Nucleic Acids Res. 2025 Jan 24;53(3):gkaf028. doi: 10.1093/nar/gkaf028.

ABSTRACT

Genome graphs, including the recently released draft human pangenome graph, can represent the breadth of genetic diversity and thus transcend the limits of traditional linear reference genomes. However, there are no genome-graph-compatible tools for analyzing whole genome bisulfite sequencing (WGBS) data. To close this gap, we introduce methylGrapher, a tool tailored for accurate DNA methylation analysis by mapping WGBS data to a genome graph. Notably, methylGrapher can reconstruct methylation patterns along haplotype paths precisely and efficiently. To demonstrate the utility of methylGrapher, we analyzed the WGBS data derived from five individuals whose genomes were included in the first Human Pangenome draft as well as WGBS data from ENCODE (EN-TEx). Along with standard performance benchmarking, we show that methylGrapher fully recapitulates DNA methylation patterns defined by classic linear genome analysis approaches. Importantly, methylGrapher captures a substantial number of CpG sites that are missed by linear methods, and improves overall genome coverage while reducing alignment reference bias. Thus, methylGrapher is a first step toward unlocking the full potential of Human Pangenome graphs in genomic DNA methylation analysis.

PMID:39868538 | DOI:10.1093/nar/gkaf028

PNAS Nexus. 2025 Jan 16;4(1):pgaf008. doi: 10.1093/pnasnexus/pgaf008. eCollection 2025 Jan.

ABSTRACT

While more data are becoming available on gene activity at different levels of biological organization, our understanding of the underlying biology remains incomplete. Here, we introduce a metabolic efficiency framework that considers highly expressed proteins (HEPs), their length, and biosynthetic costs in terms of the amino acids (AAs) they contain to address the observed balance of expression costs in cells, tissues, and cancer transformation. Notably, the combined set of HEPs in either cells or tissues shows an abundance of large and costly proteins, yet tissues compensate this with short HEPs comprised of economical AAs, indicating a stronger tendency toward mitigating costs. We additionally observe that short proteins are prevalent HEPs across individual cells and tissues, whereas long ones are more specific. Furthermore, the precise proportion of short, long, economical, or costly HEP classes indicates that particular cell types and tissues align more closely with the metabolic efficiency model, with some tissues displaying behavior akin to their constituent cells. Finally, tumors typically increase the production of short and low-cost HEPs compared with matched normal tissues, while genes that decrease their high expression levels in tumors often tend to be associated with high costs. Overall, the metabolic efficiency framework serves as a useful simplifying model for interpreting genome-wide expression data across scales.

PMID:39867669 | PMC:PMC11759310 | DOI:10.1093/pnasnexus/pgaf008

Bioinform Adv. 2025 Jan 21;5(1):vbae209. doi: 10.1093/bioadv/vbae209. eCollection 2025.

ABSTRACT

MOTIVATION: Missing values are prevalent in high-throughput measurements due to various experimental or analytical reasons. Imputation, the process of replacing missing values in a dataset with estimated values, plays an important role in multivariate and machine learning analyses. The three missingness patterns, including missing completely at random, missing at random, and missing not at random, describe unique dependencies between the missing and observed data. The optimal imputation method for each dataset depends on the type of data, the cause of the missingness, and the nature of relationships between the missing and observed data. The challenge is to identify the optimal imputation solution for a given dataset.

RESULTS: ImpLiMet: is a user-friendly web-platform that enables users to impute missing data using eight different methods. For a given dataset, ImpLiMet suggests the optimal imputation solution through a grid search-based investigation of the error rate for imputation across three missingness data simulations. The effect of imputation can be visually assessed by histogram, kurtosis, and skewness, as well as principal component analysis comparing the impact of the chosen imputation method on the distribution and overall behavior of the data.

AVAILABILITY AND IMPLEMENTATION: ImpLiMet is freely available at https://complimet.ca/shiny/implimet/ and https://github.com/complimet/ImpLiMet.

PMID:39867531 | PMC:PMC11761345 | DOI:10.1093/bioadv/vbae209

ACS Cent Sci. 2025 Jan 3;11(1):25-35. doi: 10.1021/acscentsci.4c01437. eCollection 2025 Jan 22.

