Semin Immunopathol. 2025 Apr 3;47(1):24. doi: 10.1007/s00281-025-01047-8.

ABSTRACT

Galectin-1 (Gal-1), a member of the β-galactoside-binding soluble lectin family, is a double-edged sword in immunity. On one hand, it plays a crucial role in regulating diverse immune cell functions, including the apoptosis of activated T cells. These processes are key in resolving inflammation and preventing autoimmune diseases. On the other hand, Gal-1 has significant implications in cancer, where tumor cells and the tumor microenvironment (TME) (e.g., tumor-associated fibroblasts, myeloid-derived suppressor cells) secrete Gal-1 to evade immune surveillance and promote cancer cell growth. Within the TME, Gal-1 enhances the differentiation of tolerogenic dendritic cells, induces the apoptosis of effector T cells, and enhances the proliferation of regulatory T cells, collectively facilitating tumor immune escape. Therefore, targeting Gal-1 holds the potential to boost anti-tumor immunity and improve the efficacy of cancer immunotherapy. This review provides insights into the intricate role of Gal-1 in immune cell regulation, with an emphasis on T cells, and elucidates how tumors exploit Gal-1 for immune evasion and growth. Furthermore, we discuss the potential of Gal-1 as a therapeutic target to augment current immunotherapies across various cancer types.

PMID:40178639 | DOI:10.1007/s00281-025-01047-8

Plant Dis. 2025 Apr 3. doi: 10.1094/PDIS-02-25-0251-PDN. Online ahead of print.

ABSTRACT

In July 2024, a pooled leaf sample (D760/24) was collected from several plants of three watermelon cultivars (Citrullus lanatus cvs. Crimson Sweet, Asahi Miyako Hybrid F1 and Top Gun) grown in an open field (approx. 0.5ha) in Dombrava, Slovenia. The plants which were included in the pooled sample showed virus-like symptoms, such as leaf mosaic, wilting and necrosis (eXtra Supplementary material Fig. S1). The disease incidence was estimated at 10%. DNA and RNA were extracted following Mehle et al. (2013) and RNeasy Plant Mini Kit (Qiagen, Germany) protocols, respectively. The sample was tested positive by reverse-transcription (RT)-PCR for watermelon crinkle leaf-associated virus 1 (WCLaV-1) and WCLaV-2 ( Hernandez et al. 2021) and negative for other viruses (details on viruses tested and primers used are available in eXtra Table S1). The obtained amplicons of expected sizes of WCLaV-1 and WCLaV-2 movement protein (MP) and RNA-dependent RNA polymerase (RdRp) genes (eXtra Fig S2) were then subjected to Sanger sequencing (Eurofins Genomics, Germany) and BLAST analysis. The MP (PQ570004, PQ570006) and the RdRp (PQ570005, PQ570007) sequences exhibited 100% identity with multiple accessions of WCLaV-1, such as PP792977 and PP792976, and WCLaV-2, such as LC636073 and LC636074. Illumina high-throughput sequencing (HTS, Novogene, Germany, NovaSeq X Plus, PE150) identified WCLaV-1 (PV012703-04) and WCLaV-2 (PV012705-06) reads, along with cucumis melo amalgavirus 1 (CmAV1, PV012707) and solanum nigrum ilarvirus 1 reads (insufficient reads to reconstruct genome segments, it may originate from pollen contamination of nearby infected plants in the field (Rivarez et al. 2023)). HTS data were analyzed in CLC Genomics Workbench v. 24 (Qiagen, USA) using the pipeline by (Pecman et al. 2022). Consensus genome sequences were reconstructed by iterative read mapping to the most similar reference sequence of the virus obtained from NCBI GenBank. To check for WCLaVs in watermelon seeds sold in Slovenia, we tested five seed samples from Sugar Baby, Crimstar F1, and Crimson Sweet (three lots) by RT-PCR. We also tested four leaf samples from plants grown from these seeds at 3-5 true leaves stage. Both viruses were found in all seed and leaf extracts. However, mechanical inoculations with the sap of two samples (plants grown from infected seed sample and sample D760/24) on several commonly used indicator plants including Chenopodium quinoa, Capsicum annuum, Nicotiana clevelandii, Nicotiana glutinosa, Nicotiana benthamiana, Nicotiana tabacum cv. White Burley, Nicotiana rustica, Datura stramonium, Cucurbita pepo cv. Bianca di Trieste, and Cucurbita maxima did not result in their infection. Retrospective analyses of our HTS data of two watermelon and 84 other cucurbits samples from previous years showed WCLaV-1 and WCLaV-2 reads in two pooled samples (containing equal amount of RNA of each sample): one from 2018 and another from 2019. RT-PCR confirmed the presence of WCLaVs only in watermelons. The pool from 2018 was sequenced at GATC (Germany, NovaSeq 6000 S2, PE 150) and from 2019 in-house using Oxford Nanopore Technologies (UK, MinION Mk1B device, SQK-PCS108, R9 flow cell). All HTS reads are deposited in the NCBI Short Reads Archive (PRJNA1202089). This is the first report of WCLaV-1 and WCLaV-2 in Slovenia and Europe, the two viruses which were included to the Alert list of the European and Mediterranean Plant Protection Organization, due to limited knowledge about their epidemiology (EPPO 2023). Further research is necessary to determine the incidence of these viruses in Europe, elucidate their epidemiology, symptoms association and their potential impact on the production of watermelons in the region.

