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Hyperammonemia is a dangerous life-threatening metabolic complication characterized by markedly elevated ammonia levels that can lead to irreversible brain damage if not carefully monitored. Current pharmacological treatment strategies available for hyperammonemia patients are suboptimal and associated with major side effects. In this study, we focus on developing and evaluating the

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines may target epitopes that reduce durability or increase the potential for escape from vaccine-induced immunity. Using synthetic vaccinology, we have developed rationally immune-focused SARS-CoV-2 Spike-based vaccines. Glycans can be employed to alter antibody responses to infection and vaccines. Utilizing computational modeling and in vitro screening, we have incorporated glycans into the receptor-binding domain (RBD) and assessed antigenic profiles. We demonstrate that glycan-coated RBD immunogens elicit stronger neutralizing antibodies and have engineered seven multivalent configurations. Advanced DNA delivery of engineered nanoparticle vaccines rapidly elicits potent neutralizing antibodies in guinea pigs, hamsters, and multiple mouse models, including human ACE2 and human antibody repertoire transgenics. RBD nanoparticles induce high levels of cross-neutralizing antibodies against variants of concern with durable titers beyond 6 months. Single, low-dose immunization protects against a lethal SARS-CoV-2 challenge. Single-dose coronavirus vaccines via DNA-launched nanoparticles provide a platform for rapid clinical translation of potent and durable coronavirus vaccines., Graphical abstract, Glycan engineering of immunogens is a key vaccine design strategy to focus antibodies to select epitopes. Konrath et al. create an automated pipeline to screen SARS-CoV-2 RBD for PNGS. PNGS-modified RBDs elicit enhanced ACE2-directed responses and higher neutralization. Multivalent display of glycan-engineered RBDs delivered via nucleic acid provides single-dose protection from challenge.

Ovarian cancer (OC) represents the deadliest gynecologic malignancy. Despite important advances in the field of OC therapy, recurrent OC still has very poor prognosis with a median survival of 1 year. Due to the close interaction between the ovarian cancer cells and tumor microenvironment, development of treatment strategies which not only target the tumor cells but also the components of the tumor microenvironment hold significance. Notably, a prime obstacle in the development of therapies is to identify targets with specific expression limited to the tumor surface and not the healthy tissues. The follicle-stimulating hormone receptor (FSHR) is one such target with selective expression in ovarian granulosa cells and thus, a potentially important therapeutic target in OC. Therefore, we developed monoclonal antibodies against FSHR and focused on studies of the most potent of these reagents. Anti-FSHR antibody bound to diverse FSHR expressing ovarian serous and clear cell adenocarcinoma cells suggesting these antibodies could be used to specifically target OC. We then designed a novel DNA encoded bispecific T cell engager targeting FSHR (FSHRxCD3) and evaluated this bispecific in therapeutic models for the treatment of OC. FSHRxCD3 bispecific in the presence of human PBMCs was highly specific in killing FSHR positive ovarian tumor lines. However, T cell approaches alone may not be fully effective in treating OC, referred as Immunologically “Cold” Tumors. Hence, we hypothesized that engaging the other components of immune system, would provide better tumor control. Increasing lines of evidence suggest that OC is receptive to Natural killer (NK) cell attack. We designed antibodies against human Siglec-7, an inhibitory receptor present on human NK cells and showed they could bind to NK cells. We then used these to create a novel class of bispecific NK engager (NKE) that simultaneously targets both Siglec-7 and FSHR (Siglec-7xFSHR). This NKE was potent at killing FSHR positive OC targets in both in vitro and in vivo assays. Multiple Ovarian tumors including BRCA mutated and PARPi resistant ovarian cancer cells could be targeted by this immune therapy. Our data demonstrate the therapeutic potential of these two novel bispecific molecules and initial studies suggest that their combination may be valuable in patients with OC. Citation Format: Devivasha Bordoloi, Pratik Bhojnagarwala, Abhijeet J. Kulkarni, Opeyemi S. Adeniji, Alfredo Perales-Puchalt, Ryan P. O’Connell, Xizhou Zhu, Elizabeth M. Parzych, Rugang Zhang, Mohamed Abdel-Mohsen, David B. Weiner. Immunotherapy of ovarian cancer targeting FSHR by innate and adaptive immunity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4262.

