Evodevo. 2025 May 3;16(1):5. doi: 10.1186/s13227-025-00242-w.

ABSTRACT

Homology, or relationship among characters by common descent, has been notoriously difficult to assess for many morphological features, and cell types in particular. The ontogenetic origin of morphological traits means that the only physically inherited information is encoded in the genomes. However, the complexity of the underlying gene regulatory network and often miniscule changes that can impact gene expression, make it practically impossible to postulate a clear demarcation line for what molecular signature should "define" a homologous cell type between two deeply branching animals. In this Hypothesis article, we propose the use of the recently characterized irreversible genomic states, that occur after chromosomal and sub-chromosomal mixing of genes and regulatory elements, to dissect regulatory signatures of each cell type into irreversible and reversible configurations. While many of such states will be non-functional, some may permanently impact gene expression in a given cell type. Our proposal is that such evolutionarily irreversible, and thus synapomorphic, functional genomic states can constitute a criterion for the timing of the origin of deep evolutionary cell type homologies. Our proposal thus aims to close the gap between the clearly defined homology of the individual genomic characters and their genomic states to the homology at the phenotypic level through the identification of the underlying evolutionarily irreversible and regulatory linked states.

PMID:40319312 | DOI:10.1186/s13227-025-00242-w

Sci Rep. 2025 May 3;15(1):15498. doi: 10.1038/s41598-025-00373-z.

ABSTRACT

SARS-CoV-2 significantly impacts the human metabolome. This study aims to evaluate the predictive capability of a comprehensive module clustering approach in plasma metabolomics for identifying the risk of critical complications in COVID-19 patients admitted to intensive care units (ICUs). We conducted a prospective monocenter study, gathering blood samples within 24 h of ICU admission, alongside clinical, biological, and demographic patient characteristics. Subsequently, we quantified patients' plasma metabolome using a comprehensive untargeted metabolomics approach. First, we stratified patients based on a composite outcome score indicating critical status. Analysis of potential predictors revealed that older patients with higher severity scores and pronounced alterations in key biological parameters are more likely to experience critical complications. Next, we identified 6,667 metabolic features clustered into 57 annotated metabolic modules across all patients by employing an integrative metabolomics approach. Furthermore, we identified the most differentially expressed metabolic modules related to patients' outcomes. Moreover, we defined the top five most predictive metabolites of critical status: homoserine, urobilinogen, methionine, xanthine and pipecolic acid. These five predictors alone demonstrated similar or superior performance compared to clinical and demographic variables in predicting patients' outcomes. This innovative metabolic module inference approach offers a valuable framework for identifying patients prone to complications upon ICU admission for COVID-19. Its potential applications extend to enhancing patient management across diverse clinical settings.

PMID:40319053 | DOI:10.1038/s41598-025-00373-z

Nat Commun. 2025 May 3;16(1):4131. doi: 10.1038/s41467-025-59474-y.

ABSTRACT

The colon epithelium frequently incurs damage through toxic influences. Repair is rapid, mediated by cellular plasticity and acquisition of the highly proliferative regenerative state. However, the mechanisms that promote the regenerative state are not well understood. Here, we reveal that upon injury and subsequent inflammatory response, IFN-γ drives widespread epithelial remodeling. IFN-γ promotes rapid apoptotic extrusion of fully differentiated surface colonocytes, while simultaneously causing differentiation of crypt-base stem and progenitor cells towards a colonocyte-like lineage. However, unlike homeostatic colonocytes, these IFN-γ-induced colonocytes neither respond to nor produce BMP-2 but retain regenerative capacity. The reduction of BMP-2-producing epithelial surface cells causes a remodeling of the surrounding mesenchymal niche, inducing high expression of HGF, which promotes proliferation of the IFN-γ-induced colonocytes. This mechanism of lineage replacement and subsequent remodeling of the mesenchymal niche enables tissue-wide adaptation to injury and efficient repair.

