The immune landscape of sepsis and using immune clusters for identifying sepsis endotypes.

Background: The dysregulated immune response to sepsis still remains unclear. Stratification of sepsis patients into endotypes based on immune indicators is important for the future development of personalized therapies. We aimed to evaluate the immune landscape of sepsis and the use of immune clusters for identifying sepsis endotypes. Methods: The indicators involved in innate, cellular, and humoral immune cells, inhibitory immune cells, and cytokines were simultaneously assessed in 90 sepsis patients and 40 healthy controls. Unsupervised k-means cluster analysis of immune indicator data were used to identify patient clusters, and a random forest approach was used to build a prediction model for classifying sepsis endotypes. Results: We depicted that the impairment of innate and adaptive immunity accompanying increased inflammation was the most prominent feature in patients with sepsis. However, using immune indicators for distinguishing sepsis from bacteremia was difficult, most likely due to the considerable heterogeneity in sepsis patients. Cluster analysis of sepsis patients identified three immune clusters with different survival rates. Cluster 1 (36.7%) could be distinguished from the other clusters as being an "effector-type" cluster, whereas cluster 2 (34.4%) was a "potential-type" cluster, and cluster 3 (28.9%) was a "dysregulation-type" cluster, which showed the lowest survival rate. In addition, we established a prediction model based on immune indicator data, which accurately classified sepsis patients into three immune endotypes. Conclusion: We depicted the immune landscape of patients with sepsis and identified three distinct immune endotypes with different survival rates. Cluster membership could be predicted with a model based on immune data.

Comprehensive evaluation of immune dysregulation in secondary hemophagocytic lymphohistiocytosis.

Host immune dysfunction plays a crucial role in the onset, progression, and outcome of hemophagocytic lymphohistiocytosis (HLH). This study aimed to comprehensively evaluate the peripheral immune profiles in patients with newly diagnosed secondary hemophagocytic lymphohistiocytosis (sHLH), and explore their predictive value for patient prognosis. A total of 77 patients with sHLH were enrolled in this study, with 31 of them experiencing mortality. Flow cytometry was used to assess the percentages, absolute numbers, and phenotypes of lymphocyte subsets. Simultaneously, cytokine levels and routine laboratory indicators were also collected. In sHLH patients, lymphocyte subset absolute numbers were significantly impaired, accompanied by T cell hyperactivation, B cell hyperactivation, and increased plasmablast proliferation. Prognostic analysis revealed that lower CD8+ T cell percentages, elevated APTT, IL-6, IL-10 levels, and increased CD4+CD28null T cell proportions were associated with poor patient outcomes. The study demonstrates dysregulation in the counts and phenotypes of lymphocyte subsets in sHLH patients. Several key factors, including IL-6, IL-10, APTT, and various T cell percentages, have potential as prognostic markers and therapeutic targets in sHLH.

Evaluating the diagnostic and therapeutic significance of KL-6 in patients with interstitial lung diseases.

Background: This study aimed to assess the diagnostic value of Krebs von den Lungen-6 (KL-6), Surfactant protein-A (SP-A), SP-D and molecular matrixmetalloproteinase-7 (MMP-7) in discriminating patients with interstitial lung diseases (ILDs) from disease control subjects. Methods: Serum levels of KL-6, SP-A, SP-D and MMP-7 were measured in both the ILD and non-ILD (NILD) groups. Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the diagnostic potential of these markers and laboratory indices. High-resolution computed tomography (HRCT) fibrosis scores were determined, and their correlation with the serum markers was analyzed. Results: Serum levels of KL-6 and MMP-7 were significantly elevated in the ILD group compared to the control group, while no significant differences were observed for SP-A and SP-D. ROC analysis of KL-6 demonstrated superior diagnostic accuracy, with a sensitivity of 76.36%, specificity of 91.07%, and an area under curve (AUC) of 0.902 (95%CI 0.866-0.945). These findings were consistent across an additional cohort. Correlation analysis revealed a link between KL-6 levels at initial diagnosis and HRCT fibrosis scores, indicating disease severity. Moreover, a negative correlation was found between KL-6 and pulmonary function indices, reflecting disease progression. Patients with increased 12-month HRCT fibrosis score showed higher lactate dehydrogenase (LDH) levels, with LDH exhibiting an AUC of 0.767 (95% CI: 0.520-0.927) as a predictor of progression. Conclusions: Serum KL-6 detection proves to be a valuable tool for accurately distinguishing ILDs from control subjects. While KL-6 shows a correlation with HRCT fibrosis scores and a negative association with pulmonary function indices, its predictive value for ILDs prognosis is limited. Trial registration: This study received retrospective approval from the Ethical Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (institutional review board ID: TJ-IRB20210331, date: 2021.03.30).

