PLoS Computational Biology最新文献

Spatially resolved transcriptomics (SRT) enables the simultaneous capture of gene expression profiles and spatial localization, providing valuable insights into tissue architecture. However, the preservation of spatial information requires additional experimental procedures, which often introduce substantial technical noise. Existing methods typically perform denoising and spatial domain identification in separate steps, leading to suboptimal performance and limiting their applicability. To address this limitation, we propose an integrative network model, stACN ( spatial transcriptomics Attribute Cell Network), that jointly denoises gene expression data and identifies spatial domains in SRT. Specifically, stACN first learns clean dual cell networks using a graph noise model, and then derives compatible cell features through joint tensor decomposition of the denoised networks. Experimental results demonstrate that stACN effectively enhances data quality, as measured by clustering agreement with reference annotations (Adjusted Rand Index, ARI), and facilitates spatial domain analysis in SRT datasets.

There are various well-validated taxonomic classifiers for profiling shotgun metagenomics data, with two popular methods, MetaPhlAn (marker-gene-based) and Kraken (k-mer-based), at the forefront of many studies. Despite differences between classification approaches and calls for the development of consensus methods, most analyses of shotgun metagenomics data for microbiome studies use a single taxonomic classifier. In this study, we compare inferences from two broadly used classifiers, MetaPhlAn4 and Kraken2, applied to stool metagenomic samples from participants in the Integrative Longevity Omics study to measure associations of taxonomic diversity and relative abundance with age, replicating analyses in an independent cohort. We also introduce consensus and meta-analytic approaches to compare and integrate results from multiple classifiers. While many results are consistent across the two classifiers, we find classifier-specific inferences that would be lost when using one classifier alone. Both classifiers captured similar age-associated changes in diversity across cohorts, with variability in species alpha diversity driven by differences by classifier. When using a correlated meta-analysis approach (AdjMaxP) across classifiers, differential abundance analysis captures more age-associated taxa, including 17 taxa robustly age-associated across cohorts. This study emphasizes the value of employing multiple classifiers and recommends novel approaches that facilitate the integration of results from multiple methodologies.

Genome annotations provide the essential framework for genomic analyses, capturing our current knowledge of gene structure and function as inferred from computational predictions and experimental evidence. Even as automated annotation pipelines become more sophisticated, their accuracy in representing unconventional gene expression events remains largely untested. Here, we address this gap by examining the most common form of translational recoding: the insertion of selenocysteine (Sec), a non-canonical amino acid incorporated into selenoproteins, oxidoreductase enzymes carrying essential roles in redox homeostasis. Sec insertion occurs in response to UGA, normally interpreted as stop codon, but recoded in selenoprotein mRNAs. Owing to the dual function of UGA, the identification of selenoprotein genes poses a challenge. We show that the vertebrate selenoprotein genes are widely misannotated in major public databases. Only 11% and 5% of selenoprotein genes are well annotated in Ensembl and NCBI GenBank, respectively, due to the lack of dedicated selenoprotein annotation pipelines. In most cases (81% and 84%), overlapping flawed annotations are present which lack the Sec-encoding UGA. In contrast, NCBI RefSeq employs a dedicated selenoprotein pipeline, yet with some shortcomings: its selenoprotein annotations are correct in 77% of cases, and most errors affect families with a C-terminal Sec residue. We argue that selenoproteins must be correctly annotated in public databases and that must occur via automated pipelines, to keep the pace with genome sequencing. To facilitate this task, we present a new version of Selenoprofiles, an homology based tool for selenoprotein prediction that produces predictions with accuracy comparable to manual curation, and can be easily deployed and integrated in existing annotation pipelines.

Understanding the heterogeneity of population-level viral fitness dynamics, which reflect the interplay between intrinsic viral properties and population immunity, is critical for pandemic preparedness. However, how these dynamics vary across diverse immune backgrounds and mutational landscapes remain poorly characterized. We present Geno-GNN, a graph representation learning approach for retrospectively characterizing the viral fitness dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Geno-GNN accurately predicts angiotensin-converting enzyme 2 (ACE2) binding affinity and immune escape potential across multiple external datasets. Using Geno-GNN, we identified temporal patterns in SARS-CoV-2 fitness and detected varying rates of fitness change associated with distinct immune backgrounds. Virtual mutation scanning revealed two fitness trajectories: broad immune evasion at the cost of ACE2 affinity and ACE2 affinity maintenance at or above the Wuhan-Hu-1 level along with moderate immune escape. Notably, real-world SARS-CoV-2 variants predominantly followed the latter trajectory, sustaining ACE2 affinity via fixed mutations. These findings underscore the heterogeneous, immune-contextualized nature of viral fitness dynamics and the complex evolutionary pathways of SARS-CoV-2.