PMID:40319019 | DOI:10.1038/s41467-025-59474-y

Neuroscience. 2025 May 1:S0306-4522(25)00349-5. doi: 10.1016/j.neuroscience.2025.04.050. Online ahead of print.

ABSTRACT

Parkinson's disease (PD) is a neurodegenerative disorder characterised by motor and non-motor symptoms. Recent evidence suggests a role for gut microbiome composition and diversity in PD aetiology. This study aimed to explore the association between the gut microbiome and PD in a South African population. Gut microbial sequencing data (cases: n = 16; controls: n = 42) was generated using a 16S rRNA gene (V4) primer pair. Alpha- and beta-diversity were calculated using QIIME2, and differential abundance of taxa was evaluated using Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC). Beta-diversity was found to differ significantly between cases and controls, with depletion in the relative abundance of Faecalibacterium, Roseburia, Dorea, and Veillonella, and enrichment of the relative abundance of Akkermansia and Victivallis. Our study found a reduction in butyrate-producing bacteria (e.g. Faecalibacterium and Roseburia) and an increase in mucin-degrading bacteria (Akkermansia) in PD cases compared to controls. These alterations might be associated with heightened gut permeability and inflammation. Longitudinal studies should address the question of whether these microbiome differences are a risk factor for, or are consequent to, the development of PD.

PMID:40318838 | DOI:10.1016/j.neuroscience.2025.04.050

Sci Total Environ. 2025 May 2;980:179522. doi: 10.1016/j.scitotenv.2025.179522. Online ahead of print.

ABSTRACT

Freshwater ecosystems are important natural resources but face serious threats. Nevertheless, they host diverse microorganisms crucial for biosynthetic potential and global biochemical cycles. To fully understand the enrichment and interaction of species and functional resources in freshwater ecosystems, it is essential to profile the microbial resources in the whole three-dimensional space. We profiled 131 metagenomic samples to construct the Honghu Microbial Catalog, comprising 2617 metagenome-assembled genomes, 1718 candidate species, over 60 million non-redundant gene clusters, and 7396 biosynthetic gene clusters. We emphasized surface water may be the primary source of microbial species and ARGs for Honghu Lake. We also found the impact of surface water on groundwater had an "influence sphere". Furthermore, we have identified groundwater as a potential refuge for microbial resources, enriched with CPR bacteria and ARGs. These findings are crucial for the understanding, management, and protection of freshwater ecosystems.

PMID:40318372 | DOI:10.1016/j.scitotenv.2025.179522

Front Med. 2025 May 3. doi: 10.1007/s11684-025-1132-8. Online ahead of print.

ABSTRACT

Ankylosing spondylitis (AS) is linked to an increased prevalence of myocardial infarction (MI). However, research dedicated to elucidating the pathogenesis of AS-MI is lacking. In this study, we explored the biomarkers for enhancing the diagnostic and therapeutic efficiency of AS-MI. Datasets were obtained from the Gene Expression Omnibus database. We employed weighted gene co-expression network analysis and machine learning models to screen hub genes. A receiver operating characteristic curve and a nomogram were designed to assess diagnostic accuracy. Gene set enrichment analysis was conducted to reveal the potential function of hub genes. Immune infiltration analysis indicated the correlation between hub genes and the immune landscape. Subsequently, we performed single-cell analysis to identify the expression and subcellular localization of hub genes. We further constructed a transcription factor (TF)-microRNA (miRNA) regulatory network. Finally, drug prediction and molecular docking were performed. S100A12 and MCEMP1 were identified as hub genes, which were correlated with immune-related biological processes. They exhibited high diagnostic value and were predominantly expressed in myeloid cells. Furthermore, 24 TFs and 9 miRNA were associated with these hub genes. Enzastaurin, meglitinide, and nifedipine were predicted as potential therapeutic agents. Our study indicates that S100A12 and MCEMP1 exhibit significant potential as biomarkers and therapeutic targets for AS-MI, offering novel insights into the underlying etiology of this condition.

