High-throughput phenotyping reveals expansive genetic and structural underpinnings of immune variation.

By developing a high-density murine immunophenotyping platform compatible with high-throughput genetic screening, we have established profound contributions of genetics and structure to immune variation (http://www.immunophenotype.org). Specifically, high-throughput phenotyping of 530 unique mouse gene knockouts identified 140 monogenic 'hits', of which most had no previous immunologic association. Furthermore, hits were collectively enriched in genes for which humans show poor tolerance to loss of function. The immunophenotyping platform also exposed dense correlation networks linking immune parameters with each other and with specific physiologic traits. Such linkages limit freedom of movement for individual immune parameters, thereby imposing genetically regulated 'immunologic structures', the integrity of which was associated with immunocompetence. Hence, we provide an expanded genetic resource and structural perspective for understanding and monitoring immune variation in health and disease.

A synthetic antibiotic class overcoming bacterial multidrug resistance.

The dearth of new medicines effective against antibiotic-resistant bacteria presents a growing global public health concern1. For more than five decades, the search for new antibiotics has relied heavily on the chemical modification of natural products (semisynthesis), a method ill-equipped to combat rapidly evolving resistance threats. Semisynthetic modifications are typically of limited scope within polyfunctional antibiotics, usually increase molecular weight, and seldom permit modifications of the underlying scaffold. When properly designed, fully synthetic routes can easily address these shortcomings2. Here we report the structure-guided design and component-based synthesis of a rigid oxepanoproline scaffold which, when linked to the aminooctose residue of clindamycin, produces an antibiotic of exceptional potency and spectrum of activity, which we name iboxamycin. Iboxamycin is effective against ESKAPE pathogens including strains expressing Erm and Cfr ribosomal RNA methyltransferase enzymes, products of genes that confer resistance to all clinically relevant antibiotics targeting the large ribosomal subunit, namely macrolides, lincosamides, phenicols, oxazolidinones, pleuromutilins and streptogramins. X-ray crystallographic studies of iboxamycin in complex with the native bacterial ribosome, as well as with the Erm-methylated ribosome, uncover the structural basis for this enhanced activity, including a displacement of the [Formula: see text] nucleotide upon antibiotic binding. Iboxamycin is orally bioavailable, safe and effective in treating both Gram-positive and Gram-negative bacterial infections in mice, attesting to the capacity for chemical synthesis to provide new antibiotics in an era of increasing resistance.

DNA-encoded library-enabled discovery of proximity-inducing small molecules.

Small molecules that induce protein-protein associations represent powerful tools to modulate cell circuitry. We sought to develop a platform for the direct discovery of compounds able to induce association of any two preselected proteins, using the E3 ligase von Hippel-Lindau (VHL) and bromodomains as test systems. Leveraging the screening power of DNA-encoded libraries (DELs), we synthesized ~1 million DNA-encoded compounds that possess a VHL-targeting ligand, a variety of connectors and a diversity element generated by split-and-pool combinatorial chemistry. By screening our DEL against bromodomains in the presence and absence of VHL, we could identify VHL-bound molecules that simultaneously bind bromodomains. For highly barcode-enriched library members, ternary complex formation leading to bromodomain degradation was confirmed in cells. Furthermore, a ternary complex crystal structure was obtained for our most enriched library member with BRD4BD1 and a VHL complex. Our work provides a foundation for adapting DEL screening to the discovery of proximity-inducing small molecules.

PDCM Finder: an open global research platform for patient-derived cancer models.

PDCM Finder (www.cancermodels.org) is a cancer research platform that aggregates clinical, genomic and functional data from patient-derived xenografts, organoids and cell lines. It was launched in April 2022 as a successor of the PDX Finder portal, which focused solely on patient-derived xenograft models. Currently the portal has over 6200 models across 13 cancer types, including rare paediatric models (17%) and models from minority ethnic backgrounds (33%), making it the largest free to consumer and open access resource of this kind. The PDCM Finder standardises, harmonises and integrates the complex and diverse data associated with PDCMs for the cancer community and displays over 90 million data points across a variety of data types (clinical metadata, molecular and treatment-based). PDCM data is FAIR and underpins the generation and testing of new hypotheses in cancer mechanisms and personalised medicine development.

Comparative analysis of the spatial distribution of brain metastases across several primary cancers using machine learning and deep learning models.

