Bioengineering (Basel). 2024 Dec 25;12(1):7. doi: 10.3390/bioengineering12010007.

ABSTRACT

In spite of significant advancements in diagnosis and treatment, cancer remains one of the major threats to human health due to its ability to cause disease with high morbidity and mortality. A multifactorial and multitargeted approach is required towards intervention of the multitude of signaling pathways associated with carcinogenesis inclusive of angiogenesis and metastasis. In this context, plants provide an immense source of phytotherapeutics that show great promise as anticancer drugs. There is increasing epidemiological data indicating that diets rich in vegetables and fruits could decrease the risks of certain cancers. Several studies have proved that natural plant polyphenols, such as flavonoids, lignans, phenolic acids, alkaloids, phenylpropanoids, isoprenoids, terpenes, and stilbenes, could be used in anticancer prophylaxis and therapeutics by recruitment of mechanisms inclusive of antioxidant and anti-inflammatory activities and modulation of several molecular events associated with carcinogenesis. The current review discusses the anticancer activities of principal phytochemicals with focus on signaling circuits towards targeted cancer prophylaxis and therapy. Also addressed are plant-derived anti-cancer vaccines, nanoparticles, monoclonal antibodies, and immunotherapies. This review article brings to light the importance of plants and plant-based platforms as invaluable, low-cost sources of anti-cancer molecules of particular applicability in resource-poor developing countries.

PMID:39851281 | DOI:10.3390/bioengineering12010007

Elife. 2025 Jan 24;12:RP88149. doi: 10.7554/eLife.88149.

ABSTRACT

Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed the effects of covariate stratification and interaction on body mass index (BMI) PGS (PGSBMI) across four cohorts of European (N = 491,111) and African (N = 21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R2 being nearly double between best- and worst-performing quintiles for certain covariates. Twenty-eight covariates had significant PGSBMI-covariate interaction effects, modifying PGSBMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R2 differences among strata and interaction effects - across all covariates, their main effects on BMI were correlated with their maximum R2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGSBMI individuals have highest R2 and increase in PGS effect. Using quantile regression, we show the effect of PGSBMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGSBMI performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R2 (mean 23%) across datasets. Finally, creating PGSBMI directly from GxAge genome-wide association studies effects increased relative R2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGSBMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

PMID:39851248 | DOI:10.7554/eLife.88149

Front Neural Circuits. 2025 Jan 9;18:1522421. doi: 10.3389/fncir.2024.1522421. eCollection 2024.

ABSTRACT

The substantia nigra pars compacta (SNc), one of the main dopaminergic nuclei of the brain, exerts a regulatory function on the basal ganglia circuitry via the nigro-striatal pathway but its possible dopaminergic innervation of the thalamus has been only investigated in non-human primates. The impossibility of tract-tracing studies in humans has boosted advanced MRI techniques and multi-shell high-angular resolution diffusion MRI (MS-HARDI) has promised to shed more light on the structural connectivity of subcortical structures. Here, we estimated the possible dopaminergic innervation of the human thalamus via an MS-HARDI tractography of the SNc in healthy human young adults. Two MRI data sets were serially acquired using MS-HARDI schemes from ADNI and HCP neuroimaging initiatives in a group of 10 healthy human subjects (5 males, age range: 25-30 years). High resolution 3D-T1 images were independently acquired to individually segment the thalamus and the SNc. Starting from whole-brain probabilistic tractography, all streamlines through the SNc reaching the thalamus were counted, separately for each hemisphere, after excluding streamlines through the substantia nigra pars reticulata and all those reaching the basal ganglia, the cerebellum and the cortex. We found a reproducible structural connectivity between the SNc and the thalamus, with an average of ~12% of the total number of streamlines encompassing the SNc and terminating in the thalamus, with no other major subcortical or cortical structures involved. The first principal component map of dopamine receptor density from a normative PET image data set suggested similar dopamine levels across SNc and thalamus. This is the first quantitative report from in-vivo measurements in humans supporting the presence of a direct nigro-thalamic dopaminergic projection. While histological validation and concurrent PET-MRI remains needed for ultimate proofing of existence, given the potential role of this pathway, the possibility to achieve a good reproducibility of these measurements in humans might enable the monitoring of dopaminergic-related disorders, towards targeted personalized therapies.

