Co-infection of Lactococcus garvieae and Aeromonas hydrophila in cultured Nile Tilapia in Kerala, India.

Co-infection of Lactococcus garvieae and Aeromonas hydrophila, has been confirmed from diseased Nile Tilapia (Oreochromis niloticus), Chithralada strain cultured in a freshwater rearing pond of Alappuzha district of Kerala, India. The aetiological agents behind the disease outbreak were bacteriologically proven and confirmed by 16SrRNA sequencing and phylogenetic analysis. PCR detection of the virulent genes, showed existence of adhesin and hemolysin in L. garvieae and aerolysin in A. hydrophila strain obtained. To fulfil Koch's postulates, challenge experiments were conducted and median lethal dose (LD50) of L. garvieae and A. hydrophila was calculated as 1 × 105.91 CFU per mL and 1 × 105.2 CFU per mL respectively. Histopathologically, eyes, spleen, and kidney were the predominantly infected organs by L. garvieae and A. hydrophila. Out of the 13 antibiotics tested to check antibiotic susceptibility, L. garvieae showed resistance to almost 7 antibiotics tested, with a resistance to Ciprofloxacin while A. hydrophila was found resistant to Streptomycin and Erythromycin. Understanding the complex interaction between Gram-positive and Gram-negative bacteria in the disease process and pathogenesis in fish host will contribute to efficient treatment strategies. As a preliminary investigation into this complex interaction, the present study is aimed at phenotypic and genotypic characterization, pathogenicity evaluation, and antibiotic susceptibility of the co-infecting pathogens in a diseased sample of freshwater-farmed Nile tilapia.

Performance of Large Language Models on Medical Oncology Examination Questions.

Importance: Large language models (LLMs) recently developed an unprecedented ability to answer questions. Studies of LLMs from other fields may not generalize to medical oncology, a high-stakes clinical setting requiring rapid integration of new information. Objective: To evaluate the accuracy and safety of LLM answers on medical oncology examination questions. Design, Setting, and Participants: This cross-sectional study was conducted between May 28 and October 11, 2023. The American Society of Clinical Oncology (ASCO) Oncology Self-Assessment Series on ASCO Connection, the European Society of Medical Oncology (ESMO) Examination Trial questions, and an original set of board-style medical oncology multiple-choice questions were presented to 8 LLMs. Main Outcomes and Measures: The primary outcome was the percentage of correct answers. Medical oncologists evaluated the explanations provided by the best LLM for accuracy, classified the types of errors, and estimated the likelihood and extent of potential clinical harm. Results: Proprietary LLM 2 correctly answered 125 of 147 questions (85.0%; 95% CI, 78.2%-90.4%; P < .001 vs random answering). Proprietary LLM 2 outperformed an earlier version, proprietary LLM 1, which correctly answered 89 of 147 questions (60.5%; 95% CI, 52.2%-68.5%; P < .001), and the best open-source LLM, Mixtral-8x7B-v0.1, which correctly answered 87 of 147 questions (59.2%; 95% CI, 50.0%-66.4%; P < .001). The explanations provided by proprietary LLM 2 contained no or minor errors for 138 of 147 questions (93.9%; 95% CI, 88.7%-97.2%). Incorrect responses were most commonly associated with errors in information retrieval, particularly with recent publications, followed by erroneous reasoning and reading comprehension. If acted upon in clinical practice, 18 of 22 incorrect answers (81.8%; 95% CI, 59.7%-94.8%) would have a medium or high likelihood of moderate to severe harm. Conclusions and Relevance: In this cross-sectional study of the performance of LLMs on medical oncology examination questions, the best LLM answered questions with remarkable performance, although errors raised safety concerns. These results demonstrated an opportunity to develop and evaluate LLMs to improve health care clinician experiences and patient care, considering the potential impact on capabilities and safety.

Multiplex imaging reveals spatially resolved DNA-damage response neighborhoods in TP53-mutated myelodysplastic neoplasms.