PMID:40178537 | DOI:10.1094/PDIS-02-25-0251-PDN

Plant Cell. 2025 Apr 3:koaf059. doi: 10.1093/plcell/koaf059. Online ahead of print.

ABSTRACT

The significance of research conducted on Arabidopsis thaliana cannot be overstated. This focus issue showcases how insights from Arabidopsis have opened new areas of biology and directly advanced our understanding of crops. Here, experts intimately involved in bridging between Arabidopsis and crops share their perspectives on the challenges and opportunities for translation. First, we examine the translatability of genetic modules from Arabidopsis into maize, emphasizing the need to publish well-executed translational experiments, regardless of outcome. Second, we highlight the landmark success of HB4, the first GM wheat cultivar on the market, whose abiotic tolerance is borne from direct translation and based on strategies first outlined in Arabidopsis. Third, we discuss the decades-long journey to engineer oilseed crops capable of producing omega-3 fish oils, with Arabidopsis serving as a critical intermediary. Fourth, we explore how direct translation of starch synthesizing proteins characterised in Arabidopsis helped uncover novel mechanisms and functions in crops, with potential valuable applications. Finally, we illustrate how shared molecular factors between Arabidopsis and barley exhibit distinct molecular wiring as exemplified in cuticular and stomatal development. Together, these vignettes underscore the pivotal role of Arabidopsis as a foundational model plant while highlighting the challenges of translating discoveries into field-ready, commercial cultivars with enhanced knowledge-based traits.

PMID:40178150 | DOI:10.1093/plcell/koaf059

Bioinform Adv. 2025 Mar 20;5(1):vbaf044. doi: 10.1093/bioadv/vbaf044. eCollection 2025.

ABSTRACT

SUMMARY: Modern biological research critically depends on public databases. The introduction and propagation of errors within and across databases can lead to wasted resources as scientists are led astray by bad data or have to conduct expensive validation experiments. The emergence of generative artificial intelligence systems threatens to compound this problem owing to the ease with which massive volumes of synthetic data can be generated. We provide an overview of several key issues that occur within the biological data ecosystem and make several recommendations aimed at reducing data errors and their propagation. We specifically highlight the critical importance of improved educational programs aimed at biologists and life scientists that emphasize best practices in data engineering. We also argue for increased theoretical and empirical research on data provenance, error propagation, and on understanding the impact of errors on analytic pipelines. Furthermore, we recommend enhanced funding for the stewardship and maintenance of public biological databases.

AVAILABILITY AND IMPLEMENTATION: Not applicable.

PMID:40177265 | PMC:PMC11964588 | DOI:10.1093/bioadv/vbaf044

Bioact Mater. 2025 Mar 19;49:437-455. doi: 10.1016/j.bioactmat.2025.02.008. eCollection 2025 Jul.

