Mutation characteristics and molecular evolution of ovarian metastasis from gastric cancer and potential biomarkers for paclitaxel treatment.

Ovarian metastasis is one of the major causes of treatment failure in patients with gastric cancer (GC). However, the genomic characteristics of ovarian metastasis in GC remain poorly understood. In this study, we enroll 74 GC patients with ovarian metastasis, with 64 having matched primary and metastatic samples. Here, we show a characterization of the mutation landscape of this disease, alongside an investigation into the molecular heterogeneity and pathway mutation enrichments between synchronous and metachronous metastasis. We classify patients into distinct clonal evolution patterns based on the distribution of mutations in paired samples. Notably, the parallel evolution group exhibits the most favorable prognosis. Additionally, by analyzing the differential response to chemotherapy, we identify potential biomarkers, including SALL4, CCDC105, and CLDN18, for predicting the efficacy of paclitaxel treatment. Furthermore, we validate that CLDN18 fusion mutations improve tumor response to paclitaxel treatment in GC with ovarian metastasis in vitro and vivo.

Discrepant soil microbial community and C cycling function responses to conventional and biodegradable microplastics.

Biodegradable microplastics (MPs) are promising alternatives to conventional MPs and are of high global concern. However, their discrepant effects on soil microorganisms and functions are poorly understood. In this study, polyethylene (PE) and polylactic acid (PLA) MPs were selected to investigate the different effects on soil microbiome and C-cycling genes using high-throughput sequencing and real-time quantitative PCR, as well as the morphology and functional group changes of MPs, using scanning electron microscopy and Fourier transform infrared spectroscopy, and the driving factors were identified. The results showed that distinct taxa with potential for MP degradation and nitrogen cycling were enriched in soils with PLA and PE, respectively. PLA, smaller size (150-180 µm), and 5% (w/w) of MPs enhanced the network complexity compared with PE, larger size (250-300 µm), and 1% (w/w) of MPs, respectively. PLA increased ß-glucosidase by up to 2.53 times, while PE (150-180 µm) reduced by 38.26-44.01% and PE (250-300 µm) increased by 19.00-22.51% at 30 days. Amylase was increased by up to 5.83 times by PLA (150-180 µm) but reduced by 40.26-62.96% by PLA (250-300 µm) and 16.11-43.92% by PE. The genes cbbL, cbhI, abfA, and Lac were enhanced by 37.16%- 1.99 times, 46.35%- 26.46 times, 8.41%- 69.04%, and 90.81%- 5.85 times by PLA except for PLA1B/5B at 30 days. These effects were associated with soil pH, NO3--N, and MP biodegradability. These findings systematically provide an understanding of the impact of biodegradable MPs on the potential for global climate change.

Temporal Network Embedding Enhanced With Long-Range Dynamics and Self-Supervised Learning.

Temporal network embedding (TNE) has promoted the research of knowledge discovery and reasoning on networks. It aims to embed vertices of temporal networks into a low-dimensional vector space while preserving network structures and temporal properties. However, most existing methods have limitations in capturing dynamics over long distances, which makes it difficult to explore multihop topological associations among vertices. To tackle this challenge, we propose LongTNE, which learns the long-range dynamics of vertices to endow TNE with the ability to capture high-order proximity (HP) of networks. In LongTNE, we employ graph self-supervised learning (Graph SSL) to optimize the establishment probability of deep links in each network snapshot. We also present an accumulated forward update (AFU) module to fathom global temporal evolution among multiple network snapshots. The empirical results on six temporal networks demonstrate that, in addition to achieving state-of-the-art performance on network mining tasks, LongTNE can be handily extended to existing TNE methods.

PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge.

PubTator 3.0 (https://www.ncbi.nlm.nih.gov/research/pubtator3/) is a biomedical literature resource using state-of-the-art AI techniques to offer semantic and relation searches for key concepts like proteins, genetic variants, diseases and chemicals. It currently provides over one billion entity and relation annotations across approximately 36 million PubMed abstracts and 6 million full-text articles from the PMC open access subset, updated weekly. PubTator 3.0's online interface and API utilize these precomputed entity relations and synonyms to provide advanced search capabilities and enable large-scale analyses, streamlining many complex information needs. We showcase the retrieval quality of PubTator 3.0 using a series of entity pair queries, demonstrating that PubTator 3.0 retrieves a greater number of articles than either PubMed or Google Scholar, with higher precision in the top 20 results. We further show that integrating ChatGPT (GPT-4) with PubTator APIs dramatically improves the factuality and verifiability of its responses. In summary, PubTator 3.0 offers a comprehensive set of features and tools that allow researchers to navigate the ever-expanding wealth of biomedical literature, expediting research and unlocking valuable insights for scientific discovery.

