CW-NET for multitype cell detection and classification in bone marrow examination and mitotic figure examination.

MOTIVATION: Bone marrow (BM) examination is one of the most important indicators in diagnosing hematologic disorders and is typically performed under the microscope via oil-immersion objective lens with a total 100× objective magnification. On the other hand, mitotic detection and identification is critical not only for accurate cancer diagnosis and grading but also for predicting therapy success and survival. Fully automated BM examination and mitotic figure examination from whole-slide images is highly demanded but challenging and poorly explored. First, the complexity and poor reproducibility of microscopic image examination are due to the cell type diversity, delicate intralineage discrepancy within the multitype cell maturation process, cells overlapping, lipid interference and stain variation. Second, manual annotation on whole-slide images is tedious, laborious and subject to intraobserver variability, which causes the supervised information restricted to limited, easily identifiable and scattered cells annotated by humans. Third, when the training data are sparsely labeled, many unlabeled objects of interest are wrongly defined as background, which severely confuses AI learners. RESULTS: This article presents an efficient and fully automatic CW-Net approach to address the three issues mentioned above and demonstrates its superior performance on both BM examination and mitotic figure examination. The experimental results demonstrate the robustness and generalizability of the proposed CW-Net on a large BM WSI dataset with 16 456 annotated cells of 19 BM cell types and a large-scale WSI dataset for mitotic figure assessment with 262 481 annotated cells of five cell types. AVAILABILITY AND IMPLEMENTATION: An online web-based system of the proposed method has been created for demonstration (see https://youtu.be/MRMR25Mls1A).

Structure-Guided Discovery of PD-1/PD-L1 Interaction Inhibitors: Peptide Design, Screening, and Optimization via Computation-Aided Phage Display Engineering.

Cancer immunotherapy harnesses the immune system to combat tumors and has emerged as a major cancer treatment modality. The PD-1/PD-L1 immune checkpoint modulates interactions between tumor cells and T cells and has been extensively targeted in cancer immunotherapy. However, the monoclonal antibodies known to target this immune checkpoint have considerable side effects, and novel PD-1/PD-L1 inhibitors are therefore required. Herein, a peptide inhibitor to disrupt PD-1/PD-L1 interactions was designed through structure-driven phage display engineering coupled to computational modification and optimization. BetaPb, a novel peptide library constructed by using the known structure of PD-1/PD-L, was used to develop inhibitors against the immune checkpoint, and specific peptides with high affinity toward PD-1 were screened through enzyme-linked immunosorbent assays, homogeneous time-resolved fluorescence, and biolayer interferometry. A potential inhibitor, B8, was preliminarily screened through biopanning. The binding affinity of B8 toward PD-1 was confirmed through computation-aided optimization. Assessment of B8 variants (B8.1, B8.2, B8.3, B8.4, and B8.5) demonstrated their attenuation of PD-1/PD-L1 interactions. B8.4 exhibited the strongest attenuation efficiency at a half-maximal effective concentration of 0.1 µM and the strongest binding affinity to PD-1 (equilibrium dissociation constant = 0.1 µM). B8.4 outperformed the known PD-1/PD-L1 interaction inhibitor PL120131 in disrupting PD-1/PD-L1 interactions, revealing that B8.4 has remarkable potential for modification to yield an antitumor agent. This study provides valuable information for the future development of peptide-based drugs, therapeutics, and immunotherapies for cancer.

Optimization of the stroke hospital selection strategy and the distribution of endovascular thrombectomy resources.