While increased DNA damage is a well-described feature of myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML), it is unclear whether all lineages and all regions of the marrow are homogeneously affected. In this study, we performed immunohistochemistry on formalin-fixed, paraffin-embedded whole-section bone marrow biopsies using a well-established antibody to detect pH2A.X (phosphorylated histone variant H2A.X) that recognizes DNA double-strand breaks. Focusing on TP53-mutated and complex karyotype MDS/AML, we find a greater pH2A.X+ DNA damage burden compared to TP53 wild-type neoplastic cases and non-neoplastic controls. To understand how double-strand breaks vary between lineages and spatially in TP53-mutated specimens, we applied a low-multiplex immunofluorescence staining and spatial analysis protocol to visualize pH2A.X+ cells with p53 protein staining and lineage markers. pH2A.X marked predominantly mid- to late-stage erythroids, whereas early erythroids and CD34+ blasts were relatively spared. In a prototypical example, these pH2A.X+ erythroids were organized locally as distinct colonies, and each colony displayed pH2A.X+ puncta at a synchronous level. This highly coordinated immunophenotypic expression was also seen for p53 protein staining and among presumed early myeloid colonies. Neighborhood clustering analysis showed distinct marrow regions differentially enriched in pH2A.X+/p53+ erythroid or myeloid colonies, indicating spatial heterogeneity of DNA-damage response and p53 protein expression. The lineage and architectural context within which DNA damage phenotype and oncogenic protein are expressed is relevant to current therapeutic developments that leverage macrophage phagocytosis to remove leukemic cells in part due to irreparable DNA damage. © 2024 The Pathological Society of Great Britain and Ireland.

Establishment and validation of a 2D primary gill cell culture of the sevenband grouper (Hyporthodus septemfasciatus).

A 2D primary gill cell culture system of the sevenband grouper (Hyporthodus septemfasciatus) was established to validate the pathogenesis of nervous necrosis virus (NNV) as observed in previous studies. This system, developed using the double-seeded insert (DSI) technique, yielded confluent cell layers. Upon challenge with NNV in a setup containing both autoclaved salt water and L15 media in the apical compartment, viral replication akin to that anticipated based on previous studies was observed. Consequently, we advocate for the utilization of primary gill cell culture as a viable alternative to conventional methodologies for investigating host pathogen interactions.

Impact of Automated Prognostication on Traumatic Brain Injury Care: A Focus Group Study.

BACKGROUND: Prognosticating outcomes for traumatic brain injury (TBI) patients is challenging due to the required specialized skills and variability among clinicians. Recent attempts to standardize TBI prognosis have leveraged machine learning (ML) methodologies. This study evaluates the necessity and influence of ML-assisted TBI prognostication through healthcare professionals' perspectives via focus group discussions. METHODS: Two virtual focus groups included ten key TBI care stakeholders (one neurosurgeon, two emergency clinicians, one internist, two radiologists, one registered nurse, two researchers in ML and healthcare and one patient representative). They answered six open-ended questions about their perceptions and potential ML use in TBI prognostication. Transcribed focus group discussions were thematically analyzed using qualitative data analysis software. RESULTS: The study captured diverse perceptions and interests in TBI prognostication across clinical specialties. Notably, certain clinicians who currently do not prognosticate expressed an interest in doing so independently provided they had access to ML support. Concerns included ML's accuracy and the need for proficient ML researchers in clinical settings. The consensus suggested using ML as a secondary consultation tool and promoting collaboration with internal or external research resources. Participants believed ML prognostication could enhance disposition planning and standardize care regardless of clinician expertise or injury severity. There was no evidence of perceived bias or interference during the discussions. CONCLUSION: Our findings revealed an overall positive attitude toward ML-based prognostication. Despite raising multiple concerns, the focus group discussions were particularly valuable in underscoring the potential of ML in democratizing and standardizing TBI prognosis practices.

