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Defining CDK12 as a tumor suppressor and therapeutic target in mouse models of tubo-ovarian high-grade serous carcinoma

Ovarian cancer is the sixth leading cause of cancer death among American women, with most fatalities attributable to tubo-ovarian high-grade serous carcinoma (HGSC). This malignancy usually develops resistance to conventional chemotherapy, underscoring the need for robust preclinical models to guide the development of novel therapies. Here, we introduce an HGSC mouse model generated via Ovgp1 -driven Cre recombinase effecting CRISPR/Cas9-mediated deletion of Trp53, Rb1 , and Nf1 tumor suppressors in mouse oviductal epithelium ( m-sgPRN model). Cyclin-dependent kinase 12 (CDK12) inactivation—frequently observed in human HGSC—is associated with poorer outcomes, DNA damage accumulation (including tandem duplications), and increased tumor immunogenicity. In our system, coablation of Cdk12 ( m-sgPRN;Cdk12KO ) recapitulated hallmark features of HGSC, while accelerating tumor progression and reducing survival. In a conventional (Cre-lox-mediated) Trp53/Nf1/Rb1 triple knockout model with concurrent Cdk12 ablation ( PRN ; Cdk12KO mice), we observed T cell–rich immune infiltrates mirroring those seen clinically. We established both models as subcutaneous or intraperitoneal syngeneic allografts of CDK12 -inactivated HGSC that exhibited sensitivity to immune checkpoint blockade. Furthermore, a CRISPR/Cas9 synthetic lethality screen in PRN;Cdk12KO -derived cell lines identified CDK13—an essential paralog of CDK12—as the most depleted candidate, confirming a previously reported synthetic lethal interaction. Pharmacologic CDK13/12 degradation (employing YJ1206) demonstrated enhanced efficacy in cell lines derived from both m-sgPRN;Cdk12KO and PRN ; Cdk12KO models. Our results define CDK12 as a key tumor suppressor in tubo-ovarian HGSC and highlight CDK13 targeting as a promising therapeutic approach in CDK12 -inactive disease. Additionally, we have established valuable in vivo resources to facilitate further investigation and drug development in this challenging malignancy.

Daily briefing: Judge orders the restoration of hundreds of NIH grants

Sensitivity of mass-independent Sn isotope fractionation to UV radiation and magnetic fields

Mass-independent isotope fractionation (MIF) enables powerful geochemical tracers for various geological and planetary problems, yet the mechanisms driving MIF for tin (Sn) remain ambiguous. Here, we demonstrate that distinct Sn isotope fractionation signatures were produced during photolysis of organic Sn species (i.e., methyltin) under laboratory UV irradiation and natural sunlight. UV irradiation of methyltin induced pronounced Sn-MIF in all odd Sn isotopes (Δ 115 Sn up to 21.82‰, Δ 117 Sn up to 23.16‰, Δ 119 Sn up to 24.01‰), with their ratios (Δ 117 Sn/Δ 115 Sn = 1.069; Δ 119 Sn/Δ 115 Sn = 1.099; Δ 119 Sn/Δ 117 Sn = 1.028) strongly correlating with nuclear magnetic moments. This unambiguously identifies the magnetic isotope effect (MIE) as the driving mechanism, ruling out other causes such as the nuclear volume effect (NVE). Methyl radicals (•CH 3 ) were detectable during the methyltin photolysis experiments, and the magnitude of MIF for Sn was suppressed by the presence of electron spin trapping agent (DMPO) for radicals, supporting that the pronounced Sn-MIF originated from radical-mediated singlet-triplet state transitions of Sn species. Furthermore, the magnitude of Sn-MIF depended nonmonotonically on external magnetic fields (peak suppression at 100 to 180 G), implying competition between hyperfine coupling and Zeeman interactions. Notably, Sn-MIF was absent during photolysis of methyltin by natural sunlight despite significant mass-dependent Sn isotope fractionation (e.g., >3‰ in δ 122/116 Sn), attributed to atmospheric ozone shielding of short-wavelength UV (<290 nm) required for radical generation. Our results register Sn-MIF as a sensitive tracer of UV-driven photochemistry in low-oxygen environments, underlining the potential of Sn isotopes in studies of early Earth’s atmosphere and planetary environments.