ABSTRACT

Antimicrobial drug resistance (AMR) is a pressing global human health challenge. Humans face one of their grandest challenges as climate change expands the habitat of vectors that bear human pathogens, incidences of nosocomial infections rise, and new antibiotics discovery lags. AMR is a multifaceted problem that requires a multidisciplinary and an "all-hands-on-deck" approach. As chemical microbiologists, we are well positioned to understand the complexities of AMR while seeing opportunities for tackling the challenge. In this Outlook, we focus on vulnerabilities of human pathogens and posit that they represent "opportunity targets" for which few modulatory ligands exist. We center our attention on proteins in Gram-negative organisms, which are recalcitrant to many antibiotics because of their external membrane barrier. Our hope is to highlight such targets and explore their potential as "druggable" proteins for infectious disease mitigation. We posit that success in this endeavor will introduce new classes of antibiotics that might alleviate some of the current pressing AMR concerns.

PMID:39866699 | PMC:PMC11758222 | DOI:10.1021/acscentsci.4c01437

Heliyon. 2025 Jan 2;11(1):e41633. doi: 10.1016/j.heliyon.2025.e41633. eCollection 2025 Jan 15.

ABSTRACT

Isoxazole and oxadiazole derivatives inhibiting 3-hydroxykynurenine transaminase (3HKT) are potential larvicidal candidates. This study aims to identify more suited potential inhibitors of Anopheles gambiae 3HKT (Ag3HKT) through molecular docking and molecular dynamics simulation. A total of 958 compounds were docked against Anopheles gambiae 3HKT (PDB ID: 2CH2) using Autodock vina and Autodock4. The top three identified hits were subjected to 300 ns molecular dynamics simulation using AMBER 18 and ADMET analysis using SWISSADME predictor and ADMETSAR. Replacement of alkyl attachment on C5 of isoxazole or oxadiazole derivative with a cycloalkyl group yielded compounds with lower binding energy than their straight chain counterparts. The top three compounds were brominated compounds, 2-[3-(4-bromophenyl)-1,2-oxazol-5-yl]cyclopentane-1-carboxylic acid, 2-[3-(4-bromophenyl)-1,2,4-oxadiazol-5-yl]cyclopentane-1-carboxylic acid, 3-[3-(4-bromo-2-methylphenyl)-1,2,4-oxadiazol-5-yl]cyclopentane-1-carboxylic acid, and they had binding energies of -8.58, -8.25, and -8.18 kcal/mol in virtual screening against 2CH2 protein target, respectively. These compounds were predicted to be less toxic than temephos, a standard larvicide and more biodegradable than previously reported inhibitors. The three compounds exhibited a greater stabilizing effect on 2CH2 protein target than 4-[3-(4-bromophenyl)-1,2,4-oxadiazol-5-yl]butanoic acid, a previously reported inhibitor candidate with good larvicidal activity on Aedes aegypti. Further thermodynamic calculations revealed that the top three compounds possessed total binding energies (ΔGbind) of -26.64 kcal/mol, -24.26 kcal/mol and -14.11 kcal/mol, respectively, as compared to -12.02 kcal/mol for 4-[3-(4-bromophenyl)-1,2,4-oxadiazol-5-yl]butanoic acid. These compounds could be better larvicides than previously reported isoxazole or oxadiazole derivatives and safer than temephos.

PMID:39866405 | PMC:PMC11759636 | DOI:10.1016/j.heliyon.2025.e41633

Development. 2025 Jan 27:dev.204397. doi: 10.1242/dev.204397. Online ahead of print.

ABSTRACT

Numerous regulators of cardiomyocyte (CM) proliferation have been identified, yet how they coordinate during cardiac development or regeneration is poorly understood. Here, we developed a computational model of the CM proliferation regulatory network to obtain key regulators and systems-level understanding. The model defines five modules (DNA replication, mitosis, cytokinesis, growth factor, Hippo pathway) and integrates them into a network of 72 nodes and 88 reactions that correctly predicts 73 of 78 (93.6%) independent experiments from the literature. The model predicts that in response to YAP activation, the Hippo module crosstalks to the growth factor module via PI3K and cMyc to drive cell cycle activity. This predicted YAP-cMyc axis is validated experimentally in rat cardiomyocytes and further supported by YAP-stimulated cMyc open chromatin and mRNA in mouse hearts. This validated computational model predicts how individual regulators and modules coordinate to control CM proliferation.