ABSTRACT

Peptide metabolites are emerging biomolecules with numerous possibilities in biomaterial-based regenerative medicine due to their inherent bioactivities. These small, naturally occurring compounds are intermediates or byproducts of larger proteins and peptides, and they can have profound effects, such as antiviral therapeutics, proangiogenic agents, and regenerative medicinal applications. This study is among the first to focus on using thymosin β4 protein-derived metabolites to pioneer novel applications for peptide metabolites in biomaterials. This study found that the novel peptide metabolite acetyl-thymosin β4 (amino acid 1-17) (Ac-Tβ1-17) exhibited significant protease inhibition activity against SARS-CoV-2, surpassing its precursor protein. Additionally, Ac-Tβ1-17 demonstrated beneficial effects, such as cell proliferation, wound healing, and scavenging of reactive oxygen species (ROS) in human umbilical vein endothelial cells (HUVEC). Integrating Ac-Tβ1-17 into a peptide-based scaffold facilitated cell growth and angiogenesis inside the scaffold and through gradual release into the surrounding environment. The Ac-Tβ1-17 peptide treatment induced significant biochemical responses in HUVEC, increasing Akt, ERK, PI3K, MEK, and Bcl-2 gene expression and proangiogenic proteins. Ac-Tβ1-17 peptide treatment showed similar results in ex vivo by enhancing mouse fetal metatarsal growth and angiogenesis. These findings highlight the potential of natural protein metabolites to generate biologically active peptides, offering a novel strategy for enhancing biomaterial compatibility. This approach holds promise for developing therapeutic biomaterials using peptide metabolites, presenting exciting prospects for future research and applications.

PMID:40177110 | PMC:PMC11964602 | DOI:10.1016/j.bioactmat.2025.02.008

Front Pharmacol. 2025 Mar 19;16:1580686. doi: 10.3389/fphar.2025.1580686. eCollection 2025.

NO ABSTRACT

PMID:40176899 | PMC:PMC11961961 | DOI:10.3389/fphar.2025.1580686

Front Genet. 2025 Mar 19;16:1579848. doi: 10.3389/fgene.2025.1579848. eCollection 2025.

ABSTRACT

Periodontitis, a prevalent global oral health issue, is primarily characterized by chronic inflammation resulting from bacterial infection. Periodontitis primarily affects the tissues surrounding and supporting the teeth, encompassing the gingival tissue, periodontal attachment apparatus, and the bony socket. The disease mechanism results from intricate interactions between hereditary factors, the body's defense mechanisms, and shifts in the composition of oral microbiota, with each element playing a crucial role in the initiation and advancement of the pathological process. The early symptoms of periodontitis are often not obvious, resulting in patients often not seeking medical attention until they are seriously ill, so finding biomarkers for periodontitis is essential for timely diagnosis and treatment. In this study, we selected two datasets (GSE10334 and GSE16134) by in-depth analysis of publicly available sequencing data of affected and unaffected gum tissue in periodontitis patients in the GEO database. To identify key genes associated with periodontitis pathogenesis and explore potential therapeutic biomarkers, we employed two complementary computational approaches: Random Forest, a robust machine learning algorithm for feature selection, and Weighted Gene Co-expression Network Analysis (WGCNA), a systems biology method for identifying co-expressed gene modules. Through comprehensive analysis of these combined datasets, our objective is to elucidate the underlying molecular pathways governing periodontal disease progression, thereby identifying novel therapeutic targets that may facilitate the design of improved clinical interventions for this condition. This study establishes a substantial scientific foundation that contributes to both clinical applications and fundamental research in periodontitis. The findings not only offer valuable insights for developing early diagnostic strategies and therapeutic interventions but also provide a robust theoretical framework to guide future investigations into the molecular mechanisms underlying this complex disease.

PMID:40176796 | PMC:PMC11961894 | DOI:10.3389/fgene.2025.1579848

Philos Trans R Soc Lond B Biol Sci. 2025 Apr 3;380(1923):20240021. doi: 10.1098/rstb.2024.0021. Epub 2025 Apr 3.

ABSTRACT

Animal vocalizations contain a varying degree of nonlinear phenomena (NLP) caused by irregular or chaotic vocal organ dynamics. Several hypotheses have been proposed to explain NLP presence, from unintentional by-products of poor vocal technique to having a functional communicative role. We aimed to disentangle the role of sex, age and physiological constraints in the occurrence of NLP in the songs of the lemur Indri indri, which are complex harmonic vocal displays organized in phrases. Age and sex affected the presence and type of NLP in songs. In particular, the proportion of the phenomena considered decreased with age, except for subharmonics. Subharmonics potentially mediate the perception of lower pitch, making signallers appear larger. Subharmonics and frequency jumps occurred in lower-pitched notes than regular units, while chaos and sidebands occurred in higher-pitched units. This suggests that different types of NLP can be associated with different vocal constraints. Finally, indris might present short-term vocal fatigue, with units occurring in the last position of a phrase having the highest probability of containing NLP. The presence of NLP in indris might result from proximate causes, such as physiological constraints, and ultimate causes, such as evolutionary pressures, which shaped the communicative role of NLP.This article is part of the theme issue 'Nonlinear phenomena in vertebrate vocalizations: mechanisms and communicative functions'.

