LATEST IN SCIENCE

Abstract: Chlamydia trachomatis is an obligate intracellular Gram-negative pathogen responsible for sexually transmitted infections and trachoma in humans. Although antibiotics are generally effective against acute infections, persistent chlamydial forms often exhibit reduced susceptibility during chronic infection. Chlamydia relies on its type III secretion system (T3SS) to inject effector proteins into host cells, making T3SS proteins attractive targets for antivirulence therapeutics. In this study, we employed an integrated computational pipeline to model and assemble the C. trachomatis T3SS constituent proteins. Template-based modeling using crystallographic structures of homologs from other Gram-negative bacteria revealed a highly conserved structural architecture despite low sequence identity (18-46%). Stereochemical validation confirmed high model quality, with most T3SS proteins exhibiting favorable protein-protein interactions (PPIs). Since the activity of the T3SS complex relies on extensive PPIs, we targeted these PPIs as a promising approach to attenuate bacterial virulence. CdsN, which functions as an ATPase of the T3SS, is a hexamer of which we targeted the dimerization interface. Structure-based virtual screening of compounds from the e-Drug3D and IMPPAT libraries against predicted hotspot residues and the identified druggable pocket at the CdsN dimeric interface, followed by ADMET screening, yielded three promising candidates: M Roflumilast (Drug ID: 1537), Elacestrant (Drug ID: 2081), and Tecovirimat (Drug ID: 1889). All three ligands formed thermodynamically stable complexes with the CdsN dimer, with Elacestrant demonstrating the most favourable binding free energy. This was also confirmed by 100 ns molecular dynamics simulation. This study provides new insights into the molecular architecture of C. trachomatis T3SS and identifies M Roflumilast, Elacestrant, and Tecovirimat as potential drug candidates against chlamydial infection.

Abstract: Structural alphabets have transformed protein phylogenetics by enabling sequence-style alignment and maximum-likelihood inference to be applied directly to structural data. However, a coordinate-explicit alphabet, in which character states are derived from three-dimensional atomic positions, encodes not only evolutionary signal but also the conformational variability inherent to protein structure. This source of noise has not previously been quantified in a phylogenetic context, and no framework exists for comparing alphabets with respect to their conformational sensitivity. Here, we introduce the Normalised Noise Index (NNI), a Shannon entropy-based metric for quantifying conformational sensitivity in structural alphabet encodings, and apply it alongside ensemble-wide Robinson--Foulds (RF) variance as a framework for characterising the impact of conformational noise on phylogenetic inference. Across 3,749 single-chain NMR ensembles from the Protein Data Bank, we show that 3Di character variability is a pervasive feature of experimentally observed conformational spread, with NNI negatively correlated with within-ensemble structural stability. A 100 ns molecular dynamics simulation of myoglobin confirmed that thermal fluctuations alone are sufficient to generate comparable 3Di character variation and, in 2.9% of cases, to redirect maximum-likelihood tree search away from the expected topology in a 4-taxon globin benchmark with independently established relationships. Exhaustive enumeration of 4,800 conformational replicates across three NMR ensembles revealed that topological variance under 3Di encoding is approximately 1.7-fold greater than under structural distance, based on 11,517,600 pairwise RF comparisons, a source of uncertainty invisible to standard bootstrap analysis. By contrast, TEA, a sequence-derived structure-aware alphabet inferred from ESM-2 embeddings rather than directly from atomic coordinates, is insulated from conformational sampling by construction and yields zero topological variance across all conformational replicates, serving here as a noise-insulated reference rather than a proposed replacement for 3Di. Together, these results demonstrate that alphabet choice is a methodological variable in structural phylogenetics, and that the NNI metric and RF variance framework introduced here provide a practical basis for principled noise characterisation as new structural alphabets continue to emerge.

Abstract: Genetic code expansion introduces new-to-nature chemical moieties into ribosomally synthesized proteins. In practice, the scope of functional groups that can be accessed using this method is often limited by noncanonical amino acid (ncAA) availability. Producing ncAAs directly in cells can circumvent poor ncAA uptake or commercial unavailability, but limited enzymes suitable for this application exist. In vitro evolution campaigns have been remarkably successful in yielding synthetically useful "ncAA synthases." However, these enzymes are optimized for preparative-scale synthesis and their activities often do not translate well to cellular biosynthesis. Thus, expanding strategies to engineer enzymes specifically for ncAA production within cells will benefit further implementation of genetic code expansion. Here, we use phage-assisted noncontinuous and continuous evolution to evolve enzymes for improved synthesis of non-canonical tyrosine derivatives in E. coli. Using simple serial passaging, we uncovered mutations that doubled the production of an expensive ncAA, 3-methoxytyrosine, by tyrosine phenol lyase, and furthermore evolved variants that enable 3-iodotyrosine biosynthesis, a transformation the parent enzyme is unable to catalyze. Additionally, we evolved a recently reported tyrosine synthase for improved production of 3-halogenated tyrosines, identifying variants that exhibit high activity even at low substrate concentrations owing to a ~8-fold reduction in KM. Our results demonstrate that phage assisted evolution can be used to rapidly improve the activity of enzymes for ncAA production in cells.

