Search Results (42)

Background/Objectives: Conventional imaging assesses therapy response in stage IV solid-tumor patients in 8- to 12-week intervals, delaying detection of non-responders. We evaluated a quantitative PCR (qPCR) assay that interrogates size-distributed cell-free DNA (cfDNA) fragments to provide earlier insights into treatment efficacy. Methods: In this prospective study, 128 patients with metastatic lung, breast, or colorectal cancer provided plasma 12–21 days after the first dose of a new systemic regimen. The qPCR targets multi-copy retrotransposon element fragments of greater than 80 bp, greater than 105 bp, and greater than 265 bp, as well as an internal control. A model integrates these quantities into a Progression Score (PS) ranging from 0 to 100; higher values indicate probable disease progression. Results: The PS model yielded an area under (AUC) the receiver-operating-characteristic (ROC) curve of 0.93 for predicting radiographic progression at first imaging. Scores were strongly bimodal: 92% of patients with PS > 90 progressed, whereas 95% with PS < 10 did not. Intermediate scores (10–90) comprised a mixed cohort. Assay performance was unaffected by tumor genomic profile. Conclusions: This cfDNA-based Progression Score (PS) assay enables tumor- and therapy-agnostic, non-invasive monitoring of treatment response as early as two weeks after initiation. By flagging ineffective regimens well before standard imaging, the test can accelerate clinical decision-making, reduce exposure to futile therapy, and potentially improve outcomes in stage IV cancer. Early treatment plan changes may also avoid the high costs of ineffective treatments, prevent downstream toxicity-related hospitalizations, and free up limited imaging and infusion-suite capacity—yielding savings for patients, payers, and healthcare systems. Full article

Poly (ADP-ribose) polymerase 1 (PARP1) is an important enzyme that plays a central role in the DNA damage response, facilitating repair of single-stranded DNA breaks via the base excision repair (BER) pathway and thus genomic integrity. Its therapeutic relevance is compounded in breast cancer, particularly in BRCA1 or BRCA2 mutant cancers, where compromised homologous recombination repair (HRR) leaves a synthetic lethal dependency on PARP1-mediated repair. This review comprehensively discusses the recent advances in computational chemistry for the discovery of PARP1 inhibitors, focusing on their application in breast cancer therapy. Techniques such as molecular docking, molecular dynamics (MD) simulations, quantitative structure–activity relationship (QSAR) modeling, density functional theory (DFT), time-dependent DFT (TD-DFT), and machine learning (ML)-aided virtual screening have revolutionized the discovery of inhibitors. Some of the most prominent examples are Olaparib (IC₅₀ = 5 nM), Rucaparib (IC₅₀ = 7 nM), and Talazoparib (IC₅₀ = 1 nM), which were optimized with docking scores between −9.0 to −9.3 kcal/mol and validated by in vitro and in vivo assays, achieving 60–80% inhibition of tumor growth in BRCA-mutated models and achieving up to 21-month improvement in progression-free survival in clinical trials of BRCA-mutated breast and ovarian cancer patients. These strategies enable site-specific hopping into the PARP1 nicotinamide-binding pocket to enhance inhibitor affinity and specificity and reduce off-target activity. Employing computation and experimental verification in a hybrid strategy have brought next-generation inhibitors to the clinic with accelerated development, higher efficacy, and personalized treatment for breast cancer patients. Future approaches, including AI-aided generative models and multi-omics integration, have the promise to further refine inhibitor design, paving the way for precision oncology. Full article

