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Visual inspection of histopathology slides is one of the main methods used by pathologists to assess the stage, type and subtype of lung tumors. Adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most prevalent subtypes of lung cancer, and their distinction requires visual inspection by an experienced pathologist. In this study, we trained a deep convolutional neural network (inception v3) on whole-slide images obtained from The Cancer Genome Atlas to accurately and automatically classify them into LUAD, LUSC or normal lung tissue. The performance of our method is comparable to that of pathologists, with an average area under the curve (AUC) of 0.97. Our model was validated on independent datasets of frozen tissues, formalin-fixed paraffin-embedded tissues and biopsies. Furthermore, we trained the network to predict the ten most commonly mutated genes in LUAD. We found that six of them—STK11, EGFR, FAT1, SETBP1, KRAS and TP53—can be predicted from pathology images, with AUCs from 0.733 to 0.856 as measured on a held-out population. These findings suggest that deep-learning models can assist pathologists in the detection of cancer subtype or gene mutations. Our approach can be applied to any cancer type, and the code is available at https://github.com/ncoudray/DeepPATH . A convolutional neural network model using feature extraction and machine-learning techniques provides a tool for classification of lung cancer histopathology images and predicting mutational status of driver oncogenes

Motile cilia power cell locomotion and drive extracellular fluid flow by propagating bending waves from their base to tip. The coordinated bending of cilia requires mechanoregulation by the radial spoke (RS) protein complexes and the microtubule central pair (CP). Despite their importance for ciliary motility across eukaryotes, the molecular function of the RSs is unknown. Here, we reconstituted the Chlamydomonas reinhardtii RS head that abuts the CP and determined its structure using single-particle cryo-EM to 3.1-Å resolution, revealing a flat, negatively charged surface supported by a rigid core of tightly intertwined proteins. Mutations in this core, corresponding to those involved in human ciliopathies, compromised the stability of the recombinant complex, providing a molecular basis for disease. Partially reversing the negative charge on the RS surface impaired motility in C. reinhardtii . We propose that the RS-head architecture is well-suited for mechanoregulation of ciliary beating through physical collisions with the CP. Cryo-EM analyses of the reconstituted radial spoke head and neck from Chlamydomonas reveal a rigid structure with an acidic surface, well-suited for communication with the microtubule central pair, and provide insight into human ciliopathies.

Cancer diagnosis and management depend upon the extraction of complex information from microscopy images by pathologists, which requires time-consuming expert interpretation prone to human bias. Supervised deep learning approaches have proven powerful, but are inherently limited by the cost and quality of annotations used for training. Therefore, we present Histomorphological Phenotype Learning, a self-supervised methodology requiring no labels and operating via the automatic discovery of discriminatory features in image tiles. Tiles are grouped into morphologically similar clusters which constitute an atlas of histomorphological phenotypes (HP-Atlas), revealing trajectories from benign to malignant tissue via inflammatory and reactive phenotypes. These clusters have distinct features which can be identified using orthogonal methods, linking histologic, molecular and clinical phenotypes. Applied to lung cancer, we show that they align closely with patient survival, with histopathologically recognised tumor types and growth patterns, and with transcriptomic measures of immunophenotype. These properties are maintained in a multi-cancer study. Supervised deep learning models hold promise for the interpretation of histology images, but are limited by cost and quality of training datasets. Here, the authors develop a self-supervised deep learning method that can automatically discover features in cancer histology images that are associated with diagnosis, survival, and molecular phenotypes.

Determining how microtubules (MTs) are nucleated is essential for understanding how the cytoskeleton assembles. While the MT nucleator, γ-tubulin ring complex (γ-TuRC) has been identified, precisely how γ-TuRC nucleates a MT remains poorly understood. Here, we developed a single molecule assay to directly visualize nucleation of a MT from purified Xenopus laevis γ-TuRC. We reveal a high γ-/αβ-tubulin affinity, which facilitates assembly of a MT from γ-TuRC. Whereas spontaneous nucleation requires assembly of 8 αβ-tubulins, nucleation from γ-TuRC occurs efficiently with a cooperativity of 4 αβ-tubulin dimers. This is distinct from pre-assembled MT seeds, where a single dimer is sufficient to initiate growth. A computational model predicts our kinetic measurements and reveals the rate-limiting transition where laterally associated αβ-tubulins drive γ-TuRC into a closed conformation. NME7, TPX2, and the putative activation domain of CDK5RAP2 do not enhance γ-TuRC-mediated nucleation, while XMAP215 drastically increases the nucleation efficiency by strengthening the longitudinal γ-/αβ-tubulin interaction.

YiiP is a dimeric antiporter from the cation diffusion facilitator family that uses the proton motive force to transport Zn2+ across bacterial membranes. Previous work defined the atomic structure of an outward-facing conformation, the location of several Zn2+ binding sites, and hydrophobic residues that appear to control access to the transport sites from the cytoplasm. A low-resolution cryo-EM structure revealed changes within the membrane domain that were associated with the alternating access mechanism for transport. In the current work, the resolution of this cryo-EM structure has been extended to 4.1 Å. Comparison with the X-ray structure defines the differences between inward-facing and outward-facing conformations at an atomic level. These differences include rocking and twisting of a four-helix bundle that harbors the Zn2+ transport site and controls its accessibility within each monomer. As previously noted, membrane domains are closely associated in the dimeric structure from cryo-EM but dramatically splayed apart in the X-ray structure. Cysteine crosslinking was used to constrain these membrane domains and to show that this large-scale splaying was not necessary for transport activity. Furthermore, dimer stability was not compromised by mutagenesis of elements in the cytoplasmic domain, suggesting that the extensive interface between membrane domains is a strong determinant of dimerization. As with other secondary transporters, this interface could provide a stable scaffold for movements of the four-helix bundle that confers alternating access of these ions to opposite sides of the membrane.