Development of a Multimodal Machine Learning-Based Prognostication Model for Traumatic Brain Injury Using Clinical Data and Computed Tomography Scans: A CENTER-TBI and CINTER-TBI Study.

Computed tomography (CT) is an important imaging modality for guiding prognostication in patients with traumatic brain injury (TBI). However, because of the specialized expertise necessary, timely and dependable TBI prognostication based on CT imaging remains challenging. This study aimed to enhance the efficiency and reliability of TBI prognostication by employing machine learning (ML) techniques on CT images. A retrospective analysis was conducted on the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) data set (n = 1016). An ML-driven binary classifier was developed to predict favorable or unfavorable outcomes at 6 months post-injury. The prognostic performance was assessed using the area under the curve (AUC) over fivefold cross-validation and compared with conventional models that depend on clinical variables and CT scoring systems. An external validation was performed using the Comparative Indian Neurotrauma Effectiveness Research in Traumatic Brain Injury (CINTER-TBI) data set (n = 348). The developed model achieved superior performance without the necessity for manual CT assessments (AUC = 0.846 [95% CI: 0.843-0.849]) compared with the model based on the clinical and laboratory variables (AUC = 0.817 [95% CI: 0.814-0.820]) and established CT scoring systems requiring manual interpretations (AUC = 0.829 [95% CI: 0.826-0.832] for Marshall and 0.838 [95% CI: 0.835-0.841] for International Mission for Prognosis and Analysis of Clinical Trials in TBI [IMPACT]). The external validation demonstrated the prognostic capacity of the developed model to be significantly better (AUC = 0.859 [95% CI: 0.857-0.862]) than the model using clinical variables (AUC = 0.809 [95% CI: 0.798-0.820]). This study established an ML-based model that provides efficient and reliable TBI prognosis based on CT scans, with potential implications for earlier intervention and improved patient outcomes.

Determination of the minimum infective dose of viral hemorrhagic septicemia virus (VHSV) in juvenile olive flounder, Paralichthys olivaceus using an immersion challenge model.

Viral hemorrhagic septicemia virus (VHSV) affects over 80 fish species, leading to viral hemorrhagic septicemia (VHS). Horizontal VHSV transmission is widely studied, with researchers utilizing various doses to establish infection models. Infected hosts shed the virus into the environment, elevating the risk of transmission to naïve fish within the same system. This study aimed to ascertain the minimum infective dose of VHSV in olive flounder (Paralichthys olivaceus). In olive flounder, the detection of VHSV within the kidney exhibited the highest infection rate on the third day among days 1, 3 and 5. Doses of 103.0 to 104.7 TCID50/ml were administered to juvenile olive flounder across three farms. Results showed resistance to infection below 103.4 TCID50/ml at 15 °C. While infection frequency varied by concentration, higher concentrations correlated with more infections. Nonetheless, viral copy numbers did not differ significantly among infected fish at varying concentrations. This study underscores the need for early VHSV management and contributes essential data for pathogenicity assessment and foundational knowledge.

Spatial proteo-transcriptomic profiling reveals the molecular landscape of borderline ovarian tumors and their invasive progression.

Serous borderline tumors (SBT) are epithelial neoplastic lesions of the ovaries that commonly have a good prognosis. In 10-15% of cases, however, SBT will recur as low-grade serous cancer (LGSC), which is deeply invasive and responds poorly to current standard chemotherapy1,2,3. While genetic alterations suggest a common origin, the transition from SBT to LGSC remains poorly understood4. Here, we integrate spatial proteomics5 with spatial transcriptomics to elucidate the evolution from SBT to LGSC and its corresponding metastasis at the molecular level in both the stroma and the tumor. We show that the transition of SBT to LGSC occurs in the epithelial compartment through an intermediary stage with micropapillary features (SBT-MP), which involves a gradual increase in MAPK signaling. A distinct subset of proteins and transcripts was associated with the transition to invasive tumor growth, including the neuronal splicing factor NOVA2, which was limited to expression in LGSC and its corresponding metastasis. An integrative pathway analysis exposed aberrant molecular signaling of tumor cells supported by alterations in angiogenesis and inflammation in the tumor microenvironment. Integration of spatial transcriptomics and proteomics followed by knockdown of the most altered genes or pharmaceutical inhibition of the most relevant targets confirmed their functional significance in regulating key features of invasiveness. Combining cell-type resolved spatial proteomics and transcriptomics allowed us to elucidate the sequence of tumorigenesis from SBT to LGSC. The approach presented here is a blueprint to systematically elucidate mechanisms of tumorigenesis and find novel treatment strategies.

