ISME J. 2025 Jul 25:wraf136. doi: 10.1093/ismejo/wraf136. Online ahead of print.

ABSTRACT

The Indri indri is a critically endangered lemur species that has not successfully been maintained or bred under human care. Investigating this lemur's virtually unexplored gut microbiome will deepen our understanding of the species' health determinants and inform conservation efforts. Through metagenomic assembly and integration into an updated reference database, we found the I. indri faecal microbiome remains largely uncultivated (cultivated species representing <0.1% relative abundance) and is largely specific to this primate species. After reconstructing 342 metagenome-assembled genomes encompassing 48 candidate species from a total of 22 samples (18 of which newly sequenced), we substantially improved microbiome mappability to 85% on average and found evidence for a proportionally large core microbiome. Social group membership emerged as the main determinant of both their taxonomic and functional gut microbiome composition. Using strain-level profiling, we detected extensive microbiome transmission within social groups, suggesting physical interaction is key in promoting microbiome acquisition. Strain sharing rates were highest between mothers and their offspring. Intergroup strain sharing was minimal and inversely correlated with geographical distance, aligning with the rare intergroup interactions and stable territory occupancy coupled with ongoing habitat fragmentation. No evidence of microbiome acquisition through geophagy was detected. These findings underscore the profound influence of social structure on microbiome transmission and composition in I. indri, and highlight the importance of considering social dynamics into research and conservation strategies.

PMID:40709814 | DOI:10.1093/ismejo/wraf136

Elife. 2025 Jul 25;13:RP101327. doi: 10.7554/eLife.101327.

ABSTRACT

In the last decade, activity-dependent strategies for labeling multiple immediate early gene ensembles in mice have generated unprecedented insight into the mechanisms of memory encoding, storage, and retrieval. However, few strategies exist for brain-wide mapping of multiple ensembles, including their overlapping population, and none incorporate capabilities for downstream network analysis. Here, we introduce a scalable workflow to analyze traditionally coronally sectioned datasets produced by activity-dependent tagging systems. Intrinsic to this pipeline is simple multi-ensemble atlas registration and statistical testing in R (SMARTTR), an R package which wraps mapping capabilities with functions for statistical analysis and network visualization, and support for import of external datasets. We demonstrate the versatility of SMARTTR by mapping the ensembles underlying the acquisition and expression of learned helplessness (LH), a robust stress model. Applying network analysis, we find that exposure to inescapable shock (IS), compared to context training, results in decreased centrality of regions engaged in spatial and contextual processing and higher influence of regions involved in somatosensory and affective processing. During LH expression, the substantia nigra emerges as a highly influential region that shows a functional reversal following IS, indicating a possible regulatory function of motor activity during helplessness. We also report that IS results in a robust decrease in reactivation activity across a number of cortical, hippocampal, and amygdalar regions, indicating suppression of ensemble reactivation may be a neurobiological signature of LH. These results highlight the emergent insights uniquely garnered by applying our analysis approach to multiple ensemble datasets and demonstrate the strength of our workflow as a hypothesis-generating toolkit.

PMID:40709549 | DOI:10.7554/eLife.101327

ArXiv [Preprint]. 2025 Jul 14:arXiv:2507.10714v1.

ABSTRACT

Stochastic Petri Nets (SPNs) are an increasingly popular tool of choice for modeling discrete-event dynamics in areas such as epidemiology and systems biology, yet their parameter estimation remains challenging in general and in particular when transition rates depend on external covariates and explicit likelihoods are unavailable. We introduce a neural-surrogate (neural-network--based approximation of the posterior distribution) framework that predicts the coefficients of known covariate-dependent rate functions directly from noisy, partially observed token trajectories. Our model employs a lightweight 1D Convolutional Residual Network trained end-to-end on Gillespie-simulated SPN realizations, learning to invert system dynamics under realistic conditions of event dropout. During inference, Monte Carlo dropout provides calibrated uncertainty bounds together with point estimates. On synthetic SPNs with 20% missing events, our surrogate recovers rate-function coefficients with an RMSE = 0.108 and substantially runs faster than traditional Bayesian approaches. These results demonstrate that data-driven, likelihood-free surrogates can enable accurate, robust, and real-time parameter recovery in complex, partially observed discrete-event systems.