Developing deep LSTMs with later temporal attention for predicting COVID-19 severity, clinical outcome, and antibody level by screening serological indicators over time.

OBJECTIVE: The clinical course of COVID-19, as well as the immunological reaction, is notable for its extreme variability. Identifying the main associated factors might help understand the disease progression and physiological status of COVID-19 patients. The dynamic changes of the antibody against Spike protein are crucial for understanding the immune response. This work explores a temporal attention (TA) mechanism of deep learning to predict COVID-19 disease severity, clinical outcomes, and Spike antibody levels by screening serological indicators over time. METHODS: We use feature selection techniques to filter feature subsets that are highly correlated with the target. The specific deep Long Short-Term Memory (LSTM) models are employed to capture the dynamic changes of disease severity, clinical outcome, and Spike antibody level. We also propose deep LSTMs with a TA mechanism to emphasize the later blood test records because later records often attract more attention from doctors. RESULTS: Risk factors highly correlated with COVID-19 are revealed. LSTM achieves the highest classification accuracy for disease severity prediction. Temporal Attention Long Short-Term Memory (TA-LSTM) achieves the best performance for clinical outcome prediction. For Spike antibody level prediction, LSTM achieves the best permanence. CONCLUSION: The experimental results demonstrate the effectiveness of the proposed models. The proposed models can provide a computer-aided medical diagnostics system by simply using time series of serological indicators.

Novel multiclass classification machine learning approach for the early-stage classification of systemic autoimmune rheumatic diseases.

OBJECTIVE: Systemic autoimmune rheumatic diseases (SARDs) encompass a diverse group of complex conditions with overlapping clinical features, making accurate diagnosis challenging. This study aims to develop a multiclass machine learning (ML) model for early-stage SARDs classification using accessible laboratory indicators. METHODS: A total of 925 SARDs patients were included, categorised into SLE, Sjögren's syndrome (SS) and inflammatory myositis (IM). Clinical characteristics and laboratory markers were collected and nine key indicators, including anti-dsDNA, anti-SS-A60, anti-Sm/nRNP, antichromatin, anti-dsDNA (indirect immunofluorescence assay), haemoglobin (Hb), platelet, neutrophil percentage and cytoplasmic patterns (AC-19, AC-20), were selected for model building. Various ML algorithms were used to construct a tripartite classification ML model. RESULTS: Patients were divided into two cohorts, cohort 1 was used to construct a tripartite classification model. Among models assessed, the random forest (RF) model demonstrated superior performance in distinguishing SLE, IM and SS (with area under curve=0.953, 0.903 and 0.836; accuracy= 0.892, 0.869 and 0.857; sensitivity= 0.890, 0.868 and 0.795; specificity= 0.910, 0.836 and 0.748; positive predictive value=0.922, 0.727 and 0.663; and negative predictive value= 0.854, 0.915 and 0.879). The RF model excelled in classifying SLE (precision=0.930, recall=0.985, F1 score=0.957). For IM and SS, RF model outcomes were (precision=0.793, 0.950; recall=0.920, 0.679; F1 score=0.852, 0.792). Cohort 2 served as an external validation set, achieving an overall accuracy of 87.3%. Individual classification performances for SLE, SS and IM were excellent, with precision, recall and F1 scores specified. SHAP analysis highlighted significant contributions from antibody profiles. CONCLUSION: This pioneering multiclass ML model, using basic laboratory indicators, enhances clinical feasibility and demonstrates promising potential for SARDs classification. The collaboration of clinical expertise and ML offers a nuanced approach to SARDs classification, with potential for enhanced patient care.

Using immune clusters for classifying Mycobacterium tuberculosis infection.