PubTator 3.0: an AI-powered Literature Resource for Unlocking Biomedical Knowledge.

PubTator 3.0 (https://www.ncbi.nlm.nih.gov/research/pubtator3/) is a biomedical literature resource using state-of-the-art AI techniques to offer semantic and relation searches for key concepts like proteins, genetic variants, diseases, and chemicals. It currently provides over one billion entity and relation annotations across approximately 36 million PubMed abstracts and 6 million full-text articles from the PMC open access subset, updated weekly. PubTator 3.0's online interface and API utilize these precomputed entity relations and synonyms to provide advanced search capabilities and enable large-scale analyses, streamlining many complex information needs. We showcase the retrieval quality of PubTator 3.0 using a series of entity pair queries, demonstrating that PubTator 3.0 retrieves a greater number of articles than either PubMed or Google Scholar, with higher precision in the top 20 results. We further show that integrating ChatGPT (GPT-4) with PubTator APIs dramatically improves the factuality and verifiability of its responses. In summary, PubTator 3.0 offers a comprehensive set of features and tools that allow researchers to navigate the ever-expanding wealth of biomedical literature, expediting research and unlocking valuable insights for scientific discovery.

Synthesis and Biological Activity Evaluation of Benzoxazinone-Pyrimidinedione Hybrids as Potent Protoporphyrinogen IX Oxidase Inhibitor.

Protoporphyrinogen IX oxidase (PPO/Protox, E.C. 1.3.3.4) is recognized as one of the most important targets for herbicide discovery. In this study, we report our ongoing research efforts toward the discovery of novel PPO inhibitors. Specifically, we identified a highly potent new compound series containing a pyrimidinedione moiety and bearing a versatile building block-benzoxazinone scaffold. Systematic bioassays resulted in the discovery of compound 7af, ethyl 4-(7-fluoro-6-(3-methyl-2,6-dioxo-4-(trifluoromethyl)-3,6-dihydropyrimidin-1(2H)-yl)-3-oxo-2,3-dihydro-4H-benzo[b][1,4]oxazin-4-yl)butanoate, which exhibited broad-spectrum and excellent herbicidal activity at the dosage of 37.5 g a.i./ha through postemergence application. The inhibition constant (Ki) value of 7af to Nicotiana tabacum PPO (NtPPO) was 14 nM, while to human PPO (hPPO), it was 44.8 µM, indicating a selective factor of 3200, making it the most selective PPO inhibitor to date. Moreover, molecular simulations further demonstrated the selectivity and the binding mechanism of 7af to NtPPO and hPPO. This study not only identifies a candidate that showed excellent in vivo bioactivity and high safety toward humans but also provides a paradigm for discovering PPO inhibitors with improved performance through molecular simulation and structure-guided optimization.

Monitoring of gastrointestinal carcinoma via molecular residual disease with circulating tumor DNA using a tumor-informed assay.