Nowadays, emergency medical technicians (EMTs) decide to send a suspected stroke patient to a primary stroke center (PSC) or to an endovascular thrombectomy (EVT)-capable hospital, based on the Cincinnati Prehospital Stroke Scale (CPSS) and the number of symptoms a patient presents at the scene. Based on existing studies, the patient is likely to have a better functional outcome after three months if the time between the onset of symptoms and receiving EVT treatment is shorter. However, if an acute ischemic stroke (AIS) patient with large vessel occlusion (LVO) is first sent to a PSC, and then needs to be transferred to an EVT-capable hospital, the time to get definitive treatment is significantly increased. For this purpose, We formulate an integer programming model to minimize the expected time to receive a definitive treatment for stroke patients. We then use real-world data to verify the validity of the model. Also, we expand our model to find the optimal redistribution and centralization of EVT resources. It will enable therapeutic teams to increase their experience and skills more efficiently within a short period of time.

Deep Learning Can Predict Bevacizumab Therapeutic Effect and Microsatellite Instability Directly from Histology in Epithelial Ovarian Cancer.

Epithelial ovarian cancer (EOC) remains a significant cause of mortality among gynecologic cancers, with the majority of cases being diagnosed at an advanced stage. Before targeted therapies were available, EOC treatment relied largely on debulking surgery and platinum-based chemotherapy. Vascular endothelial growth factors have been identified as inducing tumor angiogenesis. According to several clinical trials, anti-vascular endothelial growth factor-targeted therapy with bevacizumab was effective in all phases of EOC treatment. However, there are currently no biomarkers accessible for regular therapeutic use despite the importance of patient selection. Microsatellite instability (MSI), caused by a deficiency of the DNA mismatch repair system, is a molecular abnormality observed in EOC associated with Lynch syndrome. Recent evidence suggests that angiogenesis and MSI are interconnected. Developing predictive biomarkers, which enable the selection of patients who might benefit from bevacizumab-targeted therapy or immunotherapy, is critical for realizing personalized precision medicine. In this study, we developed 2 improved deep learning methods that eliminate the need for laborious detailed image-wise annotations by pathologists and compared them with 3 state-of-the-art methods to not only predict the efficacy of bevacizumab in patients with EOC using mismatch repair protein immunostained tissue microarrays but also predict MSI status directly from histopathologic images. In prediction of therapeutic outcomes, the 2 proposed methods achieved excellent performance by obtaining the highest mean sensitivity and specificity score using MSH2 or MSH6 markers and outperformed 3 state-of-the-art deep learning methods. Moreover, both statistical analysis results, using Cox proportional hazards model analysis and Kaplan-Meier progression-free survival analysis, confirm that the 2 proposed methods successfully differentiate patients with positive therapeutic effects and lower cancer recurrence rates from patients experiencing disease progression after treatment (P < .01). In prediction of MSI status directly from histopathology images, our proposed method also achieved a decent performance in terms of mean sensitivity and specificity score even for imbalanced data sets for both internal validation using tissue microarrays from the local hospital and external validation using whole section slides from The Cancer Genome Atlas archive.

Single-chain fragment antibody disrupting the EphA4 function as a therapeutic drug for gastric cancer.

Studies have shown that the high expression of EphA4 in gastric cancer tissues may correlate with unfavorable clinical pathological characteristics. Therefore, EphA4 may be an effective target for treating gastric cancer in addition to HER-2/neu. In this study, generated scFv S3 can bind endogenous EphA4 of gastric cancer cells and has significant membrane staining. Additionally, scFv S3 binding to EphA4 inhibits the growth and migration of cancer cells and the growth induction that ephrinA1 generates in gastric cancer cells. We found that EphA4 molecules may degrade through antibody treatment of cells, and the increase in LAMP1 and LAMP2 indicates that lysosome is involved in the degradation. The scFv S3 administration leads to the signals pAKT, pERK, and pSTAT3 decrease in cancer cells. The xenograft model of HER-2/neu low expressing gastric cancer cell SNU-16 exhibits better therapeutic effects by scFv S3 than trastuzumab scFv. The scFv S3 administration in vivo can degrade EphA4 molecules in tumor tissues, decreasing Ki67 and increasing cleaved C3 molecule expression. Furthermore, we identified and validated that scFv S3 generates essential ionic bonding with R162 on EphA4. The antibody may provide effective treatment for patients with gastric cancer and abnormal activation or overexpression of EphA4 signaling.