Abstract: Alternative splicing and proteolytic truncation generate tens of thousands of protein isoforms in the human proteome, but the structural consequences for quaternary state, the level at which most signaling, enzymatic and regulatory function operates, have largely been examined one molecule at a time. Leveraging the recent expansion of the AlphaFold Database to predicted human homodimers, we systematically compared 5,168 canonical-versus-truncated homodimer pairs across the human proteome. In high-confidence canonical homodimers, truncation is associated with predicted structural conservation in 56.4% of pairs (mean 85 residues lost), complete interface ablation in 26.1% (mean 178 residues lost), and partial destabilization in 17.5% (mean 134 residues lost); a distinct fourth class (4.0% of the dataset, n = 208) shows truncation-associated emergence of a predicted high-confidence interface from a sub-threshold canonical baseline. Two reproducible rules govern these transitions: a topological asymmetry in which N-terminal losses are preferentially enriched ~1.6-fold in interface preservation while C-terminal losses are rare overall (~6% of pairs) and modestly under-represented in the conservation class, and a biophysical rule in which emergence-class proteins show substantially elevated intrinsic disorder content relative to ablation-class proteins, as measured by both AlphaFold pLDDT-defined disorder of the canonical structure (Cohen's d {approx} 1.39) and AIUPred peak binding propensity of the truncated isoform (Cohen's d {approx} 0.65). Formal pathway enrichment recovered only a small nucleotide-metabolism signal, indicating that these rules operate across diverse gene-functional categories. Truncation-associated remodeling of homodimer architecture thus constitutes a structural grammar of the human proteome rather than a specialty of any single regulatory family.

Abstract: Molecular neuroimaging with positron emission tomography (PET) and single-photon emission computed tomography (SPECT) enables quantification of specific molecular targets in the living brain. Despite its scientific impact, molecular neuroimaging research has historically faced challenges due to high costs, small sample sizes, laboratory-specific analysis pipelines, and limited large-scale data sharing. These factors have hindered reproducibility and the broader reuse of valuable PET datasets. The OpenNeuroPET initiative was established to address these barriers by developing standards, infrastructure, and open-source tools for organizing, sharing, and analyzing molecular neuroimaging data. Through collaborations across Europe and North America, OpenNeuroPET has supported the PET extension of the Brain Imaging Data Structure (PET-BIDS), providing a standardized framework for PET datasets and metadata. Building on PET-BIDS, tools such as PET2BIDS, ezBIDS, and BIDSCoin facilitate data conversion and curation. In parallel, OpenNeuro now hosts PET-BIDS datasets for open sharing, while complementary platforms such as PublicnEUro enable GDPR-compliant controlled access. Emerging open-source workflows and BIDS applications further support automated, reproducible PET preprocessing and quantitative analysis, promoting harmonized processing across centers. Together, these developments mark an important step toward an open molecular neuroimaging ecosystem in which datasets, software, and workflows can be transparently shared, reused, and scaled for collaborative research.

Abstract: The three-dimensional folding of DNA is essential for genome function, but its organization remains difficult to summarize quantitatively across genomic scales. Here, we study DNA folding from Hi-C contact data using a network-based notion of fractal dimension. In this representation, genomic loci are treated as nodes, and observed Hi-C contacts define weighted edges, so that frequently interacting loci are closer in the resulting network. We then estimate fractal dimension using two complementary graph-based methods: the correlation dimension and the sandbox dimension. Validation on synthetic networks shows that the proposed estimators detect clear scaling behavior in hierarchical fractal-like networks, while distinguishing them from networks with local clustering but no stable multiscale self-similarity. Applied to intrachromosomal Hi-C data from the IMR90 human cell line, the method reveals approximate linear scaling regimes on log-log plots, suggesting fractal-like organization in chromatin contact networks. At the chromosome level, estimated fractal dimension tends to increase with chromosome size: larger chromosomes often have dimensions closer to 3, consistent with more compact and space-filling organization, whereas shorter chromosomes tend to have lower dimensions, closer to 1, consistent with simpler and more open folding patterns. A sliding-window analysis at 5 kb resolution further shows that fractal organization varies substantially along chromosomes rather than remaining uniform across genomic position. These results suggest that graph-based fractal dimension provides an interpretable summary of DNA folding complexity at both global and local scales. More broadly, the proposed framework offers a quantitative way to study multiscale genome organization from Hi-C data using tools from network geometry.