ABC transporters facilitate the movement of diverse molecules across cellular membranes, but how their activity is regulated post-translationally is not well understood. Here we report the crystal structure of MlaFB from E. coli, the cytoplasmic portion of the larger MlaFEDB ABC transporter complex, which drives phospholipid trafficking across the bacterial envelope to maintain outer membrane integrity. MlaB, a STAS domain protein, binds the ABC nucleotide binding domain, MlaF, and is required for its stability. Our structure also implicates a unique C-terminal tail of MlaF in self-dimerization. Both the C-terminal tail of MlaF and the interaction with MlaB are required for the proper assembly of the MlaFEDB complex and its function in cells. This work leads to a new model for how an important bacterial lipid transporter may be regulated by small proteins, and raises the possibility that similar regulatory mechanisms may exist more broadly across the ABC transporter family.

In double-membraned bacteria, phospholipid transport across the cell envelope is critical to maintain the outer membrane barrier, which plays a key role in virulence and antibiotic resistance. An MCE transport system called Mla has been implicated in phospholipid trafficking and outer membrane integrity, and includes an ABC transporter, MlaFEDB. The transmembrane subunit, MlaE, has minimal sequence similarity to other transporters, and the structure of the entire inner-membrane MlaFEDB complex remains unknown. Here, we report the cryo-EM structure of MlaFEDB at 3.05 Å resolution, revealing distant relationships to the LPS and MacAB transporters, as well as the eukaryotic ABCA/ABCG families. A continuous transport pathway extends from the MlaE substrate-binding site, through the channel of MlaD, and into the periplasm. Unexpectedly, two phospholipids are bound to MlaFEDB, suggesting that multiple lipid substrates may be transported each cycle. Our structure provides mechanistic insight into substrate recognition and transport by MlaFEDB.

Self-supervised learning (SSL) automates the extraction and interpretation of histopathology features on unannotated hematoxylin-eosin-stained whole slide images (WSIs). We train an SSL Barlow Twins encoder on 435 colon adenocarcinoma WSIs from The Cancer Genome Atlas to extract features from small image patches (tiles). Leiden community detection groups tiles into histomorphological phenotype clusters (HPCs). HPC reproducibility and predictive ability for overall survival are confirmed in an independent clinical trial ( N  = 1213 WSIs). This unbiased atlas results in 47 HPCs displaying unique and shared clinically significant histomorphological traits, highlighting tissue type, quantity, and architecture, especially in the context of tumor stroma. Through in-depth analyses of these HPCs, including immune landscape and gene set enrichment analyses, and associations to clinical outcomes, we shine light on the factors influencing survival and responses to treatments of standard adjuvant chemotherapy and experimental therapies. Further exploration of HPCs may unveil additional insights and aid decision-making and personalized treatments for colon cancer patients. Histomorphological phenotype clustering (HPC) is a self-supervised machine learning methodology enabling automatic feature extraction from whole slide images (WSI) in pathology. Here, the authors demonstrate the utility of this approach in colon adenocarcinoma WSIs and investigate the links between the HPCs and patient outcomes.

Approximately 30% of early-stage lung adenocarcinoma patients present with disease progression after successful surgical resection. Despite efforts of mapping the genetic landscape, there has been limited success in discovering predictive biomarkers of disease outcomes. Here we performed a systematic multi-omic assessment of 143 tumors and matched tumor-adjacent, histologically-normal lung tissue with long-term patient follow-up. Through histologic, mutational, and transcriptomic profiling of tumor and adjacent-normal tissue, we identified an inflammatory gene signature in tumor-adjacent tissue as the strongest clinical predictor of disease progression. Single-cell transcriptomic analysis demonstrated the progression-associated inflammatory signature was expressed in both immune and non-immune cells, and cell type-specific profiling in monocytes further improved outcome predictions. Additional analyses of tumor-adjacent transcriptomic data from The Cancer Genome Atlas validated the association of the inflammatory signature with worse outcomes across cancers. Collectively, our study suggests that molecular profiling of tumor-adjacent tissue can identify patients at high risk for disease progression. Lung adenocarcinoma is often curable when diagnosed at an early stage, but a subsection of patients will progress. Here, the authors use multi-omics profiling to show that gene expression data can predict clinical outcome.

Lung adenocarcinoma (LUAD) presents diverse histomorphological features within the tumor microenvironment (TME) that influence prognosis and response to immunotherapy. Leveraging contrastive learning, we developed an unbiased atlas of cell neighborhoods to systematically explore the LUAD microenvironment at the cellular scale and investigate how these neighborhoods combine to form histologic patterns. This multiscale approach enables a comprehensive understanding of both cell-specific interactions and broader histologic architecture in LUAD. Our analysis identified distinct histomorphological phenotype clusters of cellular neighborhoods (cn-HPCs) with prognostic significance. For example, cn-HPC 0 was associated with immune activation and favorable survival, while cn-HPC 23 was enriched in necrotic, immune-excluded regions and aligned with poorer outcomes. Through associations with immunophenotypes, co-expressed gene modules, and pathway enrichment, we found that cn-HPCs reflect underlying processes of immune modulation, cellular growth, and inflammation, offering insights into the functional landscape of LUAD. In an independent LUAD immunotherapy cohort, we found that cn-HPC 23 showed promising predictive value for treatment response. These findings suggest that fine-grained spatial profiling of the TME may offer complementary value for identifying patients more likely to benefit from immunotherapy. Taken together, our study highlights the potential of cn-HPCs to bridge histopathological patterns and clinical outcomes, supporting their relevance for prognosis and treatment stratification in LUAD.