An auristatin-based antibody-drug conjugate targeting EphA2 in pancreatic cancer treatment.

Pancreatic adenocarcinoma, a highly aggressive form of cancer with a poor prognosis, necessitates the development of innovative treatment strategies. Our prior research showcased the growth-inhibiting effects of the anti-EphA2 antibody drug hSD5 on pancreatic cancer tumors. This antibody targets and induces the degradation of the EphA2 receptor while also prompting the antibody's internalization. A deeper dive into the hSD5 Fab crystallographic structure and docking studies revealed that hSD5's CDRH3 drives the primary interaction between hSD5 and the EphA2 active site. In this study, we developed a novel antibody-drug conjugate (ADC)-the auristatin-based hSD5-vedotin specifically targeting EphA2 in pancreatic cancer cells. This ADC aims at the tumor-specific antigen EphA2, triggering endocytosis and releasing the conjugated payload molecule Monomethyl auristatin E (MMAE), amplifying the tumor-killing effect. Upon cellular entry, hSD5-vedotin demonstrated an impressive tumor-killing response, inhibiting tumor cell growth and promoting apoptosis even at lower antibody concentrations. In a pancreatic cancer xenograft animal model, hSD5-vedotin showcased the potential to suppress tumor growth entirely. Notably, potential immune resistance responses were also observed in recurrent pancreatic cancer tumors. Our empirical results underscore the possibility of developing hSD5-vedotin further, which we anticipate will have a broader and more potent therapeutic impact on pancreatic cancer and other EphA2-related cancers.

The Blockade of Mitogen-Activated Protein Kinase 14 Activation by Marine Natural Product Crassolide Triggers ICD in Tumor Cells and Stimulates Anti-Tumor Immunity.

Immunogenic cell death (ICD) refers to a type of cell death that stimulates immune responses. It is characterized by the surface exposure of damage-associated molecular patterns (DAMPs), which can facilitate the uptake of antigens by dendritic cells (DCs) and stimulate DC activation, resulting in T cell immunity. The activation of immune responses through ICD has been proposed as a promising approach for cancer immunotherapy. The marine natural product crassolide, a cembranolide isolated from the Formosan soft coral Lobophytum michaelae, has been shown to have cytotoxic effects on cancer cells. In this study, we investigated the effects of crassolide on the induction of ICD, the expression of immune checkpoint molecules and cell adhesion molecules, as well as tumor growth in a murine 4T1 mammary carcinoma model. Immunofluorescence staining for DAMP ectolocalization, Western blotting for protein expression and Z'-LYTE kinase assay for kinase activity were performed. The results showed that crassolide significantly increased ICD and slightly decreased the expression level of CD24 on the surface of murine mammary carcinoma cells. An orthotopic tumor engraftment of 4T1 carcinoma cells indicated that crassolide-treated tumor cell lysates stimulate anti-tumor immunity against tumor growth. Crassolide was also found to be a blocker of mitogen-activated protein kinase 14 activation. This study highlights the immunotherapeutic effects of crassolide on the activation of anticancer immune responses and suggests the potential clinical use of crassolide as a novel treatment for breast cancer.

Effect of using G-FAST to recognize emergent large vessel occlusion: A city-wide community experience.