PMID:39850841 | PMC:PMC11754968 | DOI:10.3389/fncir.2024.1522421

Comput Struct Biotechnol J. 2024 Dec 16;27:149-159. doi: 10.1016/j.csbj.2024.12.005. eCollection 2025.

ABSTRACT

Persistent infection with high-risk human papillomavirus (hrHPV) is a major cause of cervical cancer. The effectiveness of current HPV-DNA testing, which is crucial for early detection, is limited in several aspects, including low sensitivity, accuracy issues, and the inability to perform comprehensive hrHPV typing. To address these limitations, we introduce MTIOT (Multiple subTypes In One Time), a novel detection method that utilizes machine learning with a new multichannel integration scheme to enhance HPV-DNA analysis. This approach may enable more accurate and rapid identification of multiple hrHPV types within a single sample. Compared to traditional methods, MTIOT has the potential to overcome their core limitations and offer a more efficient and cost-effective solution for cervical cancer screening. When tested on both simulated samples (to mimic real-world complexities) and clinical samples, MTIOT achieved F1 scores (the harmonic mean of sensitivity and specificity) of 98 % and 92 % respectively for identifying subtypes with a sample size ≥ 50, suggesting that it may significantly improve the precision of cervical cancer screening programs. This work with MTIOT represents a significant step forward in the molecular diagnosis of hrHPV and may suggest a promising avenue for enhancing early detection strategies and potentially reducing the incidence of cervical cancer. This study also underscores the importance of methodological innovation in tackling public health challenges and sets the stage for future clinical trials to validate MTIOT's efficacy in practice.

PMID:39850660 | PMC:PMC11755069 | DOI:10.1016/j.csbj.2024.12.005

Front Pharmacol. 2025 Jan 9;15:1397995. doi: 10.3389/fphar.2024.1397995. eCollection 2024.

ABSTRACT

INTRODUCTION: TNFα inhibitor (TNFi) immunogenicity in rheumatoid arthritis (RA) is a major obstacle to its therapeutic effectiveness. Although methotrexate (MTX) can mitigate TNFi immunogenicity, its adverse effects necessitate alternative strategies. Targeting nuclear factor of activated T cells (NFAT) transcription factors may protect against biologic immunogenicity. Therefore, developing a potent NFAT inhibitor to suppress this immunogenicity may offer an alternative to MTX.

METHODS: We performed a structure-based virtual screen of the NFATC2 crystal structure to identify potential small molecules that could interact with NFATC2. For validation, we investigated the effect of the identified compound on NFAT transcriptional activity, nuclear localization, and binding to the NFAT consensus sequence. In vivo studies assessed the ability of the compound to protect against TNFi immunogenicity, while ex vivo studies evaluated its effect on CD4+ T cell proliferation and B cell antibody secretion.

RESULTS: We identified duvelisib (DV) as a novel NFATC2 and NFATC1 inhibitor that attenuates NFAT transcriptional activity without inhibiting calcineurin or NFAT nuclear localization. Our results suggest that DV inhibits NFAT independently of PI3K by interfering with nuclear NFAT binding to the NFAT consensus promoter sequence. DV significantly protected mice from adalimumab immunogenicity and attenuated ex vivo CD4+ T cell proliferation and B cell antibody secretion.

DISCUSSION: DV is a promising NFAT inhibitor that can protect against TNFi immunogenicity without inhibiting calcineurin phosphatase activity. Our results suggest that the future development of DV analogs may be of interest as agents to attenuate unwanted immune responses.

PMID:39850568 | PMC:PMC11754251 | DOI:10.3389/fphar.2024.1397995

Front Plant Sci. 2025 Jan 9;15:1502649. doi: 10.3389/fpls.2024.1502649. eCollection 2024.