OBJECTIVE: Brain metastases (BM) are associated with poor prognosis and increased mortality rates, making them a significant clinical challenge. Studying BMs can aid in improving early detection and monitoring. Systematic comparisons of anatomical distributions of BM from different primary cancers, however, remain largely unavailable. METHODS: To test the hypothesis that anatomical BM distributions differ based on primary cancer type, we analyze the spatial coordinates of BMs for five different primary cancer types along principal component (PC) axes. The dataset includes 3949 intracranial metastases, labeled by primary cancer types and with six features. We employ PC coordinates to highlight the distinctions between various cancer types. We utilized different Machine Learning (ML) algorithms (RF, SVM, TabNet DL) models to establish the relationship between primary cancer diagnosis, spatial coordinates of BMs, age, and target volume. RESULTS: Our findings revealed that PC1 aligns most with the Y axis, followed by the Z axis, and has minimal correlation with the X axis. Based on PC1 versus PC2 plots, we identified notable differences in anatomical spreading patterns between Breast and Lung cancer, as well as Breast and Renal cancer. In contrast, Renal and Lung cancer, as well as Lung and Melanoma, showed similar patterns. Our ML and DL results demonstrated high accuracy in distinguishing BM distribution for different primary cancers, with the SVM algorithm achieving 97% accuracy using a polynomial kernel and TabNet achieving 96%. The RF algorithm ranked PC1 as the most important discriminating feature. CONCLUSIONS: In summary, our results support accurate multiclass ML classification regarding brain metastases distribution.

Rational Screening for Cooperativity in Small-Molecule Inducers of Protein-Protein Associations.

The hallmark of a molecular glue is its ability to induce cooperative protein-protein interactions, leading to the formation of a ternary complex, despite weaker binding toward one or both individual proteins. Notably, the extent of cooperativity distinguishes molecular glues from bifunctional compounds, which constitute a second class of inducers of protein-protein interactions. However, apart from serendipitous discovery, there have been limited rational screening strategies for the high cooperativity exhibited by molecular glues. Here, we propose a binding-based screen of DNA-barcoded compounds on a target protein in the presence or absence of a presenter protein, using the "presenter ratio", the ratio of ternary enrichment to binary enrichment, as a predictive measure of cooperativity. Through this approach, we identified a range of cooperative, noncooperative, and uncooperative compounds in a single DNA-encoded library screen with bromodomain containing protein (BRD)9 and the VHL-elongin C-elongin B (VCB) complex. Our most cooperative hit compound, 13-7, exhibits micromolar binding affinity to BRD9 but nanomolar affinity for the ternary complex with BRD9 and VCB, with cooperativity comparable to classical molecular glues. This approach may enable the rational discovery of molecular glues for preselected proteins and thus facilitate the transition to a new paradigm of small-molecule therapeutics.

Corrigendum: High-throughput discovery of novel developmental phenotypes.

This corrects the article DOI: 10.1038/nature19356.

Mouse mutant phenotyping at scale reveals novel genes controlling bone mineral density.

The genetic landscape of diseases associated with changes in bone mineral density (BMD), such as osteoporosis, is only partially understood. Here, we explored data from 3,823 mutant mouse strains for BMD, a measure that is frequently altered in a range of bone pathologies, including osteoporosis. A total of 200 genes were found to significantly affect BMD. This pool of BMD genes comprised 141 genes with previously unknown functions in bone biology and was complementary to pools derived from recent human studies. Nineteen of the 141 genes also caused skeletal abnormalities. Examination of the BMD genes in osteoclasts and osteoblasts underscored BMD pathways, including vesicle transport, in these cells and together with in silico bone turnover studies resulted in the prioritization of candidate genes for further investigation. Overall, the results add novel pathophysiological and molecular insight into bone health and disease.

The EurOPDX Data Portal: an open platform for patient-derived cancer xenograft data sharing and visualization.

BACKGROUND: Patient-derived xenografts (PDX) mice models play an important role in preclinical trials and personalized medicine. Sharing data on the models is highly valuable for numerous reasons - ethical, economical, research cross validation etc. The EurOPDX Consortium was established 8 years ago to share such information and avoid duplicating efforts in developing new PDX mice models and unify approaches to support preclinical research. EurOPDX Data Portal is the unified data sharing platform adopted by the Consortium. MAIN BODY: In this paper we describe the main features of the EurOPDX Data Portal ( https://dataportal.europdx.eu/ ), its architecture and possible utilization by researchers who look for PDX mice models for their research. The Portal offers a catalogue of European models accessible on a cooperative basis. The models are searchable by metadata, and a detailed view provides molecular profiles (gene expression, mutation, copy number alteration) and treatment studies. The Portal displays the data in multiple tools (PDX Finder, cBioPortal, and GenomeCruzer in future), which are populated from a common database displaying strictly mutually consistent views. (SHORT) CONCLUSION: EurOPDX Data Portal is an entry point to the EurOPDX Research Infrastructure offering PDX mice models for collaborative research, (meta)data describing their features and deep molecular data analysis according to users' interests.

