bioRxiv : the preprint server for biology最新文献

Resistance to endocrine therapy (ET) is common in estrogen receptor (ER) positive breast cancer. Multiple studies have demonstrated that upregulation of MAPK signaling pathways contributes to ET resistance. Herein we show that vandetanib treatment enhances sensitivity to ET in ET-sensitive and -resistant ER+ breast cancer models. Vandetanib treatment alters the gene expression program of ER+ breast cancer cells resulting in a less proliferative and more estrogen responsive Luminal-A like character. Tyrosine kinase network reprogramming was assessed using multiplexed kinase inhibitor beads-mass spectrometry (MIB/MS) assay to identify adaptive resistance mechanisms to vandetanib treatment, including upregulation of HER2 activity. Co-treatment to inhibit HER2 with lapatinib enhanced sensitivity to vandetanib, demonstrating biologic activity of HER2 upregulation. Using a CRISPR knockout model, we demonstrate that vandetanib effects are partially mediated by RET receptor tyrosine kinase. Finally, we use our operating room-to-laboratory assay that measures drug response in individual primary tumor cells in short term cultures to demonstrate conserved gene expression changes, including increased HER2 activity signatures, in vandetanib treated cells, and identify features associated with vandetanib response. These results support future investigation of RET targeting strategies considering reprogrammed networks, such as activated HER2, in patients with ET resistant ER+ breast cancer.

Significance: Vandetanib enhances sensitivity to tamoxifen in ER+ breast cancer cells by reprograming kinase signaling networks, which can be used to select patients most likely to respond and develop more efficacious co-treatment strategies.

Esophageal adenocarcinoma (EAC) is a highly lethal cancer of the upper gastrointestinal tract with rising incidence in western populations. To decipher EAC disease progression and therapeutic response, we performed multiomic analyses of a cohort of primary and metastatic EAC tumors, incorporating single-nuclei transcriptomic and chromatin accessibility sequencing, along with spatial profiling. We identified tumor microenvironmental features previously described to associate with therapy response. We identified five malignant cell programs, including undifferentiated, intermediate, differentiated, epithelial-to-mesenchymal transition, and cycling programs, which were associated with differential epigenetic plasticity and clinical outcomes, and for which we inferred candidate transcription factor regulons. Furthermore, we revealed diverse spatial localizations of malignant cells expressing their associated transcriptional programs and predicted their significant interactions with microenvironmental cell types. We validated our findings in three external single-cell RNA-seq and three bulk RNA-seq studies. Altogether, our findings advance the understanding of EAC heterogeneity, disease progression, and therapeutic response.

Increasing evidence suggests that mechanical load on the αβ T cell receptor (TCR) is crucial for recognizing the antigenic peptide-loaded major histocompatibility complex (pMHC) molecule. Our recent all-atom molecular dynamics (MD) simulations revealed that the inter-domain motion of the TCR is responsible for the load-induced catch bond behavior of the TCR-pMHC complex and peptide discrimination. To further examine the generality of the mechanism, we perform all-atom MD simulations of the B7 TCR under different conditions for comparison with our previous simulations of the A6 TCR. The two TCRs recognize the same pMHC and have similar interfaces with pMHC in crystal structures. We find that the B7 TCR-pMHC interface stabilizes under ~15-pN load using a conserved dynamic allostery mechanism that involves the asymmetric motion of the TCR chassis. However, despite forming comparable contacts with pMHC as A6 in the crystal structure, B7 has fewer high-occupancy contacts with pMHC and exhibits higher mechanical compliance during the simulation. These results indicate that the dynamic allostery common to the TCRαβ chassis can amplify slight differences in interfacial contacts into distinctive mechanical responses and nuanced biological outcomes.