The human spleen significantly influences red blood cell (RBC) dynamics due to its ability to retain and/or remove RBCs from peripheral blood circulation. This filtering can mediate a range of malaria disease manifestations, depending on the physiological properties of the spleen. Data collected from patients undergoing splenectomy in Papua, Indonesia, revealed that in asymptomatic infections the spleen harboured substantially more infected RBCs than were circulating in the peripheral blood and that the spleen is also congested with uninfected RBCs. We hypothesise that two conditions hold for the spleen to retain such a high proportion of infected and uninfected RBCs: (i) the retention rate of uninfected RBCs is significantly higher than in uninfected patients; and (ii) phagocytosing macrophages cannot clear all of the infected RBCs from the spleen. In this paper, we present a mathematical model of RBC dynamics that includes, for the first time, the spleen as a compartment capable of retaining large numbers of infected and uninfected RBCs in Plasmodium falciparum and P. vivax infections. By calibrating the model to the Papuan data, we demonstrate that the spleen plays a significant role in removing not only infected RBCs but also uninfected RBCs. Uninfected RBC retention in the spleen, attributable to malaria, is substantially higher than circulating RBC loss due to parasitisation, for infections by both Plasmodium species. In chronic infections, the ratio of circulating uninfected RBCs lost to splenic retention per circulating uninfected RBC lost to parasitisation is 17:1 for P. falciparum and 82:1 for P. vivax. These ratios are larger than previously published estimates for acute clinical infections.

Blood vessel pruning during angiogenesis is the optimization process of the branching pattern to improve the transport properties of a vascular network. Recent studies show that part of endothelial cells (ECs) subjected to lower shear stress migrate toward vessels with higher shear stress in opposition to the blood flow for vessel regression. While dynamic changes of blood flow and local mechano-stress could coordinately modulate EC migration for vessel regression within the closed circulatory system, the effect of complexity of haemodynamic forces and vessel properties on vessel pruning remains elusive. Here, we reconstructed a 3-dimentsional (3D) vessel structure from 2D confocal images of the growing vessels in the mouse retina, and numerically obtained the local information of blood flow, shear stress and blood pressure in the vasculature. Moreover, we developed a predictive model for vessel pruning based on machine learning. We found that the combination of shear stress and blood pressure with vessel radius was tightly correlated to vessel pruning sites. Our results highlighted that orchestrated contribution of local haemodynamic parameters was important for the vessel pruning.

Previous research efforts on highly pathogenic H5N1 avian influenza (HPAI) suggest that different avian species exhibit a varied severity of clinical signs after infection. Waterfowl, such as ducks or geese, can be asymptomatic and act as silent carriers of H5N1, making detection harder and increasing the risk of further transmission, potentially leading to significant economic losses. For backyard hobby farmers, passive reporting is a common HPAI detection strategy. We aim to develop a computational, mechanistic model to quantify the effectiveness of this strategy by simulating the spread of H5N1 in a mixed-species, small-population backyard flock. Quantities such as detection time and undetected burden of infection in various scenarios are compared. Our results indicate that the presence of ducks can lead to a higher risk of an outbreak and a higher burden of infection. If most ducks within a flock are resistant to H5N1, detection can be significantly delayed. We find that within-flock infection dynamics can heavily depend on the species composition in backyard farms. Ducks, in particular, can pose a higher risk of transmission within a flock or between flocks. Our findings can help inform surveillance and intervention strategies at the flock and local levels.

In the field of biomedicine, advances in high-throughput sequencing have generated vast amounts of high-dimensional multi-omics data. Survival analysis methods with multi-omics data can comprehensively uncover the heterogeneity and complexity of diseases from multiple perspectives, thereby improving prognostic predictions for patients, which is critical for developing personalized treatment strategies in precision medicine. Recently, Transformer architecture has emerged as a dominant paradigm in multiple domains. However, due to the inherent challenges in modeling right-censored data, it remains unclear how to effectively utilize Transformer architecture in multi-omics survival analysis to fully extract complementary information across different omics for improving survival prediction performance. In this work, we propose an innovative collaborative Transformer framework for multi-omics survival analysis, namely CoFormerSurv, with two consecutive Transformer architectures including an inter-omics Transformer and an inter-sample graph Transformer. The inter-omics Transformer learns multiple meaningful feature interactions by multi-head self-attention mechanism to capture and quantify complementary information across different omics, while the inter-sample graph Transformer integrates structural information from the fused multi-omics graph into the Transformer architecture, enabling more effective exploration of neighborhood relationships among samples. The two kinds of Transformer architectures can work collaboratively to generate more comprehensive multi-omics features for improving the Cox-PH model performance in survival analysis. Experimental results on multiple real-world datasets show that our proposed method outperforms both single-Transformer architectures and existing survival prediction models by simultaneously exploring complementary information from inter-omics and cross-sample perspectives.

Transposable elements (TEs) replicate within genomes and are an active source of genetic variability in many species. Their role in immunity and domestication underscores their biological significance. However, analyzing TEs, especially within lesser-studied and wild populations, poses considerable challenges. To address this, we introduce TEPEAK, a simple and efficient approach to identify and characterize TEs in populations without any prior sequence or loci information. In addition to processing user-submitted genomes, TEPEAK integrates with the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) to increase cohort sizes or incorporate proximate species. Our application of TEPEAK to 257 horse genomes spanning 11 groups reaffirmed established genetic histories and highlighted disruptions in crucial genes. Some identified TEs were also detectable in species closely related to horses. TEPEAK paves the way for comprehensive genetic variation analysis in traditionally understudied populations by simplifying TE studies. TEPEAK is open-source and freely available at https://github.com/ryanlayerlab/TEPEAK.

[This corrects the article DOI: 10.1371/journal.pcbi.1013691.].