Metagenomic next-generation sequencing (mNGS) is transforming infectious disease diagnostics by enabling simultaneous, hypothesis-free detection of a broad array of pathogens—including bacteria, viruses, fungi, and parasites—directly from clinical specimens such as cerebrospinal fluid, blood, and bronchoalveolar lavage fluid. Unlike traditional culture and targeted molecular assays, mNGS serves as a powerful complementary approach, capable of identifying novel, fastidious, and polymicrobial infections while also characterizing antimicrobial resistance (AMR) genes. These advantages are particularly relevant in diagnostically challenging scenarios, such as infections in immunocompromised patients, sepsis, and culture-negative cases. Despite its potential, mNGS remains underutilized in clinical microbiology due to persistent gaps between its technical capabilities and routine diagnostic adoption. This review addresses key translational challenges that limit the broader implementation of mNGS, especially in resource-constrained and critical care settings. We provide a comprehensive overview of the entire workflow—from sample processing and host DNA depletion to sequencing platforms and downstream bioinformatics—and highlight sources of variability, including contamination, human DNA interference, and inconsistencies in resistance gene annotation. Additionally, we explore the ethical, legal, and privacy implications of host genomic data, as well as economic and regulatory obstacles hindering mNGS integration into standard clinical practice. To illustrate clinical relevance, we examine real-world evidence from large-scale trials such as MATESHIP, GRAIDS, DISQVER, and NGS-CAP. Finally, we outline future directions involving artificial intelligence, multi-omics integration, cloud-based analytics, and portable sequencing technologies for point-of-care diagnostics. By addressing both current limitations and emerging innovations, this review offers a translational framework for integrating mNGS into precision diagnostics and infection management across diverse healthcare environments. Full article

Treatment of Mycobacterium tuberculosis requires multi-drug regimens, and resistance to any individual antibiotic can compromise outcomes. For slow-growing organisms like M. tuberculosis, rapid detection of resistance-conferring mutations enables timely initiation of effective therapy. Conversely, confirming wild-type status in resistance-associated genes supports confidence in standard regimens. We developed an amplicon-based next generation sequencing (amplicon tNGS) assay on the Illumina platform targeting eight genes linked to resistance to isoniazid, rifampin, ethambutol, pyrazinamide, and fluoroquinolones. Sequencing results were analyzed using a custom bioinformatics pipeline. Forty-seven samples were used for assay development, and 37 additional samples underwent post-implementation clinical validation. Compared to whole genome sequencing (WGS), amplicon tNGS demonstrated 97.7% sensitivity, 98.9% specificity, and 98.7% overall accuracy for variant detection in targeted regions. Resistance prediction showed 79.3% concordance with WGS; discrepancies were primarily due to mutations outside of target regions. Among post-implementation samples, 27/37 passed quality metrics for all targets, with 95.7% concordance between amplicon tNGS results and final susceptibility results. This assay is now in use in our laboratory and offers significantly faster turnaround than both WGS and phenotypic methods on cultured isolates, enabling more rapid, informed treatment decisions for tuberculosis patients. Full article

Accurate quantification of Bifidobacterium animalis subsp. lactis HN019, a clinically validated probiotic strain conferring immune modulation, gastrointestinal health, and gut barrier integrity benefits, is essential for diverse applications. To address the critical need for strain-specific detection, we developed a quantitative PCR (qPCR) assay targeting a unique single-nucleotide polymorphism (SNP) within the galK gene, identified through comparative whole-genome sequencing (WGS) analysis of 31 B. animalis subsp. lactis strains. The assay exhibited exceptional specificity, distinguishing HN019 from 19 other Bifidobacterium strains. Sensitivity tests indicated a detection limit of 0.5 pg of DNA and 10³ CFU/mL of bacterial cells, making it suitable for industrial-scale applications. Additionally, the method exhibited strong repeatability, reproducibility across different qPCR platforms, and resistance to interference from high cell density of B. animalis subsp. lactis DSMZ 10140. Successful quantification of HN019 in complex multi-strain probiotic powders confirmed its practical reliability. This work establishes a rapid, robust, and scalable tool for precise probiotic strain tracking, addressing critical quality control and regulatory compliance needs within the rapidly expanding probiotic industry. Full article

