ChimerDB 2.0—a knowledgebase for fusion genes updated

Chromosome translocations and gene fusions are frequent events in the human genome and have been found to cause diverse types of tumor. ChimerDB is a knowledgebase of fusion genes identified from bioinformatics analysis of transcript sequences in the GenBank and various other public resources such as the Sanger cancer genome project (CGP), OMIM, PubMed and the Mitelman’s database. In this updated version, we significantly modified the algorithm of identifying fusion transcripts. Specifically, the new algorithm is more sensitive and has detected 2699 fusion transcripts with high confidence. Furthermore, it can identify interchromosomal translocations as well as the intrachromosomal deletions or inversions of large DNA segments. Importantly, results from the analysis of next-generation sequencing data in the short read archives are incorporated as well. We updated and integrated all contents (GenBank, Sanger CGP, OMIM, PubMed publications and the Mitelman’s database), and the user-interface has been improved to support diverse types of searches and to enhance the user convenience especially in browsing PubMed articles. We also developed a new alignment viewer that should facilitate examining reliability of fusion transcripts and inferring functional significance. We expect ChimerDB 2.0, available at http://ercsb.ewha.ac.kr/fusiongene, to be a valuable tool in identifying biomarkers and drug targets

Identification of MYC as an antinecroptotic protein that stifles RIPK1-RIPK3 complex formation

The underlying mechanism of necroptosis in relation to cancer is still unclear. Here, MYC, a potent oncogene, is an antinecroptotic factor that directly suppresses the formation of the RIPK1-RIPK3 complex. Gene set enrichment analyses reveal that the MYC pathway is the most prominently down-regulated signaling pathway during necroptosis. Depletion or deletion of MYC promotes the RIPK1-RIPK3 interaction, thereby stabilizing the RIPK1 and RIPK3 proteins and facilitating necroptosis. Interestingly, MYC binds to RIPK3 in the cytoplasm and inhibits the interaction between RIPK1 and RIPK3 in vitro. Furthermore, MYC-nick, a truncated form that is mainly localized in the cytoplasm, prevented TNF-induced necroptosis. Finally, down-regulation of MYC enhances necroptosis in leukemia cells and suppresses tumor growth in a xenograft model upon treatment with birinapant and emricasan. MYC-mediated suppression of necroptosis is a mechanism of necroptosis resistance in cancer, and approaches targeting MYC to induce necroptosis represent an attractive therapeutic strategy for cancer

Structure-Activity Relationship Analysis of YM155 for Inducing Selective Cell Death of Human Pluripotent Stem Cells

Despite great potential for regenerative medicine, the high tumorigenic potential of human pluripotent stem cells (hPSCs) to form undesirable teratoma is an important technical hurdle preventing safe cell therapy. Various small molecules that induce the complete elimination of undifferentiated hPSCs, referred to as “stemotoxics,” have been developed to facilitate tumor-free cell therapy, including the Survivin inhibitor YM155. In the present work, based on the chemical structure of YM155, total 26 analogs were synthesized and tested for stemotoxic activity toward human embryonic stem cells (hESCs) and induced PSCs (iPSCs). We found that a hydrogen bond acceptor in the pyrazine ring of YM155 derivatives is critical for stemotoxic activity, which is completely lost in hESCs lacking SLC35F2, which encodes a solute carrier protein. These results suggest that hydrogen bonding interactions between the nitrogens of the pyrazine ring and the SLC35F2 protein are critical for entry of YM155 into hPSCs, and hence stemotoxic activity

Reliability and Timing Aware GPU Management on Embedded Systems

The demand for low-power and high-performance computing has been driving the semiconductor industry for decades. In order to satisfy these demands, the semiconductor technology has been scaled down and multi/many-core processors have been proposed. Among the multi/many-core processors, Graphics Processing Units (GPUs) have been employed in the critical path of applications due to its programmability, high-performance, and low power consumption. Moreover, state-of-the-art GPUs have the capability to process multiple GPU workloads concurrently. Therefore, GPUs have been considered to be an essential part of embedded systems because of the increased number of throughput-oriented applications on real-time embedded systems, such as autonomous driving and advanced driving assistant applications. However, there are several challenges for using the GPUs in embedded systems. First, due to the small feature size, the state-of-the-art nano-scale multi-core processors, including GPUs, has faced severe reliability challenges like soft-error and processor degradation. Next, there is a noticeable (die-to-die and within-die) parameter variation due to the advanced semiconductor technology. Therefore, the lifetime and workload management of embedded GPUs under process variation is considered one of the most important aspects to ensure functional correctness over a long period of time. Last, existing application scheduling frameworks on a GPU do not have enough flexibility to handle the dynamic behavior of multiple event-driven applications.In order to tackle the above mentioned challenges, in this thesis, we propose a reliability and timing aware workload management framework on GPU-based real-time embedded systems. The proposed framework consists of two parts: design-time and run-time workload management. The proposed design-time workload management unit analyzes GPU kernel functions and generates PTX instruction schedules that maximizes the soft-error reliability. At the same time, the application profiles are generated for run-time workload management. The proposed run-time workload management unit includes two parts: Streaming Multiprocessor (SM) scheduling unit and aging-aware workload distribution unit. During run-time, depending on the system status and requirements, the proposed scheduling unit partitions the GPU workloads into sub-workload and generates sub-workloads launch sequences to handle the dynamic behavior of the event-driven applications. Concurrently, in the SM, the proposed aging-aware workload distribution unit jointly considers the current aging status and the process variation status and distributes the workload across the SM to maximize the lifetime of the GPU