BACKGROUND: The differential diagnosis between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) is still a challenge worldwide. METHODS: Immune indicators involved in innate, humoral, and cellular immune cells, as well as antigen-specific cells were simultaneously assessed in patients with ATB and LTBI. RESULTS: Of 54 immune indicators, no indicator could distinguish ATB from LTBI, likely due to an obvious heterogeneity of immune indicators noticed in ATB patients. Cluster analysis of ATB patients identified three immune clusters with different severity. Cluster 1 (42.1 %) was a ''Treg/Th1/Tfh unbalance type" cluster, whereas cluster 2 (42.1 %) was an "effector type'' cluster, and cluster 3 was a ''inhibition type'' cluster (15.8 %) which showed the highest severity. A prediction model based on immune indicators was established and showed potential in classifying Mycobacterium tuberculosis infection. CONCLUSIONS: We depicted the immune landscape of patients with ATB and LTBI. Three immune subtypes were identified in ATB patients with different severity.

The pathogenic and clinical characteristics of severe fever with thrombocytopenia syndrome patients with co-infections.

Objective: The study aimed to comprehensively describe and evaluate the pathogenic and clinical characteristics of severe fever with thrombocytopenia syndrome (SFTS) patients with co-infections. Methods: We retrospectively collected clinical data and laboratory indicators of the SFTS patients at Tongji Hospital from October 2021 to July 2023. Results: A total of 157 patients with SFTS virus (SFTSV) infection were involved in the analysis, including 43 co-infection and 114 non-co-infection patients. The pathogens responsible for co-infection were primarily isolated from respiratory specimens. Fungal infections, primarily Aspergillus fumigatus, were observed in 22 cases. Bacterial infections, with Klebsiella pneumoniae and carbapenem-resistant Acinetobacter baumannii as the main pathogens, were identified in 20 cases. SFTS patients with co-infection exhibited higher mortality (P=0.011) compared to non-co-infection patients. Among SFTS patients co-infected with both bacteria and fungi (8 cases) or specific drug-resistant strains (11 cases), the mortality rate was as high as 70% (14/19). In comparison with the non-co-infection group, SFTS patients with co-infection displayed significant alteration in inflammatory markers, coagulation function, and liver function indicators. Conclusion: The mortality rate of SFTS patients with co-infection is relatively high, underscoring the need for enhanced monitoring and timely, appropriate treatment to minimize the mortality rate.

The signature and predictive value of immune parameters in patients with secondary hemophagocytic lymphohistiocytosis.

BACKGROUND: Secondary hemophagocytic lymphohistiocytosis (sHLH) is a rare but fatal clinical syndrome, characterized by severe immune dysfunction and overwhelming inflammatory response. However, the host immune signature and also its role in predicting the clinical outcome are not fully described. OBJECTIVE: The present study aims to investigate the host immune status of sHLH patients in the early stage of the disease, including lymphocyte subsets, phenotypes and cytokines, and also to explore its clinical value in prognosis. METHODS: Sixty-four patients with sHLH admitted to a tertiary hospital in central China between 2018 and 2022 were enrolled, of which 21 were deceased. The subsets and phenotypes of lymphocytes, and the levels of cytokines in serum were analyzed. RESULTS: In patients with sHLH, the percentages of total T cells, CD8+ T cells, HLA-DR+ T cells, HLA-DR+CD8+ T cells, CD45RO+CD4+ T cells, and the levels of IL-1ß, IL-2R, IL-6, IL-8, IL-10 and TNF-α were significantly increased, while the percentages of CD4+ T cells, NK cells, CD45RA+CD4+ T cells, CD45RA+ regulatory T (Treg) cells, the counts of total T cells, total B cells, CD4+ T cells, CD8+ T cells, NK cells, and the ratio of CD4+ T/CD8+ T cells were significantly decreased, compared with healthy controls (HC). In addition, dysregulation of host immune response and high inflammatory status were more obvious in deceased patients than that of survivors. Kaplan-Meier survival analysis and multivariate logistic regression analysis demonstrated that lower levels of CD4+ T cells count and CD28+CD4+ T cells percentage, but higher levels of NK cells percentage and IL-1ß were poor prognostic indicators of sHLH. CONCLUSION: The evaluation of immunological markers has critical value for selecting prognostic markers and potential treatment target among adults with sHLH.

Kinetics and prognostic significance of laboratory markers in patients with severe fever with thrombocytopenia syndrome: insight from a comprehensive analysis.