The objective of this study is to investigate the molecular regulatory mechanisms of non-coding RNA (ncRNA) during the developmental process of multi-litter sheep ovaries and identify key regulatory genes that enhance the reproductive capacity of sheep. This study selected Small-Tail Han sheep (multi-litter sheep) and Ujumuqin sheep (single-litter sheep) as comparative models, constructed the expression profiles of ncRNAs and mRNAs in ovarian tissues, identified differentially expressed (DE) lncRNAs, circRNAs, miRNAs, and mRNAs, and performed target gene prediction along with functional and signaling pathway enrichment analyses. Reproduction-related pathways were further screened to construct competing endogenous RNA (ceRNA) regulatory networks (lncRNA–miRNA–mRNA and circRNA–miRNA–mRNA). Finally, the dual-luciferase reporter gene assay system was employed to perform the functional validation of the relevant targeted regulatory effects. A comprehensive screening identified 411 DE lncRNAs, 322 DE circRNAs, 26 DE miRNAs, and 29 DEGs from the ovarian tissues of Ujumqin and Small-Tail Han sheep. The results of the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses demonstrated that the DE target genes were significantly enriched in pathways associated with cell dedifferentiation, the positive regulation of embryonic development, glycosaminoglycan biosynthesis, Hippo signaling, and other signaling pathways. To identify genes associated with reproductive processes, we performed differential expression screening followed by pathway enrichment analysis, which revealed significant enrichment in reproductive regulatory pathways. Based on these findings, we constructed a ceRNA regulatory network incorporating 22 DEGs, 17 DE lncRNAs, three DE circRNAs, and one DE miRNA. Our analysis revealed that oar-let-7a is involved in signaling pathways such as oocyte meiosis and Hippo, suggesting it may serve as a key miRNA regulating the trait of multiple offspring. The dual-luciferase reporter assay was employed to confirm that oar-let-7a directly targets and regulates the expression of CPEB1. Additionally, it was demonstrated that circRNA803 and lncRNA MSTRG.19726 function as molecular sponges to competitively bind and regulate oar-let-7a. These findings suggest that oar-let-7a mediates the expression of CPEB1 via circRNA803 and lncRNA MSTRG.19726 sponge adsorption, thereby regulating the process of follicular dominance in sheep. The qRT-PCR method was employed to validate the expression patterns of nine randomly selected DEGs, and the results corroborated the reliability of the RNA-seq sequencing data. This study investigated the coordinated regulatory mechanism of DE ncRNAs and their corresponding target genes, identifying a ceRNA network, circRNA803/lncRNA MSTRG.19726-oar-let-7a-CPEB1, which plays a critical role in regulating the process of follicular dominance in sheep. These findings provide fundamental data for uncovering the reproductive potential of sheep and facilitate a comprehensive understanding of their reproductive characteristics, which hold significant guiding implications for enhancing reproductive efficiency. Full article

Background/Objectives: Hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths rising worldwide. This is leading to an increased demand for liver transplantation (LT), the most effective treatment for HCC in its initial stages. However, current patient selection criteria are limited in predicting recurrence and raise ethical concerns about equitable access to care. This study aims to enhance patient selection by refining the HepatoPredict (HP) tool, a machine learning-based model that combines molecular and clinical data to forecast LT outcomes. Methods: The updated HP algorithm was trained on a two-center dataset and assessed against standard clinical criteria. Its prognostic performance was evaluated through accuracy metrics, with additional analyses considering tumor heterogeneity and potential sampling bias. Results: HP outperformed all clinical criteria, particularly regarding negative predictive value, addressing critical limitations in existing selection strategies. It also demonstrated improved differentiation of recurrence-free and overall survival outcomes. Importantly, the prognostic accuracy of HP remained largely unaffected by intra-nodule and intra-patient heterogeneity, indicating its robustness even when biopsies were taken from smaller or non-dominant nodules. Conclusions: These findings support the usage of HP as a valuable tool for optimizing LT candidate selection, promoting fair organ allocation and enhancing patient outcomes through integrated analysis of molecular and clinical data. Full article