PMID:40709304 | PMC:PMC12288651

GEN Biotechnol. 2024 Aug;3(4):196-206. doi: 10.1089/genbio.2024.0014. Epub 2024 Aug 16.

ABSTRACT

The dissatisfaction within the postdoctoral training phase has led to the drastic reduction in the number of U.S. citizens pursuing postdoctoral positions within the biological and biomedical sciences fields. Even more so, there is an obvious disparity in not only the recruitment but the retention among underrepresented groups to pursue careers as academic scientists. The proposed social-ecological model and National Institute of Health advisory committee suggests reforming the postdoctoral training phase to overcome these downward trends and disparities. Importantly, some programs like the Stanford Propel Postdoctoral Program were integrating this framework and recommendations without knowledge that they would be released 2 years later. The goal of the Propel Program is to provide social, cohort, financial, and institutional support to diverse cohorts of postdoctoral trainee to diversify the professoriate. Within this piece, several of the Propel scholars come together to provide their perspectives on how the Propel Program has benefited their postdoctoral training experience.

PMID:40709103 | PMC:PMC12288851 | DOI:10.1089/genbio.2024.0014

Bioinform Adv. 2025 Jun 27;5(1):vbaf153. doi: 10.1093/bioadv/vbaf153. eCollection 2025.

ABSTRACT

MOTIVATION: The latest generation of deep learning-based structure prediction methods enable accurate modelling of most proteins and many complexes. However, preparing inputs for the locally installed software is not always straightforward, and the results of local runs are not always presented in an ideally accessible fashion. Furthermore, it is not yet clear whether the latest tools perform equivalently for all types of target.

RESULTS: ABCFold facilitates the use of AlphaFold 3, Boltz-1, and Chai-1 with a standardized input to predict atomic structures, with Boltz-1 and Chai-1 being installed on runtime (if required). MSAs can be generated internally using either the JackHMMER MSA search within AlphaFold 3, or with the MMseqs2 API. Alternatively, users can provide their own custom MSAs. This therefore allows AlphaFold 3 to be installed and run without downloading the large databases needed for JackHMMER. There are also straightforward options to use templates, including custom templates. Results from all packages are treated in a unified fashion, enabling easy comparison of results from different methods. A variety of visualization options are available which include information on steric clashes.

AVAILABILITY AND IMPLEMENTATION: ABCFold is coded in Python and JavaScript. All scripts and associated documentation are available from https://github.com/rigdenlab/ABCFold or https://pypi.org/project/ABCFold/.

PMID:40708869 | PMC:PMC12287924 | DOI:10.1093/bioadv/vbaf153

APL Bioeng. 2025 Jul 23;9(3):036107. doi: 10.1063/5.0246495. eCollection 2025 Sep.

ABSTRACT

Lens-free digital in-line holography (LDIH) provides a large field-of-view at micrometer-scale resolution, making it a promising tool for high-throughput cellular analysis. However, the complexity of diffraction images (holograms) produced by LDIH presents challenges for human interpretation and requires time-consuming computational reconstruction, often leading to artifacts and information loss. To address these issues, we present HoloNet, a novel deep learning architecture specifically designed for direct analysis of diffraction images in cellular diagnostics. Tailored to the unique characteristics of diffraction images, HoloNet captures multi-scale features, enabling it to outperform conventional convolutional neural networks in recognizing well-defined regions within complex holograms. HoloNet classifies breast cancer cell types with high precision and quantifies molecular marker intensities using raw diffraction images of cells stained with ER/PR and HER2. Additionally, HoloNet has proven effective in transfer learning applications, accurately classifying breast cancer cell lines and discovering previously unidentified subtypes through unsupervised learning. By integrating computational imaging with deep learning, HoloNet offers a robust solution to the challenges of holographic data analysis, significantly improving the accuracy and explainability of cellular diagnostics.

PMID:40708806 | PMC:PMC12289329 | DOI:10.1063/5.0246495

CNS Neurosci Ther. 2025 Jul;31(7):e70535. doi: 10.1111/cns.70535.