PMID:40317453 | DOI:10.1007/s11684-025-1132-8

medRxiv [Preprint]. 2025 Apr 23:2025.04.22.25326170. doi: 10.1101/2025.04.22.25326170.

ABSTRACT

Colorectal cancer (CRC) remains a major health concern, with over 150,000 new diagnoses and more than 50,000 deaths annually in the United States, underscoring an urgent need for improved screening, prognostication, disease management, and therapeutic approaches. The tumor microenvironment (TME)-comprising cancerous and immune cells interacting within the tumor's spatial architecture-plays a critical role in disease progression and treatment outcomes, reinforcing its importance as a prognostic marker for metastasis and recurrence risk. However, traditional methods for TME characterization, such as bulk transcriptomics and multiplex protein assays, lack sufficient spatial resolution. Although spatial transcriptomics (ST) allows for the high-resolution mapping of whole transcriptomes at near-cellular resolution, current ST technologies (e.g., Visium, Xenium) are limited by high costs, low throughput, and issues with reproducibility, preventing their widespread application in large-scale molecular epidemiology studies. In this study, we refined and implemented Virtual RNA Inference (VRI) to derive ST-level molecular information directly from hematoxylin and eosin (H&E)-stained tissue images. Our VRI models were trained on the largest matched CRC ST dataset to date, comprising 45 patients and more than 300,000 Visium spots from primary tumors. Using state-of-the-art architectures (UNI, ResNet-50, ViT, and VMamba), we achieved a median Spearman's correlation coefficient of 0.546 between predicted and measured spot-level expression. As validation, VRI-derived gene signatures linked to specific tissue regions (tumor, interface, submucosa, stroma, serosa, muscularis, inflammation) showed strong concordance with signatures generated via direct ST, and VRI performed accurately in estimating cell-type proportions spatially from H&E slides. In an expanded CRC cohort controlling for tumor invasiveness and clinical factors, we further identified VRI-derived gene signatures significantly associated with key prognostic outcomes, including metastasis status. Although certain tumor-related pathways are not fully captured by histology alone, our findings highlight the ability of VRI to infer a wide range of "histology-associated" biological pathways at near-cellular resolution without requiring ST profiling. Future efforts will extend this framework to expand TME phenotyping from standard H&E tissue images, with the potential to accelerate translational CRC research at scale.

PMID:40313260 | PMC:PMC12045403 | DOI:10.1101/2025.04.22.25326170

Malar J. 2025 May 2;24(1):139. doi: 10.1186/s12936-025-05376-9.

ABSTRACT

BACKGROUND: Understanding the molecular mechanisms of the malarial parasites in hosts is crucial for developing effective treatments. Epitranscriptomic research on pathogens has unveiled the significance of RNA methylation in gene regulation and pathogenesis. This is the first report investigating methylation signatures and alternative splicing events using Nanopore Direct RNA Sequencing to single-base resolution in Plasmodium falciparum and Plasmodium vivax clinical isolates with hepatic dysfunction complications.

METHODS: Direct RNA Sequencing using Nanopore from clinical isolates of P. falciparum and P. vivax showing hepatic dysfunction manifestation was performed. Subsequently, transcriptome reconstruction using FLAIR and transcript classification using SQANTI3, followed by methylation detection using CHEUI and m6Anet to identify N6-methyladenosine (m6A) and 5-methylcytosine (m5C) methylation signatures, was done. The alternative splicing events from both the datasets were documented.