Severe Fever with Thrombocytopenia Syndrome (SFTS) is an emerging infectious disease with significant mortality. Identifying prognostic factors that influence patient outcomes is crucial for effective clinical management. In this study, we assessed the dynamic changes of laboratory markers and their association with outcomes in 93 SFTS patients. We found that age and hypertension were significantly associated with poor outcomes in SFTS patients. The deceased group exhibited lower platelet counts, elevated liver and kidney function markers, coagulation profiles, inflammatory markers, and cytokines compared to the survival group. Kinetic analysis showed that these markers gradually normalized in the survival group, while they remained persistently abnormal in the deceased group. Furthermore, hypertension, elevated AST, PCT, and IL-10 were identified as independent risk factors for predicting poor prognosis of SFTS patients. These findings provide valuable insights into the prognostic significance of laboratory markers and highlight the importance of early identification of high-risk SFTS patients.

Integrated analysis of noncoding RNAs and mRNAs reveals their potential roles in chicken spleen response to Klebsiella variicola infection.

Klebsiella variicola is an emerging pathogen that has become a threat to human and animal health. There is evidence that long noncoding RNAs (lncRNAs) are involved in a host cell's response to microbial infections. However, no study has defined the link between K. variicola pathogenesis and lncRNAs until now. We used RNA sequencing to comprehensively analyze the lncRNAs and mRNAs in the chicken spleen after K. variicola infection. In total, we identified 2896 differentially expressed mRNAs and 578 differentially expressed lncRNAs. To examine the potential functions of these lncRNAs, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes signaling pathway enrichment analyses were performed on the target mRNAs of these differently expressed lncRNAs. The results suggested that lncRNAs play essential roles in modulating mRNA expression and triggering downstream immune signaling pathways to regulate the immune response in the chicken spleen. Using previous microRNA sequencing data, we constructed lncRNA-miRNA-mRNA regulatory networks to clarify the regulatory mechanisms in the chicken immune system. Several potential regulatory pairs related to K. variicola infection were found, involving XR_001467769.2, TCONS_00018386, gga-miR-132a-3p, gga-miR-132b-5p, gga-miR-2954, and novel62_mature. In conclusion, our findings make a significant contribution towards understanding the role of lncRNA in chicken spleen cells during K. variicola infection, thereby establishing a solid foundation for future research in this area.

Altered Proteomic Profile of Exosomes Secreted from Vero Cells Infected with Porcine Epidemic Diarrhea Virus.

Porcine epidemic diarrhea virus (PEDV) infection causes severe diarrhea in pigs and can be fatal in newborn piglets. Exosomes are extracellular vesicles secreted by cells that transfer biologically active proteins, lipids, and RNA to neighboring or distant cells. Herein, the morphology, particle size, and secretion of exosomes derived from a control and PEDV-infected group are examined, followed by a proteomic analysis of the exosomes. The results show that the exosomes secreted from the Vero cells had a typical cup-shaped structure. The average particle size of the exosomes from the PEDV-infected group was 112.4 nm, whereas that from the control group was 150.8 nm. The exosome density analysis and characteristic protein determination revealed that the content of exosomes in the PEDV-infected group was significantly higher than that in the control group. The quantitative proteomics assays revealed 544 differentially expressed proteins (DEPs) in the PEDV-infected group's exosomes compared with those in the controls, with 236 upregulated and 308 downregulated proteins. The DEPs were closely associated with cellular regulatory pathways, such as the phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K)-protein kinase B (Akt) signaling pathway, extracellular matrix-receptor interaction, focal adhesion, and cytoskeletal regulation. These findings provide the basis for further investigation of the pathogenic mechanisms of PEDV and the discovery of novel antiviral targets.

Irradiated Pollen-Induced Parthenogenesis for Doubled Haploid Production in Sunflowers (Helianthus spp.).

Doubled haploid (DH) technology is a tool used to develop large numbers of inbred lines and increase the rate of genetic gain by shortening the breeding cycles. However, previous attempts to produce DH sunflower plants (Helianthus spp.) have resulted in limited success. In this research, we applied gamma-induced parthenogenesis to assist the production of DH sunflowers. The objectives of the study included (1) identifying optimal gamma ray doses for inducing DH sunflowers using two cytoplasmic male sterility (CMS) lines as female plants and two male pollinators with recognizable morphological markers, (2) selecting new male pollinators from wild sunflower varieties, and (3) testing the efficacy of the selected male pollinators using emasculated non-male sterile sunflower lines as female plants. In these experiments, pollen grains were irradiated with gamma ray doses ranging from 50 to 200 Gy. The optimal gamma ray dose for pollen grain irradiation and DH plant production was identified to be 100 Gy. In addition, a cultivated (G11/1440) and a wild-type (ANN1811) sunflower line can be used as common male pollinators for their distinctive morphological markers and wide capacity for DH induction by gamma-irradiated pollen grains.