ABSTRACT

AIMS: Albiflorin, a key compound from Paeonia lactiflora, has shown therapeutic potential in neuropsychiatric and neurodegenerative disorders (NPDs and NDDs), especially depression and Alzheimer's disease (AD). This review aimed to summarize its pharmacological effects, mechanisms, pharmacokinetics, and therapeutic prospects.

DISCUSSION: Albiflorin exhibits multi-target actions, including modulation of monoamine neurotransmitters, inhibition of neuroinflammation, and enhancement of neuroplasticity. In AD, it reduces Aβ accumulation, improves mitochondrial function, and activates MAPK/ERK and Nrf2/HO-1 signaling pathways. In depression, it restores phospholipid and tryptophan metabolism, regulates HPA axis function, and increases BDNF expression. Albiflorin crosses the blood-brain barrier (BBB) and may act indirectly via the gut-brain axis through its metabolite benzoic acid. Though brain concentrations are low, its pharmacological effects remain significant. Albiflorin also shows potential benefits in conditions like cerebral ischemia and hypoxic-ischemic brain injury. Toxicological data indicate low systemic toxicity and good safety margins in vivo and in vitro.

CONCLUSIONS: Albiflorin demonstrates promising therapeutic potential for NPDs and NDDs via multi-pathway regulation. However, further studies are needed to optimize brain delivery, understand gut microbiota interactions, and confirm efficacy through clinical trials. The advancement of formulation strategies and pharmacokinetic research will be considered key to achieving clinical translation.

PMID:40708436 | DOI:10.1111/cns.70535

Birth Defects Res. 2025 Aug;117(8):e2514. doi: 10.1002/bdr2.2514.

ABSTRACT

BACKGROUND: Dexamethasone (DEX) is used during pregnancies at risk of early delivery or congenital adrenal hyperplasia. DEX exposure is also known to cause placental damage. Although placental cytokines/chemokines protect the fetus and regulate placental development, few studies have examined placental cytokine/chemokine transcript levels in DEX-dosed pregnant mice.

METHODS: To examine this, quantitative PCR and histological analysis in humanized mice were performed. Mice were injected once daily for five consecutive days with DEX (5 mg/kg) or saline (0.9%) via the tail vein on gestation days (GDs) 10-14, respectively (n = 3-5). All mice were intravenously injected with human immunoglobulin G (2 mg/kg) on GD14.

RESULTS: No statistically significant changes in maternal body weights by GD 12, absolute or relative placental weights in the dosed group were observed compared to concurrent controls. Fetal weights in the DEX-dosed group were lower than in concurrent controls, and statistically significant changes were observed on GD 18. Necrosis/apoptosis of cytotrophoblasts in the placenta's labyrinth zone was observed in the DEX-dosed dams. The placental transcript levels of interferon lambda receptor 1, interleukin 6, and C-X-C motif chemokine ligand 10 (Cxcl10) were higher in the DEX-dosed than the control group on GDs 15 and 16; the difference of Cxcl10 transcript level was statistically significant (p = 0.016) on GD 16.

CONCLUSIONS: Cxcl10 is overexpressed during DEX-induced placental damage in the mouse models, suggesting it as a potential biomarker of placental damage. Further studies are needed to confirm Cxcl10 changes during placental damage induced by other placental toxicants.

PMID:40708190 | DOI:10.1002/bdr2.2514

Biotechnol Biofuels Bioprod. 2025 Jul 24;18(1):81. doi: 10.1186/s13068-025-02684-9.

ABSTRACT

Plant specialised metabolism generates a vast array of compounds with significant potential across agriculture, medicine, cosmetics, and the food industry. A key challenge lies in optimising their production in the plant, as these compounds are often present in trace amounts in a complex metabolic cocktail. Given their high economic value, extensive efforts have been made to elucidate their biosynthetic pathways and pinpoint key regulatory and enzymatic targets. This knowledge has been applied for metabolic engineering to enhance the carbon flux towards metabolites of interest, thereby broadening the utility of plants as a source of high-value compounds. This review examines different metabolic engineering strategies employed today using the phenylpropanoid pathway as a case study and highlights the potential of integrating plant and microbial research to drive cross-disciplinary innovation.