ABSTRACT

Sugar, the primary product of photosynthesis, is a vital requirement for cell activities. Allocation of sugar from source to sink tissues is facilitated by sugar transporters (ST). These STs belong to the Major Facilitator Superfamily (MFS), the largest family of STs in plants. In this study, we performed genome wide and gene expression data analysis to identify the putative ST genes in Erianthus rufipilus (E. rufipilus) and in Saccharum officinarum (S. officinarum). We identified 78 ST gene families in E. rufipilus and 86 ST gene families in S. officinarum. Phylogenetic analysis distributed the ST genes into eight distinct subfamilies (INT, MST, VGT, pGlcT, PLT, STP, SFP and SUT). Chromosomal distribution of ST genes clustered them on 10 respective chromosomes. Furthermore, synteny analysis with S. spontaneum and Sorghum bicolor (S. bicolor) revealed highly colinear regions. Synonymous and non-synonymous ratio (Ka/Ks) showed purifying selection in gene evolution. Promoter analysis identified several cis-regulatory elements, mainly associated with light responsiveness. We also examined the expression pattern of ST genes in different developing tissues (mature leaf, pre-mature stem, mature stem and seedling stem). Under sugar stress, we identified the significant ST genes showing differential expression patterns. Moreover, our yeast one-hybrid (Y1H) assays identified NAM, ATAF and CUC (NAC) and Lesion Simulating Disease (LSD) potential transcription factors (TFs) that may bind to the SUT1-T1 promoter in S. officinarum, showing negative correlation pattern with SUT1-T1. Our results deepen our understanding of ST gene evolution in Saccharum species and will facilitate the future investigation of functional analysis of the ST gene family.

PMID:39850208 | PMC:PMC11755103 | DOI:10.3389/fpls.2024.1502649

G3 (Bethesda). 2025 Jan 24:jkaf012. doi: 10.1093/g3journal/jkaf012. Online ahead of print.

ABSTRACT

Sex-determining region Y box 2 (Sox2) is a critical transcription factor for embryogenesis and neural stem and progenitor cell (NSPC) maintenance. While distal enhancers control Sox2 in embryonic stem cells (ESCs), enhancers closer to the gene are implicated in Sox2 transcriptional regulation in neural development. We hypothesize that a downstream enhancer cluster, termed Sox2 regulatory regions 2-18 (SRR2-18), regulates Sox2 transcription in neural stem cells and we investigate this in NSPCs derived from mouse ESCs. Using functional genomics and CRISPR-Cas9 mediated deletion analyses we investigate the role of SRR2-18 in Sox2 regulation during neural differentiation. Transcriptome analyses demonstrate that loss of even one copy of SRR2-18 disrupts the region-specific identity of NSPCs, reducing the expression of genes associated with more anterior regions of the embryonic nervous system. Homozygous deletion of this Sox2 neural enhancer cluster causes reduced SOX2 protein, less frequent interaction with transcriptional machinery, and leads to perturbed chromatin accessibility genome-wide further affecting the expression of neurodevelopmental and anterior-posterior regionalization genes. Furthermore, homozygous NSPC deletants exhibit self-renewal defects and impaired differentiation into cell types found in the brain. Altogether, our data define a cis-regulatory enhancer cluster controlling Sox2 transcription in NSPCs and highlight the sensitivity of neural differentiation processes to decreased Sox2 transcription, which causes differentiation into posterior neural fates, specifically the caudal neural tube. This study highlights the importance of precise Sox2 regulation by SRR2-18 in neural differentiation.

PMID:39849901 | DOI:10.1093/g3journal/jkaf012

J Am Chem Soc. 2025 Jan 23. doi: 10.1021/jacs.4c13464. Online ahead of print.

ABSTRACT

The technology of native chemical ligation and postligation desulfurization has greatly expanded the scope of modern chemical protein synthesis. Here, we report that ultrasonic energy can trigger robust and clean protein desulfurization, and we developed an ultrasound-induced desulfurization (USID) strategy that is simple to use and generally applicable to peptides and proteins. The USID strategy involves a simple ultrasonic cleaning bath and an easy-to-use and easy-to-remove sonosensitizer, titanium dioxide. It features mild and convenient reaction conditions and excellent functional group compatibility, e.g., with thiazolidine (Thz) and serotonin, which are sensitive to other desulfurization strategies. The USID strategy is robust: without reoptimizing the reaction conditions, the same USID procedure can be used for the clean desulfurization of a broad range of proteins with one or more sulfhydryl groups, even in multi-hundred-milligram scale reactions. The utility of USID was demonstrated by the one-pot synthesis of bioactive cyclopeptides such as Cycloleonuripeptide E and Segetalin F, as well as convergent chemical synthesis of functionally important proteins such as histone H3.5 using Thz as a temporary protecting group. A mechanistic investigation indicated that USID proceeds via a radical-based mechanism promoted by low-frequency and low-intensity ultrasonication. Overall, our work introduces a mechanically triggered approach with the potential to become a robust desulfurization method for general use in chemical protein synthesis by both academic and industrial laboratories.