T cell receptor ß repertoires in patients with COVID-19 reveal disease severity signatures.

Background: The immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are crucial in maintaining a delicate balance between protective effects and harmful pathological reactions that drive the progression of coronavirus disease 2019 (COVID-19). T cells play a significant role in adaptive antiviral immune responses, making it valuable to investigate the heterogeneity and diversity of SARS-CoV-2-specific T cell responses in COVID-19 patients with varying disease severity. Methods: In this study, we employed high-throughput T cell receptor (TCR) ß repertoire sequencing to analyze TCR profiles in the peripheral blood of 192 patients with COVID-19, including those with moderate, severe, or critical symptoms, and compared them with 81 healthy controls. We specifically focused on SARS-CoV-2-associated TCR clonotypes. Results: We observed a decrease in the diversity of TCR clonotypes in COVID-19 patients compared to healthy controls. However, the overall abundance of dominant clones increased with disease severity. Additionally, we identified significant differences in the genomic rearrangement of variable (V), joining (J), and VJ pairings between the patient groups. Furthermore, the SARS-CoV-2-associated TCRs we identified enabled accurate differentiation between COVID-19 patients and healthy controls (AUC > 0.98) and distinguished those with moderate symptoms from those with more severe forms of the disease (AUC > 0.8). These findings suggest that TCR repertoires can serve as informative biomarkers for monitoring COVID-19 progression. Conclusions: Our study provides valuable insights into TCR repertoire signatures that can be utilized to assess host immunity to COVID-19. These findings have important implications for the use of TCR ß repertoires in monitoring disease development and indicating disease severity.

Detection of serum human neutrophil lipocalin is an effective biomarker for the diagnosis and monitoring of children with bacterial infection.

BACKGROUND: The study aimed to investigate the diagnostic efficiency of human neutrophil lipocalin (HNL) in bacterial infections in children. METHODS: This study included 49 pediatric patients with bacterial infections, 37 patients with viral infections, 30 patients with autoimmune diseases (AID) and 41 healthy controls (HCs). HNL, procalcitonin (PCT), C-reactive protein (CRP), white blood cell (WBC) and neutrophil counts were detected in the initial diagnosis and the following days. RESULTS: In the patients with bacterial infections, the levels of HNL, PCT, CRP, WBC and neutrophils were significantly increased than that of disease controls and HCs. The dynamic of these markers was monitored during antibiotic treatment. The level of HNL was decreased rapidly in patients with effective treatment, but maintained at high levels in deteriorated patients according to the clinical progression. CONCLUSIONS: HNL detection is an effective biomarker to identify bacterial infections from viral infections and other AIDs, and has potential value to evaluate the effect of antibiotic treatment in pediatric patients.

Prevalence and Characterization of Salmonella Isolated from Chickens in Anhui, China.

Salmonella is one of the most important zoonotic pathogens that can cause both acute and chronic illnesses in poultry flocks, and can also be transmitted to humans from infected poultry. The purpose of this study was to investigate the prevalence, antimicrobial resistance, and molecular characteristics of Salmonella isolated from diseased and clinically healthy chickens in Anhui, China. In total, 108 Salmonella isolates (5.66%) were successfully recovered from chicken samples (n = 1908), including pathological tissue (57/408, 13.97%) and cloacal swabs (51/1500, 3.40%), and S. Enteritidis (43.52%), S. Typhimurium (23.15%), and S. Pullorum (10.19%) were the three most prevalent isolates. Salmonella isolates showed high rates of resistance to penicillin (61.11%), tetracyclines (47.22% to tetracycline and 45.37% to doxycycline), and sulfonamides (48.89%), and all isolates were susceptible to imipenem and polymyxin B. In total, 43.52% isolates were multidrug-resistant and had complex antimicrobial resistance patterns. The majority of isolates harbored cat1 (77.78%), blaTEM (61.11%), and blaCMY-2 (63.89%) genes, and the antimicrobial resistance genes in the isolates were significantly positively correlated with their corresponding resistance phenotype. Salmonella isolates carry high rates of virulence genes, with some of these reaching 100% (invA, mgtC, and stn). Fifty-seven isolates (52.78%) were biofilm-producing. The 108 isolates were classified into 12 sequence types (STs), whereby ST11 (43.51%) was the most prevalent, followed by ST19 (20.37%) and ST92 (13.89%). In conclusion, Salmonella infection in chicken flocks is still serious in Anhui Province, and not only causes disease in chickens but might also pose a threat to public health security.