RESULTS: The reference genome of Plasmodium reports > 5000 genes out of which ~ 50% was identified as expressed in the two sequenced isolates, including novel isoforms and intergenic transcripts, highlighting extensive transcriptome diversity. The distinct RNA methylation profiles of m6A and m5C from the expressed transcripts were observed in sense, Natural Antisense Transcripts (NATs) and intergenic categories hinting at species-specific regulatory mechanisms. Dual modification events were observed in a significant number of transcripts in both the parasites. Modified transcripts originating from apicoplast and mitochondrial genomes have also been detected. These modifications are unevenly present in the annotated regions of the mRNA, potentially influencing mRNA export and translation. Several splicing events were observed, with alternative 3' and 5' end splicing predominating in the datasets suggesting differences in translational kinetics and possible protein characteristics in these disease conditions.

CONCLUSION: The data shows the presence of modified sense, NATs and alternatively spliced transcripts. These phenomena together suggest the presence of multiple regulatory layers which decides the post-translational proteome of the parasites in particular disease conditions. Studies like these will help to decipher the post-translational environments of malaria parasites in vivo and elucidate their inherent proteome plasticity, thus allowing the conceptualization of novel strategies for interventions.

PMID:40316999 | DOI:10.1186/s12936-025-05376-9

Nat Biotechnol. 2025 May 2. doi: 10.1038/s41587-025-02659-z. Online ahead of print.

ABSTRACT

Despite progress in mapping protein-protein interactions, their tissue specificity is understudied. Here, given that protein coabundance is predictive of functional association, we compiled and analyzed protein abundance data of 7,811 proteomic samples from 11 human tissues to produce an atlas of tissue-specific protein associations. We find that this method recapitulates known protein complexes and the larger structural organization of the cell. Interactions of stable protein complexes are well preserved across tissues, while cell-type-specific cellular structures, such as synaptic components, are found to represent a substantial driver of differences between tissues. Over 25% of associations are tissue specific, of which <7% are because of differences in gene expression. We validate protein associations for the brain through cofractionation experiments in synaptosomes, curation of brain-derived pulldown data and AlphaFold2 modeling. We also construct a network of brain interactions for schizophrenia-related genes, indicating that our approach can functionally prioritize candidate disease genes in loci linked to brain disorders.

PMID:40316700 | DOI:10.1038/s41587-025-02659-z

J Mol Biol. 2025 Apr 30:169181. doi: 10.1016/j.jmb.2025.169181. Online ahead of print.

ABSTRACT

Understanding of complex biological systems remains a significant challenge due to their high dimensionality, nonlinearity, and context-specific behavior. Artificial intelligence (AI) and mechanistic modeling are becoming essential tools for studying such complex systems. Mechanistic modeling can facilitate the construction of simulatable models that are interpretable but often struggle with scalability and parameters estimation. AI can integrate multi-omics data to create predictive models, but it lacks interpretability. The gap between these two modeling methods limits our ability to develop comprehensive and predictive models for biomedical applications. This article reviews the most recent advancements in the integration of AI and mechanistic modeling to fill this gap. Recently, with omics availability, AI has led to new discoveries in mechanistic computational modeling. The mechanistic models can also help in getting insight into the mechanism for prediction made by AI models. This integration is helpful in modeling complex systems, estimating the parameters that are hard to capture in experiments, and creating surrogate models to reduce computational costs because of expensive mechanistic model simulations. This article focuses on advancements in mechanistic computational models and AI models and their integration for scientific discoveries in biology, pharmacology, drug discovery and diseases. The mechanistic models with AI integration can facilitate biological discoveries to advance our understanding of disease mechanisms, drug development, and personalized medicine. The article also highlights the role of AI and mechanistic model integration in the development of more advanced models in the biomedical domain, such as medical digital twins and virtual patients for pharmacological discoveries.

PMID:40316010 | DOI:10.1016/j.jmb.2025.169181

Cell Rep Methods. 2025 Apr 25:101033. doi: 10.1016/j.crmeth.2025.101033. Online ahead of print.