BACKGROUND/PURPOSE: A prehospital bypass strategy was suggested for large vessel occlusion. This study aimed to evaluate the effect of a bypass strategy using the gaze-face-arm-speech-time test (G-FAST) implemented in a metropolitan community. METHODS: Pre-notified patients with positive Cincinnati Prehospital Stroke Scale and symptom onset <3 h from July 2016 to December 2017 (pre-intervention period) and those with positive G-FAST and symptom onset <6 h from July 2019 to December 2020 (intervention period) were included. Patients aged <20 years and those with missing in-hospital data were excluded. The primary outcomes were the rates of receiving endovascular thrombectomy (EVT) and intravenous thrombolysis (IVT). The secondary outcomes were total prehospital time, door-to-computed tomography (CT) time, door-to-needle (DTN) time, and door-to-puncture (DTP) time. RESULTS: We included 802 and 695 pre-notified patients from the pre-intervention and intervention periods, respectively. The characteristics of the patients in the two periods were similar. In the primary outcomes, pre-notified patients during the intervention period showed higher rates of receiving EVT (4.49% vs. 15.25%, p < 0.001) and IVT (15.34% vs. 21.58%, p = 0.002). In the secondary outcomes, pre-notified patients during intervention period had longer total prehospital time (mean 23.38 vs 25.23 min, p < 0.001), longer door-to-CT time (median 10 vs 11 min, p < 0.001), longer DTN time (median 53 vs 54.5 min, p < 0.001) but shorter DTP time (median 141 vs 139.5 min, p < 0.001). CONCLUSION: The prehospital bypass strategy with G-FAST showed benefits for stroke patients.

Annotation-Free Deep Learning-Based Prediction of Thyroid Molecular Cancer Biomarker BRAF (V600E) from Cytological Slides.

Thyroid cancer is the most common endocrine cancer. Papillary thyroid cancer (PTC) is the most prevalent form of malignancy among all thyroid cancers arising from follicular cells. Fine needle aspiration cytology (FNAC) is a non-invasive method regarded as the most cost-effective and accurate diagnostic method of choice in diagnosing PTC. Identification of BRAF (V600E) mutation in thyroid neoplasia may be beneficial because it is specific for malignancy, implies a worse prognosis, and is the target for selective BRAF inhibitors. To the authors' best knowledge, this is the first automated precision oncology framework effectively predict BRAF (V600E) immunostaining result in thyroidectomy specimen directly from Papanicolaou-stained thyroid fine-needle aspiration cytology and ThinPrep cytological slides, which is helpful for novel targeted therapies and prognosis prediction. The proposed deep learning (DL) framework is evaluated on a dataset of 118 whole slide images. The results show that the proposed DL-based technique achieves an accuracy of 87%, a precision of 94%, a sensitivity of 91%, a specificity of 71% and a mean of sensitivity and specificity at 81% and outperformed three state-of-the-art deep learning approaches. This study demonstrates the feasibility of DL-based prediction of critical molecular features in cytological slides, which not only aid in accurate diagnosis but also provide useful information in guiding clinical decision-making in patients with thyroid cancer. With the accumulation of data and the continuous advancement of technology, the performance of DL systems is expected to be improved in the near future. Therefore, we expect that DL can provide a cost-effective and time-effective alternative tool for patients in the era of precision oncology.

Expanding resources of endovascular thrombectomy: An optimization model.

BACKGROUND/PURPOSE: Recently optimized models for selecting the locations of hospitals capable of providing endovascular thrombectomy (EVT) did not consider the accuracy of the prehospital stroke scale assessment and possibility of secondary transport. Our study aimed to propose a new model for selecting existing hospitals with intravenous thrombolysis capability to become EVT-capable hospitals. METHODS: A sequential order was provided to upgrade hospitals providing intravenous thrombolysis, using a mixed integer programming model based on current medical resource allocation. In addition, we drafted a centralized plan to redistribute existing EVT resources by redetermining locations of EVT-capable hospitals. Using historical data of 7679 on-scene patients with suspected stroke, the model was implemented to determine the hospital that maximizes the number of patients receiving EVT treatment within call-to-definitive-treatment time. RESULTS: All suspected stroke patients were sent to EVT-capable hospitals directly under the current medical resource allocation model. After upgrading one additional hospital to become an EVT-capable hospital, the percentage of patients receiving definitive treatment within the standard call-to-definitive-treatment time was elevated from 68.82% to 72.97%. In the model, assuming that there is no hospital providing EVT, all patients suspected of stroke will be sent to EVT-capable hospitals directly after upgrading three or more hospitals to be able to provide treatment. CONCLUSION: All patients eligible for acute stroke treatment are sent to EVT-capable hospitals in the simulation under the current medical resource allocation model. This model can be utilized to provide insights for capacity redistribution in other regions.