PMID:39866065 | DOI:10.1242/dev.204397

Plant J. 2025 Jan;121(2):e17210. doi: 10.1111/tpj.17210.

ABSTRACT

Bacterial wilt caused by Ralstonia solanacearum is a devastating disease affecting a great many crops including peanut. The pathogen damages plants via secreting type Ш effector proteins (T3Es) into hosts for pathogenicity. Here, we characterized RipAU was among the most toxic effectors as ΔRipAU completely lost its pathogenicity to peanuts. A serine residue of RipAU is the critical site for cell death. The RipAU targeted a subtilisin-like protease (AhSBT1.7) in peanut and both protein moved into nucleus. Heterotic expression of AhSBT1.7 in transgenic tobacco and Arabidopsis thaliana significantly improved the resistance to R. solanacearum. The enhanced resistance was linked with the upregulating ERF1 defense marker genes and decreasing pectin methylesterase (PME) activity like PME2&4 in cell wall pathways. The RipAU played toxic effect by repressing R-gene, defense hormone signaling, and AhSBTs metabolic pathways but increasing PMEs expressions. Furthermore, we discovered AhSBT1.7 interacted with AhPME4 and was colocalized at nucleus. The AhPME speeded plants susceptibility to pathogen via mediated cell wall degradation, which inhibited by AhSBT1.7 but upregulated by RipAU. Collectively, RipAU impaired AhSBT1.7 defense for pathogenicity by using PME-mediated cell wall degradation. This study reveals the mechanism of RipAU pathogenicity and AhSBT1.7 resistance, highlighting peanut immunity to bacterial wilt for future improvement.

PMID:39866050 | DOI:10.1111/tpj.17210

Curr Neuropharmacol. 2025 Jan 24. doi: 10.2174/011570159X323666241029171256. Online ahead of print.

ABSTRACT

Epilepsy is a neurological disorder affecting millions of people worldwide. Antiseizure medications (ASM) remain a critical therapeutic intervention for treating epilepsy, notwithstanding the rapid development of other therapies. There have been substantial advances in epilepsy medications over the past three decades, with over 20 ASMs now available commercially. Here we describe the conventional and unique mechanisms of action of ASMs, focusing on everolimus, cannabidiol, cenobamate, fenfluramine, and ganaxolone, the five most recently marketed ASMs. Major obstacles in the development of ASMs are also addressed, particularly drug-resistant epilepsy as well as psychiatric and behavioral adverse effects of ASMs. Moreover, we delve into the mechanisms and comparative efficacy of ASM polytherapy, with remarks on the benefits and challenges in their application in clinical practice. In addition, the characteristics of the ideal ASM are outlined in this review. The review also discusses the development of new potential ASMs, including modifying existing ASMs to improve efficacy and tolerability. Furthermore, we expound on the modulation of γ- aminobutyric acid type A receptor (GABAAR) as a strategy for the treatment of epilepsy and the identification of a GABAAR agonist, isoguvacine, as a potential ASM.

PMID:39865817 | DOI:10.2174/011570159X323666241029171256

Protein Sci. 2025 Feb;34(2):e70023. doi: 10.1002/pro.70023.

ABSTRACT

Proteins' flexibility is a feature in communicating changes in cell signaling instigated by binding with secondary messengers, such as calcium ions, associated with the coordination of muscle contraction, neurotransmitter release, and gene expression. When binding with the disordered parts of a protein, calcium ions must balance their charge states with the shape of calcium-binding proteins and their versatile pool of partners depending on the circumstances they transmit. Accurately determining the ionic charges of those ions is essential for understanding their role in such processes. However, it is unclear whether the limited experimental data available can be effectively used to train models to accurately predict the charges of calcium-binding protein variants. Here, we developed a chemistry-informed, machine-learning algorithm that implements a game theoretic approach to explain the output of a machine-learning model without the prerequisite of an excessively large database for high-performance prediction of atomic charges. We used the ab initio electronic structure data representing calcium ions and the structures of the disordered segments of calcium-binding peptides with surrounding water molecules to train several explainable models. Network theory was used to extract the topological features of atomic interactions in the structurally complex data dictated by the coordination chemistry of a calcium ion, a potent indicator of its charge state in protein. Our design created a computational tool of CaXML, which provided a framework of explainable machine learning model to annotate ionic charges of calcium ions in calcium-binding proteins in response to the chemical changes in an environment. Our framework will provide new insights into protein design for engineering functionality based on the limited size of scientific data in a genome space.