ABSTRACT

High-quality human genome assemblies derived from lymphoblastoid cell lines (LCLs) provide reference genomes and pangenomes for genomics studies. However, LCLs pose technical challenges for profiling immunoglobulin (IG) genes, as their IG loci contain a mixture of germline and somatically recombined haplotypes, making genotyping and assembly difficult with widely used frameworks. To address this, we introduce IGLoo, a software tool that analyzes sequence data and assemblies derived from LCLs, characterizing somatic V(D)J recombination events and identifying breakpoints and missing IG genes in the assemblies. Furthermore, IGLoo implements a reassembly framework to improve germline assembly quality by integrating information on somatic events and population structural variations in IG loci. Applying IGLoo to the assemblies from the Human Pangenome Reference Consortium, we gained valuable insights into the mechanisms, gene usage, and patterns of V(D)J recombination and the causes of assembly artifacts in the IG heavy-chain (IGH) locus, and we improved the representation of IGH assemblies.

PMID:40315852 | DOI:10.1016/j.crmeth.2025.101033

Cell Stem Cell. 2025 Apr 28:S1934-5909(25)00141-9. doi: 10.1016/j.stem.2025.04.006. Online ahead of print.

ABSTRACT

The repertoire of neurons and their progenitors depends on their location along the antero-posterior and dorso-ventral axes of the neural tube. To model these axes, we designed the Dual Orthogonal-Morphogen Assisted Patterning System (Duo-MAPS) diffusion device to expose spheres of induced pluripotent stem cells (iPSCs) to concomitant orthogonal gradients of a posteriorizing and a ventralizing morphogen, activating WNT and SHH signaling, respectively. Comparison with single-cell transcriptomes from the fetal human brain revealed that Duo-MAPS-patterned organoids generated an extensive diversity of neuronal lineages from the forebrain, midbrain, and hindbrain. WNT and SHH crosstalk translated into early patterns of gene expression programs associated with the generation of specific brain lineages with distinct functional networks. Human iPSC lines showed substantial interindividual and line-to-line variations in their response to morphogens, highlighting that genetic and epigenetic variations may influence regional specification. Morphogen gradients promise to be a key approach to model the brain in its entirety.

PMID:40315847 | DOI:10.1016/j.stem.2025.04.006

Cell Host Microbe. 2025 Apr 25:S1931-3128(25)00143-X. doi: 10.1016/j.chom.2025.04.010. Online ahead of print.

ABSTRACT

Adult human facial skin microbiomes are remarkably similar at the species level, dominated by Cutibacterium acnes and Staphylococcus epidermidis, yet each person harbors a unique community of strains. Understanding how person-specific communities assemble is critical for designing microbiome-based therapies. Here, using 4,055 isolate genomes and 356 metagenomes, we reconstruct on-person evolutionary history to reveal on- and between-person strain dynamics. We find that multiple cells are typically involved in transmission, indicating ample opportunity for migration. Despite this accessibility, family members share only some of their strains. S. epidermidis communities are dynamic, with each strain persisting for an average of only 2 years. C. acnes strains are more stable and have a higher colonization rate during the transition to an adult facial skin microbiome, suggesting this window could facilitate engraftment of therapeutic strains. These previously undetectable dynamics may influence the design of microbiome therapeutics and motivate the study of their effects on hosts.

PMID:40315837 | DOI:10.1016/j.chom.2025.04.010

Phys Rev Lett. 2025 Apr 18;134(15):158401. doi: 10.1103/PhysRevLett.134.158401.

ABSTRACT

Spatial information from cell-surface receptors is crucial for processes that require signal processing and sensing of the environment. Here, we investigate the optimal placement of such receptors through a theoretical model that minimizes uncertainty in gradient estimation. Without requiring a priori knowledge of the physical limits of sensing or biochemical processes, we reproduce the emergence of clusters that closely resemble those observed in real cells. On perfect spherical surfaces, optimally placed receptors spread uniformly. When perturbations break their symmetry, receptors cluster in regions of high curvature, massively reducing estimation uncertainty. This agrees in many scenarios with mechanistic models that minimize elastic preference discrepancies between receptors and cell membranes. We further extend our model to motile receptors responding to cell-shape changes and external fluid flow, demonstrating the biological relevance of our model. Our findings provide a simple and utilitarian explanation for receptor clustering at high-curvature regions when high sensing accuracy is paramount.