Machine learning in computational histopathology: Challenges and opportunities.

Digital histopathological images, high-resolution images of stained tissue samples, are a vital tool for clinicians to diagnose and stage cancers. The visual analysis of patient state based on these images are an important part of oncology workflow. Although pathology workflows have historically been conducted in laboratories under a microscope, the increasing digitization of histopathological images has led to their analysis on computers in the clinic. The last decade has seen the emergence of machine learning, and deep learning in particular, a powerful set of tools for the analysis of histopathological images. Machine learning models trained on large datasets of digitized histopathology slides have resulted in automated models for prediction and stratification of patient risk. In this review, we provide context for the rise of such models in computational histopathology, highlight the clinical tasks they have found success in automating, discuss the various machine learning techniques that have been applied to this domain, and underscore open problems and opportunities.

Automated screening of computed tomography using weakly supervised anomaly detection.

BACKGROUND: Current artificial intelligence studies for supporting CT screening tasks depend on either supervised learning or detecting anomalies. However, the former involves a heavy annotation workload owing to requiring many slice-wise annotations (ground truth labels); the latter is promising, but while it reduces the annotation workload, it often suffers from lower performance. This study presents a novel weakly supervised anomaly detection (WSAD) algorithm trained based on scan-wise normal and anomalous annotations to provide better performance than conventional methods while reducing annotation workload. METHODS: Based on surveillance video anomaly detection methodology, feature vectors representing each CT slice were trained on an AR-Net-based convolutional network using a dynamic multiple-instance learning loss and a center loss function. The following two publicly available CT datasets were retrospectively analyzed: the RSNA brain hemorrhage dataset (normal scans: 12,862; scans with intracranial hematoma: 8882) and COVID-CT set (normal scans: 282; scans with COVID-19: 95). RESULTS: Anomaly scores of each slice were successfully predicted despite inaccessibility to any slice-wise annotations. Slice-level area under the curve (AUC), sensitivity, specificity, and accuracy from the brain CT dataset were 0.89, 0.85, 0.78, and 0.79, respectively. The proposed method reduced the number of annotations in the brain dataset by 97.1% compared to an ordinary slice-level supervised learning method. CONCLUSION: This study demonstrated a significant annotation reduction in identifying anomalous CT slices compared to a supervised learning approach. The effectiveness of the proposed WSAD algorithm was verified through higher AUC than existing anomaly detection techniques.

Isolation and identification of Streptococcus agalactiae in cage-cultured green chromide Etroplus suratensis in Kerala, India.

Streptococcus agalactiae is one of the main aetiological agents in large-scale mortalities of tilapia, having caused major economic losses to the aquaculture industry in recent years. This study describes the isolation and identification of the bacteria from cage-cultured Etroplus suratensis that experienced moderate to severe mortalities in Kerala, India. Gram-positive, catalase-negative S. agalactiae was identified from brain, eye and liver of the fish by antigen grouping and 16S rDNA sequencing. Multiplex PCR confirmed that the isolate belonged to capsular serotype Ia. Antibiotic susceptibility tests showed that the isolate was resistant to methicillin, vancomycin, tetracycline, kanamycin, streptomycin, ampicillin, oxacillin and amikacin. Histological sections of the infected E. suratensis brain revealed infiltration of inflammatory cells, vacuolation and meningitis. This report is the first description of S. agalactiae as a primary pathogen causing mortalities in E. suratensis culture in Kerala.