The Peripheral Immune Traits Changed in Patients with Multiple System Atrophy.

A growing body of evidence suggests immune involvement in the pathology of multiple system atrophy (MSA). Research on detailed peripheral immune indices, however, is relatively sparse, and is one of the intriguing aspects of MSA yet to be elucidated. A total of 26 MSA patients and 56 age-and sex-matched healthy controls (HC) were enrolled in the current case-control study to delineate the peripheral immune traits of MSA patients. The ratio of CD4+/CD8+ T cells, natural killer cells, CD28 expression on both CD4+ T cells and CD8+ T cells increased in MSA patients compared to HC, but CD8+ T cells and active marker (HLA-DR) expression on total T cells decreased (p < 0.05). This study sheds light on the dysregulation of cellular immunity in MSA, pointing to future mechanistic research.

The clinical and immunological characteristics in fatal severe fever with thrombocytopenia syndrome virus (SFTSV) infection.

OBJECTIVE: This study aimed to make a comprehensive evaluation of peripheral immune profiles for further understanding the immunopathogenesis of severe fever with thrombocytopenia syndrome (SFTS). METHODS: Forty-seven patients with SFTS virus infection were included, of which 24 were deceased. The percentages, absolute numbers, phenotype of lymphocyte subsets were detected by flow cytometry. RESULTS: In patients with SFTS, the numbers of CD3+T, CD4+T, CD8+T and NKT cells were decreased compared with healthy controls (HCs), accompanied with highly active and exhausted phenotypes for T cells, and overproliferating plasmablasts. High inflammatory status, dysregulation of coagulation and host immune response were more obvious in deceased patients than that of survivors. Higher levels of PCT, IL-6, IL-10, TNF-α, APTT, TT and the occurrence of hemophagocytic lymphohistiocytosis were poor prognostic indicators of SFTS. CONCLUSIONS: The evaluation of immunological markers in combination with laboratory tests has critical value for selecting prognostic markers and potential treatment target.

Prevalence and Importance of the Necrotrophic Effector Gene ToxA in Bipolaris sorokiniana Populations Collected from Spring Wheat and Barley.

Bipolaris sorokiniana is a necrotrophic fungal pathogen that causes foliar and root diseases on wheat and barley. These diseases are common in all wheat- and barley-growing regions, with more severe outbreaks occurring under warm and humid conditions. B. sorokiniana can also infect a wide range of grass species in the family Poaceae and secrete ToxA, an important necrotrophic effector also identified other wheat leaf spotting pathogens. In this study, the prevalence and virulence role of ToxA were investigated in a collection of 278 B. sorokiniana isolates collected from spring wheat and barley in the Upper Midwest of the United States or other places, including 169 from wheat leaves, 75 from wheat roots, 30 from barley leaves, and 4 from wild quack grass leaves. ToxA was present in the isolates from wheat leaves, wheat roots, and wild grass leaves but was absent from isolates collected from barley leaves. Prevalence of ToxA in wheat leaf isolates (34.3%) was much higher than that in wheat root isolates (16%). Sequencing analysis revealed the presence of two haplotypes, with the majority being BsH2. All ToxA+ isolates produced the functional effector in liquid cultures. Pathogenicity assays revealed that ToxA+ isolates caused significantly more disease on spring wheat lines harboring Tsn1 than their tsn1 mutants, suggesting that the ToxA-Tsn1 interaction plays an important role in spot blotch development. This work confirms the importance of ToxA in B. sorokiniana populations infecting wheat and, thus, the need to eliminate Tsn1 from spring wheat cultivars to reduce susceptibility to spot blotch.