PMID:39865355 | DOI:10.1002/pro.70023

Life Sci Space Res (Amst). 2025 Feb;44:10-22. doi: 10.1016/j.lssr.2024.11.004. Epub 2024 Nov 22.

ABSTRACT

The space environment presents unique stressors, such as microgravity and space radiation, which can induce molecular and physiological changes in living organisms. To identify key reproducible transcriptomic features and explore potential biological roles in space-flown C. elegans, we integrated transcriptomic data from C. elegans subjected to four spaceflights aboard the International Space Station (ISS) and identified 32 reproducibly differentially expressed genes (DEGs). These DEGs were enriched in pathways related to the structural constituent of cuticle, defense response, unfolded protein response, longevity regulation, extracellular structural organization, and signal receptor regulation. Among these 32 DEGs, 13 genes were consistently downregulated across four spaceflight conditions, primarily associated with the structural constituent of the cuticle. The remaining genes, involved in defense response, unfolded protein response, and longevity regulation pathway, exhibited distinct patterns depending on spaceflight duration: they were downregulated during short-term spaceflights but upregulated during long-term spaceflights. To explore the potential space stressors responsible for these transcriptomic changes, we performed qRT-PCR experiments on C. elegans exposed to simulated microgravity and low-dose radiation. Our results demonstrated that cuticle-related gene expression was significantly downregulated under both simulated microgravity and low-dose radiation conditions. In contrast, almost all genes involved in defense response, unfolded protein response, and longevity regulation pathway were downregulated under simulated microgravity but upregulated under low-dose radiation exposure. These findings suggest that both microgravity and space radiation inhibit cuticle formation; microgravity as the primary stressor inhibit defense response, unfolded protein response, and longevity regulation pathway during short-term spaceflights, while space radiation may promote these processes during long-term spaceflights. In summary, through integrated spaceflight transcriptomic analyses and simulated space experiments, we identified key transcriptomic features and potential biological functions in space-flown C. elegans, shedding light on the space stressors responsible for these changes. This study provides new insights into the molecular and physiological adaptations of C. elegans to spaceflight, highlighting the distinct impacts of microgravity and space radiation.

PMID:39864902 | DOI:10.1016/j.lssr.2024.11.004

Environ Toxicol Chem. 2025 Jan 6:vgae063. doi: 10.1093/etojnl/vgae063. Online ahead of print.

ABSTRACT

An adverse outcome pathway (AOP) framework maps the sequence of events leading to adverse outcomes from chemical exposures, providing a mechanistic understanding often absent in traditional methods. The quantitative AOP (qAOP) advances AOP by integrating quantitative data and mathematical modeling, thereby providing a more precise comprehension of relationships between molecular initiating events, key events, and adverse outcomes. This review critically examines three primary methodologies: systems toxicology, regression modeling, and Bayesian network modeling, highlighting their strengths, limitations, and specific data requirements within toxicology. Through an analysis of current methodologies and challenges, this review emphasizes the integration of experimental and computational approaches to elucidate key event relationships and proposes strategies for overcoming limitations through standardized protocols and advanced computational tools. By outlining future research directions and the potential of qAOPs to transform chemical risk assessment, this review aims to contribute to the advancement of regulatory science and the protection of public health and the environment.

PMID:39864436 | DOI:10.1093/etojnl/vgae063

Cell Rep. 2025 Jan 25;44(2):115241. doi: 10.1016/j.celrep.2025.115241. Online ahead of print.