External validation of prehospital stroke scales for emergent large vessel occlusion.

BACKGROUND: It is suggested that a prehospital scale should be utilized to identify patients with emergent large vessel occlusion (ELVO). We aimed to perform external validation of nine ELVO scales. METHODS: This single center retrospective observational study included patients with ischemic stroke visiting the emergency department (ED) within 6 h of symptom onset. Participants were excluded if individual items of the National Institute of Health Stroke Scale scores were not recorded or they did not receive brain computed tomography angiography or magnetic resonance imaging before intravenous thrombolysis or endovascular thrombectomy, and within 24 h of ED admission. The first definition of ELVO was emergent occlusion of the internal carotid artery (ICA) and middle cerebral artery segment 1 (M1). The second definition was emergent occlusion of ICA, M1, basilar artery, middle cerebral artery segment 2, anterior cerebral artery segment 1, and posterior cerebral artery segment 1. Area under the receiver operating characteristic curve (AUROC) was constructed to examine discrimination. The sensitivity, specificity, positive predictive value, and negative predictive value of the nine scales under the two ELVO definitions were calculated. RESULTS: A total of 1231 patients were included in the study. No significant differences were observed in the AUROC under the two ELVO definitions. However, sensitivity values of these scales were largely different, ranging from 44.56% to 93.68% under the first ELVO definition. The sensitivity values among scales were also different under the second ELVO definition. CONCLUSION: Stakeholders in the community should choose suitable scales according to their own system conditions.

Fully Automatic Registration Methods for Chest X-Ray Images.

PURPOSE: Image registration is important in medical applications accomplished by improving healthcare technology in recent years. Various studies have been proposed in medical applications, including clinical track of events and updating the treatment plan for radiotherapy and surgery. This study presents a fully automatic registration system for chest X-ray images to generate fusion results for difference analysis. Using the accurate alignment of the proposed system, the fusion result indicates the differences in the thoracic area during the treatment process. METHODS: The proposed method consists of a data normalization method, a hybrid L-SVM model to detect lungs, ribs and clavicles for object recognition, a landmark matching algorithm, two-stage transformation approaches and a fusion method for difference analysis to highlight the differences in the thoracic area. In evaluation, a preliminary test was performed to compare three transformation models, with a full evaluation process to compare the proposed method with two existing elastic registration methods. RESULTS: The results show that the proposed method produces significantly better results than two benchmark methods (P-value ≤ 0.001). The proposed system achieves the lowest mean registration error distance (MRED) (8.99 mm, 23.55 pixel) and the lowest mean registration error ratio (MRER) w.r.t. the length of image diagonal (1.61%) compared to the two benchmark approaches with MRED (15.64 mm, 40.97 pixel) and (180.5 mm, 472.69 pixel) and MRER (2.81%) and (32.51%), respectively. CONCLUSIONS: The experimental results show that the proposed method is capable of accurately aligning the chest X-ray images acquired at different times, assisting doctors to trace individual health status, evaluate treatment effectiveness and monitor patient recovery progress for thoracic diseases.

Generation and Characterization of Single Chain Variable Fragment against Alpha-Enolase of Candida albicans.