PMID:40708042 | DOI:10.1186/s13068-025-02684-9

Genome Biol. 2025 Jul 24;26(1):220. doi: 10.1186/s13059-025-03680-w.

ABSTRACT

Advances in single-cell technology enable large-scale generation of omics data, promising for clarifying gene regulatory networks governing different cell type/states. Nonetheless, prevailing methods fail to account for universal and reusable regulatory modules in GRNs, which are fundamental underpinnings of cell type landscape. We introduce cRegulon to infer regulatory modules by modeling combinatorial regulation of transcription factors based on diverse GRNs from single-cell multi-omics data. Through benchmarking and applications using simulated datasets and real datasets, cRegulon outperforms existing approaches in identifying TF combinatorial modules as regulatory units and annotating cell types. cRegulon offers new insights and methodology into combinatorial regulation.

PMID:40707940 | DOI:10.1186/s13059-025-03680-w

Discov Oncol. 2025 Jul 24;16(1):1403. doi: 10.1007/s12672-025-03272-x.

ABSTRACT

BACKGROUND: Microbial lung infections may promote development of lung cancer through overlapping molecular mechanisms. This analysis aimed to identify a co-regulated peripheral blood gene signature in lung adenocarcinoma (LUAD) and microbial lung infections.

METHODS: A total of 403 peripheral blood transcriptomic profiles from five GEO test datasets-two LUAD (GSE39345, GSE103527) and three infection-related (GSE40012, GSE65682, GSE103119)-were analyzed using the limma package. Differentially expressed genes (DEGs) were defined by|log2FC| >1 and p < 0.05. Two additional GEO datasets (GSE42826 and GSE42830), comprising 30 blood samples (16 LUAD, 14 lung infection), served as validation sets. Shared DEGs were subjected to KEGG and GO enrichment analyses. Protein-protein interaction (PPI) networks were constructed in Cytoscape, and the top 10 hub genes were identified. Expression data of hub genes were compared between validation LUAD and lung infection samples using the Mann-Whitney U test, followed by linear regression and Pearson correlation to confirm co-regulation. Immune cell infiltration was assessed using xCell deconvolution algorithm.

RESULTS: Ninety-three significant DEGs were shared between LUAD and infection datasets, including 40 upregulated and 53 downregulated genes. Eight hub genes showed consistent differential expression in both LUAD and lung infection: BCL6, CD163, S100A12 (upregulated); and FLT3LG, RPL13, RPL14, RPL22, RPS4X (downregulated), of which BCL6, S100A12, FLT3LG, RPL13, RPL14, RPL22 and RPS4X were significantly co-regulated (R2 >0.8, p < 0.001) and correlated (p < 0.05). Immune profiling revealed that upregulated genes were associated with immunosuppressive cells such as Tregs and M2 macrophages, while downregulated genes were positively correlated with antitumor immune cell infiltration including CD8+ T cells and M1 macrophages. Consistent immune, stroma and microenvironment scores were observed between LUAD and lung infection.

CONCLUSION: This analysis identified a blood-based 7-gene signature shared between LUAD and microbial lung infections, associated with immunosuppressive microenvironment features, suggesting a potential link between infection-driven inflammation and tumor-promoting immune modulation.

PMID:40707667 | DOI:10.1007/s12672-025-03272-x

Nat Commun. 2025 Jul 24;16(1):6831. doi: 10.1038/s41467-025-62099-w.

ABSTRACT

Induced proximity by molecular glues refers to strategies that leverage the recruitment of proteins to facilitate their modification, regulation or degradation. As prospective design of molecular glues remains challenging, unbiased discovery methods are necessary to discover new chemical targets. Here we establish a high throughput affinity proteomics workflow leveraging E3 ligase activity-impaired CRBN-DDB1ΔB in cell lysates for the unbiased identification of molecular glue targets. By mapping the interaction landscape of CRBN-binding molecular glues, we unveil 298 protein targets and demonstrate the utility of enrichment methods for identifying targets overlooked by established methods. We use a computational workflow to estimate target confidence and perform biochemical and structural validation of uncharacterized neo-substrates. We further identify a lead compound for the previously untargeted non-zinc finger PPIL4 through a biochemical screen. Our study provides a comprehensive inventory of targets chemically recruited to CRBN and delivers a robust and scalable workflow for identifying drug-induced protein interactions in cell lysates.