Today, most research evaluation frameworks are designed to assess mature projects with well-defined data and clearly articulated outcomes. Yet, few, if any, are equipped to evaluate the promise of early-stage biotechnology research, which is inherently characterized by limited evidence, high uncertainty, and evolving objectives. These early-stage projects require nuanced assessments that can adapt to incomplete information, project maturity, and shifting research questions. Furthermore, these challenges are compounded by the difficulty of systematically scaling evaluations with the increasing volume of research projects. As a step toward addressing this gap, we introduce the biotechnology-oriented Early-Stage Research Assessment Framework for Artificial Intelligence (ERAF-AI), a systematic approach to evaluate research at Technology Readiness Levels (TRLs) 1 to 3 - research maturity levels where ideas are more conceptual and only preliminary evidence exists to indicate potential viability. By leveraging AI-driven methodologies and platforms such as the Coordination.Network, ERAF-AI ensures transparent, scalable, and context-sensitive evaluations that integrate research maturity classification, adaptive scoring, and strategic decision-making. Importantly, ERAF-AI aligns criteria with the unique demands of early-stage research, guiding evaluation through the 4P framework (Promote, Pause, Pivot, Perish) to inform next steps. As an initial demonstration of its potential, we apply ERAF-AI to a high-impact early-stage project, providing actionable insights and measurable improvement over conventional practices. Although ERAF-AI shows significant promise in improving the prioritization of early-stage research, further refinement, and validation across a wider range of disciplines and datasets is required to refine its scalability and adaptability. Overall, we expect this framework to serve as a valuable tool for empowering researchers to make informed decisions and to prioritize high-potential initiatives in the face of uncertainty and limited data.

CRISPR/Cas9 is a powerful tool for targeted genome engineering experiments. With CRISPR/Cas9, genes can be deleted or modified by inserting small peptides, fluorescent proteins or other tags for protein labelling experiments. Such experiments are important for detailed protein characterization in vivo. However, designing and cloning the corresponding constructs can be repetitive, time consuming and laborious. To aid users in CRISPR/Cas9-based genome engineering experiments, we built CrisprBuildr, a web-based application that allows users to delete genes or insert fluorescent proteins at the N- or C-terminus of their gene of choice. The application is built on the Drosophila melanogaster genome but can be used as a template for other available genomes. We have also generated new tagging vectors, using EGFP and mCherry combined with the small peptide SspB-Q73R for use in iLID-based optogenetic experiments. CrisprBuildr guides users through the process of designing guide RNAs and repair template vectors. CrisprBuildr is an open-source application and future releases could incorporate additional tagging or deletion vectors, genomes or CRISPR applications.

Plasma cell subsets vary in their lifespans and ability to sustain humoral immunity. We conducted a genome-wide CRISPR-Cas9 screen in a myeloma cell line for factors that promote surface expression of CD98, a marker of longevity in primary mouse plasma cells. A large fraction of genes found to promote CD98 expression in this screen are involved in secretory and other vesicles, including many subunits of the V-type ATPase complex. Chemical inhibition or genetic ablation of V-type ATPases in myeloma cells reduced antibody secretion. Primary mouse and human long-lived plasma cells had greater numbers of acidified vesicles than did their short-lived counterparts, and this correlated with increased secretory capacity of IgM, IgG, and IgA. The screen also identified PI4KB, which promoted acidified vesicle numbers and secretory capacity, and DDX3X, an ATP-dependent RNA helicase, the deletion of which reduced immunoglobulin secretion independently of vesicular acidification. Finally, we report a plasma-cell intrinsic function of the signaling adapter MYD88 in both antibody secretion and plasma cell survival in vivo. These data reveal novel regulators of plasma cell secretory capacity, including those that also promote lifespan.

Online vision testing enables efficient data collection from diverse participants but requires accurate fixation. Fixation accuracy is traditionally ensured by using a camera to track gaze. That works well in the lab, but tracking during online testing with a built-in webcam is not yet sufficiently precise. Kurzawski, Pombo, et al. (2023) introduced a fixation task that improves fixation through hand-eye coordination, requiring participants to track a moving crosshair with a mouse-controlled cursor. This dynamic fixation task greatly reduces peeking at peripheral targets relative to a stationary fixation task, but does not eliminate it. Here, we enhance fixation further by leveraging "crowding," adding clutter around the fixation mark-a method we call crowded dynamic fixation. We assessed fixation accuracy during peripheral threshold measurement. Relative to the RMS gaze error during the stationary fixation task, dynamic fixation error was 61%, while crowded dynamic fixation error was only 47%. With a 1.5° tolerance, peeking occurred on 9% of trials with stationary fixation, 4% with dynamic fixation, and 0% with crowded dynamic fixation. This improvement eliminated implausibly low peripheral thresholds, likely by preventing peeking. We conclude that crowded dynamic fixation enables accurate gaze control for online testing.