Epstein Barr Virus (EBV) latent infection is associated with multiple types of cancer. Several clinical studies have targeted EBV antigens as immune therapeutic targets with limited efficacy of EBV+ malignancies, suggesting additional targets might be important. BamHI-A rightward frame 1 (BARF1) is an EBV antigen that is highly expressed in EBV-associated gastric carcinoma (EBVaGC) and EBV+ nasopharyngeal carcinoma (NPC) and has not been evaluated as an antigen in the context of therapeutic immunization. Here, we developed a synthetic DNA-based expression cassette as immunotherapy targeting BARF1 (pBARF1). Immunization with pBARF1 induced potent antigen-specific humoral and polyfunctional effector T cell responses in vivo. We observed significant antigen-specific increases of IFN-γ+, IFN-γ+ TNF-α+, and IFN-γ+ TNF-α+ IL-2+ populations in CD8+ T cells from the pBARF1 immunized animals in both C57BL/6 and BALB/c mice. Novel BARF1 expressing carcinoma lines that grow in immune-competent animals were developed as surrogates for human EBV+ tumors. We confirmed that BARF1 expression in these new cancer cell lines, MC38-BARF1 and CT26-BARF1, was within the range of human EBV+ cancer cell lines, SNU719 and C666-1. Immunization with pBARF1 plasmid demonstrated impact of tumor progression through induction of CD8+ T cell in these BARF1+ carcinoma models. We also observed long-term antitumor immunity, at least up to 446 days, against BARF1 in a CT26-BARF1 rechallenge study. Using IVIS in vivo imaging, we observed that pBARF1 immunized animals rapidly controlled and cleared cancer cells. These studies demonstrate the potential for pBARF1 immunity to induce antigen-specific immune responses impacting tumor progression. Further study of this immune target and these EBV+ tumor models is likely important as part of therapeutic approaches for EBV+ malignancies. Citation Format: Xizhou Zhu, Alfredo Perales-Puchalt, Krzysztof Wojtak, Ziyang Xu, Kun Yun, Pratik S. Bhojnagarwala, Devivasha Bordoloi, Daniel H. Park, Kevin Liaw, Mamadou A. Bah, Paul M. Lieberman, Ebony N. Gary, Ami Patel, David B. Weiner. DNA immunotherapy targeting BARF1 demonstrates therapeutic impact in novel murine carcinoma models expressing EBV latent antigens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3573.

Antibodies from SARS-CoV-2 vaccines may target epitopes which reduce durability or increase the potential for escape from vaccine-induced immunity. Using a novel synthetic vaccinology pipeline, we developed rationally immune focused SARS-CoV-2 Spike-based vaccines. N-linked glycans can be employed to alter antibody responses to infection and vaccines. Utilizing computational modeling and comprehensive in vitro screening, we incorporated glycans into the Spike Receptor-Binding Domain (RBD) and assessed antigenic profiles. We developed glycan coated RBD immunogens and engineered seven multivalent configurations. Advanced DNA delivery of engineered nanoparticle vaccines rapidly elicited potent neutralizing antibodies in guinea pigs, hamsters and multiple mouse models, including human ACE2 and human B cell repertoire transgenics. RBD nanoparticles encoding wild-type and the P.1 SARS-CoV-2 variant induced high levels of cross-neutralizing antibodies. Single, low dose immunization protected against a lethal SARS-CoV-2 challenge. Single-dose coronavirus vaccines via DNA-launched nanoparticles provide a platform for rapid clinical translation of novel, potent coronavirus vaccines.

Self-supervised learning has shown its great potential to extract powerful visual representations without human annotations. Various works are proposed to deal with self-supervised learning from different perspectives: (1) contrastive learning methods (e.g., MoCo, SimCLR) utilize both positive and negative samples to guide the training direction; (2) asymmetric network methods (e.g., BYOL, SimSiam) get rid of negative samples via the introduction of a predictor network and the stop-gradient operation; (3) feature decorrelation methods (e.g., Barlow Twins, VICReg) instead aim to reduce the redundancy between feature dimensions. These methods appear to be quite different in the designed loss functions from various motivations. The final accuracy numbers also vary, where different networks and tricks are utilized in different works. In this work, we demonstrate that these methods can be unified into the same form. Instead of comparing their loss functions, we derive a unified formula through gradient analysis. Furthermore, we conduct fair and detailed experiments to compare their performances. It turns out that there is little gap between these methods, and the use of momentum encoder is the key factor to boost performance. From this unified framework, we propose UniGrad, a simple but effective gradient form for self-supervised learning. It does not require a memory bank or a predictor network, but can still achieve state-of-the-art performance and easily adopt other training strategies. Extensive experiments on linear evaluation and many downstream tasks also show its effectiveness. Code is released at https://github.com/fundamentalvision/UniGrad., Comment: CVPR2022

The coronavirus family member, SARS-CoV-2 has been identified as the causal agent for the pandemic viral pneumonia disease, COVID-19. At this time, no vaccine is available to control further dissemination of the disease. We have previously engineered a synthetic DNA vaccine targeting the MERS coronavirus Spike (S) protein, the major surface antigen of coronaviruses, which is currently in clinical study. Here we build on this prior experience to generate a synthetic DNA-based vaccine candidate targeting SARS-CoV-2 S protein. The engineered construct, INO-4800, results in robust expression of the S protein in vitro. Following immunization of mice and guinea pigs with INO-4800 we measure antigen-specific T cell responses, functional antibodies which neutralize the SARS-CoV-2 infection and block Spike protein binding to the ACE2 receptor, and biodistribution of SARS-CoV-2 targeting antibodies to the lungs. This preliminary dataset identifies INO-4800 as a potential COVID-19 vaccine candidate, supporting further translational study., There is currently no licensed SARS-CoV-2 vaccine. Here, the authors generate an optimized DNA vaccine candidate encoding the SARS-CoV-2 spike antigen, demonstrating induction of specific T cells and neutralizing antibody responses in mice and guinea pigs. These initial results support further development of this vaccine candidate.

