached 89.47% and the item missing rate was 0.33%, the monitoring revealed that a considerable number of asymptomatic patients were experiencing undesirable symptoms during the isolation period. In its real-world application in 2022, the response rate was 85.93% and the item missing rate was 4.84%, the monitoring found the symptom burden was higher in the younger group (18-40 years old) than in the older group (40-67 years old), and over 30% of patients reported symptoms such as cough (36.08%), dry mouth (35.67%), sleep disorders (32.27%), appetite loss (32.17%), and sputum (30.79%) during the isolation period. CONCLUSIONS: Electronic patient-reported outcome measurement was demonstrated to be sensitive and feasible for monitoring symptoms in patients with COVID-19. By integrating smartphone-based data collection with real-time online data transmission and secure data storage using Secure Sockets Layer encryption, an electronic platform for monitoring critical symptoms can be rapidly established in emergency situations.

Correction: Incomplete dominance of deleterious alleles contributes substantially to trait variation and heterosis in maize.

[This corrects the article DOI: 10.1371/journal.pgen.1007019.].

Domestication reshaped the genetic basis of inbreeding depression in a maize landrace compared to its wild relative, teosinte.

Inbreeding depression is the reduction in fitness and vigor resulting from mating of close relatives observed in many plant and animal species. The extent to which the genetic load of mutations contributing to inbreeding depression is due to large-effect mutations versus variants with very small individual effects is unknown and may be affected by population history. We compared the effects of outcrossing and self-fertilization on 18 traits in a landrace population of maize, which underwent a population bottleneck during domestication, and a neighboring population of its wild relative teosinte. Inbreeding depression was greater in maize than teosinte for 15 of 18 traits, congruent with the greater segregating genetic load in the maize population that we predicted from sequence data. Parental breeding values were highly consistent between outcross and selfed offspring, indicating that additive effects determine most of the genetic value even in the presence of strong inbreeding depression. We developed a novel linkage scan to identify quantitative trait loci (QTL) representing large-effect rare variants carried by only a single parent, which were more important in teosinte than maize. Teosinte also carried more putative juvenile-acting lethal variants identified by segregation distortion. These results suggest a mixture of mostly polygenic, small-effect partially recessive effects in linkage disequilibrium underlying inbreeding depression, with an additional contribution from rare larger-effect variants that was more important in teosinte but depleted in maize following the domestication bottleneck. Purging associated with the maize domestication bottleneck may have selected against some large effect variants, but polygenic load is harder to purge and overall segregating mutational burden increased in maize compared to teosinte.

Image Filtering to Improve Maize Tassel Detection Accuracy Using Machine Learning Algorithms.

Unmanned aerial vehicle (UAV)-based imagery has become widely used to collect time-series agronomic data, which are then incorporated into plant breeding programs to enhance crop improvements. To make efficient analysis possible, in this study, by leveraging an aerial photography dataset for a field trial of 233 different inbred lines from the maize diversity panel, we developed machine learning methods for obtaining automated tassel counts at the plot level. We employed both an object-based counting-by-detection (CBD) approach and a density-based counting-by-regression (CBR) approach. Using an image segmentation method that removes most of the pixels not associated with the plant tassels, the results showed a dramatic improvement in the accuracy of object-based (CBD) detection, with the cross-validation prediction accuracy (r2) peaking at 0.7033 on a detector trained with images with a filter threshold of 90. The CBR approach showed the greatest accuracy when using unfiltered images, with a mean absolute error (MAE) of 7.99. However, when using bootstrapping, images filtered at a threshold of 90 showed a slightly better MAE (8.65) than the unfiltered images (8.90). These methods will allow for accurate estimates of flowering-related traits and help to make breeding decisions for crop improvement.

CD25: A potential tumor therapeutic target.

CD25 is the alpha-chain of the heterotrimer IL-2 receptor. CD25 is expressed on the surface of both immune and non-immune cells with different frequencies. For cancers, CD25 is expressed at high levels in many types of hematological malignancies, but at low levels in most solid tumors. CD25 is also highly expressed in activated circulating immune cells and regulatory T cells (Tregs). Infiltration of Tregs in the tumor microenvironment can lead to an imbalanced ratio of effector T cells (Teffs) and Tregs, which is associated with the progression of cancers. A rescued Teff/Treg cell ratio indicates an efficient anti-tumor response to immunotherapy. CD25 as a potential target for the depletion of Tregs is critical in developing new immunotherapeutic strategies. Few articles have summarized the relationships between CD25 and tumors, or the recent progress of drugs targeting CD25. In this paper, we will discuss the structures of IL-2 and IL-2R, the biological function of CD25 and its important role in tumor therapy. In addition, the latest research on drugs targeting CD25 has been summarized, providing guidance for future drug development.

The Heterogeneity in the Landscape of Gene Dominance in Maize is Accompanied by Unique Chromatin Environments.