Lassa fever (LF), a viral hemorrhagic fever disease with a case fatality rate that can be over 20% among hospitalized LF patients, is endemic to many West African countries. Currently, no vaccines or therapies are specifically licensed to prevent or treat LF, hence the significance of developing therapeutics against the mammarenavirus Lassa virus (LASV), the causative agent of LF. We used in silico docking approaches to investigate the binding affinities of 2015 existing drugs to LASV proteins known to play critical roles in the formation and activity of the virus ribonucleoprotein complex (vRNP) responsible for directing replication and transcription of the viral genome. Validation of docking protocols were achieved with reference inhibitors of the respective targets. Our in silico docking screen identified five drugs (dexamethasone, tadalafil, mefloquine, ergocalciferol, and flunarizine) with strong predicted binding affinity to LASV proteins involved in the formation of the vRNP. We used cell-based functional assays to evaluate the antiviral activity of the five selected drugs. We found that flunarizine, a calcium-entry blocker, inhibited the vRNP activity of LASV and LCMV and virus surface glycoprotein fusion activity required for mammarenavirus cell entry. Consistently with these findings, flunarizine significantly reduced peak titers of LCMV in a multi-step growth kinetics assay in human A549 cells. Flunarizine is being used in several countries worldwide to treat vertigo and migraine, supporting the interest in exploring its repurposing as a candidate drug to treat LASV infections. Full article

Enterotoxigenic Escherichia coli (ETEC) is a major pathogen causing diarrhea in humans and animals, with increasing antimicrobial resistance posing a growing challenge in recent years. Lytic bacteriophages (phages) offer a targeted and environmentally sustainable approach to combating bacterial infections, particularly in eliminating drug-resistant strains. In this study, ETEC strains were utilized as indicators, and a stable, high-efficiency phage, designated vB_EcoM_JE01 (JE01), was isolated from pig farm manure. The genome of JE01 was a dsDNA molecule, measuring 168.9 kb, and a transmission electron microscope revealed its characteristic T4-like Myoviridae morphology. JE01 effectively lysed multi-drug-resistant ETEC isolates. Stability assays demonstrated that JE01 retained its activity across a temperature range of 20 °C to 50 °C and a pH range of 3–11, showing resilience to ultraviolet radiation and chloroform exposure. Furthermore, JE01 effectively suppressed ETEC adhesion to porcine intestinal epithelial cells (IPEC-J2), mitigating the inflammatory response triggered by ETEC. To investigate the in vivo antibacterial efficacy of phage JE01 preparations, a diarrhea model was established using germ-free mice infected with a drug-resistant ETEC strain. The findings indicated that 12 h post-ETEC inoculation, intragastric administration of phage JE01 significantly reduced mortality, alleviated gastrointestinal lesions, decreased ETEC colonization in the jejunum, and suppressed the expression of the cytokines IL-6 and IL-8. These results demonstrate a therapeutic benefit of JE01 in treating ETEC-induced diarrhea in mice. Additionally, a fluorescent phage incorporating red fluorescent protein (RFP) was engineered, and the pharmacokinetics of phage therapy were preliminarily assessed through intestinal fluorescence imaging in mice. The results showed that the phage localized to ETEC in the jejunum rapidly, within 45 min. Moreover, the pharmacokinetics of the phage were markedly slowed in the presence of its bacterial target in the gut, suggesting sustained bacteriolytic activity in the ETEC-infected intestine. In conclusion, this study establishes a foundation for the development of phage-based therapies against ETEC. Full article