PMID:40315515 | DOI:10.1103/PhysRevLett.134.158401

PLoS Pathog. 2025 May 2;21(5):e1013156. doi: 10.1371/journal.ppat.1013156. Online ahead of print.

ABSTRACT

Motile bacteria use chemotaxis to navigate complex environments like the mammalian gut. These bacteria sense a range of chemoeffector molecules, which can either be of nutritional value or provide a cue for the niche best suited for their survival and growth. One such cue molecule is the intra- and interspecies quorum sensing signaling molecule, autoinducer-2 (AI-2). Apart from controlling collective behavior of Escherichia coli, chemotaxis towards AI-2 contributes to its ability to colonize the murine gut. However, the impact of AI-2-dependent niche occupation by E. coli on interspecies interactions in vivo is not fully understood. Using the C57BL/6J mouse infection model, we show that chemotaxis towards AI-2 contributes to nutrient competition and thereby affects colonization resistance conferred by E. coli against the enteric pathogen Salmonella enterica serovar Typhimurium (S. Tm). Like E. coli, S. Tm also relies on chemotaxis, albeit not towards AI-2, to compete against residing E. coli in a gut inflammation-dependent manner. Finally, utilizing a barcoded S. Tm mutant pool, we investigated the impact of AI-2 signaling in E. coli on S. Tm's carbohydrate utilization and central metabolism. Interestingly, AI-2-dependent niche colonization by E. coli was highly specific, impacting only a limited number of S. Tm mutants at distinct time points during infection. Notably, it significantly altered the fitness of mutants deficient in mannose utilization (ΔmanA, early stage infection) and, to a lesser extent, fumarate respiration (ΔdcuABC, late stage infection). The role of quorum sensing and chemotaxis in metabolic competition among bacteria remains largely unexplored. Here, we provide initial evidence that AI-2-dependent nutrient competition occurs between S. Tm and E. coli at specific time points during infection. These findings represent a crucial step toward understanding how bacteria navigate the gastrointestinal tract and engage in targeted nutrient competition within this complex three-dimensional environment.

PMID:40315408 | DOI:10.1371/journal.ppat.1013156

J Mammary Gland Biol Neoplasia. 2025 May 2;30(1):8. doi: 10.1007/s10911-025-09580-w.

ABSTRACT

Circular RNAs (circRNAs) have emerged as critical regulators in various biological processes including diseases. In the mammary gland (MG), which undergoes most of its development postnatally, circRNAs play pivotal roles in both physiological and pathological contexts. This review highlights the involvement of circRNAs during key developmental stages of the MG, with particular emphasis on lactation, where circRNA-miRNA networks significantly influence milk secretion and composition. CircRNAs exhibit stage-, breed- and species-specific expression patterns during lactation, which underscores their complexity. This intricate regulation also plays a significant role in pathological conditions of the MG, where dysregulated circRNA expression contributes to disease progression such as mastitis, early breast cancer (BC) stages, and epithelial-to-mesenchymal transition in BC (EMT). In mastitis, altered circRNA expression disrupts immune responses and compromises epithelial integrity. During early BC progression, circRNAs drive cell proliferation, while in EMT, they facilitate metastatic processes. By focusing on the circRNA-miRNA interactions underlying these processes, this review highlights their potential use as biomarkers for MG development, disease progression, and as therapeutic targets.

PMID:40314719 | DOI:10.1007/s10911-025-09580-w

Anal Chem. 2025 May 2. doi: 10.1021/acs.analchem.4c06809. Online ahead of print.