Abstract: High-throughput affinity-based proteomics has advanced biomedical research, yet fundamental, persistent discordance between mainstream platforms (SomaScan and Olink) routinely undermines the replication of findings. This platform-driven non-replication complicates downstream biological validation and biomarker prioritization. Here, we develop a machine learning-based framework for cross-platform protein value imputation to resolve this translational bottleneck. Using paired proteomic data measured by both SomaScan and Olink from 5,325 participants of the Multi-Ethnic Study of Atherosclerosis, we developed models to impute cross-platform measurements and applied them to two independent and demographically distinct cohorts (Cardiovascular Health Study [N=3,171] and UK Biobank [UKB; N=41,405]) for external validation. Our bi-directional model 1) established an imputation performance-based protein fidelity index, validated against gold-standard measurements from Atherosclerosis Risk in Communities study (N=101) and Nurses' Health Study (N=54), 2) enabled imputation of platform-exclusive protein measurements, and 3) facilitated calibration of overlapping proteins. We demonstrate the utility of this framework through three applications: 1) fidelity-informed analyses enhanced the replication of biomarker discovery, 2) recovery of SomaScan signals that were previously inaccessible in UKB's original Olink measurements, and 3) improved replication performance for overlapping proteins. Our study offers a translational roadmap that allows researchers to achieve reliable epidemiological replication, target specific assays for future optimization, and prioritize biological signal over platform noise.

Abstract: Mechanical remodeling of the extracellular matrix (ECM) influences glioma progression, yet the molecular regulators that control tumor matrix mechanics remain poorly understood. By comparing low-grade gliomas (LGGs), associated with improved patient survival, with glioblastomas (GBMs), which carry a poor prognosis, we identified the cartilage-derived ECM protein CRTAC1-A as enriched in LGGs and significantly reduced in GBMs, with elevated expression correlating with improved patient survival. Restoration of CRTAC1-A suppressed glioma cell proliferation and invasion and enhanced temozolomide efficacy in three-dimensional tumor spheroid models. Mechanistically, CRTAC1-A directly interacts with collagen I and reorganizes collagen networks across multiple length scales, generating a mechanically compliant yet structurally resilient ECM with reduced stiffness, enhanced elastic recovery, and resistance to persistent remodeling. This viscoelastic normalization limits invasive remodeling while preserving matrix permeability and drug penetration. Together, these findings identify CRTAC1-A as a reversible regulator of tumor ECM mechanics that suppresses glioma malignancy.

Abstract: Oncogenic KRAS mutations promote tumorigenesis by constitutive activation of multiple, well-characterised signalling pathways. However, there is significant heterogeneity across mutant KRAS tumours in terms of mutation present, mutant allele abundance and downstream signalling strength. It is unclear whether these variations can impact responses to specific therapies. Here, we demonstrate that ~20% of lung adenocarcinomas (LUAD) show an increase in mutant KRAS dosage (KRASmutant allele fraction > KRASwild-type). Furthermore, we show that KRAS mutant dosage can directly influence clinical outcome and therapeutic susceptibilities in lung cancer. Our findings show that mutant KRAS copy gains specifically affect platinum lung cancer response, promoting resistance to this standard-of-care therapy. Importantly, increases in KRAS mutant dosage are also associated with an increased vulnerability to pS6K inhibition, due to the unique metabolic rewiring of these cells. Together, we show that mutant KRAS dosage contributes to the phenotypic heterogeneity of mutant KRAS NSCLC and that assessment of mutant KRAS content or signalling strength can help optimise treatments strategies for these patients.

Abstract: Tumor-associated macrophages (TAM) exert essential functions during the immune response to cancer. However, investigations of TAM within a native human tumor microenvironment (TME) have been impeded by a lack of appropriate model systems. Here, patient-derived organoids (PDO) from air-liquid interface (ALI)-grown tumor fragments, containing a human TME that encompassed stroma and immune subsets, robustly preserved TAM that were maintained by endogenous CSF-1 and appropriately responded to polarization signals. Antibody blockade of the CD47 regulatory checkpoint in organoids stimulated phagocytosis and remodeled TAM cytokine secretion profiles that were confirmed in anti-CD47 phase I trial patients. Amongst PDO histologies screened, anti-CD47 tumor killing was notable in clear cell renal cell carcinoma (ccRCC) which was associated with increased TAM infiltration. PDO contained diverse previously described TAM subsets; however, anti-CD47 reprogrammed organoid TAM toward an immunosuppressive SPP1+ phenotype, highlighting a negative feedback mechanism. Our findings uncover a resistance circuit engaged by macrophage checkpoint blockade and position ALI PDO as a robust translational platform for dissecting human macrophage biology and informing precision immunotherapy.