Pleiotropy data resource as a primer for investigating co-morbidities/multi-morbidities and their role in disease.

Most current biomedical and protein research focuses only on a small proportion of genes, which results in a lost opportunity to identify new gene-disease associations and explore new opportunities for therapeutic intervention. The International Mouse Phenotyping Consortium (IMPC) focuses on elucidating gene function at scale for poorly characterized and/or under-studied genes. A key component of the IMPC initiative is the implementation of a broad phenotyping pipeline, which is facilitating the discovery of pleiotropy. Characterizing pleiotropy is essential to identify gene-disease associations, and it is of particular importance when elucidating the genetic causes of syndromic disorders. Here we show how the IMPC is effectively uncovering pleiotropy and how the new mouse models and gene function hypotheses generated by the IMPC are increasing our understanding of the mammalian genome, forming the basis of new research and identifying new gene-disease associations.

A quantitative characterization of the spatial distribution of brain metastases from breast cancer and respective molecular subtypes.

PURPOSE: Brain metastases (BM) remain a significant cause of morbidity and mortality in breast cancer (BC) patients. Specific factors promoting the process of BM and predilection for selected neuro-anatomical regions remain unknown, yet may have major implications for prevention or treatment. Anatomical spatial distributions of BM from BC suggest a predominance of metastases in the hindbrain and cerebellum. Systematic approaches to quantifying BM location or location-based analyses based on molecular subtypes, however, remain largely unavailable. METHODS: We analyzed stereotactic Cartesian coordinates derived from 134 patients undergoing gamma- knife radiosurgery (GKRS) for treatment of 407 breast cancer BMs to quantitatively study BM spatial distribution along principal component axes and by intrinsic molecular subtype (ER, PR, Herceptin). We used kernel density estimators (KDE) to highlight clustering and distribution regions in the brain, and we used the metric of mutual information (MI) to tease out subtle differences in the BM distributions associated with different molecular subtypes of BC. BM location maps according to vascular and anatomical distributions using Cartesian coordinates to aid in systematic classification of tumor locations were additionally developed. RESULTS: We corroborated that BC BMs show a consistent propensity to arise posteriorly and caudally, and that Her2+ tumors are relatively more likely to arise medially rather than laterally. To compare the distributions among varying BC molecular subtypes, the mutual information metric reveal that the ER-PR-Her2+ and ER-PR-Her2- subtypes show the smallest amount of mutual information and are most molecularly distinct. The kernel density contour plots show a propensity for triple negative BC to arise in more superiorly or cranially situated BMs. CONCLUSIONS: We present a novel and shareable workflow for characterizing and comparing spatial distributions of BM which may aid in identifying therapeutic or diagnostic targets and interactions with the tumor microenvironment. Further characterization of these patterns with larger multi-institutional data-sets may have major impacts on treatment or management of cancer patients.

Machine Learning on DNA-Encoded Library Count Data Using an Uncertainty-Aware Probabilistic Loss Function.

DNA-encoded library (DEL) screening and quantitative structure-activity relationship (QSAR) modeling are two techniques used in drug discovery to find novel small molecules that bind a protein target. Applying QSAR modeling to DEL selection data can facilitate the selection of compounds for off-DNA synthesis and evaluation. Such a combined approach has been done recently by training binary classifiers to learn DEL enrichments of aggregated "disynthons" in order to accommodate the sparse and noisy nature of DEL data. However, a binary classification model cannot distinguish between different levels of enrichment, and information is potentially lost during disynthon aggregation. Here, we demonstrate a regression approach to learning DEL enrichments of individual molecules, using a custom negative-log-likelihood loss function that effectively denoises DEL data and introduces opportunities for visualization of learned structure-activity relationships. Our approach explicitly models the Poisson statistics of the sequencing process used in the DEL experimental workflow under a frequentist view. We illustrate this approach on a DEL dataset of 108,528 compounds screened against carbonic anhydrase (CAIX), and a dataset of 5,655,000 compounds screened against soluble epoxide hydrolase (sEH) and SIRT2. Due to the treatment of uncertainty in the data through the negative-log-likelihood loss used during training, the models can ignore low-confidence outliers. While our approach does not demonstrate a benefit for extrapolation to novel structures, we expect our denoising and visualization pipeline to be useful in identifying structure-activity trends and highly enriched pharmacophores in DEL data. Further, this approach to uncertainty-aware regression modeling is applicable to other sparse or noisy datasets where the nature of stochasticity is known or can be modeled; in particular, the Poisson enrichment ratio metric we use can apply to other settings that compare sequencing count data between two experimental conditions.