BACKGROUND: Circulating tumor DNA (ctDNA)-based minimal residual disease (MRD) detection, which can identify disease relapse ahead of radiological imaging, has shown promising performance. The objective of this study was to develop and validate OriMIRACLE S (Minimal Residual Circulating Nucleic Acid Longitudinal Detection in Solid Tumor), a highly sensitive and specific tumor-informed assay for MRD detection. METHODS: Tumor-specific somatic single nucleotide variants (SNVs) were identified via whole exome sequencing of tumor tissue and matched germline DNA. Clonal SNVs were selected using the OriSelector algorithm for patient-specific, multiplex PCR-based NGS assays in MRD detection. Plasma-free DNA from patients with gastrointestinal tumors prior to and following an operation, and during monitoring, were ultradeep sequenced. RESULTS: The detection of three positive sites was sufficient to achieve nearly 100% overall sample level sensitivity and specificity and was determined by calculating binomial probability based on customized panels containing 21 to 30 variants. A total of 127 patients with gastrointestinal tumors were enrolled in our study. Preoperatively, MRD was positive in 18 of 26 patients (69.23%). Following surgery, MRD was positive in 24 of 82 patients (29.27%). The positivity rate for MRD was 33.33% (n = 18) for gastric adenocarcinoma and 32.26% (n = 62) for colorectal cancer. Twenty (20) of 59 patients (34.48%) experienced a change in MRD status over the monitoring period. Patients 8 and 31 responded to 3 cycles of systemic therapy, after which levels for all ctDNA dropped below the detection limit. Patient 53 was an example of using MRD to predict tumor metastasis. Patient 55 showed a weak response to treatments first and respond to new systemic therapy after tumor progression. CONCLUSION: Our study identified a sensitive and specific clinical detection method for low frequency ctDNA, and explored the detection performance of this technology in gastrointestinal tumors.

Protocol for the automatic extraction of epidemiological information via a pre-trained language model.

The lack of systems to automatically extract epidemiological fields from open-access COVID-19 cases restricts the timeliness of formulating prevention measures. Here we present a protocol for using CCIE, a COVID-19 Cases Information Extraction system based on the pre-trained language model.1 We describe steps for preparing supervised training data and executing python scripts for named entity recognition and text category classification. We then detail the use of machine evaluation and manual validation to illustrate the effectiveness of CCIE. For complete details on the use and execution of this protocol, please refer to Wang et al.2.

Discovery of a Subnanomolar Inhibitor of Protoporphyrinogen IX Oxidase via Fragment-Based Virtual Screening.

Protoporphyrinogen IX oxidase (PPO, E.C. 1.3.3.4), a key functional enzyme existing in various organisms, is acknowledged to be one of the most important action targets in the development of herbicides due to its pivotal roles in chlorophyll and heme biosynthesis pathways. As our persistent research work on the discovery of novel PPO-inhibiting herbicides, a new compound methyl 2-((5-(3-chloro-4,5,6,7-tetrahydro-2H-indazol-2-yl)-6-fluorobenzo[d]thiazol-2-yl)thio)acetate (8aj, Ki = 16 nM) was screened out as a hit compound via a fragment-based virtual screening method performed in the Auto Core Fragment in silico Screening web server. Subsequently, through a fused process of "hit-to-lead" optimization guided by molecular simulation, a total of 30 3-chloro-4,5,6,7-tetrahydro-2H-indazol-benzo[d]thiazole derivatives were synthesized and characterized. The results of the enzymatic inhibition bioassay showed that more than half of the newly synthesized compounds displayed higher activity against Nicotiana tabacum PPO (NtPPO) than oxadiazon, a commercial PPO-inhibiting herbicide. In particular, compound 8ab, a subnanomolar inhibitor with a Ki value of 380 pM against NtPPO, was discovered, which showed to be 71-fold more active than the commercial control oxadiazon (Ki = 27 nM), and was proven to be the most potent PPO inhibitor so far. Furthermore, the greenhouse assay demonstrated that most of the synthetic compounds showed good herbicidal activity toward the tested weeds. Especially, compound 8ad (Ki = 670 pM) showed the most promising post-emergence herbicidal activity with a broad spectrum of weed control even at a concentration as low as 37.5 g a.i./ha and relatively safe to rice at a dosage of 150 g a.i./ha, indicating that 8ad has the greatest potential to be developed as a new herbicide for weed control in paddy fields. This work provides a paradigm for the rational design and discovery of a novel PPO-inhibiting herbicide guided by the fragment-based drug design.

ACFIS 2.0: an improved web-server for fragment-based drug discovery via a dynamic screening strategy.

