Literature Watch
Expression and purification of E140 protein antigen fragments of Plasmodium vivax and Plasmodium berghei for serological assays
FEBS Open Bio. 2025 Jan 15. doi: 10.1002/2211-5463.13939. Online ahead of print.
ABSTRACT
Malaria, a life-threatening disease caused by Plasmodium parasites, continues to pose a significant global health threat, with nearly 250 million infections and over 600 000 deaths reported annually by the WHO. Fighting malaria is particularly challenging partly due to the complex life cycle of the parasite. However, technological breakthroughs such as the development of the nucleoside-modified mRNA lipid nanoparticle (mRNA-LNP) vaccine platform, along with the discovery of novel conserved Plasmodium antigens such as the E140 protein, present new opportunities in malaria prevention. Importantly, production of recombinant proteins for malaria vaccine evaluation by serological assays often represents an additional hurdle because many Plasmodium proteins are complex and often contain transmembrane domains that make production and purification particularly difficult. This research protocol provides a step-by-step guide for the production and purification of P. berghei and P. vivax E140 protein fragments that can be used to test humoral immune responses against this novel malaria vaccine target. We demonstrate that the purified proteins can be successfully used in enzyme-linked immunosorbent assay (ELISA) to evaluate antigen-specific binding antibody responses in sera obtained from E140 mRNA-LNP-vaccinated mice. Therefore, these proteins can contribute to the development and evaluation of E140-based malaria vaccines.
PMID:39815669 | DOI:10.1002/2211-5463.13939
Leadership and Coordination Center (LACC) for the MACS/WIHS Combined Cohort Study (MWCCS) (U01 Clinical Trials Not Allowed)
Limited Competition: Data Analysis and Sharing Center (DASC) for the MACS/WIHS Combined Cohort Study (MWCCS) (U01 Clinical Trials Not Allowed)
Limited Competition: Clinical Research Sites (CRS) for the MACS/WIHS Combined Cohort Study (MWCCS) (U01 Clinical Trial Not Allowed)
Asthma and Allergic Diseases Cooperative Research Centers (U19 Clinical Trial Optional)
Notice to Extend the Response Date listed in NOT-AG-24-086, Request for Information on the National Institute on Aging Health Disparities Research Framework
T32 Training Program to Promote Broad Participation (T32 Clinical Trial Not Allowed)
February 17 (Monday), 2025: NIH Closed for the Federal Holiday
NIH (including help desks) will be closed on Monday, February 17, 2025 for the federal holiday (Washington’s Birthday). If a grant application due date falls on a federal holiday, the application deadline is automatically extended to the next business day.
Explore NIH Research Enhancement Award and Fellowship Programs: Upcoming Webinars
We invite you to join us for two webinars that spotlight our NIH Research Enhancement Award (R15) and Fellowship programs.
NIH Research Enhancement Award (R15): What You Need to Know and Recent Changes
January 30, 2025, 2:30-4:00 PM ET
Register now!
In this virtual event, you will learn about the two NIH R15 programs:
- Academic Research Enhancement Award for Undergraduate-Focused Institutions (AREA)
- Research Enhancement Award Program for Health Professional and Graduate Schools (REAP).
R15 research project grants are designed to provide support for meritorious research at institutions that have not been major recipients of NIH support, to strengthen the research environment at these institutions, and to give students an opportunity to gain significant biomedical research experience.
An Introduction to the NIH Fellowship Program for Prospective Candidates
February 11, 2025, 10:00 – 11:30 AM ET
You may already know that there’s a lot that goes into an NIH fellowship application, and you may be wondering where to start. At this live, virtual event NIH experts will:
- Discuss NIH’s fellowship programs
- Break down the fellowship application and peer review process
- Share Practical tips for preparing a competitive application
- Answer your questions at live Q&A sessions
The NIH fellowship program provides individual training opportunities to support fellows at various career stages, including at graduate, and postdoctoral levels. This webinar is valuable for fellowship candidates, sponsors, and research program administrators new to the NIH and the fellowship application process.
Don’t miss out on the opportunities to explore these exciting programs.
National Cooperative Drug/Device Discovery/Development Groups (NCDDG) for the Treatment of Mental Disorders (U19 Clinical Trial Optional)
To Do in 2025: Keep Your eRA Personal Profile Updated
The Personal Profile module in eRA Commons is where you — as a principal investigator, award recipient, trainee, reviewer or other Commons user — tell NIH and other awarding agencies about yourself. Awarding agencies need to know about you to grant awards, process those awards and more. Here are a few reasons that it is extremely important to keep your Personal Profile updated.
- All communications between awarding agencies and you are sent to contact methods that you enter into your Personal Profile.