Background: Patients with hormone receptor-positive (HR+)/HER2- metastatic breast cancer (mBC) generally receive hormonal therapy (HT) combined with CDK4/6 inhibitors (CDK4/6i). Despite this treatment, resistance mechanisms to CDK4/6i emerge and the majority of these patients experience disease progression (PD). This highlight the necessity to uncover the resistance mechanism to CDK4/6i through the identification of specific biomarkers. The primary objective is to assess the accuracy and feasibility of a novel multi-gene target panel NGS assay on circulating tumor DNA (ctDNA) to detect molecular alterations of AKT1, ERBB2, ESR1, KRAS, PIK3CA, and TP53 genes in women with BC undergoing HT plus CDK4/6i treatment. Secondarily, the study aims to explore the relationship between genomic profiling and clinical outcomes. Materials and Methods: Plasma samples were collected from 16 patients diagnosed with advanced/locally advanced HR+/HER2- BC at 2 time points: T0 (baseline) and at T1 (3 months after CDK4/6i treatment). Starting from 2 mL of plasma, ctDNA was isolated and libraries were set up using the Plasma-SeqSensei (PQS)^® Breast Cancer IVD Kit, sequenced on Nextseq 550 and analyzed using the Plasma-SeqSensei™ IVD Software^®. Results: Among the five patients who presented PD, three had PIK3CA mutations and, of these, two showed a higher mutant allele frequency (MAF) at T1. In three patients with stable disease and in eight patients with partial response, the MAF of the detected alterations decreased dramatically or disappeared during CDK4/6i treatment. Conclusions: Based on our findings, the liquid biopsy analysis using the PQS panel seems to be both feasible and accurate, demonstrating a strong sensitivity in detecting mutations. This exploratory analysis of the clinical outcome associated to the mutational status of patients highlights the potential of molecular analysis on liquid biopsy for disease monitoring, although further validation with a larger patient cohort is necessary to confirm these preliminary observations. Full article

Sclerotinia sclerotiorum (Ss) is one of the most devastating fungal pathogens, causing huge yield loss in multiple economically important crops including oilseed rape. Plant resistance to Ss pertains to quantitative disease resistance (QDR) controlled by multiple minor genes. Genome-wide identification of genes involved in QDR to Ss is yet to be conducted. In this study, we integrated several assays including genome-wide association study (GWAS), multi-omics co-localization, and machine learning prediction to identify, on a genome-wide scale, genes involved in the oilseed rape QDR to Ss. Employing GWAS and multi-omics co-localization, we identified seven resistance-associated loci (RALs) associated with oilseed rape resistance to Ss. Furthermore, we developed a machine learning algorithm and named it Integrative Multi-Omics Analysis and Machine Learning for Target Gene Prediction (iMAP), which integrates multi-omics data to rapidly predict disease resistance-related genes within a broad chromosomal region. Through iMAP based on the identified RALs, we revealed multiple calcium signaling genes related to the QDR to Ss. Population-level analysis of selective sweeps and haplotypes of variants confirmed the positive selection of the predicted calcium signaling genes during evolution. Overall, this study has developed an algorithm that integrates multi-omics data and machine learning methods, providing a powerful tool for predicting target genes associated with specific traits. Furthermore, it makes a basis for further understanding the role and mechanisms of calcium signaling genes in the QDR to Ss. Full article

Genome-wide association studies (GWAS) constitute a powerful tool to identify the different biochemical pathways associated with disease. This knowledge can be used to prioritize drugs targeting these routes, paving the road to clinical application. Here, we describe DAGGER (Drug Repositioning by Analysis of GWAS and Gene Expression in R), a straightforward pipeline to find currently approved drugs with repurposing potential. As a proof of concept, we analyzed a meta-GWAS of 1.6 × 10⁷ single-nucleotide polymorphisms performed on Alzheimer’s disease (AD). Our pipeline uses the Genotype-Tissue Expression (GTEx) and Drug Gene Interaction (DGI) databases for a rational prioritization of 22 druggable targets. Next, we performed a two-stage in vivo functional assay. We used a C. elegans humanized model over-expressing the Aβ_1-42 peptide. We assayed the five top-scoring candidate drugs, finding midostaurin, a multitarget protein kinase inhibitor, to be a protective drug. Next, 3xTg AD transgenic mice were used for a final evaluation of midostaurin’s effect. Behavioral testing after three weeks of 20 mg/kg intraperitoneal treatment revealed a significant improvement in behavior, including locomotion, anxiety-like behavior, and new-place recognition. Altogether, we consider that our pipeline might be a useful tool for drug repurposing in complex diseases. Full article