ABSTRACT

N-heterocyclic carbenes (NHCs) are versatile catalysts for organic reactions, characterized by their unique electron-donating properties and high activity. This study introduces NHCs as innovative derivatization reagents for liquid chromatography-mass spectrometry (LC-MS) analysis of aliphatic aldehydes. Five distinct NHC reagents were evaluated, and 2-mesityl-2,5,6,7-tetrahydropyrrolo[2,1-c][1,2,4]triazol-4-ium chloride (MTPTC) was identified as the most promising candidate due to its rapid reaction kinetics, high selectivity, and excellent product stability. The MTPTC-based derivatization reaction effectively addressed the issue of stereoisomeric products, resulting in well-resolved single peaks in the LC separation. Additionally, the derivatized products exhibited high stability, facilitating accurate and reliable quantitative analysis. Using MTPTC as the derivatization reagent, an LC-MS quantitative analysis strategy was developed for the determination of eight aliphatic aldehydes in human sera. The method demonstrated a broad linear range, low limits of detection and quantification, and satisfactory reproducibility and accuracy. The applicability of this method was further validated through the quantification of aliphatic aldehydes in the serum of sepsis patients. This work extends NHCs' utility to analytical chemistry and introduces a novel derivatization reagent for the analysis of carbonyl compounds by LC-MS.

PMID:40314613 | DOI:10.1021/acs.analchem.4c06809

mSystems. 2025 May 2:e0002125. doi: 10.1128/msystems.00021-25. Online ahead of print.

ABSTRACT

Gihawi et al. (mBio 14:e01607-23, 2023, https://doi.org/10.1128/mbio.01607-23) argued that the analysis of tumor-associated microbiome data by Poore et al. (Nature 579:567-574, 2020, https://doi.org/10.1038/s41586-020-2095-1) is invalid because features that were originally very sparse (genera with mostly zero read counts) became associated with the phenotype following batch correction. Here, we examine whether such an observation should necessarily indicate issues with processing or machine learning pipelines. We show counterexamples using the centered log ratio (CLR) transformation, which is often used for analysis of compositional microbiome data. The CLR transformation has similarities to voom-SNM, the batch-correction method brought into question by Gihawi et al., and yet is a sample-wise operation that cannot, in itself, "leak" information or invalidate downstream analyses. We show that because the CLR transformation divides each value by the geometric mean of its sample, common imputation strategies for missing or zero values result in transformed features that are associated with the geometric mean. Through analyses of both synthetic and vaginal microbiome data sets, we demonstrate that when the geometric mean is associated with a phenotype, sparse and CLR-transformed features will also become associated with it. We re-analyze features highlighted by Gihawi et al. and demonstrate that the phenomenon of sparse features becoming phenotype-associated can also be observed after a CLR transformation, which serves as a counterexample to the claim that such an observation necessarily means information leakage. While we do not intend to address other concerns regarding tumor microbiome analyses, validate Poore et al.'s results, or evaluate batch-correction pipelines, we conclude that because phenotype-associated features that were initially sparse can be created by a sample-wise transformation that cannot artifactually inflate machine learning performance, their detection is not independently sufficient to demonstrate information leakage in machine learning pipelines. Microbiome data are multivariate, and as such, a value of 0 carries a different meaning for each sample. Many transformations, including CLR and other batch-correction methods, are likewise multivariate, and, as these issues demonstrate, each individual feature should be interpreted with caution.

IMPORTANCE: Gihawi et al. claim that finding that a transformation turned highly sparse (mostly zero) features into features that are associated with a phenotype is sufficient to conclude that there is information leakage and to invalidate an analysis. This claim has critical implications for both the debate regarding The Cancer Genome Atlas (TCGA) cancer microbiome analysis and for interpretation and evaluation of analyses in the microbiome field at large. We show by counterexamples and by reanalysis that such transformations can be valid.

