bioRxiv : the preprint server for biology最新文献

Metagenomic read classification is a fundamental task in computational biology, yet it remains challenging due to the scale, diversity, and complexity of sequencing datasets. We propose a novel, lossless, run-length compressed index that enables efficient multi-class metagenomic classification in O(r) space, based on the move structure. Our method identifies all super-maximal exact matches (SMEMs) of length at least L between a read and the reference dataset and associates each SMEM with one class identifier using a sampled tag array. A consensus algorithm then compacts these SMEMs with their class identifier into a single classification per read. We are the first to perform run-length compressed read classification based on full SMEMs instead of semi-SMEMs. We evaluate our approach on both long and short reads in two conceptually distinct datasets: a large bacterial pan-genome with few metagenomic classes and a smaller 16S rRNA gene database spanning thousands of genera or classes. Our method consistently outperforms SPUMONI 2 in accuracy and runtime, with only a modest memory overhead. Compared to Cliffy, we demonstrate better memory efficiency while achieving superior accuracy on the simpler dataset and comparable performance on the more complex one. Overall, our implementation carefully balances accuracy, runtime, and memory usage, offering a versatile solution for metagenomic classification across diverse datasets. The open-source C++11 implementation is available at https://github.com/biointec/tagger under the AGPL-3.0 license.

Numerous computational approaches have been developed to infer cell state transition trajectories from snapshot single-cell data. Most approaches first require projecting high-dimensional data onto a low-dimensional representation, raising the question of whether the dynamics of the system become distorted. Using epithelial-to-mesenchymal transition (EMT) as a test system, we show that both biology-guided low-dimensional representations and stochastic trajectory simulations in high-dimensional state space, not representations obtained with brute force dimension-reduction methods, reveal multiple distinct paths of TGF-β-induced EMT. The paths arise from coupling between EMT and cell cycle arrest at either the G1/S, G2/M or M checkpoints, contributing to cell-cycle related EMT heterogeneity. The present study emphasizes that caution should be taken when inferring transition dynamics from snapshot single-cell data in two- or three-dimensional representations, and that incorporating dynamical information can improve prediction accuracy.

Endothelial cell (EC) injury is a major contributing factor to vascular surgical failure. As such, understanding the mechanisms of endothelial healing is essential to the development of vascular therapeutics and procedures. Gap junctions formed by connexin 43 (Cx43) are implicated in regulating skin wound healing, but their role in endothelial healing is unknown. Secondary analysis of RNAseq data from in vivo injured mouse aortas (GEO: GSE115618), identified significant Cx43 upregulation in EC post-injury. We developed a novel in vivo model of EC injury using mouse carotid artery ligation to test the role of Cx43. We identified that EC immediately adjacent to the wound edge upregulate Cx43 protein expression, predominantly at cell-cell junctions. We show significantly delayed EC healing in a mouse model of inducible EC-specific Cx43 deletion (EC-Cx43 KO) at 24 hr post ligation. Single cell RNAseq analysis of 10,829 cells from 18 hr injured EC-WT and EC-Cx43 KO carotids revealed a Cx43-associated reduction in enrichment of EC pathways associated with migration, proliferation, and ERK/MAPK signaling pathways. Finally, the importance of Cx43 phosphorylation on EC healing was tested in mice with single-point alanine mutations (phospho-null) in known phosphorylation sites that alter Cx43 channel assembly and opening. Mice containing alanine mutations at ERK phosphorylated Cx43 serines (Cx43 ^{S255/262/279/282A} ) reduces healing rates similar to EC-Cx43 KO. These data suggest that EC injury-induced Cx43 upregulation, and subsequent Cx43 gap junction-mediated cell-to-cell communication are required for normal EC migration during wound healing after vascular injury.

New and noteworthy: These findings demonstrate for the first time that mechanical injury to large artery endothelium induces the expression of gap junction protein Cx43. This upregulation improves migratory and proliferative capacity of endothelial cells at the wound edge, facilitating timely wound closure. This phenomenon is dependent on appropriate gap junction function and turnover.

Evolutionary Rate Covariation (ERC) is an established comparative genomics method that identifies sets of genes sharing patterns of sequence evolution, which suggests shared function. Whereas many functional predictions of ERC have been empirically validated, its predictive power has hitherto been limited by its inability to tackle the large numbers of species in contemporary comparative genomics datasets. This study introduces ERC2.0, an enhanced methodology for studying ERC across phylogenies with hundreds of species and tens of thousands of genes. ERC2.0 improves upon previous iterations of ERC in algorithm speed, normalizing for heteroskedasticity, and normalizing correlations via Fisher transformations. These improvements have resulted in greater statistical power to predict biological function. In exemplar yeast and mammalian datasets, we demonstrate that the predictive power of ERC2.0 is improved relative to the previous method, ERC1.0, and that further improvements are obtained by using larger yeast and mammalian phylogenies. We attribute the improvements to both the larger datasets and improved rate normalization. We demonstrate that ERC2.0 has high predictive accuracy for known annotations and can predict the functions of genes in non-model systems. Our findings underscore the potential for ERC2.0 to be used as a single-pass computational tool in candidate gene screening and functional predictions.