PMID:40176518 | DOI:10.1098/rstb.2024.0021

Philos Trans R Soc Lond B Biol Sci. 2025 Apr 3;380(1923):20240019. doi: 10.1098/rstb.2024.0019. Epub 2025 Apr 3.

ABSTRACT

African penguins (Spheniscus demersus) extensively use high-frequency food solicitation signals (begging calls) to request food from parents. We studied the occurrence of nonlinear vocal phenomena (NLP) in begging calls in 91 hand-reared penguin chicks at the Southern African Foundation for the Conservation of Coastal Birds. For each chick, we recorded the begging calls daily, from the hatching of wild abandoned eggs to the release of the chicks into the wild approximately three months later. We found that most (70%) of begging calls contain NLP. The most frequently observed are sidebands (54.1%) and deterministic chaos (71.4%), and these phenomena often coexist (26.5%). We suggest that the aperiodic chaotic features of begging calls assist in increasing adults' attention and avoiding habituation. The occurrence of NLP also depends on the penguins' age, with older chicks producing more NLP in their calls. Moreover, we found that NLP significantly increased in chicks after contracting a respiratory disease (for example, bacterial infections or aspergillosis). Such findings might be useful for the timely diagnosis of penguins needing veterinary treatment, contributing to conservation efforts for this endangered species.This article is part of the theme issue 'Nonlinear phenomena in vertebrate vocalizations: mechanisms and communicative functions'.

PMID:40176507 | DOI:10.1098/rstb.2024.0019

Philos Trans R Soc Lond B Biol Sci. 2025 Apr 3;380(1923):20240009. doi: 10.1098/rstb.2024.0009. Epub 2025 Apr 3.

ABSTRACT

Nonlinear acoustic phenomena (NLP) likely facilitate the expression of distress in animal vocalizations, making calls perceptually rough and hard to ignore. Yet, their function in adult human vocal communication remains poorly understood. Here, to examine the production and perception of acoustic correlates of pain in spontaneous human nonverbal vocalizations, we take advantage of childbirth-a natural context in which labouring women typically produce a range of highly evocative loud vocalizations, including moans and screams-as they experience excruciating pain. We combine acoustic analyses of these real-life pain vocalizations with psychoacoustic experiments involving the playback of natural and synthetic calls to both naïve and expert listeners. We show that vocalizations become acoustically rougher, higher in fundamental frequency (pitch), less stable, louder and longer as child labour progresses, paralleling a rise in women's self-assessed pain. In perception experiments, we show that both naïve listeners and obstetric professionals assign the highest pain ratings to vocalizations produced in the final expulsion phase of labour. Experiments with synthetic vocal stimuli confirm that listeners rely largely on nonlinear phenomena to assess pain. Our study confirms that nonlinear phenomena communicate intense, pain-induced distress in humans, consistent with their widespread function to signal distress and arousal in vertebrate vocal signals.This article is part of the theme issue 'Nonlinear phenomena in vertebrate vocalizations: mechanisms and communicative functions'.

PMID:40176506 | DOI:10.1098/rstb.2024.0009

mBio. 2025 Apr 3:e0077825. doi: 10.1128/mbio.00778-25. Online ahead of print.

NO ABSTRACT

PMID:40176258 | DOI:10.1128/mbio.00778-25

Cardiovasc Diabetol. 2025 Apr 2;24(1):152. doi: 10.1186/s12933-025-02701-z.

ABSTRACT

BACKGROUND: Disruption of lipid metabolism contributes to increased cardiovascular risk in diabetes.