PMID:39849831 | DOI:10.1021/jacs.4c13464

BMC Bioinformatics. 2025 Jan 23;26(1):27. doi: 10.1186/s12859-025-06049-9.

ABSTRACT

BACKGROUND: Currently, synthetic genomics is a rapidly developing field. Its main tasks, such as the design of synthetic sequences and the assembly of DNA sequences from synthetic oligonucleotides, require specialized software. In this article, we present a program with a graphical interface that allows non-bioinformatics to perform the tasks needed in synthetic genomics.

RESULTS: We developed BAC-browser v.2.1. It helps to design nucleotide sequences and features the following tools: generate nucleotide sequence from amino acid sequences using a codon frequency table for a specific organism, as well as visualization of restriction sites, GC composition, GC skew and secondary structure. To assemble DNA sequences, a fragmentation tool was created: regular breakdown into oligonucleotides of a certain length and breakdown into oligonucleotides with thermodynamic alignment. We demonstrate the possibility of DNA fragments assemblies designed in different modes of BAC-browser.

CONCLUSIONS: The BAC-browser has a large number of tools for working in the field of systemic genomics and is freely available at the link with a direct link https://sysbiomed.ru/upload/BAC-browser-2.1.zip .

PMID:39849360 | DOI:10.1186/s12859-025-06049-9

Sci Rep. 2025 Jan 23;15(1):2948. doi: 10.1038/s41598-025-86854-7.

ABSTRACT

Many cellular patterns exhibit a reaction-diffusion component, suggesting that Turing instability may contribute to pattern formation. However, biological gene-regulatory pathways are more complex than simple Turing activator-inhibitor models and generally do not require fine-tuning of parameters as dictated by the Turing conditions. To address these issues, we employ random matrix theory to analyze the Jacobian matrices of larger networks with robust statistical properties. Our analysis reveals that Turing patterns are more likely to occur by chance than previously thought and that the most robust Turing networks have an optimal size, consisting of only a handful of molecular species, thus significantly increasing their identifiability in biological systems. Broadly speaking, this optimal size emerges from a trade-off between the highest stability in small networks and the greatest instability with diffusion in large networks. Furthermore, we find that with multiple immobile nodes, differential diffusion ceases to be important for Turing patterns. Our findings may inform future synthetic biology approaches and provide insights into bridging the gap to complex developmental pathways.

PMID:39849094 | DOI:10.1038/s41598-025-86854-7

Sci Rep. 2025 Jan 23;15(1):2982. doi: 10.1038/s41598-024-82517-1.

ABSTRACT

Recent research has identified sex-dependent links between risk taking behaviors, approach-avoidance bias and alcohol intake. However, preclinical studies have typically assessed alcohol drinking using a singular dimension of intake (i.e. drinking level), failing to capture the multidimensional pattern of aberrant alcohol-seeking observed in alcohol use disorder. In this study, we sought to further explore individual and sex differences in the relationship between approach-avoidance bias, frontloading (bingeing and onset skew) and multiple addiction-like indices of ethanol seeking that included motivation for ethanol, persistence despite its absence (extinction), and ethanol-taking in the face of mild footshock. We found that female rats displayed more addiction-like phenotypes than males overall, and that frontloading patterns differed by sex, with females outdrinking males in the early part of access sessions (bingeing), but males strongly concentrating their lever pressing for ethanol in that period (onset skew). Multiple regression analyses revealed that bingeing was a strong positive predictor and onset skew a negative predictor of motivational breakpoint. Cued-conflict preference - a measure of approach-avoidance bias towards a mixed-valence conflict cue - was predictive of both extinction and footshock in males, but not females. Our data highlight key sex differences, and the relevance of both frontloading patterns and conflict preference in predicting future addiction-like phenotypes.

PMID:39848982 | DOI:10.1038/s41598-024-82517-1

Free Radic Biol Med. 2025 Jan 21:S0891-5849(25)00052-8. doi: 10.1016/j.freeradbiomed.2025.01.042. Online ahead of print.