Drug discovery, which plays a vital role in maintaining human health, is a persistent challenge. Fragment-based drug discovery (FBDD) is one of the strategies for the discovery of novel candidate compounds. Computational tools in FBDD could help to identify potential drug leads in a cost-efficient and time-saving manner. The Auto Core Fragment in silico Screening (ACFIS) server is a well-established and effective online tool for FBDD. However, the accurate prediction of protein-fragment binding mode and affinity is still a major challenge for FBDD due to weak binding affinity. Here, we present an updated version (ACFIS 2.0), that incorporates a dynamic fragment growing strategy to consider protein flexibility. The major improvements of ACFIS 2.0 include (i) increased accuracy of hit compound identification (from 75.4% to 88.5% using the same test set), (ii) improved rationality of the protein-fragment binding mode, (iii) increased structural diversity due to expanded fragment libraries and (iv) inclusion of more comprehensive functionality for predicting molecular properties. Three successful cases of drug lead discovery using ACFIS 2.0 are described, including drugs leads to treat Parkinson's disease, cancer, and major depressive disorder. These cases demonstrate the utility of this web-based server. ACFIS 2.0 is freely available at http://chemyang.ccnu.edu.cn/ccb/server/ACFIS2/.

Fragment-based drug discovery supports drugging 'undruggable' protein-protein interactions.

Protein-protein interactions (PPIs) have important roles in various cellular processes, but are commonly described as 'undruggable' therapeutic targets due to their large, flat, featureless interfaces. Fragment-based drug discovery (FBDD) has achieved great success in modulating PPIs, with more than ten compounds in clinical trials. Here, we highlight the progress of FBDD in modulating PPIs for therapeutic development. Targeting hot spots that have essential roles in both fragment binding and PPIs provides a shortcut for the development of PPI modulators via FBDD. We highlight successful cases of cracking the 'undruggable' problems of PPIs using fragment-based approaches. We also introduce new technologies and future trends. Thus, we hope that this review will provide useful guidance for drug discovery targeting PPIs.

Whole-exome sequencing of rectal neuroendocrine tumors.

The genetic characteristics of rectal neuroendocrine tumors (R-NETs) were poorly understood. Depicting the genetic characteristics may provide a biological basis for prognosis prediction and novel treatment development. Tissues of 18 R-NET patients were analyzed using whole-exome sequencing. The median tumor mutation burden (TMB) and microsatellite instability (MSI) were 1.15 Muts/MB (range, 0.03-23.28) and 0.36 (range, 0.00-10.97), respectively. Genes involved in P53 signaling, PI3K-AKT signaling, DNA damage repair, WNT signaling, etc. were frequently altered. Higher TMB (P = 0.078), higher CNV (P = 0.110), somatic mutation of CCDC168 (P = 0.049), HMCN1 (P = 0.040), MYO10 (P = 0.007), and amplification of ZC3H13 (P < 0.001) were associated with shorter OS. Potentially targetable gene alterations (PTGAs) were seen in 72% of the patients. FGFR1 amplification (22%) was the most common PTGA followed by BARD1 and BRCA2 mutation (each 17%). As for gene variations associated with the efficacy of immune checkpoint blockade (ICB), FAT1 alteration (39%) and PTEN depletion (28%) were commonly observed. In conclusion, frequently altered oncogenic pathways might contribute to the development and progression of R-NETs. Gene alterations significantly associated with prognosis might be potential novel targets. Targeted therapy might be a promising strategy as targetable alterations were prevalent in R-NETs. FAT1 alteration and PTEN depletion might be the main genetic alterations influencing the response to ICB besides overall low TMB and MSI in R-NETs.

Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter.

Inadequate bioavailability is one of the most critical reasons for the failure of oral drug development. However, the way that substructures affect bioavailability remains largely unknown. Serotonin transporter (SERT) inhibitors are first-line drugs for major depression disorder, and improving their bioavailability may be able to decrease side-effects by reducing daily dose. Thus, it is an excellent model to probe the relationship between substructures and bioavailability. Here, we proposed the concept of "nonbioavailable substructures", referring to substructures that are unfavorable to bioavailability. A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. A more potent SERT inhibitor DH4 was discovered with a bioavailability of 83.28% in rats by replacing the nonbioavailable substructure of approved drug vilazodone. DH4 exhibits promising anti-depression efficacy in animal experiments. The concept of nonbioavailable substructures may open up a new venue for the improvement of drug bioavailability.

Protein Kinases as Potential Targets Contribute to the Development of Agrochemicals.