PMID:40707481 | DOI:10.1038/s41467-025-62099-w

Nat Commun. 2025 Jul 24;16(1):6487. doi: 10.1038/s41467-025-61483-w.

ABSTRACT

Trichomonas vaginalis, the causative agent of the venereal disease trichomoniasis, infects men and women globally and is associated with serious outcomes during pregnancy, increased risk of HIV-1 infection, and cancers of the human reproductive tract. Species of trichomonad parasitize a range of hosts in addition to humans, including birds, livestock, and pets. Genetic analysis of trichomonads recovered from columbid birds has provided evidence that they undergo frequent host-switching, and that a spillover event from columbids likely gave rise to T. vaginalis in humans. Here we describe a comparative genomics study of seven trichomonad species, generating chromosome-scale reference genomes for T. vaginalis and its avian sister species Trichomonas stableri, and assemblies of five other species that infect birds and mammals. Human-infecting trichomonad lineages have undergone recent and convergent genome size expansions compared to their avian sister species, a result of extensive repeat expansions specifically of multicopy gene families and transposable elements, with genetic drift likely a driver due to relaxed selection. Trichomonads are thought to have independently host-switched twice from birds to mammals/humans. We identify gene functions implicated in the transition, including host tissue adherence and phagocytosis, extracellular vesicle formation, and CAZyme virulence factors, which are all associated with pathogenesis phenotypes.

PMID:40707449 | DOI:10.1038/s41467-025-61483-w

Fungal Biol. 2025 Aug;129(5):101590. doi: 10.1016/j.funbio.2025.101590. Epub 2025 May 6.

ABSTRACT

The Fifth International Symposium on Fungal Stress (ISFUS) brought together in Brazil many of the leaders in the field of fungal stress responses, from fourteen countries, for four days of outstanding science ranging from basic research to studies with agricultural, medical, industrial, and environmental significance. In addition to the excellent oral and poster presentations, the Symposium organisers ensured that all participants had ample opportunity to engage, socialise, and network to exchange ideas and share research. The conference was enhanced by the world-class venue near Iguazu Falls, probably the greatest natural phenomenon in South America.

PMID:40707112 | DOI:10.1016/j.funbio.2025.101590

Mol Cell Proteomics. 2025 Jul 22:101038. doi: 10.1016/j.mcpro.2025.101038. Online ahead of print.

ABSTRACT

African swine fever virus (ASFV) causes a lethal disease in pigs and represents a significant threat to the global pork industry due to the lack of effective vaccines or treatments. Despite intensive research, many ASFV proteins remain uncharacterized. This study aimed to elucidate the functions of two ASFV proteins, pMGF360-21R and pA151R, through comprehensive analysis of their interactions with host proteins. Using affinity purification-mass spectrometry and yeast two-hybrid screening approaches, we identified the host protein barrier-to-autointegration factor 1 (BANF1) as a key interactor of both viral proteins. Biochemical and colocalization assays confirmed these interactions and demonstrated that MGF360-21R and A151R expression leads to cytoplasmic relocation of BANF1. Functionally, BANF1 silencing significantly reduced ASFV replication, indicating its proviral role. Given BANF1's established function in regulating the cGAS/STING-dependent type I interferon (IFN-I) response, we postulated that A151R and MGF360-21R could inhibit this pathway. Using different strategies, we showed that both A151R and MGF360-21R did indeed inhibit IFN-I induction. Generation of ASFV deficient of A151R or MGF360-21R showed that both mutant viruses enhanced the host IFN response in primary porcine macrophages compared to wild-type virus. However, their capacity to inhibit this pathway could occur through mechanisms independent of BANF1. Proteomic analysis of BANF1 interactors during ASFV infection highlighted potentially roles in chromatin remodeling, nuclear transport, and innate immune response pathways. Altogether, our data provide new insights into ASFV-host interactions, identifying BANF1 as an important new host factor required for replication and uncovering novel functions for A151R and MGF360-21R.