High-throughput discovery of novel developmental phenotypes.

Approximately one-third of all mammalian genes are essential for life. Phenotypes resulting from knockouts of these genes in mice have provided tremendous insight into gene function and congenital disorders. As part of the International Mouse Phenotyping Consortium effort to generate and phenotypically characterize 5,000 knockout mouse lines, here we identify 410 lethal genes during the production of the first 1,751 unique gene knockouts. Using a standardized phenotyping platform that incorporates high-resolution 3D imaging, we identify phenotypes at multiple time points for previously uncharacterized genes and additional phenotypes for genes with previously reported mutant phenotypes. Unexpectedly, our analysis reveals that incomplete penetrance and variable expressivity are common even on a defined genetic background. In addition, we show that human disease genes are enriched for essential genes, thus providing a dataset that facilitates the prioritization and validation of mutations identified in clinical sequencing efforts.

Characterization of BCMA Expression in Circulating Rare Single Cells of Patients with Plasma Cell Neoplasms.

B-cell maturation antigen (BCMA), a key regulator of B-cell proliferation and survival, is highly expressed in almost all cases of plasma cell neoplasms and B-lymphoproliferative malignancies. BCMA is a robust biomarker of plasma cells and a therapeutic target with substantial clinical significance. However, the expression of BCMA in circulating tumor cells of patients with hematological malignancies has not been validated for the detection of circulating plasma and B cells. The application of BCMA as a biomarker in single-cell detection and profiling of circulating tumor cells in patients' blood could enable early disease profiling and therapy response monitoring. Here, we report the development and validation of a slide-based immunofluorescence assay (i.e., CD138, BCMA, CD45, DAPI) for enrichment-free detection, quantification, and morphogenomic characterization of BCMA-expressing cells in patients (N = 9) with plasma cell neoplasms. Varying morphological subtypes of circulating BCMA-expressing cells were detected across the CD138(+/-) and CD45(+/-) compartments, representing candidate clonotypic post-germinal center B cells, plasmablasts, and both normal and malignant plasma cells. Genomic analysis by single-cell sequencing and correlation to clinical FISH cytogenetics provides validation, with data showing that patients across the different neoplastic states carry both normal and altered BCMA-expressing cells. Furthermore, altered cells harbor cytogenetic events detected by clinical FISH. The reported enrichment-free liquid biopsy approach has potential applications as a single-cell methodology for the early detection of BCMA+ B-lymphoid malignancies and in monitoring therapy response for patients undergoing anti-BCMA treatments.

Practical Gram-Scale Synthesis of Iboxamycin, a Potent Antibiotic Candidate.

A gram-scale synthesis of iboxamycin, an antibiotic candidate bearing a fused bicyclic amino acid residue, is presented. A pivotal transformation in the route involves an intramolecular hydrosilylation-oxidation sequence to set the ring-fusion stereocenters of the bicyclic scaffold. Other notable features of the synthesis include a high-yielding, highly diastereoselective alkylation of a pseudoephenamine amide, a convergent sp3-sp2 Negishi coupling, and a one-pot transacetalization-reduction reaction to form the target compound's oxepane ring. Implementation of this synthetic strategy has provided ample quantities of iboxamycin to allow for its in vivo profiling in murine models of infection.

Soft windowing application to improve analysis of high-throughput phenotyping data.