Reliability and Timing Aware GPU Management on Embedded Systems

The demand for low-power and high-performance computing has been driving the semiconductor industry for decades. In order to satisfy these demands, the semiconductor technology has been scaled down and multi/many-core processors have been proposed. Among the multi/many-core processors, Graphics Processing Units (GPUs) have been employed in the critical path of applications due to its programmability, high-performance, and low power consumption. Moreover, state-of-the-art GPUs have the capability to process multiple GPU workloads concurrently. Therefore, GPUs have been considered to be an essential part of embedded systems because of the increased number of throughput-oriented applications on real-time embedded systems, such as autonomous driving and advanced driving assistant applications. However, there are several challenges for using the GPUs in embedded systems. First, due to the small feature size, the state-of-the-art nano-scale multi-core processors, including GPUs, has faced severe reliability challenges like soft-error and processor degradation. Next, there is a noticeable (die-to-die and within-die) parameter variation due to the advanced semiconductor technology. Therefore, the lifetime and workload management of embedded GPUs under process variation is considered one of the most important aspects to ensure functional correctness over a long period of time. Last, existing application scheduling frameworks on a GPU do not have enough flexibility to handle the dynamic behavior of multiple event-driven applications.In order to tackle the above mentioned challenges, in this thesis, we propose a reliability and timing aware workload management framework on GPU-based real-time embedded systems. The proposed framework consists of two parts: design-time and run-time workload management. The proposed design-time workload management unit analyzes GPU kernel functions and generates PTX instruction schedules that maximizes the soft-error reliability. At the same time, the application profiles are generated for run-time workload management. The proposed run-time workload management unit includes two parts: Streaming Multiprocessor (SM) scheduling unit and aging-aware workload distribution unit. During run-time, depending on the system status and requirements, the proposed scheduling unit partitions the GPU workloads into sub-workload and generates sub-workloads launch sequences to handle the dynamic behavior of the event-driven applications. Concurrently, in the SM, the proposed aging-aware workload distribution unit jointly considers the current aging status and the process variation status and distributes the workload across the SM to maximize the lifetime of the GPU

Drug Repositioning for Cancer Therapy Based on Large-Scale Drug-Induced Transcriptional Signatures.

An in silico chemical genomics approach is developed to predict drug repositioning (DR) candidates for three types of cancer: glioblastoma, lung cancer, and breast cancer. It is based on a recent large-scale dataset of ~20,000 drug-induced expression profiles in multiple cancer cell lines, which provides i) a global impact of transcriptional perturbation of both known targets and unknown off-targets, and ii) rich information on drug's mode-of-action. First, the drug-induced expression profile is shown more effective than other information, such as the drug structure or known target, using multiple HTS datasets as unbiased benchmarks. Particularly, the utility of our method was robustly demonstrated in identifying novel DR candidates. Second, we predicted 14 high-scoring DR candidates solely based on expression signatures. Eight of the fourteen drugs showed significant anti-proliferative activity against glioblastoma; i.e., ivermectin, trifluridine, astemizole, amlodipine, maprotiline, apomorphine, mometasone, and nortriptyline. Our DR score strongly correlated with that of cell-based experimental results; the top seven DR candidates were positive, corresponding to an approximately 20-fold enrichment compared with conventional HTS. Despite diverse original indications and known targets, the perturbed pathways of active DR candidates show five distinct patterns that form tight clusters together with one or more known cancer drugs, suggesting common transcriptome-level mechanisms of anti-proliferative activity

If You Have a Reliable Source, Say Something: Effects of Correction Comments on COVID-19 Misinformation

