Cell systems最新文献

The circulating antibody (Ab) repertoire is crucial for immune protection, holding significant immunological and biotechnological value. While bottom-up mass spectrometry (MS) is widely used for profiling the sequence diversity of circulating Abs (Ab repertoire sequencing [Ab-seq]), it has not been thoroughly benchmarked. We quantified the replicability and robustness of Ab-seq using six monoclonal Ab spike-ins in 70 combinations of concentration and oligoclonality, with and without polyclonal serum immunoglobulin G (IgG) background. Each combination underwent four protease treatments and was analyzed across four experimental and three technical replicates, totaling 3,360 liquid chromatography-tandem MS (LC-MS/MS) runs. We quantified the dependence of Ab-seq identification on Ab sequence, concentration, protease, presence of background IgGs, and bioinformatics methods. Integrating the data from experimental replicates, proteases, and bioinformatics tools enhanced Ab identification. De novo sequencing performed similarly to database-dependent methods at higher Ab concentrations, but de novo Ab reconstruction remains challenging. Our work provides a foundational resource for the field of MS-based Ab profiling. A record of this paper's transparent peer review process is included in the supplemental information.

Uncovering genotype-phenotype relationships is hampered by genetic redundancy. For example, most genes in Streptococcus pneumoniae are non-essential under laboratory conditions. A powerful approach to unravel genetic redundancy is by identifying gene-gene interactions. We developed a broadly applicable dual CRISPRi-seq method and analysis pipeline to probe genetic interactions (GIs) genome-wide. A library of 869 dual single-guide RNAs (sgRNAs) targeting high-confidence operons was created, covering over 70% of the genetic elements in the pneumococcal genome. Testing these 378,015 unique combinations, 4,026 significant GIs were identified. Besides known GIs, we found previously unknown positive and negative interactions involving genes in fundamental cellular processes such as division and chromosome segregation. The presented methods and bioinformatic approaches can serve as a roadmap for genome-wide gene interaction studies in other organisms. All interactions are available for exploration via the Pneumococcal Genetic Interaction Network (PneumoGIN), which can serve as a starting point for new biological discoveries. A record of this paper's transparent peer review process is included in the supplemental information.

Bacterial host factors regulate the infection cycle of bacteriophages. Except for some well-studied host factors (e.g., receptors or restriction-modification systems), the contribution of the rest of the host genome on phage infection remains poorly understood. We developed phage-host analysis using genome-wide CRISPR interference and phage packaging ("PHAGEPACK"), a pooled assay that systematically and comprehensively measures each host gene's impact on phage fitness. PHAGEPACK combines CRISPR interference with phage packaging to link host perturbation to phage fitness during active infection. Using PHAGEPACK, we constructed a genome-wide map of genes impacting T7 phage fitness in permissive E. coli, revealing pathways that affect phage packaging. When applied to the non-permissive E. coli O121, PHAGEPACK identified pathways leading to host resistance; their removal increased phage susceptibility up to a billion-fold. Bioinformatic analysis indicates that phage genomes carry homologs or truncations of key host factors, potentially for fitness advantage. In summary, PHAGEPACK offers insights into phage-host interactions, phage evolution, and bacterial resistance.

To determine whether HIV persistence arises from the natural dynamics of memory (m)CD4+ T cells, we compare clonal dynamics of HIV proviruses and mCD4+ T cells from the same people living with HIV (PWH) on antiretroviral therapy and from matched HIV-seronegative people (N = 51). HIV proviruses are more clonal than mCD4+ T cells but similarly clonal to antigen-specific cells. Increasing reservoir clonality over time and differential decay of intact and defective proviruses are not explained by mCD4+ T cell kinetics alone. We develop and validate a stochastic model trained on 10 quantitative data metrics, which shows that negative selection against HIV-infected cells is necessary to explain all metrics. We estimate the strength of negative selection, finding that death of cells harboring intact and defective proviruses is infrequently (∼6% and ∼2% on average) due to HIV-specific factors. Thus, our data indicate that HIV persistence is mostly, but not entirely, driven by natural mCD4+ kinetics.

Controlling cell states is pivotal in biological research, yet understanding the specific perturbations that induce desired changes remains challenging. To address this, we present PAIRING (perturbation identifier to induce desired cell states using generative deep learning), which identifies cellular perturbations leading to the desired cell state. PAIRING embeds cell states in the latent space and decomposes them into basal states and perturbation effects. The identification of optimal perturbations is achieved by comparing the decomposed perturbation effects with the vector representing the transition toward the desired cell state in the latent space. We demonstrate that PAIRING can identify perturbations transforming given cell states into desired states across different types of transcriptome datasets. PAIRING is employed to identify perturbations that lead colorectal cancer cells to a normal-like state. Moreover, simulating gene expression changes using PAIRING provides mechanistic insights into the perturbation. We anticipate that it will have a broad impact on therapeutic development, potentially applicable across various biological domains.