Using agrochemicals against pest insects, fungi, and weeds plays a major part in maintaining and improving crop yields, which helps to solve the issue of food security. Due to the limited targets and resistance of agrochemicals, protein kinases are regarded as attractive potential targets to develop new agrochemicals. Recently, a lot of investigations have shown the extension of agrochemicals by targeting protein kinases, implying an increasing concern for this kind of method. However, few people have summarized and discussed the targetability of protein kinases contributing to the development of agrochemicals. In this work, we introduce the research on protein kinases as potential targets used in crop protection and discuss the prospects of protein kinases in the field of agrochemical development. This study may not only provide guidance for the contribution of protein kinases to the development of agrochemicals but also help nonprofessionals such as students learn and understand the role of protein kinases quickly.

Discovery of Macrocycle-Based HPK1 Inhibitors for T-Cell-Based Immunotherapy.

Hematopoietic progenitor kinase 1 (HPK1) is a negative regulator of T-cell activation, and targeting HPK1 is considered a promising strategy for improving responses to antitumor immune therapies. The biggest challenge of HPK1 inhibitor design is to achieve a higher selectivity to GLK, an HPK1 homology protein as a positive regulator of T-cell activation. Herein, we report the design of a series of macrocycle-based HPK1 inhibitors via a conformational constraint strategy. The identified candidate compound 5i exhibited HPK1 inhibition with an IC50 value of 0.8 nM and 101.3-fold selectivity against GLK. Compound 5i also displayed good oral bioavailability (F = 27-49%) in mice and beagles and favorable metabolic stability (T1/2 > 186.4 min) in human liver microsomes. More importantly, compound 5i demonstrated a clear synergistic effect with anti-PD-1 in both MC38 (MSI) and CT26 (MSS) syngeneic tumor mouse models. These results showed that compound 5i has a great potential in immunotherapy.

Metabolic alteration of Tetrahymena thermophila exposed to CdSe/ZnS quantum dots to respond to oxidative stress and lipid damage.

CdSe/ZnS Quantum dots (QDs) are possibly released to surface water due to their extensive application. Based on their high reactivity, even small amounts of toxicant QDs will disturb water microbes and pose a risk to aquatic ecology. Here, we evaluated CdSe/ZnS QDs toxicity to Tetrahymena thermophila (T. thermophila), a model organism of the aquatic environment, and performed metabolomics experiments. Before the omics experiment was conducted, QDs were found to induce inhibition of cell proliferation, and reactive oxygen species (ROS) production along with Propidium iodide labeled cell membrane damage indicated oxidative stress stimulation. In addition, mitochondrial ultrastructure alteration of T. thermophila was also confirmed by Transmission Electron Microscope results after 48 h of exposure to QDs. Further results of metabolomics detection showed that 0.1 µg/mL QDs could disturb cell physiological and metabolic metabolism characterized by 18 significant metabolite changes, of which twelve metabolites improved and three decreased significantly compared to the control. Kyoto Encyclopedia of Genes and Genomes analysis showed that these metabolites were involved in the ATP-binding cassette transporter and purine metabolism pathways, both of which respond to ROS-induced cell membrane damage. In addition, purine metabolism weakness might also reflect mitochondrial dysfunction associated with energy metabolism and transport abnormalities. This research provides deep insight into the potential risks of quantum dots in aquatic ecosystems.

Unveiling toxicity profile for food risk components: A manually curated toxicological databank of food-relevant chemicals.

Rigorous risk assessment of chemicals in food and feed is essential to address the growing worldwide concerns about food safety. High-quality toxicological data on food-relevant chemicals are fundamental for risk modeling and assessment in the food safety area. The organization and analysis of substantial toxicity information can positively support decision-making by providing insight into toxicity trends. However, it remains challenging to systematically obtain fragmented toxicity data, and related toxicological resources are required to meet the current demands. In this study, we collected 221,439 experimental toxicity records for 5,657 food-relevant chemicals identified from extensive databases and literature, along with their information on chemical identification, physicochemical properties, environmental fates, and biological targets. Based on the aggregated data, a freely available web-based databank, Food-Relevant Available Chemicals Toxicology Databank (FRAC-TD) is presented, which supports multiple browsing ways and search criterions. Applying FRAC-TD for data-driven analysis, we revealed the underlying toxicity profiles of food-relevant chemicals in humans, mammals, and other species in the food chain. Expectantly, FRAC-TD could positively facilitate toxicological studies, toxicity prediction, and risk assessments in the food industry.