Candida albicans (C. albicans) is an opportunistic human pathogen responsible for approximately a half of clinical candidemia. The emerging Candida spp. with resistance to azoles is a major challenge in clinic, suggesting an urgent demand for new drugs and therapeutic strategies. Alpha-enolase (Eno1) is a multifunctional protein and represents an important marker for invasive candidiasis. Thus, C. albicans Eno1 (CaEno1) is believed to be an important target for the development of therapeutic agents and antibody drugs. Recombinant CaEno1 (rCaEno1) was first used to immunize chickens. Subsequently, we used phage display technology to construct two single chain variable fragment (scFv) antibody libraries. A novel biopanning procedure was carried out to screen anti-rCaEno1 scFv antibodies, whose specificities were further characterized. The polyclonal IgY antibodies showed binding to rCaEno1 and native CaEno1. A dominant scFv (CaS1) and its properties were further characterized. CaS1 attenuated the growth of C. albicans and inhibited the binding of CaEno1 to plasminogen. Animal studies showed that CaS1 prolonged the survival rate of mice and zebrafish with candidiasis. The fungal burden in kidney and spleen, as well as level of inflammatory cytokines were significantly reduced in CaS1-treated mice. These results suggest CaS1 has potential of being immunotherapeutic drug against C. albicans infections.

A benchmark for comparing precision medicine methods in thyroid cancer diagnosis using tissue microarrays.

Motivation: The aim of precision medicine is to harness new knowledge and technology to optimize the timing and targeting of interventions for maximal therapeutic benefit. This study explores the possibility of building AI models without precise pixel-level annotation in prediction of the tumor size, extrathyroidal extension, lymph node metastasis, cancer stage and BRAF mutation in thyroid cancer diagnosis, providing the patients' background information, histopathological and immunohistochemical tissue images. Results: A novel framework for objective evaluation of automatic patient diagnosis algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2017- A Grand Challenge for Tissue Microarray Analysis in Thyroid Cancer Diagnosis. Here, we present the datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark. The main contributions of the challenge include the creation of the data repository of tissue microarrays; the creation of the clinical diagnosis classification data repository of thyroid cancer; and the definition of objective quantitative evaluation for comparison and ranking of the algorithms. With this benchmark, three automatic methods for predictions of the five clinical outcomes have been compared, and detailed quantitative evaluation results are presented in this paper. Based on the quantitative evaluation results, we believe automatic patient diagnosis is still a challenging and unsolved problem. Availability and implementation: The datasets and the evaluation software will be made available to the research community, further encouraging future developments in this field. (http://www-o.ntust.edu.tw/cvmi/ISBI2017/). Contact: cweiwang@mail.ntust.edu.tw. Supplementary information: Supplementary data are available at Bioinformatics online.

Correction to: Development and clinical applications of cancer immunotherapy against PD-1 signaling pathway.

In the original publication of this article [1] the name of the fifth author is uncorrect. The correct name of the fifth author should be Wei-Chiao Chang rather than Wei-Chao Chang. The original publication has been corrected.

Development and clinical applications of cancer immunotherapy against PD-1 signaling pathway.

Dramatic advances in immune therapy have emerged as a promising strategy in cancer therapeutics. In addition to chemotherapy and radiotherapy, inhibitors targeting immune-checkpoint molecules such as cytotoxic T-lymphocyte antigen-4 (CTLA-4), programmed cell death receptor-1 (PD-1) and its ligand (PD-L1) demonstrate impressive clinical benefits in clinical trials. In this review, we present background information about therapies involving PD-1/PD-L1 blockade and provide an overview of current clinical trials. Furthermore, we present recent advances involving predictive biomarkers associated with positive therapeutic outcomes in cancer immunotherapy.

Antigenic epitopes on the outer membrane protein A of Escherichia coli identified with single-chain variable fragment (scFv) antibodies.