Romero-Pérez et al. reveal that protein surface properties-hydrophilicity, negative charge, and disorder content-confer innate tolerance to desiccation, mirroring protein solubility principles. Tolerant proteins are enriched in metabolic enzymes needed for recovery after rehydration. These insights into proteins' "molecular armor" could be leveraged to improve biologics design.

The identification of T cell neoantigens is fundamental and computationally challenging in tumor immunotherapy study. Current prediction methods mainly focus on peptide properties, human leukocyte antigen (HLA) binding affinity, or single peptide-major histocompatibility complex-T cell receptor (pMHC-TCR) interactions, often overlooking the patient-specific TCR profile in evaluating neoantigen immunogenicity. This limited scope has constrained the performance and application of these tools in real-world settings for neoantigen identification. To address these limitations, we developed "TCRBagger," a weakly supervised learning framework that uses the bagging of sample-specific TCR profiles to enhance personalized neoantigen identification. TCRBagger integrates three learning strategies-self-supervised, denoising, and multi-instance learning (MIL)-for modeling peptide-TCR binding to identify immunogenic neoantigens. Our comprehensive tests and applications reveal that TCRBagger outperforms existing tools by modeling peptide-TCR profile interactions, accordingly enhancing the capability of immunogenic neoantigen identification. Collectively, TCRBagger provides an unprecedented perspective and methodology for modeling the interaction between a peptide and patient-specific TCR profiles, facilitating neoantigen identification for personalized tumor immunotherapy. A record of this paper's Transparent Peer Review process is included in the supplemental information.

Cellular desiccation-the loss of nearly all water from the cell-is a recurring stress that drives widespread protein dysfunction. To survive, part of the proteome must resume function upon rehydration. Which proteins tolerate desiccation, and the molecular determinants that underlie this tolerance, are largely unknown. Here, we use quantitative mass spectrometry and structural proteomics to show that certain proteins possess an innate capacity to tolerate extreme water loss. Structural analyses point to protein surface chemistry as a key determinant of desiccation tolerance, which we test by showing that rational surface mutants can convert a desiccation-sensitive protein into a tolerant one. We also find that highly tolerant proteins are responsible for the production of small-molecule building blocks, while intolerant proteins are involved in energy-consuming processes such as ribosome biogenesis. We propose that this functional bias enables cells to kickstart their metabolism and promote cell survival following desiccation and rehydration. A record of this paper's transparent peer review process is included in the supplemental information.

Whole-cell models (WCMs) are multi-scale computational models that aim to simulate the function of all genes and processes within a cell. This approach is promising for designing genomes tailored for specific tasks. However, a limitation of WCMs is their long runtime. Here, we show how machine learning (ML) surrogates can be used to address this limitation by training them on WCM data to accurately predict cell division. Our ML surrogate achieves a 95% reduction in computational time compared with the original WCM. We then show that the surrogate and a genome-design algorithm can generate an in silico-reduced E. coli cell, where 40% of the genes included in the WCM were removed. The reduced genome is validated using the WCM and interpreted biologically using Gene Ontology analysis. This approach illustrates how the holistic understanding gained from a WCM can be leveraged for synthetic biology tasks while reducing runtime. A record of this paper's transparent peer review process is included in the supplemental information.

Our understanding of metabolic thermodynamics is limited by the lack of genome-scale data on the standard Gibbs free energy change (Δ_rG°) of metabolic reactions. Here, we present dGbyG, a graph neural network (GNN)-based model for predicting Δ_rG° with superior accuracy, versatility, robustness, and generalization ability. Integration of dGbyG predictions into metabolic networks facilitated model curation, improved flux prediction accuracy, and identified thermodynamic driver reactions (TDRs) with substantial negative values of the reaction Gibbs free energy change (Δ_rG). TDRs showed distinctive network topological features and heterogeneous enzyme expression, implying coupling between reaction thermodynamics and network topology for efficient metabolic regulation. We also discovered a universal pattern of thermodynamics in linear metabolic pathways, explained by a multi-objective optimization model balancing the needs to maximize pathway flux and minimize enzyme and metabolite loads. Our work expands accessible thermodynamic data and elucidates optimality principles in metabolism at the genome scale. A record of this paper's transparent peer review process is included in the supplemental information.