Thermal property evaluation of a 2.5D integration method with device level microchannel direct cooling for a high-power GaN HEMT device.

Gallium nitride high electron mobility transistor (GaN HEMT) devices have become critical components in the manufacturing of high-performance radio frequency (RF) or power electronic modules due to their superior characteristics, such as high electron saturation speeds and high power densities. However, the high heat characteristics of GaN HEMTs make device level cooling a critical problem to solve since performance degradation or even failure may occur under high temperatures. In this paper, we proposed a 2.5D integration method with device-level microchannel direct cooling for a high-power GaN HEMT device. To demonstrate this technological concept, a multigate GaN HEMT device featuring a gate length/width/source drain spacing of 0.5 µm/300 µm/6 µm that underwent in-house backside thinning and metallization was used as the test vehicle. A high-resistivity silicon (HR Si) interposer embedded with four-layer microchannels was designed, having widths/pitches of 30 µm/30 µm at the top microchannel. The high-power GaN HEMT device was soldered on a Si interposer embedded with open microchannels for heat dissipation. A pair of GSG Pad chips was soldered simultaneously to display the capacity for the heterogeneous integration of other chip types. Thermal property evaluation was conducted with experiments and simulations. The test results showed that the maximum surface temperature of the GaN HEMT device decreased to 93.8 °C when it experienced a heat dissipation density of 32 kW/cm2 in the gate finger area and an average heat dissipation density of 5 kW/cm2 was found in the active area with the DI water coolant at a flow rate of 3 mL/min. To our knowledge, among recently reported works, this finding was the best cooling capacity of heterogeneously integrated microchannels for GaN HEMT devices. In addition, this technology was scalable regarding the numbers of gate fingers or GaN HEMT devices.

Epidemiologic information discovery from open-access COVID-19 case reports via pretrained language model.

Although open-access data are increasingly common and useful to epidemiological research, the curation of such datasets is resource-intensive and time-consuming. Despite the existence of a major source of COVID-19 data, the regularly disclosed case reports were often written in natural language with an unstructured format. Here, we propose a computational framework that can automatically extract epidemiological information from open-access COVID-19 case reports. We develop this framework by coupling a language model developed using deep neural networks with training samples compiled using an optimized data annotation strategy. When applied to the COVID-19 case reports collected from mainland China, our framework outperforms all other state-of-the-art deep learning models. The information extracted from our approach is highly consistent with that obtained from the gold-standard manual coding, with a matching rate of 80%. To disseminate our algorithm, we provide an open-access online platform that is able to estimate key epidemiological statistics in real time, with much less effort for data curation.

Exploring the kinase-inhibitor fragment interaction space facilitates the discovery of kinase inhibitor overcoming resistance by mutations.

Protein kinases play crucial roles in many cellular signaling processes, making them become important targets for drug discovery. But drug resistance mediated by mutation puts a barrier to the therapeutic effect of kinase inhibitors. Fragment-based drug discovery has been successfully applied to overcome such resistance. However, the complicate kinase-inhibitor fragment interaction and fragment-to-lead process seriously limit the efficiency of kinase inhibitor discovery against resistance caused by mutation. Here, we constructed a comprehensive web platform KinaFrag for the fragment-based kinase inhibitor discovery to overcome resistance. The kinase-inhibitor fragment space was investigated from 7783 crystal kinase-inhibitor fragment complexes, and the structural requirements of kinase subpockets were analyzed. The core fragment-based virtual screening workflow towards specific subpockets was developed to generate new kinase inhibitors. A series of tropomyosin receptor kinase (TRK) inhibitors were designed, and the most potent compound YT9 exhibits up to 70-fold activity improvement than marketed drugs larotrectinib and selitrectinib against G595R, G667C and F589L mutations of TRKA. YT9 shows promising antiproliferative against tumor cells in vitro and effectively inhibits tumor growth in vivo for wild type TRK and TRK mutants. Our results illustrate the great potential of KinaFrag in the kinase inhibitor discovery to combat resistance mediated by mutation. KinaFrag is freely available at http://chemyang.ccnu.edu.cn/ccb/database/KinaFrag/.