METHODS: We evaluated the associations between serum lipidomic profile and subclinical carotid atherosclerosis (SCA) in type 1 (T1D) and type 2 (T2D) diabetes, and in subjects without diabetes (controls) in a cross-sectional study. All subjects underwent a lipidomic analysis using ultra-high performance liquid chromatography-electrospray ionization tandem mass spectrometry, carotid ultrasound (mode B) to assess SCA, and clinical assessment. Multiple linear regression models were used to assess the association between features and the presence and burden of SCA in subjects with T1D, T2D, and controls separately. Additionally, multiple linear regression models with interaction terms were employed to determine features significantly associated with SCA within risk groups, including smoking habit, hypertension, dyslipidaemia, antiplatelet use and sex. Depending on the population under study, different confounding factors were considered and adjusted for, including sample origin, sex, age, hypertension, dyslipidaemia, body mass index, waist circumference, glycated haemoglobin, glucose levels, smoking habit, diabetes duration, antiplatelet use, and alanine aminotransferase levels.

RESULTS: A total of 513 subjects (151 T1D, 155 T2D, and 207 non-diabetic control) were included, in whom the percentage with SCA was 48.3%, 49.7%, and 46.9%, respectively. A total of 27 unique lipid species were associated with SCA in subjects with T2D, in former/current smokers with T2D, and in individuals with T2D without dyslipidaemia. Phosphatidylcholines and diacylglycerols were the main SCA-associated lipidic classes. Ten different species of phosphatidylcholines were up-regulated, while 4 phosphatidylcholines containing polyunsaturated fatty acids were down-regulated. One diacylglycerol was down-regulated, while the other 3 were positively associated with SCA in individuals with T2D without dyslipidaemia. We discovered several features significantly associated with SCA in individuals with T1D, but only one sterol could be partially annotated.

CONCLUSIONS: We revealed a significant disruption of lipid metabolism associated with SCA in subjects with T2D, and a larger SCA-associated disruption in former/current smokers with T2D and individuals with T2D who do not undergo lipid-lowering treatment.

PMID:40176064 | DOI:10.1186/s12933-025-02701-z

Biotechnol Lett. 2025 Apr 2;47(2):38. doi: 10.1007/s10529-025-03579-3.

ABSTRACT

OBJECTIVE: The aim of this study is to identify therapeutic cargos within mesenchymal stem cell (MSC)-derived small extracellular vesicles (sEVs) for the treatment of liver fibrosis, a condition that poses significant health risks.

RESULTS: sEVs from human umbilical cord-derived MSCs (UCMSCs) and their differentiated hepatocyte-like cells (hpUCMSCs) were found to alleviate liver fibrosis in mouse models, reduce fibrogenic gene expression in the liver, and inhibit hepatic stellate cell (HSC) activation, a central driver of liver fibrosis, in vitro. Deep sequencing identified differentially abundant microRNAs (miRNAs) (high-abundance: 57, low-abundance: 22) in both UCMSC- and hpUCMSC-derived sEVs, compared to HeLa cell-derived sEVs, which lack anti-liver fibrotic activity. Functional enrichment analysis of the high-abundance sEV miRNA targets revealed their involvement in transcriptional regulation, apoptosis, and cancer-related pathways, all of which are linked to liver fibrosis and hepatocellular carcinoma. Notably, many of the top 10 most abundant miRNAs reduced pro-fibrotic marker levels in activated HSCs in vitro.

CONCLUSION: The therapeutic potential of the high-abundance miRNAs shared by UCMSC- and hpUCMSC-derived sEVs in treating liver fibrosis is highlighted.

PMID:40175803 | DOI:10.1007/s10529-025-03579-3

Nat Med. 2025 Apr 2. doi: 10.1038/s41591-025-03610-0. Online ahead of print.

ABSTRACT

The human gut microbiome is most dynamic in early life. Although sweeping changes in taxonomic architecture are well described, it remains unknown how, and to what extent, individual strains colonize and persist and how selective pressures define their genomic architecture. In this study, we combined shotgun sequencing of 1,203 stool samples from 26 mothers and their twins (52 infants), sampled from childbirth to 8 years after birth, with culture-enhanced, deep short-read and long-read stool sequencing from a subset of 10 twins (20 infants) to define transmission, persistence and evolutionary trajectories of gut species from infancy to middle childhood. We constructed 3,995 strain-resolved metagenome-assembled genomes across 399 taxa, and we found that 27.4% persist within individuals. We identified 726 strains shared within families, with Bacteroidales, Oscillospiraceae and Lachnospiraceae, but not Bifidobacteriaceae, vertically transferred. Lastly, we identified weaning as a critical inflection point that accelerates bacterial mutation rates and separates functional profiles of genes accruing mutations.