ABSTRACT

Hematopoietic stem cells (HSCs) possess the capacity to regenerate the entire hematopoietic system. However, the precise HSC dynamics in the early post-transplantation phase remain an enigma. Clinically, the initial hematopoiesis in the post-transplantation period is critical, necessitating strategies to accelerate hematopoietic recovery. Here, we uncovered the spatiotemporal dynamics of early active hematopoiesis, "hematopoietic cell inflation," using a highly sensitive in vivo imaging system. Hematopoietic cell inflation occurs in three peaks in the spleen after transplantation, with common myeloid progenitors (CMPs), notably characterized by HSC-like signatures, playing a central role. Leveraging these findings, we developed expanded CMPs (exCMPs), which exhibit a gene expression pattern that selectively proliferates in the spleen and promotes hematopoietic expansion. Moreover, universal exCMPs supported early hematopoiesis in allogeneic transplantation. Human universal exCMPs have the potential to be a viable therapeutic enhancement for all HSC transplant patients.

PMID:39864058 | DOI:10.1016/j.celrep.2025.115241

J Immunother Cancer. 2025 Jan 25;13(1):e010083. doi: 10.1136/jitc-2024-010083.

ABSTRACT

BACKGROUND: B7 homolog 3 (B7-H3), an overexpressed antigen across multiple solid cancers, represents a promising target for CAR T cell therapy. This study investigated the expression of B7-H3 across various solid tumors and developed novel monoclonal antibodies (mAbs) targeting B7-H3 for CAR T cell therapy.

METHODS: Expression of B7-H3 across various solid tumors was evaluated using RNA-seq data from TCGA, TARGET, and GTEx datasets and by flow cytometry staining. B7-H3-specific mAbs were developed by immunizing mice with human B7-H3, screening with ELISA, and analyzing kinetics with surface plasmon resonance. These mAbs were used to create second-generation CAR constructs, which were evaluated in vitro and in vivo for their antitumor function.

RESULTS: We identified four mAb clones from immunized mice, with three demonstrating high specificity and affinity. The second-generation B7-H3 CAR T cells derived from these mAbs exhibited robust cytotoxicity against B7-H3-positive targets and successfully infiltrated and eliminated tumor spheroids in vitro. In a xenograft mouse model of glioblastoma, these CAR T cells, particularly those derived from clone A2H4, eradicated the primary tumor, and effectively controlled rechallenge tumor, resulting in prolonged survival of the xenograft mice. In vivo T cell trafficking revealed high accumulation and persistence of A2H4-derived CAR T cells at the tumor site.

CONCLUSIONS: Our results provide novel B7-H3-targeted CAR T cells with high efficacy, paving the way for clinical translation of solid tumor treatment.

PMID:39863300 | DOI:10.1136/jitc-2024-010083

Biochim Biophys Acta Mol Basis Dis. 2025 Jan 23:167688. doi: 10.1016/j.bbadis.2025.167688. Online ahead of print.

ABSTRACT

Cancer, a heterogeneous disease, presents significant challenges for drug development due to its complex etiology. Drug repurposing, particularly through network medicine approaches, offers a promising avenue for cancer treatment by analyzing how drugs influence cellular networks on a systemic scale. The advent of large-scale proteomics data provides new opportunities to elucidate regulatory mechanisms specific to cancer subtypes. Herein, we present NetSDR, a Network-based Subtype-specific Drug Repurposing framework for prioritizing repurposed drugs specific to certain cancer subtypes, guided by subtype-specific proteomic signatures and network perturbations. First, by integrating cancer subtype information into a network-based method, we developed a pipeline to recognize subtype-specific functional modules. Next, we conducted drug response analysis for each module to identify the "therapeutic module" and then used deep learning to construct weighted drug response network for the particular subtype. Finally, we employed a perturbation response scanning-based drug repurposing method, which incorporates dynamic information, to facilitate the prioritization of candidate drugs. Applying the framework to gastric cancer, we attested the significance of the extracellular matrix module in treatment strategies and discovered a promising potential drug target, LAMB2, as well as a series of possible repurposed drugs. This study demonstrates a systems biology framework for precise drug repurposing in cancer and other complex diseases.

PMID:39862994 | DOI:10.1016/j.bbadis.2025.167688

Cell. 2025 Jan 22:S0092-8674(25)00032-7. doi: 10.1016/j.cell.2025.01.003. Online ahead of print.