Fluorescence Lifetime Imaging Microscopy (FLIM) quantifies the autofluorescence lifetime to measure cellular metabolism, therapeutic efficacy, and disease progression. These dynamic processes are intrinsically heterogeneous, increasing the complexity of the signal analysis. Often noise reduction strategies that combine thresholding and non-selective data smoothing filters are applied. These can result in error introduction and data loss. To mitigate these issues, we develop noise-corrected principal component analysis (NC-PCA). This approach isolates the signal of interest by selectively identifying and removing the noise. To validate NC-PCA, a secondary analysis of FLIM images of patient-derived colorectal cancer organoids exposed to a range of therapeutics was performed. First, we demonstrate that NC-PCA decreases the uncertainty up to 4-fold in comparison to conventional analysis with no data loss. Then, using a merged data set, we show that NC-PCA, unlike conventional methods, identifies multiple metabolic states. Thus, NC-PCA provides an enabling tool to advance FLIM analysis across fields.

Microbiome data, like other high-throughput data, suffer from technical heterogeneity stemming from differential experimental designs and processing. In addition to measured artifacts such as batch effects, there is heterogeneity due to unknown or unmeasured factors, which lead to spurious conclusions if unaccounted for. With the advent of large-scale multi-center microbiome studies and the increasing availability of public datasets, this issue becomes more pronounced. Current approaches for addressing unmeasured heterogeneity in high-throughput data were developed for microarray and/or RNA sequencing data. They cannot accommodate the unique characteristics of microbiome data such as sparsity and over-dispersion. Here, we introduce Quantile Thresholding (QuanT), a novel non-parametric approach for identifying unmeasured heterogeneity tailored to microbiome data. QuanT applies quantile regression across multiple quantile levels to threshold the microbiome abundance data and uncovers latent heterogeneity using thresholded binary residual matrices. We validated QuanT using both synthetic and real microbiome datasets, demonstrating its superiority in capturing and mitigating heterogeneity and improving the accuracy of downstream analyses, such as prediction analysis, differential abundance tests, and community-level diversity evaluations.

Pseudomonas aeruginosa is a major contributor to human infections and is widely distributed in the environment. Its ability for growth under aerobic and anaerobic conditions provides adaptability to environmental changes and to confront immune responses. We applied high-throughput native 2-dimensional metalloproteomics to P. aeruginosa to examine how use of iron within the metallome responds to oxic and anoxic conditions. Metalloproteomic analyses revealed four major iron peaks, each comprised of metalloproteins with synergistic functions, including: 1) respiratory and metabolic enzymes, 2) oxidative stress response enzymes, 3) DNA synthesis and nitrogen assimilation enzymes, and 4) denitrification enzymes and related copper enzymes. Three ferritins co-eluted with the first and third iron peaks, localizing iron storage with these functions. Several metalloenzymes were more abundant at low oxygen, including alkylhydroperoxide reductase C that deactivates organic radicals produced by denitrification, all three classes of ribonucleotide reductases, ferritin (increasing in ratio relative to bacterioferritin), and denitrification enzymes. Superoxide dismutase and homogentisate 1,2-dioxygenase were more abundant at high oxygen. The co-eluting Fe peaks contained multiple iron metalloproteins of varying size, implying the presence protein supercomplexes with related functionality and co-localized iron storage. This coordination with functionality implies cellular organization at the protein complex level optimized for metal trafficking that contributes to efficient iron use and prioritization, particularly for Pseudomonas with its large genome and flexible metabolism. This study provides insight into prokaryotic metallome dynamics in response to oxygen availability and demonstrates the capabilities of native metalloproteomic methods in understanding metal use and protein-protein interactions in biological systems.