Bacterial meningitis is a severe disease that is fatal to one-third of patients. The major cause of meningitis in neonates is Escherichia coli (E. coli) K1. This bacterium synthesizes an outer membrane protein A (OmpA) that is responsible for the adhesion to (and invasion of) endothelial cells. Thus, the OmpA protein represents a potential target for developing diagnostic and therapeutic agents for meningitis. In this study, we expressed recombinant OmpA proteins with various molecular weights in E. coli. The sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) was performed to check the molecular size of OmpA's full length (FL) and truncated proteins. OmpA-FL protein was purified for immunizing chickens to produce immunoglobulin yolk (IgY) antibodies. We applied phage display technology to construct antibody libraries (OmpA-FL scFv-S 1.1 × 107 and OmpA-FL scFv-L 5.01 × 106) to select specific anti-OmpA-FL scFv antibodies; these were characterized by their binding ability to recombinant or endogenous OmpA using ELISA, immunofluorescent staining, and confirmed with immunoblotting. We found 12 monoclonal antibodies that react to OmpA fragments; seven scFvs recognize fragments spanning amino acid (aa) residues 1-346, aa 1-287, aa 1-167, and aa 60-192, while five scFvs recognize fragments spanning aa 1-346 and aa 1-287 only. Two fragments (aa 246-346 and aa 287-346) were not recognized with any of the 12 scFvs. Together, the data suggest three antigenic epitopes (60 aa-160 aa, 161 aa-167 aa, 193 aa-245 aa) recognized by monoclonal antibodies. These scFv antibodies show strong reactivity against OmpA proteins. We believe that antibodies show promising diagnostic agents for E. coli K1 meningitis.

Generation and characterization of avian-derived anti-human CD19 single chain fragment antibodies.

The human cluster of differentiation 19 (CD19) is highly expressed in most leukemia, rendering is a promising therapeutic target. In this study, we generated anti-CD19 single-chain variable fragments (scFv) from immunized chickens by phage display technology. After constructing a scFv antibody library with 2.5 × 108 compositional diversity for panning, one representative scFv clone S2 which can specifically recognize to the CD19 protein was isolated and characterized. The binding reactivity of the scFv S2 to the endogenous CD19 protein of the ARH-77 leukemia cancer cell was verified through flow cytometry and the binding affinity of scFv S2 is 6.9 × 10-8 M determined by the surface plasmon resonance system. Compared with the chicken germline, hyper mutation in the complementarity-determining regions (CDRs) suggested that scFv S2 could be generated through an antigen-driven humoral response. By molecular modeling, the possible CDR configurations of scFv S2 were constructed rationally. Furthermore, the characteristics of chicken antibodies of a protein database were investigated. The findings in this study contribute to antibody development and engineering because they reveal the geometric structures and properties of the CDRs in chicken antibodies.

Cellular Nuclear Export Factors TAP and Aly Are Required for HDAg-L-mediated Assembly of Hepatitis Delta Virus.

Hepatitis delta virus (HDV) is a satellite virus of hepatitis B virus (HBV). HDV genome encodes two forms of hepatitis delta antigen (HDAg), small HDAg (HDAg-S), which is required for viral replication, and large HDAg (HDAg-L), which is essential for viral assembly. HDAg-L is identical to HDAg-S except that it bears a 19-amino acid extension at the C terminus. Both HDAgs contain a nuclear localization signal (NLS), but only HDAg-L contains a CRM1-independent nuclear export signal at its C terminus. The nuclear export activity of HDAg-L is important for HDV particle formation. However, the mechanisms of HDAg-L-mediated nuclear export of HDV ribonucleoprotein are not clear. In this study, the host cellular RNA export complex TAP-Aly was found to form a complex with HDAg-L, but not with an export-defective HDAg-L mutant, in which Pro205 was replaced by Ala. HDAg-L was found to colocalize with TAP and Aly in the nucleus. The C-terminal domain of HDAg-L was shown to directly interact with the N terminus of TAP, whereas an HDAg-L mutant lacking the NLS failed to interact with full-length TAP. In addition, small hairpin RNA-mediated down-regulation of TAP or Aly reduced nuclear export of HDAg-L and assembly of HDV virions. Furthermore, a peptide, TAT-HDAg-L(198-210), containing the 10-amino acid TAT peptide and HDAg-L(198-210), inhibited the interaction between HDAg-L and TAP and blocked HDV virion assembly and secretion. These data demonstrate that formation and release of HDV particles are mediated by TAP and Aly.