Location of Early Stage Tumor Detection using Microwave Imaging in the Breast Phantom.

BACKGROUND: Universally, the most predominant cause of female mortality is mainly due to breast cancer. Owing to numerous constraints in the existing imaging technique, researchers are trying out an alternative tool to detect the tumor before going to the miserable stage. METHODS: This article presents a novel method to detect the mean value system for detecting the location of the tumor in different depths by shifting the antenna anywhere in the breast tissue. In addition, an algorithm to reconstruct the breast image, namely Delay-Multiply-and-Sum (DMAS) is followed to identify the tumor implanted in the breast tissue. RESULTS: The analysis shows that the maximum mean value occurs while the antenna moves very near to the tumor while the mean value reduces while the antenna shifts apart from the tumor location. The mean value in different locations is converted into a microwave image. The high intensity in the image exhibits the precise position of the tumor. This technique can identify the location of early-stage tumor of size 3mm. Multiple tumors of sizes 6mm and 7mm can identify at a depth of 12mm and 18mm in the homogeneous breast phantom. DMAS can provide better imaging results in the early stage tumor of size 3mm embedded in the breast phantom. CONCLUSION: Microwave imaging is an efficient technique to differentiate healthy and malignant tissue in the breast. Antenna plays a major role in identifying tumors in the breast in the early stage. Hence a high-performance Ultra Wideband Dielectric Resonator Antenna (DRA-UWB) is used to identify the tumor in the breast. An antenna is sketched in different locations of the breast phantom. On account of the hemispherical structure, the mean value of the reflected signal is high at the center than at the edge. Hence, the difference in mean value is calculated with and without breast phantom for identifying the tumor location. The overall efficiency of this technique can be improved by using a high-performance UWB antenna. The image of the breast is reformed by the DMAS beamforming algorithm.

Angioleiomyoma in a 54-year-old Female: A Case Report of Distal Leg Tumor and Review of the Literature.

Introduction: Angioleiomyomas are rare benign tumors originating from smooth muscle cells of blood vessels. Although they can occur in various anatomical locations, angioleiomyomas of the distal leg are relatively uncommon. Due to its clinical resemblance to other soft-tissue tumors, misdiagnosis can occur leading to inadequate treatment. Case Report: We present a case of angioleiomyoma in a 54-year-old female who presented with a palpable mass in her distal leg. The tumor was surgically excised, and histopathological examination confirmed the diagnosis of angioleiomyoma. In this article, we discuss the clinical presentation, diagnostic evaluation, and management of angioleiomyoma, with a focus on distal leg tumors. Furthermore, we provide a comprehensive review of the existing literature on angioleiomyomas, emphasizing findings and treatment outcomes reported in previous studies. Conclusion: Angioleiomyomas are uncommon soft-tissue tumors that can mimic other more common lesions such as ganglion cysts. Hence, diagnosis requires a high index of suspicion. Surgical excision is the treatment of choice for angioleiomyoma. Complete resection is generally curative, with a low rate of recurrence.

Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet?

BACKGROUND: The purpose of this study was to conduct a systematic review for understanding the availability and limitations of artificial intelligence (AI) approaches that could automatically identify and quantify computed tomography (CT) findings in traumatic brain injury (TBI). METHODS: Systematic review, in accordance with PRISMA 2020 and SPIRIT-AI extension guidelines, with a search of 4 databases (Medline, Embase, IEEE Xplore, and Web of Science) was performed to find AI studies that automated the clinical tasks for identifying and quantifying CT findings of TBI-related abnormalities. RESULTS: A total of 531 unique publications were reviewed, which resulted in 66 articles that met our inclusion criteria. The following components for identification and quantification regarding TBI were covered and automated by existing AI studies: identification of TBI-related abnormalities; classification of intracranial hemorrhage types; slice-, pixel-, and voxel-level localization of hemorrhage; measurement of midline shift; and measurement of hematoma volume. Automated identification of obliterated basal cisterns was not investigated in the existing AI studies. Most of the AI algorithms were based on deep neural networks that were trained on 2- or 3-dimensional CT imaging datasets. CONCLUSION: We identified several important TBI-related CT findings that can be automatically identified and quantified with AI. A combination of these techniques may provide useful tools to enhance reproducibility of TBI identification and quantification by supporting radiologists and clinicians in their TBI assessments and reducing subjective human factors.