In the post-truth era, particularly during the COVID-19 pandemic, an effective correction on misinformation is necessary to promote personal and public health. To better understand the effect of “correcting” misinformation, therefore, we investigated correction from different users on social media (e.g., individual users, fact-checking websites, and health organizations) and the frequency of correction (e.g., once vs. twice) in three online experiments. In each experiment, we evaluated participants’ perceived accuracy and willingness to share in terms of real and fake news of COVID-19, respectively. Across all experiments, a single correction from the health organizations effectively reduced participants’ perceived accuracy rating on the COVID-19 fake news. Experiments 2 and 3 revealed the effects of a single correction from individual users and fact-checking websites. Moreover, results of post-session questionnaires indicated that participants counted on the reliability of the sources in the correction. We did not obtain the consistent effects of frequent correction but verified the vulnerability of participants with high health anxiety to the COVID-19 fake news across all experiments. Overall, our study highlights the effects of user-initiated correction regardless of whether the user is an individual or an organization, as long as the correction contains a reliable source

Associative Inference Can Increase People’s Susceptibility to Misinformation

Associative inference is an adaptive, constructive process of memory that allows people to link related information to make novel connections. We conducted three online human-subjects experiments investigating participants’ susceptibility to associatively inferred misinformation and its interaction with their cognitive ability and how news articles were presented. In each experiment, participants completed recognition and perceived accuracy rating tasks for the snippets of news articles in a tweet format across two phases. At Phase 1, participants viewed real news only. At Phase 2, participants viewed both real and fake news. Critically, we varied whether the fake news at Phase 2 was inferred from (i.e., associative inference), associated with (i.e., association only), or irrelevant to (i.e., control) the corresponding real news pairs at Phase 1. Both recognition and perceived accuracy results showed that participants in the associative inference condition were more susceptible to fake news than those in the other conditions. Furthermore, hashtags embedded within the tweets made the obtained effects evident only for the participants of higher cognitive ability. Our findings reveal that associative inference can be a basis for individuals’ susceptibility to misinformation, especially for those of higher cognitive ability. We conclude by discussing the implications of our results for understanding and mitigating misinformation on social media platforms

Molecular Identification of Borrelia spp. from Ticks in Pastures Nearby Livestock Farms in Korea

Ticks are vectors that spread pathogenic bacteria, viruses, and protozoa. As the number of ticks increases due to climate change, the importance of the study of tick-borne pathogens has also increased. This study was conducted to investigate the distribution of the major tick species causing Lyme borreliosis and regional differences in the prevalence of Borrelia spp. by tick species. Borrelia infection was confirmed not only in Ixodes ticks, which are the major vectors of Borrelia spp., but also in Haemaphysalis and Amblyomma ticks. PCR targeting the 5S-23S rRNA intergenic spacer region (rrf-rrl) was performed to confirm Borrelia positivity. A total of 6102 ticks (736 pools) were tested, and the proportion was Haemaphysalis longicornis nymphs and adults at 69.2%, Haemaphysalis flava nymphs and adults at 13.9%, Haemaphysalis spp. larva at 14.3%, Ixodes nipponensis at 0.8%, and Amblyomma testudinarium at 1.9%. Ixodes nipponensis showed the highest minimum infection rate (MIR: 34.00; 17 pools/50 ticks) for Borrelia spp., followed by A. testudinarium (MIR: 0.88), and H. longicornis (MIR: 0.05). In particular, to our knowledge Borrelia infection was first confirmed in A. testudinarium in Korea. As a result of phylogenetic analysis, all sequences were grouped with Borreliaafzelii isolates and showed a close relationship with high identity. Considering that B. afzelii causes infectious zoonotic diseases, continuous monitoring and attention are needed, although it has a low prevalence in this study

Enhanced Asymptomatic Systemic Infection Caused by <i>Plesiomonas shigelloides</i> in a Captive Gray Wolf (<i>Canis lupus</i>)

A 7-year-old male gray wolf was found dead at a zoo during exhibition. To determine the cause of death, histological and gross necropsy diagnoses and a molecular analysis were performed. The gross necropsy revealed a swollen abdomen, hemorrhagic exudates around the mouth, splenomegaly, a discolored liver, a congested kidney, hemorrhagic ascites, and dark gray-colored membranes and air bubbles in the fundus of the stomach. Rod-shaped bacteria were found in the liver parenchyma and hemorrhagic ascites using Giemsa staining. The nucleotide sequencing of the cultured bacteria identified the causative agent as Plesiomonas shigelloides, which is rarely responsible for systemic infections. This study describes a rare case and the first reported systemic gastrointestinal infection due to P. shigelloides in a zoo animal