MOTIVATION: High-throughput phenomic projects generate complex data from small treatment and large control groups that increase the power of the analyses but introduce variation over time. A method is needed to utlize a set of temporally local controls that maximizes analytic power while minimizing noise from unspecified environmental factors. RESULTS: Here we introduce 'soft windowing', a methodological approach that selects a window of time that includes the most appropriate controls for analysis. Using phenotype data from the International Mouse Phenotyping Consortium (IMPC), adaptive windows were applied such that control data collected proximally to mutants were assigned the maximal weight, while data collected earlier or later had less weight. We applied this method to IMPC data and compared the results with those obtained from a standard non-windowed approach. Validation was performed using a resampling approach in which we demonstrate a 10% reduction of false positives from 2.5 million analyses. We applied the method to our production analysis pipeline that establishes genotype-phenotype associations by comparing mutant versus control data. We report an increase of 30% in significant P-values, as well as linkage to 106 versus 99 disease models via phenotype overlap with the soft-windowed and non-windowed approaches, respectively, from a set of 2082 mutant mouse lines. Our method is generalizable and can benefit large-scale human phenomic projects such as the UK Biobank and the All of Us resources. AVAILABILITY AND IMPLEMENTATION: The method is freely available in the R package SmoothWin, available on CRAN http://CRAN.R-project.org/package=SmoothWin. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PDX Finder: A portal for patient-derived tumor xenograft model discovery.

Patient-derived tumor xenograft (PDX) mouse models are a versatile oncology research platform for studying tumor biology and for testing chemotherapeutic approaches tailored to genomic characteristics of individual patients' tumors. PDX models are generated and distributed by a diverse group of academic labs, multi-institution consortia and contract research organizations. The distributed nature of PDX repositories and the use of different metadata standards for describing model characteristics presents a significant challenge to identifying PDX models relevant to specific cancer research questions. The Jackson Laboratory and EMBL-EBI are addressing these challenges by co-developing PDX Finder, a comprehensive open global catalog of PDX models and their associated datasets. Within PDX Finder, model attributes are harmonized and integrated using a previously developed community minimal information standard to support consistent searching across the originating resources. Links to repositories are provided from the PDX Finder search results to facilitate model acquisition and/or collaboration. The PDX Finder resource currently contains information for 1985 PDX models of diverse cancers including those from large resources such as the Patient-Derived Models Repository, PDXNet and EurOPDX. Individuals or organizations that generate and distribute PDXs are invited to increase the 'findability' of their models by participating in the PDX Finder initiative at www.pdxfinder.org.

Water-Compatible Cycloadditions of Oligonucleotide-Conjugated Strained Allenes for DNA-Encoded Library Synthesis.

DNA-encoded libraries of small molecules are being explored extensively for the identification of binders in early drug-discovery efforts. Combinatorial syntheses of such libraries require water- and DNA-compatible reactions, and the paucity of these reactions currently limit the chemical features of resulting barcoded products. The present work introduces strain-promoted cycloadditions of cyclic allenes under mild conditions to DNA-encoded library synthesis. Owing to distinct cycloaddition modes of these reactive intermediates with activated olefins, 1,3-dipoles, and dienes, the process generates diverse molecular architectures from a single precursor. The resulting DNA-barcoded compounds exhibit unprecedented ring and topographic features, related to elements found to be powerful in phenotypic screening.

A comprehensive and comparative phenotypic analysis of the collaborative founder strains identifies new and known phenotypes.

The collaborative cross (CC) is a large panel of mouse-inbred lines derived from eight founder strains (NOD/ShiLtJ, NZO/HILtJ, A/J, C57BL/6J, 129S1/SvImJ, CAST/EiJ, PWK/PhJ, and WSB/EiJ). Here, we performed a comprehensive and comparative phenotyping screening to identify phenotypic differences and similarities between the eight founder strains. In total, more than 300 parameters including allergy, behavior, cardiovascular, clinical blood chemistry, dysmorphology, bone and cartilage, energy metabolism, eye and vision, immunology, lung function, neurology, nociception, and pathology were analyzed; in most traits from sixteen females and sixteen males. We identified over 270 parameters that were significantly different between strains. This study highlights the value of the founder and CC strains for phenotype-genotype associations of many genetic traits that are highly relevant to human diseases. All data described here are publicly available from the mouse phenome database for analyses and downloads.

Distribution of Topological Types in Grain-Growth Microstructures.

An open question in studying normal grain growth concerns the asymptotic state to which microstructures converge. In particular, the distribution of grain topologies is unknown. We introduce a thermodynamiclike theory to explain these distributions in two- and three-dimensional systems. In particular, a bendinglike energy E_{i} is associated to each grain topology t_{i}, and the probability of observing that particular topology is proportional to [1/s(t_{i})]e^{-ßE_{i}}, where s(t_{i}) is the order of an associated symmetry group and ß is a thermodynamiclike constant. We explain the physical origins of this approach and provide numerical evidence in support.