PMID:40707000 | DOI:10.1016/j.mcpro.2025.101038

Toxicol Lett. 2025 Jul 22:S0378-4274(25)01552-8. doi: 10.1016/j.toxlet.2025.07.1415. Online ahead of print.

ABSTRACT

Endosulfan, an organochlorine pesticide, is implicated in human cardiovascular diseases. Protein-tyrosine phosphatase 4A3 (PTP4A3) has been identified to play a critical role in endothelial cell migration when exposure to endosulfan. In the present study, we aim to explore the epigenetic mechanism by which endosulfan upregulates PTP4A3 expression to enhance cell migration in human umbilical vein endothelial cells (HUVECs). Bioinformatics analysis showed that there were complementary sequences in the 3'-UTR of PTP4A3 and lncRNA KCNQ1OT1 to the seed regions of miR-140-5p. Dual luciferase reporter assay confirmed that miR-140-5p had potential binding capacity to PTP4A3 and KCNQ1OT1. Endosulfan upregulated PTP4A3 and KCNQ1OT1, but downregulated miR-140-5p expression, promoting cell migration through the activation of MAPK/ERK and PI3K/AKT pathways in HUVECs, which were inhibited by miR-140-5p overexpression or KCNQ1OT1 silencing Anti-Ago2 RNA immunoprecipitation experiments confirmed the binding interaction between miR-140-5p and KCNQ1OT1. Transfection of miR-140-5p mimics downregulated PTP4A3 and KCNQ1OT1, while si-KCNQ1OT1 downregulated PTP4A3 and upregulated miR-140-5p in HUVECs. Either miR-140-5p mimic or si-KCNQ1OT1 attenuated cell migration and influenced MAPK/ERK and PI3K/AKT signaling pathways in HUVECs, which were counteracted by co-transfection with pEGFP-PTP4A3 or anti-miR-140-5p. We observed the upregulation of PTP4A3 and KCNQ1OT1, as well as the downregulation of miR-140-5p in the aorta of the ApoE-/- atherosclerotic mice. These findings suggest the involvement of KCNQ1OT1/miR-140-5p/PTP4A3 axis in endosulfan-induced cell migration, providing new insights into the epigenetic mechanisms of endothelial dysfunction in cardiovascular diseases when exposure to endosulfan.

PMID:40706910 | DOI:10.1016/j.toxlet.2025.07.1415

Adv Drug Deliv Rev. 2025 Jul 22:115658. doi: 10.1016/j.addr.2025.115658. Online ahead of print.

ABSTRACT

Carbon-based nanomaterials (CBMs) and their polymeric composites have garnered widespread interest in treating neurotrauma and neurodegenerative diseases, where restoring damaged central and peripheral nervous systems remains a persistent clinical challenge. These materials provide exceptional electrical conductivity, mechanical robustness, and tunable nanoscale architectures conducive to guiding neuronal growth, synaptic connectivity, and targeted biomolecule delivery. In this review, we explore the rationale, recent advances, and translational potential of CBM scaffolds in promoting neuronal survival, neurite outgrowth, and functional maturity across various experimental models. We detail key fabrication strategies, including electrospinning, phase inversion, 3D bioprinting, and pyrolysis that enable precise control over scaffolds' structural and mechanical properties while facilitating the incorporation of neurotrophic factors, genes, and therapeutic drugs. Emerging in vivo findings suggest that CBM nanocomposites promote regenerative outcomes in peripheral nerve injuries at levels comparable to, or exceeding conventional autografts, underscoring their promise as off-the-shelf solutions. Nonetheless, concerns persist regarding large-scale manufacturing, cytotoxicity, and meeting regulatory standards for clinical use. By highlighting cutting-edge innovations and remaining bottlenecks, this review aims to guide future research endeavors in harnessing CBM scaffolds for safe and effective neural tissue repair.

PMID:40706866 | DOI:10.1016/j.addr.2025.115658

Bioresour Technol. 2025 Jul 22:133030. doi: 10.1016/j.biortech.2025.133030. Online ahead of print.