Arginase is a complex and unique enzyme that plays diverse roles in health and disease. By metabolizing arginine, it can shape the outcome of innate and adaptive immune responses. The immunomodulatory capabilities of arginase could potentially be applied for local immunosuppression or induction of immune tolerance. With the use of an enhanced DNA delivery approach, we designed and studied a DNA-encoded secretable arginase enzyme as a tool for immune modulation and evaluated its immunomodulatory function in vivo. Strong immunosuppression of cluster of differentiation 4 (CD4) and CD8 T cells, as well as macrophages and dendritic cells, was observed in vitro in the presence of an arginase-rich supernatant. To further evaluate the efficacy of DNA-encoded arginase on in vivo immunosuppression against an antigen, a cancer antigen vaccine model was used in the presence or absence of DNA-encoded arginase. Significant in vivo immunosuppression was observed in the presence of DNA-encoded arginase. The efficacy of this DNA-encoded arginase delivery was examined in a local, imiquimod-induced, psoriasis-like, skin-inflammation model. Pretreatment of animals with the synthetic DNA-encoded arginase led to significant decreases in skin acanthosis, proinflammatory cytokines, and costimulatory molecules in extracted macrophages and dendritic cells. These results draw attention to the potential of direct in vivo-delivered arginase to function as an immunomodulatory agent for treatment of local inflammation or autoimmune diseases., Graphical Abstract, Arginase is a unique enzyme that plays a critical role in regulating the outcome of innate and adaptive immune responses. The immunomodulatory capabilities of arginase enzyme could potentially be applied for local inflammation or autoimmune diseases. Here, we report on a DNA-encoded secretable arginase enzyme as a tool for therapeutic immunomodulation.

In article number https://doi.org/10.1002/advs.201902802, David B. Weiner, Daniel W. Kulp, and co‐workers demonstrate that synthetic DNA delivery and adaptive electroporation can launch de novo assembly of different nanoparticle vaccines in the hosts to elicit significantly improved responses while reducing required doses. Next‐generation designer vaccines can now be rapidly evaluated in vivo to facilitate clinical translation for the betterment of human and animal health.

The coronavirus family member, SARS-CoV-2 has been identified as the causal agent for the outbreak of viral pneumonia disease, COVID-19 which first emerged in mid-December 2019 in the city of Wuhan in central China. As of February 25, 2020 there are 80,994 people infected and 2,760 deaths, and documented human-to-human transmission across multiple continents. At this time, no vaccine is available to control further dissemination of the disease. We have previously developed a synthetic DNA vaccine targeting the MERS coronavirus Spike (S) protein that was deployed in response to the MERS outbreak in South Korea. This vaccine induced potent antibody and CTL responses, and provided protection in a NHP challenge model. In the clinic, the vaccine generated humoral immunity including neutralizing antibody responses, as well as T cell immunity. Here we build on this prior work and report on the rapid development of a synthetic DNA-based vaccine targeting the major surface antigen Spike protein of SARS-CoV-2. The engineered construct, INO-4800 induced robust expression of the Spike protein in vitro, and generated antibody and T cell responses following a single immunization in mice and guinea pigs. This preliminary dataset identifies INO-4800 as a potential COVID-19 vaccine candidate, supporting further study for mobilization against this emerging disease threat.

Despite the importance of unsupervised object detection, to the best of our knowledge, there is no previous work addressing this problem. One main issue, widely known to the community, is that object boundaries derived only from 2D image appearance are ambiguous and unreliable. To address this, we exploit LiDAR clues to aid unsupervised object detection. By exploiting the 3D scene structure, the issue of localization can be considerably mitigated. We further identify another major issue, seldom noticed by the community, that the long-tailed and open-ended (sub-)category distribution should be accommodated. In this paper, we present the first practical method for unsupervised object detection with the aid of LiDAR clues. In our approach, candidate object segments based on 3D point clouds are firstly generated. Then, an iterative segment labeling process is conducted to assign segment labels and to train a segment labeling network, which is based on features from both 2D images and 3D point clouds. The labeling process is carefully designed so as to mitigate the issue of long-tailed and open-ended distribution. The final segment labels are set as pseudo annotations for object detection network training. Extensive experiments on the large-scale Waymo Open dataset suggest that the derived unsupervised object detection method achieves reasonable accuracy compared with that of strong supervision within the LiDAR visible range. Code shall be released.