PMID:40314439 | DOI:10.1128/msystems.00021-25

Elife. 2025 May 2;13:RP98033. doi: 10.7554/eLife.98033.

ABSTRACT

Protein engineering is a pivotal aspect of synthetic biology, involving the modification of amino acids within existing protein sequences to achieve novel or enhanced functionalities and physical properties. Accurate prediction of protein variant effects requires a thorough understanding of protein sequence, structure, and function. Deep learning methods have demonstrated remarkable performance in guiding protein modification for improved functionality. However, existing approaches predominantly rely on protein sequences, which face challenges in efficiently encoding the geometric aspects of amino acids' local environment and often fall short in capturing crucial details related to protein folding stability, internal molecular interactions, and bio-functions. Furthermore, there lacks a fundamental evaluation for developed methods in predicting protein thermostability, although it is a key physical property that is frequently investigated in practice. To address these challenges, this article introduces a novel pre-training framework that integrates sequential and geometric encoders for protein primary and tertiary structures. This framework guides mutation directions toward desired traits by simulating natural selection on wild-type proteins and evaluates variant effects based on their fitness to perform specific functions. We assess the proposed approach using three benchmarks comprising over 300 deep mutational scanning assays. The prediction results showcase exceptional performance across extensive experiments compared to other zero-shot learning methods, all while maintaining a minimal cost in terms of trainable parameters. This study not only proposes an effective framework for more accurate and comprehensive predictions to facilitate efficient protein engineering, but also enhances the in silico assessment system for future deep learning models to better align with empirical requirements. The PyTorch implementation is available at https://github.com/ai4protein/ProtSSN.

PMID:40314227 | DOI:10.7554/eLife.98033

Neural Regen Res. 2025 Apr 29. doi: 10.4103/NRR.NRR-D-24-00794. Online ahead of print.

ABSTRACT

Retinal ganglion cells, a crucial component of the central nervous system, are often affected by irreversible visual impairment due to various conditions, including trauma, tumors, ischemia, and glaucoma. Studies have shown that the optic nerve crush model and glaucoma model are commonly used to study retinal ganglion cell injury. While these models differ in their mechanisms, both ultimately result in retinal ganglion cell injury. With advancements in high-throughput technologies, techniques such as microarray analysis, RNA sequencing, and single-cell RNA sequencing have been widely applied to characterize the transcriptomic profiles of retinal ganglion cell injury, revealing underlying molecular mechanisms. This review focuses on optic nerve crush and glaucoma models, elucidating the mechanisms of optic nerve injury and neuron degeneration induced by glaucoma through single-cell transcriptomics, transcriptome analysis, and chip analysis. Research using the optic nerve crush model has shown that different retinal ganglion cell subtypes exhibit varying survival and regenerative capacities following injury. Single-cell RNA sequencing has identified multiple genes associated with retinal ganglion cell protection and regeneration, such as Gal, Ucn, and Anxa2. In glaucoma models, high-throughput sequencing has revealed transcriptomic changes in retinal ganglion cells under elevated intraocular pressure, identifying genes related to immune response, oxidative stress, and apoptosis. These genes are significantly upregulated early after optic nerve injury and may play key roles in neuroprotection and axon regeneration. Additionally, CRISPR-Cas9 screening and ATAC-seq analysis have identified key transcription factors that regulate retinal ganglion cell survival and axon regeneration, offering new potential targets for neurorepair strategies in glaucoma. In summary, single-cell transcriptomic technologies provide unprecedented insights into the molecular mechanisms underlying optic nerve injury, aiding in the identification of novel therapeutic targets. Future researchers should integrate advanced single-cell sequencing with multi-omics approaches to investigate cell-specific responses in retinal ganglion cell injury and regeneration. Furthermore, computational models and systems biology methods could help predict molecular pathways interactions, providing valuable guidance for clinical research on optic nerve regeneration and repair.

PMID:40313107 | DOI:10.4103/NRR.NRR-D-24-00794