A molecular atlas of the human postmenopausal fallopian tube and ovary from single-cell RNA and ATAC sequencing.

As part of the Human Cell Atlas Initiative, our goal is to generate single-cell transcriptomics (single-cell RNA sequencing [scRNA-seq], 86,708 cells) and regulatory (single-cell assay on transposase accessible chromatin sequencing [scATAC-seq], 59,830 cells) profiles of the normal postmenopausal ovary and fallopian tube (FT). The FT contains 11 major cell types, and the ovary contains 6. The dominating cell type in the FT and ovary is the stromal cell, which expresses aging-associated genes. FT epithelial cells express multiple ovarian cancer risk-associated genes (CCDC170, RND3, TACC2, STK33, and ADGB) and show active communication between fimbrial epithelial cells and ovarian stromal cells. Integrated single-cell transcriptomics and chromatin accessibility data show that the regulatory landscape of the fimbriae is different from other anatomic regions. Cell types with similar gene expression in the FT display transcriptional profiles. These findings allow us to disentangle the cellular makeup of the postmenopausal FT and ovary, advancing our knowledge of gynecologic diseases in menopause.

Βeta-glucan stimulation induces trained immunity markers in common carp, Cyprinus carpio.

Trained immunity refers to the memory acquired by innate immune cells, leading to cross-protection and non-specific responses to subsequent infection, thereby improving host survival. Trained immunity induction is a combined effect of immune signaling, metabolic changes, and epigenetic modifications. The present study evaluated the induction of markers of the phenomenon of trained immunity in common carp, which is trained using ß-glucan. The mammalian target of rapamycin (mtor) and hypoxia-inducible factor (hif1α), the metabolic basis of trained immunity; the histone deacetylase (hdac7), one of the markers of epigenetic modifications, metabolic activity of activated cells and expression profiles of proinflammatory cytokines viz. il6a, tnfαa2, and ifnγ were targeted in the study and analyzed in vivo. Besides in vivo analysis, in vitro analysis of mtorc2, hif1α, hdac7, and ifnγ were analyzed. In vitro analyses were performed on head kidney macrophages isolated and maintained in L-15 media and double trained with ß-glucan at 100µg/mL. The culture supernatant was collected at different time intervals and processed for expression studies. Healthy common carp were injected with ß-glucan at 20 mg/kg body weight for training followed by a resting phase for 6 days and were restimulated with the same dose. Head kidney was collected from the fish post-induction as well as post-restimulation. The expression profile of mtorc2, hdac7, and hif1α were found elevated post-stimulation of ß-glucan. Further, a significantly upregulated expression profile of proinflammatory cytokines (ifnγ, il6a and tnfαa2) was observed. Increased glycolysis in the cells post-ß-glucan stimulation was confirmed by the high lactate and LDH production detected in the cell culture supernatant. Overall, the study revealed the expression profile of the trained immunity markers and the increased metabolic activity in cells induced with ß-glucan, which further validates that the action of trained immunity is indispensable in fish on encounter with a potential ligand. The study supports the existing reports on trained immunity in teleost fish with evidence at the genomic level. However, further studies are required to understand the responses and actions of trained immune cells during infection in detail.

Temperature dependent cellular, and epigenetic regulatory mechanisms underlying the antiviral immunity in sevenband grouper to nervous necrosis virus infection.