ABSTRACT

Cuproptosis, a copper-dependent form of regulated cell death, has been implicated in the progression and treatment of various tumors. The copper ionophores, such as Disulfiram (DSF), an FDA-approved drug previously used to treat alcohol dependence, have been found to induce cuproptosis. However, the limited solubility and effectiveness of the combination of DSF and copper ion restrict its widespread application. In this study, through a random screening of our in-house compound library, we identified a novel cuproptosis inducer, YL21, comprising a naphthoquinone core substituted by two dithiocarbamate groups. The combination of YL21 with copper ion induces cuproptosis by disrupting mitochondrial function and promoting the oligomerization of lipoylated protein DLAT. Further, this combination induces endoplasmic reticulum (ER) stress and oxidative stress, triggering immunogenic cell death (ICD) and subsequently promoting the activation of antitumor immune responses to suppress tumor growth in the mice breast cancer model. Notably, the combination of YL21 and copper ion demonstrated improved solubility and increased antitumor activity compared to the combination of DSF and copper ion. Thus, YL21 functions as a novel cuproptosis inducer and may serve as a promising candidate for antitumor immunotherapy.

PMID:39848344 | DOI:10.1016/j.freeradbiomed.2025.01.042

J Psychiatr Res. 2025 Jan 20;182:329-337. doi: 10.1016/j.jpsychires.2025.01.039. Online ahead of print.

ABSTRACT

Major depressive disorder (MDD) is a highly prevalent and debilitating disorder, yet its pathophysiology has not been fully elucidated. The aim of this study is to identify novel potential proteins and biological processes associated with MDD through a systems biology approach. Original articles involving the measurement of proteins in the blood of patients diagnosed with MDD were selected. Data on the differentially expressed proteins (DEPs) in each article were extracted and imported into R, and the pathfindR package was used to identify the main gene ontology terms involved. Data from the STRING database were combined with the DEPs identified in the original studies to create expanded networks of protein-protein interactions (PPIs). An R script was developed to obtain the five most reliable connections from each DEP and to create the networks, which were visualized through Cytoscape software. Out of 510 articles found, eight that contained all the values necessary for the analysis were selected, including 1112 adult patients with MDD and 864 controls. A total of 240 DEPs were identified, with the most significant gene ontology term being "endoplasmic reticulum lumen" (46 DEPs, p-value = 5.5x10-13). An extended PPI network was obtained, where Anoctamin-8 was the most central protein. Using systems biology contributed to the interpretation of data obtained in proteomic studies on MDD and expanded the findings of these studies. The combined use of these methodologies can provide new insights into the pathophysiology of psychiatric disorders, identifying novel biomarkers to improve diagnostic, prognostic, and treatment strategies in MDD.

PMID:39848100 | DOI:10.1016/j.jpsychires.2025.01.039

Int Immunopharmacol. 2025 Jan 22;148:114090. doi: 10.1016/j.intimp.2025.114090. Online ahead of print.

ABSTRACT

INTRODUCTION: Biomarkers play a crucial role across various fields by providing insights into biological responses to interventions. High-throughput gene expression profiling technologies facilitate the discovery of data-driven biomarkers through extensive datasets. This study focuses on identifying biomarkers in gene expression data related to chemical injuries by mustard gas, covering a spectrum from healthy individuals to severe injuries.

MATERIALS AND METHODS: The study utilized RNA-Seq data comprising 52 expression data samples for 54,583 gene transcripts. These samples were categorized into four classes based on the GOLD classification for chemically injured individuals: Severe (n = 14), Moderate (n = 11), Mild (n = 16), and healthy controls (n = 11). Data preparation involved examining an Excel file created in the R programming environment using MLSeq and devtools packages. Feature selection was performed using Genetic Algorithm and Simulated Annealing, with Random Forest algorithm employed for classification. Ab initio methods ensured computational efficiency and result accuracy, while molecular dynamics simulation acted as a virtual experiment bridging the gap between experimental and theoretical experiences.

RESULTS: A total of 12 models were created, each introducing a list of differentially expressed genes as potential biomarkers. The performance of models varied across group comparisons, with the Genetic Algorithm generally outperforming Simulated Annealing in most cases. For the Severe vs. Moderate group, GA achieved the best performance with an accuracy of 94.38%, recall of 91.64%, and specificity of 97.10%. The results highlight the effectiveness of GA in most group comparisons, while SA performed better in specific cases involving Moderate and Mild groups. These biomarkers were evaluated against the gene expression data to assess their expression changes between different groups of chemically injured individuals. Four genes were selected based on level expression for further investigation: CXCR1, EIF2B2, RAD51, and RXFP2. The expression levels of these genes were analyzed to determine their differential expression between the groups.