The timed contraction and relaxation of myofibers in tissues such as the heart and skeletal muscle occurs via the tightly regulated movement of calcium ions into and out of the sarcoplasmic reticulum (SR). In skeletal muscle, this phenomenon enables humans to exercise, perform day-to-day tasks, and to breathe. Sarcolipin, a small regulatory protein, prevents calcium ions from entering the SR by binding to and inhibiting SERCA, contributing to myofiber contraction. Disruptions in sarcolipin expression are implicated in the pathophysiology of obesity and musculoskeletal disease. However, the mechanisms regulating sarcolipin expression are not clearly understood. We recently showed that Site-1 Protease (S1P) is a regulator of skeletal muscle function and mass. Here, we report that deleting S1P in mouse skeletal muscle increases sarcolipin expression, without impacting calcium SR flux. In cultured cells, S1P negatively regulates sarcolipin by activating the transcription factor ATF6, which inhibits basal- and calcineurin-stimulated sarcolipin promoter activity. We identified a cAMP response element binding protein (CREB) binding site on the sarcolipin promoter that is necessary for promoter activation, and show that in muscle, CREB binds to the sarcolipin promoter and that this binding is enhanced when S1P is deleted. These discoveries expand our knowledge of S1P biology and the mechanisms controlling calcium regulatory genes.

Using CLAP methodology, Guo et al. recently concluded that PRC2 is not an RNA binding protein (RBP). They suggest that prior findings were CLIP artifacts and argue against RNA's direct role in PRC2 regulation. Here, we re-analyze their raw datasets and reach contrary conclusions. Through an independent computational pipeline, we observe significant PRC2 enrichment throughout the transcriptome, including XIST. Applying the authors' published computational pipeline also reaffirms PRC2 as an RBP. Detailed investigation of the authors' pipeline reveals several unconventional practices. First, Guo et al. retained reads from foreign species and other unmappable reads to obtain a normalization factor. Second, they selectively removed read duplicates from the mappable fraction, while retaining them in the unmappable fraction. Finally, the authors applied an arbitrary cutoff for enrichment values in XIST. Their pipeline thereby inflated PRC2's background reads and suppressed mappable signals, creating the impression that PRC2 is not a robust RBP.

Using a phylogenetic framework to characterize natural selection, we investigate the hypothesis that zoonotic viruses require adaptation prior to zoonosis to sustain human-to-human transmission. Examining the zoonotic emergence of Ebola virus, Marburg virus, influenza A virus, SARS-CoV, and SARS-CoV-2, we find no evidence of a change in the intensity of natural selection immediately prior to a host switch, compared with typical selection within reservoir hosts. We conclude that extensive pre-zoonotic adaptation is not necessary for human-to-human transmission of zoonotic viruses. In contrast, the reemergence of H1N1 influenza A virus in 1977 showed a change in selection, consistent with the hypothesis of passage in a laboratory setting prior to its reintroduction into the human population, purportedly during a vaccine trial. Holistic phylogenetic analysis of selection regimes can be used to detect evolutionary signals of host switching or laboratory passage, providing insight into the circumstances of past and future viral emergence.

Multidrug transport by ATP binding cassette (ABC) exporters entails a mechanism to modulate drug affinity across the transport cycle. Here, we combine cryo-EM and molecular dynamics (MD) simulations to illuminate how lipid competition modulates substrate affinity to drive its translocation by ABC exporters. We determined cryo-EM structures of the ABC transporter BmrCD in drug-loaded inward-facing (IF) and outward-facing (OF) conformations in lipid nanodiscs to reveal the structural basis of alternating access, details of drug-transporter interactions, and the scale of drug movement between the two conformations. Remarkably, the structures uncovered lipid molecules bound in or near the transporter vestibule along with the drugs. MD trajectories from the IF structure show that these lipids stimulate drug disorder and translocation towards the vestibule apex. Similarly, bound lipids enter the OF vestibule and weaken drug-transporter interactions facilitating drug release. Our results complete a near-atomic model of BmrCD's conformational cycle and advance a general mechanism of lipid-driven drug transport by ABC exporters.

K-RAS mutations drive oncogenesis in multiple cancers, yet the lack of druggable sites has long hindered therapeutic development. Here, we use multi-temperature X-ray crystallography (MT-XRC) to capture functionally relevant K-RAS conformations across a temperature gradient, spanning cryogenic to physiological and even "fever" conditions, and show how cryogenic conditions may obscure key dynamic states as targets for new drug development. This approach revealed a temperature-dependent conformational landscape of K-RAS, shedding light on the dynamic nature of key regions. We identified significant conformational changes occurring at critical sites, including known allosteric and drug-binding pockets, which were hidden under cryogenic conditions but later discovered to be critically important for drug-protein interactions and inhibitor design. These structural changes align with regions previously highlighted by large-scale mutational studies as functionally significant. However, our MT-XRC analysis provides precise structural snapshots, capturing the exact conformations of these potentially important allosteric sites in unprecedented detail. Our findings underscore the necessity of advancing tools like MT-XRC to visualize conformational transitions that may be important in signal propagation which are missed by standard cryogenic XRC and to address hard-to-drug targets through rational drug design. This approach not only provides unique structural insights into K-RAS signaling events and identifies new potential sites to target with drug candidates but also establishes a powerful framework for discovering therapeutic opportunities against other challenging drug targets.