SARS-CoV-2 Delta and Omicron variants resist spike cleavage by human airway trypsin-like protease.

Soluble host factors in the upper respiratory tract can serve as the first line of defense against SARS-CoV-2 infection. In this study, we described the identification and function of a human airway trypsin-like protease (HAT), capable of reducing the infectivity of ancestral SARS-CoV-2. Further, in mouse models, HAT analogue expression was upregulated by SARS-CoV-2 infection. The antiviral activity of HAT functioned through the cleavage of the SARS-CoV-2 spike glycoprotein at R682. This cleavage resulted in inhibition of the attachment of ancestral spike proteins to host cells, which inhibited the cell-cell membrane fusion process. Importantly, exogenous addition of HAT notably reduced the infectivity of ancestral SARS-CoV-2 in vivo. However, HAT was ineffective against the Delta variant and most circulating Omicron variants, including the BQ.1.1 and XBB.1.5 subvariants. We demonstrate that the P681R mutation in Delta and P681H mutation in the Omicron variants, adjacent to the R682 cleavage site, contributed to HAT resistance. Our study reports what we believe to be a novel soluble defense factor against SARS-CoV-2 and resistance of its actions in the Delta and Omicron variants.

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.

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.

Genomic investigation of 18,421 lines reveals the genetic architecture of rice.

Understanding how numerous quantitative trait loci (QTL) shape phenotypic variation is an important question in genetics. To address this, we established a permanent population of 18,421 (18K) rice lines with reduced population structure. We generated reference-level genome assemblies of the founders and genotyped all 18K-rice lines through whole-genome sequencing. Through high-resolution mapping, 96 high-quality candidate genes contributing to variation in 16 traits were identified, including OsMADS22 and OsFTL1 verified as causal genes for panicle number and heading date, respectively. We identified epistatic QTL pairs and constructed a genetic interaction network with 19 genes serving as hubs. Overall, 170 masking epistasis pairs were characterized, serving as an important factor contributing to genetic background effects across diverse varieties. The work provides a basis to guide grain yield and quality improvements in rice.

Dissecting the Genetic Architecture of Morphological Traits in Sunflower (Helianthus annuus L.).

The sunflower (Helianthus annuus L.) is one of the most essential oil crops in the world. Several component traits, including flowering time, plant height, stem diameter, seed weight, and kernel weight, determine sunflower seed and oil yield. Although the genetic mechanisms governing the variation of these yield-related traits have been studied using various approaches, genome-wide association studies (GWAS) have not been widely applied to sunflowers. In this study, a set of 342 sunflower accessions was evaluated in 2019 and 2020 using an incomplete randomized block design, and GWAS was conducted utilizing two complementary approaches: the mixed linear model (MLM) and the fixed and random model circulating probability unification (farmCPU) model by fitting 226,779 high-quality SNPs. As a result, GWAS identified a number of trait-associated SNPs. Those SNPs were located close to several genes that may serve as a basis for further molecular characterization and provide promising targets for sunflower yield improvement.

Antibody-drug conjugates targeting DDR1 as a novel strategy for treatment of breast cancer.

Antibody-drug conjugates (ADCs) have emerged as a novel class of targeted cancer therapies and been successfully applied in the treatment of breast cancer (BC). Discoidin domain receptor 1 (DDR1) is a single transmembrane receptor tyrosine kinase and has been identified as a possible target for cancer. In this study, we explored the potential of an anti-DDR1 ADC, named T4H11-DM4, for the treatment of DDR1-positive BC. We demonstrated that high protein expression and RNA expression of DDR1 in BC tissues. In vitro, T4H11-DM4 was potently cytotoxic to DDR1-expressing BC cells, with IC50 in the nanomolar range. In mice BC xenograft models, T4H11-DM4 dramatically eliminated BC tumours, without observable toxicity. Taken together, our findings demonstrated that DDR1 can serve as a promising therapeutic target for BC.

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.

TWAS facilitates gene-scale trait genetic dissection through gene expression, structural variations, and alternative splicing in soybean.