Changes in the thermal optima of fish impacts changes in the physiology and immune response associated with infections. The present study showed that at suboptimal temperatures (17 °C), the host tries to evade viral infection by downregulating the inflammatory response through enhanced neuronal protection. There was significantly less abundance of IgM + B cells in the 17 °C group compared to that in the 25 °C group. An increased macrophage population (Iba1+) during the survival phase in fish challenged at 25 °C demonstrated inflammation. Optimal temperature challenge activated virus-induced senescence in brain cells, demonstrated with a heterochromatin-associated H3K9me3 histone mark. There was an abundant expression of anti-inflammatory cytokines in the brain of fish at the suboptimal challenge. Besides the cytokines, the expression of BDNF was significantly higher in the suboptimally challenged group, suggesting that its neuronal protection activity following NNV infection is mediated through TGFß. The suboptimal challenge resulted in H3k9ac displaying transcriptional competency, activation of trained immunity H3K4me3, and enrichment of H3 histone-lysine-4 monomethylation (H3K4me1), resulting in a robust re-stimulatory immune response. The observations from the H4 modifications showed that besides H4K12ac and H4K20m3, all the assayed modifications were significantly higher in suboptimal convalescent fishes. The suboptimally challenged fish acquired more methylation along cytosine residues than the optimally infected fish. Together, these observations suggest that optimal temperature results in an immune priming effect, whereas the protection enabled in suboptimal convalescent fishes is operated through epigenetically controlled trained immune functions.

NLRC3 attenuates antiviral immunity and activates inflammasome responses in primary grouper brain cells following nervous necrosis virus infection.

NLRC3 is identified as a unique regulatory NLR involved in the modulation of cellular processes and inflammatory responses. In this study, a novel Nod like receptor C3 (NLRC3) was functionally characterized from seven band grouper in the context of nervous necrosis virus infection. The grouper NLRC3 is highly conserved and homologous with other vertebrate proteins with a NACHT domain and a C-terminal leucine-rich repeat (LRR) domain and an N-terminal CARD domain. Quantitative gene expression analysis revealed the highest mRNA levels of NLRC3 were in the brain and gill followed by the spleen and kidney following NNV infection. Overexpression of NLRC3 augmented the NNV replication kinetics in primary grouper brain cells. NLRC3 attenuated the interferon responses in the cells following NNV infection by impacting the TRAF6/NF-κB activity and exhibited reduced IFN sensitivity, ISRE promoter activity, and IFN pathway gene expression. In contrast, NLRC3 expression positively regulated the inflammasome response and pro-inflammatory gene expression during NNV infection. NLRC3 negatively regulates the PI3K-mTOR axis and activated the cellular autophagic response. Delineating the complexity of NLRC3 regulation of immune response in the primary grouper brain cells following NNV infection suggests that the protein acts as a virally manipulated host factor that negatively regulated the antiviral immune response to augment the NNV replication.

Beta glucan induced immune priming protects against nervous necrosis virus infection in sevenband grouper.

In the present study, we studied the effect of ß-glucan on the activation of antiviral immune responses against nervous necrosis virus (NNV) taking into consideration the role of innate immune training. Sevenband grouper primary macrophages showed an attenuated proinflammatory response and elevated antiviral response to NNV infection. In vitro, priming of ß-glucan enhanced macrophage viability against NNV infection which is associated with the activation of sustained inflammatory cytokines gene expression. Observations were clear to understand that NLR Family CARD Domain Containing 3 (NLRC3) and caspase-1 activation and subsequent IL-1ß production were reduced in ß-glucan-primed macrophages. Subsequent markers for training including Lactate and abundance of HIF-1α were elevated in the cells following training. However, the lactate dehydrogenase (LDH) concentrations remained stable among the ß-glucan stimulated infected and uninfected groups suggesting similar macrophage health in both groups. In vivo, the NNV-infected fish primed with ß-glucan had a higher survival rate (60%) than the control NNV-infected group (40%). Our findings demonstrate that ß-glucan induced protective responses against NNV infection and studies are underway to harness its potential applicability for prime and boost vaccination strategies.