CONCLUSION: This study was designed as a computational effort to identify diagnostic biomarkers in basic biological system research. Our findings proposed a list of discriminative biomarkers capable of distinguishing between different groups of chemically injured individuals. The identification of key genes highlights the potential for biomarkers to serve as indicators of chemical injury severity, warranting further investigation to validate their clinical relevance and utility in diagnosis and treatment.

PMID:39847951 | DOI:10.1016/j.intimp.2025.114090

Science. 2025 Jan 24;387(6732):eadn7277. doi: 10.1126/science.adn7277. Epub 2025 Jan 24.

ABSTRACT

Vascular inflammation regulates endothelial pathophenotypes, particularly in pulmonary arterial hypertension (PAH). Dysregulated lysosomal activity and cholesterol metabolism activate pathogenic inflammation, but their relevance to PAH is unclear. Nuclear receptor coactivator 7 (NCOA7) deficiency in endothelium produced an oxysterol and bile acid signature through lysosomal dysregulation, promoting endothelial pathophenotypes. This oxysterol signature overlapped with a plasma metabolite signature associated with human PAH mortality. Mice deficient for endothelial Ncoa7 or exposed to an inflammatory bile acid developed worsened PAH. Genetic predisposition to NCOA7 deficiency was driven by single-nucleotide polymorphism rs11154337, which alters endothelial immunoactivation and is associated with human PAH mortality. An NCOA7-activating agent reversed endothelial immunoactivation and rodent PAH. Thus, we established a genetic and metabolic paradigm that links lysosomal biology and oxysterol processes to endothelial inflammation and PAH.

PMID:39847635 | DOI:10.1126/science.adn7277

Science. 2025 Jan 23:eadk9688. doi: 10.1126/science.adk9688. Online ahead of print.

ABSTRACT

Genomes contain mosaics of discordant evolutionary histories, challenging the accurate inference of the tree of life. While genome-wide data are routinely used for discordance-aware phylogenomic analyses, due to modeling and scalability limitations, the current practice leaves out large chunks of genomes. As more high-quality genomes become available, we urgently need discordance-aware methods to infer the tree directly from a multiple genome alignment. Here, we introduce CASTER, a theoretically justified site-based method that eliminates the need to predefine recombination-free loci. CASTER is scalable to hundreds of mammalian whole genomes. We demonstrate the accuracy and scalability of CASTER in simulations that include recombination and apply CASTER to several biological datasets, showing that its per-site scores can reveal both biological and artefactual patterns of discordance across the genome.

PMID:39847611 | DOI:10.1126/science.adk9688

PLoS Pathog. 2025 Jan 23;21(1):e1012903. doi: 10.1371/journal.ppat.1012903. Online ahead of print.

ABSTRACT

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) continues to persist, demonstrating the risks posed by emerging infectious diseases to national security, public health, and the economy. Development of new vaccines and antibodies for emerging viral threats requires substantial resources and time, and traditional development platforms for vaccines and antibodies are often too slow to combat continuously evolving immunological escape variants, reducing their efficacy over time. Previously, we designed a next-generation synthetic humanized nanobody (Nb) phage display library and demonstrated that this library could be used to rapidly identify highly specific and potent neutralizing heavy chain-only antibodies (HCAbs) with prophylactic and therapeutic efficacy in vivo against the original SARS-CoV-2. In this study, we used a combination of high throughput screening and machine learning (ML) models to identify HCAbs with potent efficacy against SARS-CoV-2 viral variants of interest (VOIs) and concern (VOCs). To start, we screened our highly diverse Nb phage display library against several pre-Omicron VOI and VOC receptor binding domains (RBDs) to identify panels of cross-reactive HCAbs. Using HCAb affinity for SARS-CoV-2 VOI and VOCs (pre-Omicron variants) and model features from other published data, we were able to develop a ML model that successfully identified HCAbs with efficacy against Omicron variants, independent of our experimental biopanning workflow. This biopanning informed ML approach reduced the experimental screening burden by 78% to 90% for the Omicron BA.5 and Omicron BA.1 variants, respectively. The combined approach can be applied to other emerging viruses with pandemic potential to rapidly identify effective therapeutic antibodies against emerging variants.

PMID:39847604 | DOI:10.1371/journal.ppat.1012903

PLoS Comput Biol. 2025 Jan 23;21(1):e1012198. doi: 10.1371/journal.pcbi.1012198. Online ahead of print.