A genome-wide association study (GWAS) identifies trait-associated loci, but identifying the causal genes can be a bottleneck, due in part to slow decay of linkage disequilibrium (LD). A transcriptome-wide association study (TWAS) addresses this issue by identifying gene expression-phenotype associations or integrating gene expression quantitative trait loci with GWAS results. Here, we used self-pollinated soybean (Glycine max [L.] Merr.) as a model to evaluate the application of TWAS to the genetic dissection of traits in plant species with slow LD decay. We generated RNA sequencing data for a soybean diversity panel and identified the genetic expression regulation of 29 286 soybean genes. Different TWAS solutions were less affected by LD and were robust to the source of expression, identifing known genes related to traits from different tissues and developmental stages. The novel pod-color gene L2 was identified via TWAS and functionally validated by genome editing. By introducing a new exon proportion feature, we significantly improved the detection of expression variations that resulted from structural variations and alternative splicing. As a result, the genes identified through our TWAS approach exhibited a diverse range of causal variations, including SNPs, insertions or deletions, gene fusion, copy number variations, and alternative splicing. Using this approach, we identified genes associated with flowering time, including both previously known genes and novel genes that had not previously been linked to this trait, providing insights complementary to those from GWAS. In summary, this study supports the application of TWAS for candidate gene identification in species with low rates of LD decay.

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).

Emerging trends in small molecule inhibitors targeting aldosterone synthase: A new paradigm in cardiovascular disease treatment.

Aldosterone synthase (CYP11B2) is the rate-limiting enzyme in aldosterone production. In recent years, CYP11B2 has become an appealing target for treating conditions associated with excess aldosterone, such as hypertension, heart failure, and cardiometabolic diseases. Several small-molecule inhibitors of CYP11B2 have demonstrated efficacy in both preclinical studies and clinical trials. Among them, the tetrahydroisoquinoline derivative Baxdrostat has entered clinical trial phases and demonstrated efficacy in treating patients with hypertension. However, the high homology (>93 %) between CYP11B2 and steroid-11ß-hydroxylase (CYP11B1), which catalyzes cortisol production, implies that insufficient drug specificity can lead to severe side effects. Developing selective inhibitors for CYP11B2 remains a considerable challenge that requires ongoing attention. This review summarizes recent research progress on small-molecule inhibitors targeting CYP11B2, focusing on structure-activity relationships (SAR) and structural optimization. It discusses strategies for enhancing the specificity and inhibitory activity of inhibitors, while also exploring potential applications and future prospects for CYP11B2 inhibitors, providing a theoretical foundation for developing the new generation of CYP11B2-targeted medications.

TrG2P: A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield.

Yield prediction is the primary goal of genomic selection (GS)-assisted crop breeding. Because yield is a complex quantitative trait, making predictions from genotypic data is challenging. Transfer learning can produce an effective model for a target task by leveraging knowledge from a different, but related, source domain and is considered a great potential method for improving yield prediction by integrating multi-trait data. However, it has not previously been applied to genotype-to-phenotype prediction owing to the lack of an efficient implementation framework. We therefore developed TrG2P, a transfer-learning-based framework. TrG2P first employs convolutional neural networks (CNN) to train models using non-yield-trait phenotypic and genotypic data, thus obtaining pre-trained models. Subsequently, the convolutional layer parameters from these pre-trained models are transferred to the yield prediction task, and the fully connected layers are retrained, thus obtaining fine-tuned models. Finally, the convolutional layer and the first fully connected layer of the fine-tuned models are fused, and the last fully connected layer is trained to enhance prediction performance. We applied TrG2P to five sets of genotypic and phenotypic data from maize (Zea mays), rice (Oryza sativa), and wheat (Triticum aestivum) and compared its model precision to that of seven other popular GS tools: ridge regression best linear unbiased prediction (rrBLUP), random forest, support vector regression, light gradient boosting machine (LightGBM), CNN, DeepGS, and deep neural network for genomic prediction (DNNGP). TrG2P improved the accuracy of yield prediction by 39.9%, 6.8%, and 1.8% in rice, maize, and wheat, respectively, compared with predictions generated by the best-performing comparison model. Our work therefore demonstrates that transfer learning is an effective strategy for improving yield prediction by integrating information from non-yield-trait data. We attribute its enhanced prediction accuracy to the valuable information available from traits associated with yield and to training dataset augmentation. The Python implementation of TrG2P is available at https://github.com/lijinlong1991/TrG2P. The web-based tool is available at http://trg2p.ebreed.cn:81.

A chimeric adenovirus-vectored vaccine based on Beta spike and Delta RBD confers a broad-spectrum neutralization against Omicron-included SARS-CoV-2 variants.