PMID:40175737 | DOI:10.1038/s41591-025-03610-0

Nature. 2025 Apr 2. doi: 10.1038/s41586-025-08814-5. Online ahead of print.

ABSTRACT

Anthropogenic biodiversity decline threatens the functioning of ecosystems and the many benefits they provide to humanity1. As well as causing species losses in directly affected locations, human influence might also reduce biodiversity in relatively unmodified vegetation if far-reaching anthropogenic effects trigger local extinctions and hinder recolonization. Here we show that local plant diversity is globally negatively related to the level of anthropogenic activity in the surrounding region. Impoverishment of natural vegetation was evident only when we considered community completeness: the proportion of all suitable species in the region that are present at a site. To estimate community completeness, we compared the number of recorded species with the dark diversity-ecologically suitable species that are absent from a site but present in the surrounding region2. In the sampled regions with a minimal human footprint index, an average of 35% of suitable plant species were present locally, compared with less than 20% in highly affected regions. Besides having the potential to uncover overlooked threats to biodiversity, dark diversity also provides guidance for nature conservation. Species in the dark diversity remain regionally present, and their local populations might be restored through measures that improve connectivity between natural vegetation fragments and reduce threats to population persistence.

PMID:40175550 | DOI:10.1038/s41586-025-08814-5

Sci Rep. 2025 Apr 2;15(1):11312. doi: 10.1038/s41598-025-95276-4.

ABSTRACT

Infectious mononucleosis (IM), predominantly caused by primary Epstein-Barr virus (EBV) infection, is a common disease in adolescents and young adults. EBV infection is nearly ubiquitous globally. Although primary EBV infection is asymptomatic in most individuals, IM manifests in a subset infected during adolescence or young adulthood. IM occurrence is linked to sibship structure, and is associated with increased risk of multiple sclerosis, other autoimmune diseases, and cancer later in life. We analyzed 47 biomarkers in 5,526 Danish individuals aged 18-60 years, of whom 604 had a history of IM, examining their associations with IM history up to 48 years after IM diagnosis. No significant long-term associations were observed after adjusting for multiple comparisons. When restricting the analysis to individuals measured within 10 years post-IM diagnosis, a statistically significant increase in CRP levels was observed in females. This association was not driven by oral contraceptive use. No significant associations between sibship structure and biomarker levels were detected. In conclusion, our study shows that while IM may lead to a transient increase in CRP levels in females, it does not result in long-term alterations in plasma biomarkers related to immune function, suggesting other mechanisms may be responsible for the long-term health impacts associated with IM.

PMID:40175486 | DOI:10.1038/s41598-025-95276-4

Microsyst Nanoeng. 2025 Apr 2;11(1):56. doi: 10.1038/s41378-025-00880-z.

ABSTRACT

Chronic kidney disease (CKD) significantly affects people's health and quality of life and presents a high economic burden worldwide. There are well-established biomarkers for CKD diagnosis. However, the existing routine standard tests are lab-based and governed by strict regulations. Creatinine is commonly measured as a filtration biomarker in blood to determine estimated Glomerular Filtration Rate (eGFR), as well as a normalization factor to calculate urinary Albumin-to-Creatinine Ratio (uACR) for CKD evaluation. In this study, we developed a passive flow microreactor for colorimetric urine creatinine measurement (uCR-Chip), which is highly amenable to integration with our previously developed microfluidic urine albumin assay. The combination of the 2-phase pressure compensation (2-PPC) technique and microfluidic channel network design accurately controls the fluidic mixing ratio and chemical reaction. Together with an optimized observation window (OW) design, a uniform and stable detection signal was achieved within 7 min. The color signal was measured by a simple USB microscope-based platform to quantify creatinine concentration in the sample. The combination of the custom in-house photomask production techniques and dry-film photoresist-based lithography enabled rapid iterative design optimization and precise chip fabrication. The developed assay achieved a dynamic linear detection range up to 40 mM and a lower limit of detection (LOD) of 0.521 mM, meeting the clinical precision requirements (comparable to existing point-of-care (PoC) systems). The microreactor was validated using creatinine standards spiked into commercial artificial urine that mimics physiological matrix. Our results showed acceptable recovery rate and low matrix effect, especially for the low creatinine concentration range in comparison to a commercial PoC uACR test. Altogether, the developed uCR-Chip offers a viable PoC test for CKD assessment and provides a potential platform technology to measure various disease biomarkers.