ABSTRACT

Systems biology tackles the challenge of understanding the high complexity in the internal regulation of homeostasis in the human body through mathematical modelling. These models can aid in the discovery of disease mechanisms and potential drug targets. However, on one hand the development and validation of knowledge-based mechanistic models is time-consuming and does not scale well with increasing features in medical data. On the other hand, data-driven approaches such as machine learning models require large volumes of data to produce generalisable models. The integration of neural networks and mechanistic models, forming universal differential equation (UDE) models, enables the automated learning of unknown model terms with less data than neural networks alone. Nevertheless, estimating parameters for these hybrid models remains difficult with sparse data and limited sampling durations that are common in biological applications. In this work, we propose the use of physiology-informed regularisation, penalising biologically implausible model behavior to guide the UDE towards more physiologically plausible regions of the solution space. In a simulation study we show that physiology-informed regularisation not only results in a more accurate forecasting of model behaviour, but also supports training with less data. We also applied this technique to learn a representation of the rate of glucose appearance in the glucose minimal model using meal response data measured in healthy people. In that case, the inclusion of regularisation reduces variability between UDE-embedded neural networks that were trained from different initial parameter guesses.

PMID:39847592 | DOI:10.1371/journal.pcbi.1012198

PLoS Biol. 2025 Jan 23;23(1):e3002707. doi: 10.1371/journal.pbio.3002707. eCollection 2025 Jan.

ABSTRACT

Antibodies are extensively used in biomedical research, clinical fields, and disease treatment. However, to enhance the reproducibility and reliability of antibody-based experiments, it is crucial to have a detailed understanding of the antibody's target specificity and epitope. In this study, we developed a high-throughput and precise epitope analysis method, DECODE (Decoding Epitope Composition by Optimized-mRNA-display, Data analysis, and Expression sequencing). This method allowed identifying patterns of epitopes recognized by monoclonal or polyclonal antibodies at single amino acid resolution and predicted cross-reactivity against the entire protein database. By applying the obtained epitope information, it has become possible to develop a new 3D immunostaining method that increases the penetration of antibodies deep into tissues. Furthermore, to demonstrate the applicability of DECODE to more complex blood antibodies, we performed epitope analysis using serum antibodies from mice with experimental autoimmune encephalomyelitis (EAE). As a result, we were able to successfully identify an epitope that matched the sequence of the peptide inducing the disease model without relying on existing antigen information. These results demonstrate that DECODE can provide high-quality epitope information, improve the reproducibility of antibody-dependent experiments, diagnostics and therapeutics, and contribute to discover pathogenic epitopes from antibodies in the blood.

PMID:39847587 | DOI:10.1371/journal.pbio.3002707

Mol Biol Evol. 2025 Jan 23:msaf005. doi: 10.1093/molbev/msaf005. Online ahead of print.

ABSTRACT

Copy-number variants (CNVs) are an important class of genetic variation that can mediate rapid adaptive evolution. Whereas CNVs can increase the relative fitness of the organism, they can also incur a cost due to the associated increased gene expression and repetitive DNA. We previously evolved populations of Saccharomyces cerevisiae over hundreds of generations in glutamine-limited (Gln-) chemostats and observed the recurrent evolution of CNVs at the GAP1 locus. To understand the role that gene expression plays in adaptation, both in relation to the adaptation of the organism to the selective condition and as a consequence of the CNV, we measured the transcriptome, translatome, and proteome of 4 strains of evolved yeast, each with a unique CNV, and their ancestor in Gln- conditions. We find CNV-amplified genes correlate with higher mRNA abundance; however, this effect is reduced at the level of the proteome, consistent with post-transcriptional dosage compensation. By normalizing each level of gene expression by the abundance of the preceding step we were able to identify widespread differences in the efficiency of each level of gene expression. Genes with significantly different translational efficiency were enriched for potential regulatory mechanisms including either upstream open reading frames (uORFs), RNA binding sites for Ssd1, or both. Genes with lower protein expression efficiency were enriched for genes encoding proteins in protein complexes. Taken together, our study reveals widespread changes in gene expression at multiple regulatory levels in lineages containing adaptive CNVs highlighting the diverse ways in which genome evolution shapes gene expression.

PMID:39847535 | DOI:10.1093/molbev/msaf005