- Reports, such as the Research Performance Progress Report (RPPR), cannot be submitted if award recipients have incomplete Personal Profiles.
- Trainees cannot be appointed without complete Personal Profiles.
- Early Stage Investigator (ESI) status is calculated from Personal Profile data entered by principal investigators in the Education section.
- Personal Profile data is used to help agency review staff identify reviewers’ conflict of interest with applications they are reviewing; and is the place reviewers enter information to get their review meeting honorariums.
Personal Profile is the central repository of information about all Commons registered users. It lets you own and maintain the accuracy for your own personal information. This profile information is integrated throughout eRA systems. It is used to determine reviewer conflicts, link publications, populate application data, track trainee effort, and to ensure that Early Stage Investigator status is accurately calculated. In addition, you have the option to provide demographic information to help NIH better understand its research workforce. Read more about how to manage your Personal Profile.
Linking your ORCID iD (Open Researcher and Contributor ID) to your eRA Commons account enables automatic importation of publications into biosketches, reducing burden and saving you time. You can use Personal Profile to connect your eRA Commons account to your ORCID iD (Open Researcher and Contributor ID), which is a unique 16-digit identifier that enables connections between researchers and their research and scholarship. A linked ORCID iD is required for all senior/ key personnel listed on an application for a due date on or after May 25, 2025. See the video titled Link Your ORCID iD to Your eRA Commons Account.
So, if you have not recently updated your Personal Profile, please do so soon. It will benefit both the awarding agency and you.
Also see:
National Cooperative Drug/Device Discovery/Development Groups (NCDDG) for the Treatment of Mental Disorders (U01 Clinical Trial Optional)
Notice of Change to Receipt Date and the Expiration Date for RFA-RM-24-010
Notice of Special Interest (NOSI): Common Mechanisms and Interactions Among Neurodegenerative Diseases
Notice of the Discontinuation of NCI's Participation in PA-24-193, PA-24-194 and PA-24-195 NIH Pathway to Independence Award (Parent K99/R00)
Molecular docking to investigate HLA-associated idiosyncratic drug reactions
Drug Metab Rev. 2025 Jan 20:1-24. doi: 10.1080/03602532.2025.2453521. Online ahead of print.
ABSTRACT
Idiosyncratic drug reactions (IDRs) pose severe threats to patient health. Unlike conventionally dose-dependent side effects, they are unpredictable and more frequently manifest as life-threatening conditions, such as severe cutaneous adverse reactions (SCARs) and drug-induced liver injury (DILI). Some HLA alleles, such as HLA-B*57:01, HLA-B*15:02, and HLA-B*58:01, are known risk factors for adverse reactions induced by multiple drugs. However, the structural basis underlying most HLA-associated adverse events remains poorly understood. This review summarizes the application of molecular docking to reveal the mechanisms of IDR-related HLA associations, covering studies using this technique to examine drug-HLA binding pockets and identify key binding residues. We provide a comprehensive overview of risk HLA alleles associated with IDRs, followed by a discussion of the utility and limitations of commonly used molecular docking tools in simulating complex molecular interactions within the HLA binding pocket. Through examples, including the binding of abacavir and flucloxacillin to HLA-B*57:01, carbamazepine to HLA-B*15:02, and allopurinol to HLA-B*58:01, we demonstrate how docking analyses can provide insights into the drug and HLA allele-specificity of adverse events. Furthermore, the use of molecular docking to screen drugs with unknown IDR liability is examined, targeting either multiple HLA variants or a single specific variant. Despite multiple challenges, molecular docking presents a promising toolkit for investigating drug-HLA interactions and understanding IDR mechanisms, with significant implications for preemptive HLA typing and safer drug development.
PMID:39811883 | DOI:10.1080/03602532.2025.2453521
Predicting drug combination side effects based on a metapath-based heterogeneous graph neural network
BMC Bioinformatics. 2025 Jan 15;26(1):16. doi: 10.1186/s12859-024-06028-6.
ABSTRACT
In recent years, combined drug screening has played a very important role in modern drug discovery. Generally, synergistic drug combinations are crucial in treatment for many diseases. However, the toxic side effects of drug combinations are probably increased with the increase of drugs numbers, so the accurate prediction of toxic side effects of drug combinations is equally important. In this paper, we built a Metapath-based Aggregated Embedding Model on Single Drug-Side Effect Heterogeneous Information Network (MAEM-SSHIN), which extracts feature from a heterogeneous information network of single drug side effects, and a Graph Convolutional Network on Combinatorial drugs and Side effect Heterogeneous Information Network (GCN-CSHIN), which transforms the complex task of predicting multiple side effects between drug pairs into the more manageable prediction of relationships between combinatorial drugs and individual side effects. MAEM-SSHIN and GCN-CSHIN provided a united novel framework for predicting potential side effects in combinatorial drug therapies. This integration enhances prediction accuracy, efficiency, and scalability. Our experimental results demonstrate that this combined framework outperforms existing methodologies in predicting side effects, and marks a significant advancement in pharmaceutical research.