ABSTRACT

Knowledge of protein-metabolite interactions can enhance mechanistic understanding and chemical probing of biochemical processes, but the discovery of endogenous ligands remains challenging. Here, we combined rapid affinity purification with precision mass spectrometry and high-resolution molecular docking to precisely map the physical associations of 296 chemically diverse small-molecule metabolite ligands with 69 distinct essential enzymes and 45 transcription factors in the gram-negative bacterium Escherichia coli. We then conducted systematic metabolic pathway integration, pan-microbial evolutionary projections, and independent in-depth biophysical characterization experiments to define the functional significance of ligand interfaces. This effort revealed principles governing functional crosstalk on a network level, divergent patterns of binding pocket conservation, and scaffolds for designing selective chemical probes. This structurally resolved ligand interactome mapping pipeline can be scaled to illuminate the native small-molecule networks of complete cells and potentially entire multi-cellular communities.

PMID:39862855 | DOI:10.1016/j.cell.2025.01.003

EBioMedicine. 2025 Jan 24;112:105558. doi: 10.1016/j.ebiom.2025.105558. Online ahead of print.

ABSTRACT

BACKGROUND: Aging increases disease susceptibility and reduces vaccine responsiveness, highlighting the need to better understand the aging immune system and its clinical associations. Studying the human immune system, however, remains challenging due to its complexity and significant inter-individual variability.

METHODS: We conducted an immune profiling study of 550 elderly participants (≥60 years) and 100 young controls (20-40 years) from the RESIST Senior Individuals (SI) cohort. Extensive demographic, clinical, and laboratory data were collected. Multi-color spectral flow cytometry and 48-plex plasma cytokine assays were used for deep immune phenotyping. Data were analyzed using unsupervised clustering and multi-dataset integration approaches.

FINDINGS: We studied 97 innate and adaptive immune cell populations, revealing intricate age- and sex-related changes in the elderly immune system. Our large sample size allowed detection of even subtle changes in cytokines and immune cell clusters. Integrative analysis combining clinical, laboratory, and immunological data revealed systems-level aging signatures, including shifts in specific immune cell subpopulations and cytokine concentrations (e.g., HGF and CCL27). Additionally, we identified unique immune signatures associated with smoking, obesity, and diseases such as osteoporosis, heart failure, and gout.

INTERPRETATION: This study provides one of the most comprehensive immune profiles of elderly individuals, uncovering high-resolution immune changes associated with aging. Our findings highlight clinically relevant immune signatures that enhance our understanding of aging-related diseases and could guide future research into new treatments, offering translational insights into human health and aging.

FUNDING: Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy-EXC 2155-project number 390874280.

PMID:39862806 | DOI:10.1016/j.ebiom.2025.105558

Placenta. 2025 Jan 20;161:31-38. doi: 10.1016/j.placenta.2025.01.009. Online ahead of print.

ABSTRACT

INTRODUCTION: Fusion of mononucleated cytotrophoblasts into syncytium leads to trophoblast senescence. Yet, premature senescence is associated with preeclampsia, fetal growth restriction (FGR), and related obstetrical syndromes. A set of 28 transcripts that comprise senescence-associated secretory phenotype (SASP) was recently described in placentas from women with preeclampsia. We posited that this transcript set is uniquely regulated in late-term placentas or in placentas derived from participants with major obstetrical syndromes.

METHODS: Using our large placental RNAseq bank, we analyzed data from healthy participants (n = 33) with histologically normal placentas, representing delivery at 37-41 weeks. To represent diseases, we included RNAseq data from participants (n = 220) with severe preeclampsia, FGR, FGR with a hypertensive disorder (FGR + HDP), or spontaneous preterm delivery, and healthy controls (n = 129). We also assessed the expression of several SASPs in primary human trophoblasts that were exposed in vitro to hypoxia, reduced differentiation, or ferroptotic or apoptotic signals.

RESULTS: Among the 28 SASP transcripts analyzed, eight had a significant change between deliveries at <37 weeks vs ≥ 41 weeks, including upregulation of FSTL3, IL1RL1, INHBA, and VEGFA and downregulation of STC1, RARRES2, MRC2, and SELP. The expression of SASP mRNAs was enriched in the placentas from the assessed syndromes, with most expression changes in placentas from FGR/HDP. Our in vitro analysis associated hypoxia or apoptosis with altered expression of FSTL3, VEGFA, and DKK1.