Urgent research into innovative severe acute respiratory coronavirus-2 (SARS-CoV-2) vaccines that may successfully prevent various emerging emerged variants, particularly the Omicron variant and its subvariants, is necessary. Here, we designed a chimeric adenovirus-vectored vaccine named Ad5-Beta/Delta. This vaccine was created by incorporating the receptor-binding domain from the Delta variant, which has the L452R and T478K mutations, into the complete spike protein of the Beta variant. Both intramuscular (IM) and intranasal (IN) vaccination with Ad5-Beta/Deta vaccine induced robust broad-spectrum neutralization against Omicron BA.5-included variants. IN immunization with Ad5-Beta/Delta vaccine exhibited superior mucosal immunity, manifested by higher secretory IgA antibodies and more tissue-resident memory T cells (TRM) in respiratory tract. The combination of IM and IN delivery of the Ad5-Beta/Delta vaccine was capable of synergically eliciting stronger systemic and mucosal immune responses. Furthermore, the Ad5-Beta/Delta vaccination demonstrated more effective boosting implications after two dosages of mRNA or subunit recombinant protein vaccine, indicating its capacity for utilization as a booster shot in the heterologous vaccination. These outcomes quantified Ad5-Beta/Delta vaccine as a favorable vaccine can provide protective immunity versus SARS-CoV-2 pre-Omicron variants of concern and BA.5-included Omicron subvariants.

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.

Heritable microbiome variation is correlated with source environment in locally adapted maize varieties.

Beneficial interactions with microorganisms are pivotal for crop performance and resilience. However, it remains unclear how heritable the microbiome is with respect to the host plant genotype and to what extent host genetic mechanisms can modulate plant-microbiota interactions in the face of environmental stresses. Here we surveyed 3,168 root and rhizosphere microbiome samples from 129 accessions of locally adapted Zea, sourced from diverse habitats and grown under control and different stress conditions. We quantified stress treatment and host genotype effects on the microbiome. Plant genotype and source environment were predictive of microbiome abundance. Genome-wide association analysis identified host genetic variants linked to both rhizosphere microbiome abundance and source environment. We identified transposon insertions in a candidate gene linked to both the abundance of a keystone bacterium Massilia in our controlled experiments and total soil nitrogen in the source environment. Isolation and controlled inoculation of Massilia alone can contribute to root development, whole-plant biomass production and adaptation to low nitrogen availability. We conclude that locally adapted maize varieties exert patterns of genetic control on their root and rhizosphere microbiomes that follow variation in their home environments, consistent with a role in tolerance to prevailing stress.

Generation and characterization of mAb 61H9 against junctional adhesion molecule-a with potent antitumor activity.

Junctional adhesion molecule-A (JAM-A) is an adhesion molecule that exists on the surface of certain types of cells, including white blood cells, endothelial cells, and dendritic cells. In this study, the cDNA sequences of JAM-A-Fc were chemically synthesized with optimization for mammalian expression. Afterward, we analyzed JAM-A protein expression through transient transfection in HEK293 cell lines. Mice were immunized with JAM-A-Fc protein, and hybridoma was prepared by fusing myeloma cells and mouse spleen cells. Antibodies were purified from the hybridoma supernatant and four monoclonal strains were obtained and numbered 61H9, 70E5, 71A8, and 74H3 via enzyme-linked immunosorbent assay screening. Immunofluorescence staining assay showed 61H9 was the most suitable cell line for mAb production due to its fluorescence signal being the strongest. Flow cytometric analysis proved that 61H9 possessed high affinity. Moreover, antagonism of JAM-A mAb could attenuate the proliferative, migrative, and invasive abilities of ESCC cells and significantly inhibit tumor growth in mice. By examining hematoxylin-eosin staining mice tumor tissues, we found inflammatory cells infiltrated lightly in the anti-JAM-A group. The expression of BCL-2 and IκBα in the anti-JAM-A group were decreased in mice tumor tissues compared to the control group. Ultimately, a method for preparing high-yield JAM-A-Fc protein was created and a high affinity mAb against JAM-A with an antitumor effect was prepared.

MCT1-governed pyruvate metabolism is essential for antibody class-switch recombination through H3K27 acetylation.

Monocarboxylate transporter 1 (MCT1) exhibits essential roles in cellular metabolism and energy supply. Although MCT1 is highly expressed in activated B cells, it is not clear how MCT1-governed monocarboxylates transportation is functionally coupled to antibody production during the glucose metabolism. Here, we report that B cell-lineage deficiency of MCT1 significantly influences the class-switch recombination (CSR), rendering impaired IgG antibody responses in Mct1f/fMb1Cre mice after immunization. Metabolic flux reveals that glucose metabolism is significantly reprogrammed from glycolysis to oxidative phosphorylation in Mct1-deficient B cells upon activation. Consistently, activation-induced cytidine deaminase (AID), is severely suppressed in Mct1-deficient B cells due to the decreased level of pyruvate metabolite. Mechanistically, MCT1 is required to maintain the optimal concentration of pyruvate to secure the sufficient acetylation of H3K27 for the elevated transcription of AID in activated B cells. Clinically, we found that MCT1 expression levels are significantly upregulated in systemic lupus erythematosus patients, and Mct1 deficiency can alleviate the symptoms of bm12-induced murine lupus model. Collectively, these results demonstrate that MCT1-mediated pyruvate metabolism is required for IgG antibody CSR through an epigenetic dependent AID transcription, revealing MCT1 as a potential target for vaccine development and SLE disease treatment.