Subgenome dominance after whole-genome duplication (WGD) has been observed in many plant species. However, the degree to which the chromatin environment affects this bias has not been explored. Here, we compared the dominant subgenome (maize1) and the recessive subgenome (maize2) with respect to patterns of sequence substitutions, genes expression, transposable element accumulation, small interfering RNAs, DNA methylation, histone modifications, and accessible chromatin regions (ACRs). Our data show that the degree of bias between subgenomes for all the measured variables does not vary significantly when both of the WGD genes are located in pericentromeric regions. Our data further indicate that the location of maize1 genes in chromosomal arms is pivotal for maize1 to maintain its dominance, but location has a less effect on maize2 homoeologs. In addition to homoeologous genes, we compared ACRs, which often harbor cis-regulatory elements, between the two subgenomes and demonstrate that maize1 ACRs have a higher level of chromatin accessibility, a lower level of sequence substitution, and are enriched in chromosomal arms. Furthermore, we find that a loss of maize1 ACRs near their nearby genes is associated with a reduction in purifying selection and expression of maize1 genes relative to their maize2 homoeologs. Taken together, our data suggest that chromatin environment and cis-regulatory elements are important determinants shaping the divergence and evolution of duplicated genes.

Filamin A is overexpressed in non-alcoholic steatohepatitis and contributes to the progression of inflammation and fibrosis.

Non-alcoholic steatohepatitis (NASH) is a chronic and progressive liver disease characterized by steatosis, inflammation, and fibrosis. Filamin A (FLNA), an actin-binding protein, is involved in various cell functions, including the regulation of immune cells and fibroblasts. However, its role in the development of NASH through inflammation and fibrogenesis is not fully understood. In this study, we found that FLNA expression was increased in liver tissues of patients with cirrhosis and mice with non-alcoholic fatty liver disease (NAFLD)/NASH and fibrosis. Immunofluorescence analysis showed that FLNA was primarily expressed in macrophages and hepatic stellate cells (HSCs). Knocking down of FLNA by specific shRNA in phorbol-12-myristate-13-acetate (PMA)-derived THP-1 macrophages reduced lipopolysaccharide (LPS)-stimulated inflammatory response. The decreased mRNA levels of inflammatory cytokines and chemokines and suppression of the STAT3 signaling were observed in FLNA-downregulated macrophages. In addition, knockdown of FLNA in immortalized human hepatic stellate cells (LX-2 cells) resulted in decreased mRNA levels of fibrotic cytokines and enzymes involved in collagen synthesis, as well as increased levels of metalloproteinases and pro-apoptotic proteins. Overall, these results suggest that FLNA may contribute to the pathogenesis of NASH through its role in the regulation of inflammatory and fibrotic mediators.

A leaf-level spectral library to support high-throughput plant phenotyping: predictive accuracy and model transfer.

Leaf-level hyperspectral reflectance has become an effective tool for high-throughput phenotyping of plant leaf traits due to its rapid, low-cost, multi-sensing, and non-destructive nature. However, collecting samples for model calibration can still be expensive, and models show poor transferability among different datasets. This study had three specific objectives: first, to assemble a large library of leaf hyperspectral data (n=2460) from maize and sorghum; second, to evaluate two machine-learning approaches to estimate nine leaf properties (chlorophyll, thickness, water content, nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur); and third, to investigate the usefulness of this spectral library for predicting external datasets (n=445) including soybean and camelina using extra-weighted spiking. Internal cross-validation showed satisfactory performance of the spectral library to estimate all nine traits (mean R2=0.688), with partial least-squares regression outperforming deep neural network models. Models calibrated solely using the spectral library showed degraded performance on external datasets (mean R2=0.159 for camelina, 0.337 for soybean). Models improved significantly when a small portion of external samples (n=20) was added to the library via extra-weighted spiking (mean R2=0.574 for camelina, 0.536 for soybean). The leaf-level spectral library greatly benefits plant physiological and biochemical phenotyping, whilst extra-weight spiking improves model transferability and extends its utility.

Leaf-Counting in Monocot Plants Using Deep Regression Models.

Leaf numbers are vital in estimating the yield of crops. Traditional manual leaf-counting is tedious, costly, and an enormous job. Recent convolutional neural network-based approaches achieve promising results for rosette plants. However, there is a lack of effective solutions to tackle leaf counting for monocot plants, such as sorghum and maize. The existing approaches often require substantial training datasets and annotations, thus incurring significant overheads for labeling. Moreover, these approaches can easily fail when leaf structures are occluded in images. To address these issues, we present a new deep neural network-based method that does not require any effort to label leaf structures explicitly and achieves superior performance even with severe leaf occlusions in images. Our method extracts leaf skeletons to gain more topological information and applies augmentation to enhance structural variety in the original images. Then, we feed the combination of original images, derived skeletons, and augmentations into a regression model, transferred from Inception-Resnet-V2, for leaf-counting. We find that leaf tips are important in our regression model through an input modification method and a Grad-CAM method. The superiority of the proposed method is validated via comparison with the existing approaches conducted on a similar dataset. The results show that our method does not only improve the accuracy of leaf-counting, with overlaps and occlusions, but also lower the training cost, with fewer annotations compared to the previous state-of-the-art approaches.The robustness of the proposed method against the noise effect is also verified by removing the environmental noises during the image preprocessing and reducing the effect of the noises introduced by skeletonization, with satisfactory outcomes.