ABSTRACT

Free fatty acids (FFA) serve as versatile precursors for biofuels and oleochemicals, and their microbial production offers a renewable alternative to petrochemical processes. Escherichia coli has been extensively engineered for high-titer FFA production and currently serves as the benchmark chassis, achieving titers exceeding 35 g L-1 through advanced metabolic and systems biology approaches. In contrast, Pseudomonas putida KT2440 has recently gained attention due to its innate stress tolerance, redox flexibility, and broad substrate utilization, though reported FFA titers remain modest (∼0.67 g L-1). P. putida KT2440 exhibits innate tolerance to FFA toxicity, attributed to its surplus NADPH supply and robust membrane composition. This review provides a side-by-side comparison of the two hosts with respect to fatty acid biosynthetic pathways, redox metabolism, native tolerance mechanisms, and substrate scope. We summarize key metabolic engineering strategies used to enhance FFA production and examine recent advances in genetic toolkits and regulatory rewiring that have accelerated strain development in both organisms. While E. coli remains the leading host in terms of performance, P. putida offers distinct advantages for next-generation biomanufacturing. This comparative review outlines the key opportunities and challenges in FFA biosynthesis and offers a framework to guide future strain engineering for sustainable production.

PMID:40706765 | DOI:10.1016/j.biortech.2025.133030

J Microbiol Methods. 2025 Jul 22:107201. doi: 10.1016/j.mimet.2025.107201. Online ahead of print.

ABSTRACT

Nitrification inhibitors are valuable in mitigating nitrogen (N) losses from agricultural fertilizers, enhancing fertilizer use efficiency, and minimizing their environmental and climatic impacts. Currently, the portfolio of approved inhibitors is limited, creating a strong demand for new alternatives. Traditional nitrification inhibition assays rely on large soil systems, batch cultures, or, at best, microbial cultures in deep-well 96-well plates, none of which are suited for high-throughput screening. Here, we present a highly robust method that enables rapid and efficient screening of thousands of molecules on the soil-borne ammonia-oxidizing bacteria Nitrosomonas europaea and Nitrosospira multiformis. Our assay utilizes 384-well plates for both screening and read-out, requiring only 50 μl of culture per sample and low amounts of compound. The assay also allows for further characterization of nitrification inhibitors and differentiation between those targeting the ammonia monooxygenase (AMO) and hydroxylamine oxidoreductase (HAO) pathways. Finally, we applied the assay to test several oxazolidine variants and discovered goitrin as a novel biological nitrification inhibitor (BNI). Overall, this assay offers promising tools for the rapid identification of novel nitrification inhibitors, contributing to sustainable agriculture.

PMID:40706639 | DOI:10.1016/j.mimet.2025.107201

Nucleic Acids Res. 2025 Jul 19;53(14):gkaf670. doi: 10.1093/nar/gkaf670.

ABSTRACT

The 3D organization of the genome-in particular, which two regions of DNA are in contact with each other-plays a role in regulating gene expression. Several factors influence genome 3D organization. Nucleosomes (where ∼100 base pairs of DNA wrap around histone proteins) bend, twist, and compactify chromosomal DNA, altering its polymer mechanics. How much does the positioning of nucleosomes between gene loci influence contacts between those gene loci? And to what extent are polymer mechanics responsible for this? To address this question, we combine a stochastic polymer mechanics model of chromosomal DNA including twists and wrapping induced by nucleosomes with two data-driven pipelines. The first estimates nucleosome positioning from ATAC-seq data in regions of high accessibility. Most of the genome is low accessibility, so we combine this with a novel image analysis method that estimates the distribution of nucleosome spacing from electron microscopy data. There are no fit parameters in the biophysical model. We apply this method to IL-6, IL-15, CXCL9, and CXCL10, inflammatory marker genes in macrophages, before and after inflammatory stimulation, and compare the predictions with contacts measured by conformation capture experiments (4C-seq). We find that within a 500-kb genomic region, polymer mechanics with nucleosomes can explain 71% of close contacts. These results suggest that, while genome contacts on 100 kb scales are multifactorial, they may be amenable to mechanistic, physical explanation. Our work also highlights the role of nucleosomes, not just at the loci of interest, but between them, and not just the total number of nucleosomes, but their specific placement. The method generalizes to other genes, and can be used to address whether a contact is under active regulation by the cell (e.g. a macrophage during inflammatory stimulation).

PMID:40705928 | DOI:10.1093/nar/gkaf670