PMID:39815175 | DOI:10.1186/s12859-024-06028-6
Anticancer effect of the antirheumatic drug leflunomide on oral squamous cell carcinoma by the inhibition of tumor angiogenesis
Discov Oncol. 2025 Jan 16;16(1):53. doi: 10.1007/s12672-025-01763-5.
ABSTRACT
OBJECTIVES: Leflunomide (LEF) is a conventional synthetic disease-modifying antirheumatic drug and suppresses T-cell proliferation and activity by inhibiting pyrimidine synthesis using dihydroorotase dehydrogenase (DHODH); however, several studies have demonstrated that LEF possesses anticancer and antiangiogenic effects in some malignant tumors. Therefore, we investigated the anticancer and antiangiogenic effects of LEF on oral squamous cell carcinoma (OSCC).
METHODS: To evaluate the inhibitory effect of LEF on OSCC, cell proliferation and wound-healing assays using human OSCC cell lines were performed. The DHODH inhibitory effect of LEF was evaluated by Western blot. To assess the suppression of pyrimidine biosynthesis induced by LEF on OSCC, cell proliferation assays with or without uridine supplementation were performed. The antiangiogenic effect of LEF was evaluated by in vitro tube formation assay using immortalized human umbilical vein endothelial cells, which were electroporatically transfected with hTERT. The tumor-suppressive effect of LEF in vivo was examined in both immunodeficient and syngeneic mice by implanting mouse OSCC cells. Tumor vascularization was evaluated by immunohistochemistry of the tumor extracted from syngeneic mice.
RESULTS: LEF dose-dependently inhibited OSCC proliferation and migration. LEF significantly inhibited DHODH expression, and uridine supplementation rescued the inhibitory effect of LEF. LEF dose-dependently suppressed endothelial tube formation. In the animal study, LEF significantly suppressed tumor growth in both immunodeficient and syngeneic mice. Histologically, LEF decreased DHODH expression and tumor vascularization.
CONCLUSION: LEF is a potent anticancer agent with antiangiogenic effects on OSCC and might be clinically applicable to OSCC by drug repositioning.
PMID:39815040 | DOI:10.1007/s12672-025-01763-5
DeepDrug as an expert guided and AI driven drug repurposing methodology for selecting the lead combination of drugs for Alzheimer's disease
Sci Rep. 2025 Jan 15;15(1):2093. doi: 10.1038/s41598-025-85947-7.
ABSTRACT
Alzheimer's Disease (AD) significantly aggravates human dignity and quality of life. While newly approved amyloid immunotherapy has been reported, effective AD drugs remain to be identified. Here, we propose a novel AI-driven drug-repurposing method, DeepDrug, to identify a lead combination of approved drugs to treat AD patients. DeepDrug advances drug-repurposing methodology in four aspects. Firstly, it incorporates expert knowledge to extend candidate targets to include long genes, immunological and aging pathways, and somatic mutation markers that are associated with AD. Secondly, it incorporates a signed directed heterogeneous biomedical graph encompassing a rich set of nodes and edges, and node/edge weighting to capture crucial pathways associated with AD. Thirdly, it encodes the weighted biomedical graph through a Graph Neural Network into a new embedding space to capture the granular relationships across different nodes. Fourthly, it systematically selects the high-order drug combinations via diminishing return-based thresholds. A five-drug lead combination, consisting of Tofacitinib, Niraparib, Baricitinib, Empagliflozin, and Doxercalciferol, has been selected from the top drug candidates based on DeepDrug scores to achieve the maximum synergistic effect. These five drugs target neuroinflammation, mitochondrial dysfunction, and glucose metabolism, which are all related to AD pathology. DeepDrug offers a novel AI-and-big-data, expert-guided mechanism for new drug combination discovery and drug-repurposing across AD and other neuro-degenerative diseases, with immediate clinical applications.
PMID:39814937 | DOI:10.1038/s41598-025-85947-7
'Pharmacogenetics, health and ethnicity in Latin American populations' call for the "Dr Jose Maria Cantu Award 2024"
Drug Metab Pers Ther. 2025 Jan 2;39(4):163-165. doi: 10.1515/dmpt-2024-0091. eCollection 2024 Dec 1.
NO ABSTRACT
PMID:39814711 | DOI:10.1515/dmpt-2024-0091
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