Time series canopy phenotyping enables the identification of genetic variants controlling dynamic phenotypes in soybean.

Advances in plant phenotyping technologies are dramatically reducing the marginal costs of collecting multiple phenotypic measurements across several time points. Yet, most current approaches and best statistical practices implemented to link genetic and phenotypic variation in plants have been developed in an era of single-time-point data. Here, we used time-series phenotypic data collected with an unmanned aircraft system for a large panel of soybean (Glycine max (L.) Merr.) varieties to identify previously uncharacterized loci. Specifically, we focused on the dissection of canopy coverage (CC) variation from this rich data set. We also inferred the speed of canopy closure, an additional dimension of CC, from the time-series data, as it may represent an important trait for weed control. Genome-wide association studies (GWASs) identified 35 loci exhibiting dynamic associations with CC across developmental stages. The time-series data enabled the identification of 10 known flowering time and plant height quantitative trait loci (QTLs) detected in previous studies of adult plants and the identification of novel QTLs influencing CC. These novel QTLs were disproportionately likely to act earlier in development, which may explain why they were missed in previous single-time-point studies. Moreover, this time-series data set contributed to the high accuracy of the GWASs, which we evaluated by permutation tests, as evidenced by the repeated identification of loci across multiple time points. Two novel loci showed evidence of adaptive selection during domestication, with different genotypes/haplotypes favored in different geographic regions. In summary, the time-series data, with soybean CC as an example, improved the accuracy and statistical power to dissect the genetic basis of traits and offered a promising opportunity for crop breeding with quantitative growth curves.

Preparation and characterization of a high-affinity monoclonal antibody against nerve growth factor.

Nerve growth factor (NGF) is produced and released in injured tissues or chronic pain tissues caused by other diseases. Studies have shown that monoclonal antibodies targeting NGF have a good efficacy in the treatment of osteoarthritis (OA), low back pain and chronic pain, which may be a promising therapy. In this study, DNA sequences of NGF-his and NGF-hFc were synthesized using eukaryotic expression system and subcloned into pTT5 expression vector. After that, NGF proteins were expressed by transient expression in HEK293E cells. We immunized mice with NGF-hFc protein and fused mouse spleen cells to prepare hybridomas. NGF-His protein was used to screen out the hybridoma supernatant that could directly bind to NGF. Antibodies were purified from hybridioma supernatant. Futhermore, via surface plasmon resonance (SPR) screening, six anti-NGF mAbs were screened to block the binding of NGF and TrkA receptor in the treatment of chronic pain. Among them, 58F10G10H showed high affinity (KD = 1.03 × 10-9 M) and even better than that of positive control antibody Tanezumab (KD = 1.53 × 10-9 M). Moreover, the specific reactivity of 58F10G10H was demonstrated by TF-1 cell proliferation activity experiments, competitive binding Enzyme-linked immunosorbent assay (ELISA) and the arthritis animal models in mice, respectively. In conclusion, in this study, a method for the preparation of high-yield NGF-HFC and NGF-His proteins was designed, and a high-affinity monoclonal antibody against NGF with potential for basic research and clinical application was prepared.

Small-Molecule Inhibitor of 8-Oxoguanine DNA Glycosylase 1 Regulates Inflammatory Responses during Pseudomonas aeruginosa Infection.

The DNA repair enzyme 8-oxoguanine DNA glycosylase 1 (OGG1), which excises 8-oxo-7,8-dihydroguanine lesions induced in DNA by reactive oxygen species, has been linked to the pathogenesis of lung diseases associated with bacterial infections. A recently developed small molecule, SU0268, has demonstrated selective inhibition of OGG1 activity; however, its role in attenuating inflammatory responses has not been tested. In this study, we report that SU0268 has a favorable effect on bacterial infection both in mouse alveolar macrophages (MH-S cells) and in C57BL/6 wild-type mice by suppressing inflammatory responses, particularly promoting type I IFN responses. SU0268 inhibited proinflammatory responses during Pseudomonas aeruginosa (PA14) infection, which is mediated by the KRAS-ERK1-NF-κB signaling pathway. Furthermore, SU0268 induces the release of type I IFN by the mitochondrial DNA-cGAS-STING-IRF3-IFN-ß axis, which decreases bacterial loads and halts disease progression. Collectively, our results demonstrate that the small-molecule inhibitor of OGG1 (SU0268) can attenuate excessive inflammation and improve mouse survival rates during PA14 infection. This strong anti-inflammatory feature may render the inhibitor as an alternative treatment for controlling severe inflammatory responses to bacterial infection.