The CRISPR/Cas system has a broad range of possible medical applications, but its clinical translation has been hampered, particularly by the lack of safe and efficient vector systems mediating the short-term expression of its components. Recently, different virus-like particles (VLPs) have been introduced as promising vectors for the delivery of CRISPR/Cas genome editing components. Here, we characterized and directly compared three different types of retrovirus-based (R) VLPs, two derived from the γ-retrovirus murine leukemia virus (gRVLPs and “enhanced” egRVLPs) and one from the lentivirus human immunodeficiency virus, HIV (LVLPs). First, we unified and optimized the production of the different RVLPs. To ensure maximal comparability of the produced RVLPs, we adapted several assays, including nanoparticle tracking analysis (NTA), multi-parametric imaging flow cytometry (IFC), and Cas9-ELISA, to analyze their morphology, surface composition, size, and concentration. Next, we comparatively tested the three RVLPs targeting different genes in 293T model cells. Using identical gRNAs, we found egRVLPs to mediate the most efficient editing. Functional analyses indicated better cargo (i.e., Cas9) transfer and/or release as the underlying reason for their superior performance. Finally, we compared on- and off-target activities of the three RVLPs in human-induced pluripotent stem cells (hiPSC) exploiting the clinically relevant C-C motif chemokine receptor 5 (CCR5) as the target. Again, egRVLPs facilitated the highest, almost 100% knockout rates, importantly with minimal off-target activity. In conclusion, in direct comparison, egRVLPs were the most efficient RVLPs. Moreover, we established methods for in-depth characterization of VLPs, facilitating their validation and thus more predictable and safe application. Full article

Human colorectal cancer (CRC) is one of the most common malignancies in men and women across the globe, albeit CRC incidence and mortality shows a substantial racial and ethnic disparity, with the highest burden in African American patients. Even with effective screening tools such as colonoscopy and diagnostic detection assays, CRC remains a substantial health burden. In addition, primary tumors located in the proximal (right) or distal (left) sides of the colorectum have been shown to be unique tumor types that require unique treatment schema. Distal metastases in the liver and other organ systems are the major causes of mortality in CRC patients. Characterizing genomic, epigenomic, transcriptomic and proteomic (multi-omics) alterations has led to a better understanding of primary tumor biology, resulting in targeted therapeutic advancements. In this regard, molecular-based CRC subgroups have been developed that show correlations with patient outcomes. Molecular characterization of CRC metastases has highlighted similarities and differences between metastases and primary tumors; however, our understanding as to how to improve patient outcomes based on metastasis biology is lagging and remains a major obstacle to improving CRC patient outcomes. In this review, we will summarize the multi-omics features of primary CRC tumors and their metastases across racial and ethnic groups, the differences in proximal and distal tumor biology, molecular-based CRC subgroups, treatment strategies and challenges for improving patient outcomes. Full article

Rapid, specific, and sensitive identification of microbial pathogens is critical to infectious disease diagnosis and surveillance. Classical culture-based methods can be applied to a broad range of pathogens but have long turnaround times. Molecular methods, such as PCR, are time-effective but are not comprehensive and may not detect novel strains. Metagenomic shotgun next-generation sequencing (NGS) promises specific identification and characterization of any pathogen (viruses, bacteria, fungi, and protozoa) in a less biased way. Despite its great potential, NGS has yet to be widely adopted by clinical microbiology laboratories due in part to the absence of standardized workflows. Here, we describe a sample-to-answer workflow called PanGIA (Pan-Genomics for Infectious Agents) that includes simplified, standardized wet-lab procedures and data analysis with an easy-to-use bioinformatics tool. PanGIA is an end-to-end, multi-use workflow that can be used for pathogen detection and related applications, such as biosurveillance and biothreat detection. We performed a comprehensive survey and assessment of current, commercially available wet-lab technologies and open-source bioinformatics tools for each workflow component. The workflow includes total nucleic acid extraction from clinical human whole blood and environmental microbial forensic swabs as sample inputs, host nucleic acid depletion, dual DNA and RNA library preparation, shotgun sequencing on an Illumina MiSeq, and sequencing data analysis. The PanGIA workflow can be completed within 24 h and is currently compatible with bacteria and viruses. Here, we present data from the development and application of the clinical and environmental workflows, enabling the specific detection of pathogens associated with bloodstream infections and environmental biosurveillance, without the need for targeted assay development. Full article