DISCUSSION: A set of placental SASPs defines late-term placentas, placental dysfunction-related clinical syndromes, and in vitro-defined trophoblast injury. Trophoblastic SASP signatures may assist in characterizing placental senescence in health and disease.

PMID:39862734 | DOI:10.1016/j.placenta.2025.01.009

Genomics Proteomics Bioinformatics. 2025 Jan 25:qzaf002. doi: 10.1093/gpbjnl/qzaf002. Online ahead of print.

ABSTRACT

Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) techniques hold great value in evaluating the heterogeneity and spatial characteristics of hematopoietic cells within tissues. These two techniques are highly complementary, with scRNA-seq offering single-cell resolution and ST retaining spatial information. However, there is an urgent demand for well-organized and user-friendly toolkits capable of handling single-cell and spatial information. Here, we present HemaScope, a specialized bioinformatics toolkit featuring modular designs to analyze scRNA-seq and ST data generated from hematopoietic cells. It enables users to perform quality control, basic analysis, cell atlas construction, cellular heterogeneity exploration, and dynamical examination on scRNA-seq data. Also, it can perform spatial analysis and microenvironment analysis on ST data. Meanwhile, HemaScope takes into consideration hematopoietic cell-specific features, including lineage affiliation evaluation, cell cycle prediction, and marker gene collection. To enhance the user experience, we have deployed the toolkit in user-friendly forms: HemaScopeR (an R package), HemaScopeCloud (a web server), HemaScopeDocker (a Docker image), and HemaScopeShiny (a graphical interface). In case studies, we employed it to construct a cell atlas of human bone marrow, analyze age-related changes, and identify acute myeloid leukemia cells in mice. Moreover, we characterized the microenvironments in angioimmunoblastic T cell lymphoma and primary central nervous system lymphoma, elucidating tumor boundaries. HemaScope is freely available at https://zhenyiwangthu.github.io/HemaScope_Tutorial/.

PMID:39862439 | DOI:10.1093/gpbjnl/qzaf002

STAR Protoc. 2025 Jan 24;6(1):103589. doi: 10.1016/j.xpro.2024.103589. Online ahead of print.

ABSTRACT

The intestine features a two-front nutrient supply environment, comprising an enteral side enriched with microbial and dietary metabolites and a serosal side supplied by systemic nutrients, collectively supporting intestinal and systemic homeostasis, but there is currently no optimal approach for extracting and assessing the local intestinal microenvironment. Here, we present a protocol for constructing a nutrient supply model in mice and extracting gut interstitial fluid (GIF) via centrifugation. This model and the extracted GIF are suitable for downstream analyses. For complete details on the use and execution of this protocol, please refer to Zhang et al.1.

PMID:39862429 | DOI:10.1016/j.xpro.2024.103589

Bioinformatics. 2025 Jan 25:btaf043. doi: 10.1093/bioinformatics/btaf043. Online ahead of print.

ABSTRACT

SUMMARY: Gene and genome duplications are major evolutionary forces that shape the diversity and complexity of life. However, different duplication modes have distinct impacts on gene function, expression, and regulation. Existing tools for identifying and classifying duplicated genes are either outdated or not user-friendly. Here, we present doubletrouble, an R/Bioconductor package that provides a comprehensive and robust framework for analyzing duplicated genes from genomic data. doubletrouble can detect and classify gene pairs as derived from six duplication modes (segmental, tandem, proximal, retrotransposon-derived, DNA transposon-derived, and dispersed duplications), calculate substitution rates, detect signatures of putative whole-genome duplication events, and visualize results as publication-ready figures. We applied doubletrouble to classify the duplicated gene repertoire in 822 eukaryotic genomes, and results were made available through a user-friendly web interface.

AVAILABILITY AND IMPLEMENTATION: doubletrouble is available on Bioconductor (https://bioconductor.org/packages/doubletrouble), and the source code is available in a GitHub repository (https://github.com/almeidasilvaf/doubletrouble). doubletroubledb is available online at https://almeidasilvaf.github.io/doubletroubledb/.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online and at https://github.com/almeidasilvaf/doubletrouble_paper.

PMID:39862387 | DOI:10.1093/bioinformatics/btaf043