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.

Microbiome-enabled genomic selection improves prediction accuracy for nitrogen-related traits in maize.

Root-associated microbiomes in the rhizosphere (rhizobiomes) are increasingly known to play an important role in nutrient acquisition, stress tolerance, and disease resistance of plants. However, it remains largely unclear to what extent these rhizobiomes contribute to trait variation for different genotypes and if their inclusion in the genomic selection protocol can enhance prediction accuracy. To address these questions, we developed a microbiome-enabled genomic selection method that incorporated host SNPs and amplicon sequence variants from plant rhizobiomes in a maize diversity panel under high and low nitrogen (N) field conditions. Our cross-validation results showed that the microbiome-enabled genomic selection model significantly outperformed the conventional genomic selection model for nearly all time-series traits related to plant growth and N responses, with an average relative improvement of 3.7%. The improvement was more pronounced under low N conditions (8.4-40.2% of relative improvement), consistent with the view that some beneficial microbes can enhance N nutrient uptake, particularly in low N fields. However, our study could not definitively rule out the possibility that the observed improvement is partially due to the amplicon sequence variants being influenced by microenvironments. Using a high-dimensional mediation analysis method, our study has also identified microbial mediators that establish a link between plant genotype and phenotype. Some of the detected mediator microbes were previously reported to promote plant growth. The enhanced prediction accuracy of the microbiome-enabled genomic selection models, demonstrated in a single environment, serves as a proof-of-concept for the potential application of microbiome-enabled plant breeding for sustainable agriculture.

Multi-integrated genomic data for Passiflora foetida provides insights into genome size evolution and floral development in Passiflora.

Passiflora is a plant genus known for its extremely distinctive and colorful flowers and a wide range of genome size variation. However, how genome characteristics are related to flower traits among Passiflora species remains poorly understood. Here, we assembled a chromosome-scale genome of P. foetida, which belongs to the same subgenus as the commercial passionfruit P. edulis. The genome of P. foetida is smaller (424.16 Mb) and contains fewer copies of long terminal repeat retrotransposons (LTR-RTs). The disparity in LTR-RTs is one of the main contributors to the differences in genome sizes between these two species and possibly in floral traits. Additionally, we observed variation in insertion times and copy numbers of LTR-RTs across different transposable element (TE) lineages. Then, by integrating transcriptomic data from 33 samples (eight floral organs and flower buds at three developmental stages) with phylogenomic and metabolomic data, we conducted an in-depth analysis of the expression, phylogeny, and copy number of MIKC-type MADS-box genes and identified essential biosynthetic genes responsible for flower color and scent from glandular bracts and other floral organs. Our study pinpoints LRT-RTs as an important player in genome size variation in Passiflora species and provides insights into future genetic improvement.

A patient with a pulmonary hernia and chance fracture of the thoracic vertebra following a fall: A case report and literature review.

INTRODUCTION AND IMPORTANCE: Pulmonary hernia is a rare disease caused mostly by chest trauma. Patients often present with non-specific signs and symptoms. Currently, there is no unified approach to treating it. For asymptomatic pulmonary hernias, conservative treatment has been chosen in the past. However, the increasing number of cases has shown that surgery has a more positive effect on some patients with asymptomatic pulmonary hernias. CASE PRESENTATION: A 63-year-old female patient who accidentally fell from an agricultural vehicle while doing farm work presented with back pain and lower limb paralysis. Her vital signs were stable. A chest computed tomography (CT) scan showed the patient had a pulmonary hernia, thoracic vertebra Chance fracture, rib fractures, and right hydropneumothorax. The patient received an open reduction internal fixation (ORIF) of the thoracic vertebra at the trauma emergency center. The postoperative chest CT scan showed that the pulmonary hernia had reset to the chest cavity. Subsequently, the patient got a pulmonary hernia repair at the thoracic surgery department. The patient was discharged on the 19th day after the injury. Long-term follow-up showed good recovery from the thoracic trauma. CLINICAL DISCUSSION: The patient had a pulmonary hernia combined with thoracic Chance fracture and other injuries. Surgical repair achieved satisfactory results. CONCLUSION: We lack guidelines on whether to manage pulmonary hernias surgically. Patients with asymptomatic pulmonary hernias particularly should have their long-term prognoses fully evaluated. Surgery is needed if the patient has high-risk factors and severe intercostal muscle defects.