PMID:40175342 | DOI:10.1038/s41378-025-00880-z

Trends Plant Sci. 2025 Apr 1:S1360-1385(25)00063-9. doi: 10.1016/j.tplants.2025.03.003. Online ahead of print.

ABSTRACT

The field of mass spectrometry (MS)-based proteomics is rapidly advancing with technological and computational improvements, including leveraging the power of artificial intelligence (AI) to drive innovation. Such innovation has been particularly apparent in human disease research, where the intersection of these disciplines has pioneered a new age of disease diagnostics and pharmaceutical discovery. However, applications within plant sciences remains woefully under-represented and yet provides exceptional promise and potential to support new, interdisciplinary areas of research. Timely and novel examples of proteomics advancing plant science encompass biotechnology, climatic resiliency, agricultural production systems, and disease management. Herein, we propose new scientific avenues that leverage the power of proteomics and AI within plant science research to drive new discoveries and innovation.

PMID:40175191 | DOI:10.1016/j.tplants.2025.03.003

Adv Pharmacol. 2025;103:287-311. doi: 10.1016/bs.apha.2025.01.012. Epub 2025 Feb 26.

ABSTRACT

Bioinformatics has taken a pivotal place in the life sciences field. Not only does it improve, but it also fine-tunes and complements the wet lab experiments. It has been a driving force in the so-called biological sciences, converting them into hypothesis and data-driven fields. This study highlights the translational impact of bioinformatics on experimental biology and discusses its evolution and the advantages it has brought to advancing biological research. Computational analyses make labor-intensive wet lab work cost-effective by reducing the use of expensive reagents. Genome/proteome-wide studies have become feasible due to the efficiency and speed of bioinformatics tools, which can hardly be compared with wet lab experiments. Computational methods provide the scalability essential for manipulating large and complex data of biological origin. AI-integrated bioinformatics studies can unveil important biological patterns that traditional approaches may otherwise overlook. Bioinformatics contributes to hypothesis formation and experiment design, which is pivotal for modern-day multi-omics and systems biology studies. Integrating bioinformatics in the experimental procedures increases reproducibility and helps reduce human errors. Although today's AI-integrated bioinformatics predictions have significantly improved in accuracy over the years, wet lab validation is still unavoidable for confirming these predictions. Challenges persist in multi-omics data integration and analysis, AI model interpretability, and multiscale modeling. Addressing these shortcomings through the latest developments is essential for advancing our knowledge of disease mechanisms, therapeutic strategies, and precision medicine.

PMID:40175046 | DOI:10.1016/bs.apha.2025.01.012

Adv Pharmacol. 2025;103:231-263. doi: 10.1016/bs.apha.2025.01.003. Epub 2025 Feb 6.

ABSTRACT

Computational precision in drug discovery integrates algorithms and high-performance computing to analyze complex biological data with unprecedented accuracy, revolutionizing the identification of therapeutic targets. This process encompasses diverse computational and experimental approaches that enhance drug discovery's speed and precision. Advanced techniques like next-generation sequencing enable rapid genetic characterization, while proteomics explores protein expression and interactions driving disease progression. In-silico methods, including molecular docking, virtual screening, and pharmacophore modeling, predict interactions between small molecules and biological targets, accelerating early drug candidate identification. Structure-based drug design and molecular dynamics simulations refine drug designs by elucidating target structures and molecular behaviors. Ligand-based methods utilize known chemical properties to anticipate new compound activities. AI and machine learning optimizes data analysis, offering novel insights and improving predictive accuracy. Systems biology and network pharmacology provide a holistic view of biological networks, identifying critical nodes as potential drug targets, which traditional methods might overlook. Computational tools synergize with experimental techniques, enhancing the treatment of complex diseases with personalized medicine by tailoring therapies to individual patients. Ethical and regulatory compliance ensures clinical applicability, bridging computational predictions to effective therapies. This multi-dimensional approach marks a paradigm shift in modern medicine, delivering safer, more effective treatments with precision. By integrating bioinformatics, genomics, and proteomics, computational drug discovery has transformed how therapeutic interventions are developed, ensuring an era of personalized, efficient healthcare.

PMID:40175044 | DOI:10.1016/bs.apha.2025.01.003