How I carved out a career as a ‘pracademic’

Local and distal changes in dynamics are caused by an L205R Cushing’s syndrome mutant in PRKACA

Cushing’s syndrome (CS) is an abnormal condition characterized by elevated cortisol levels, often resulting from genetic alterations in the PRKACA gene, which encodes the catalytic subunit of cAMP-dependent protein kinase A (PKA-C). The most common CS mutation, L205R, lies at the P + 1 loop. Understanding how this mutation alters the internal allosteric network within PKA-C and changes nucleotide and substrate cooperativity is a major goal. Using molecular dynamics (MD) simulations and protein residue networks based on local spatial pattern (LSP) method, we compare crystal structures of wild-type PKA-C and L205R. Our findings indicate that L205R not only locally disrupts the P + 1 hydrophobic pocket, leading to the displacement of the P + 1-residue and altered substrate specificity, but also has long-range effects in the linker connecting the A helix to β strand 1. The MD simulations and LSP analyses also reveal critical changes at the phosphoryl transfer site. Some of these changes are captured in the L205R crystal structure while others are not. With this strategy, we also show how the dynamics of local and distal allosteric networks are differentially influenced by backbone and side-chain dynamics.

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Macroevolutionary analysis of polysynthesis shows that language complexity is more likely to evolve in small, isolated populations

Evolution of complexity in human languages has been vigorously debated, including the proposal that complexity can build in small, isolated populations but is often lost in situations of language contact. If it is generally true that small, isolated languages can build morphological complexity over time, but complexity tends to be lost in situations of language contact, then we should find that forms of language complexity that have evolved multiple times will tend to be associated with population size, isolation, and language age. We test this hypothesis by focusing on one particular form of morphological complexity, polysynthesis, where words built from many parts embody complex phrases. By assembling a global database of polysynthetic languages and conducting phylospatial analyses, we show that languages with highly complex word morphology are more likely to have small population sizes, less likely to occur with many other languages in direct contact, and have a greater tendency to be on long phylogenetically isolated lineages. These findings are consistent with the hypothesis that languages that evolve in isolation for long periods may be more likely to accrue morphological complexity. Polysynthetic languages also tend to have higher levels of endangerment. Our results provide phylogenetically informed evidence that one particular form of complex language morphology is more likely to occur in small, isolated languages and is prone to loss in contact.

Representation of locomotive action affordances in human behavior, brains, and deep neural networks

To decide how to move around the world, we must determine which locomotive actions (e.g., walking, swimming, or climbing) are afforded by the immediate visual environment. The neural basis of our ability to recognize locomotive affordances is unknown. Here, we compare human behavioral annotations, functional MRI (fMRI) measurements, and deep neural network (DNN) activations to both indoor and outdoor real-world images to demonstrate that the human visual cortex represents locomotive action affordances in complex visual scenes. Hierarchical clustering of behavioral annotations of six possible locomotive actions show that humans group environments into distinct affordance clusters using at least three separate dimensions. Representational similarity analysis of multivoxel fMRI responses in the scene-selective visual cortex shows that perceived locomotive affordances are represented independently from other scene properties such as objects, surface materials, scene category, or global properties and independent of the task performed in the scanner. Visual feature activations from DNNs trained on object or scene classification as well as a range of other visual understanding tasks correlate comparatively lower with behavioral and neural representations of locomotive affordances than with object representations. Training DNNs directly on affordance labels or using affordance-centered language embeddings increases alignment with human behavior, but none of the tested models fully captures locomotive action affordance perception. These results uncover a type of representation in the human brain that reflects locomotive action affordances.

Improving polygenic prediction from whole-genome sequencing data by leveraging predicted epigenomic features

Polygenic risk scores (PRS) are essential tools for estimating individual susceptibility to complex diseases by aggregating the effects of many genetic variants. With the advent of whole-genome sequencing (WGS), rare and de novo variants can now be detected at scale, presenting new opportunities to enhance PRS performance. Additionally, regulatory mechanisms that govern gene expression play a critical role in disease manifestation, suggesting further potential for improvement. However, most existing PRS methods are not well-equipped to incorporate nonlinear variant effects, rare variant contributions, or regulatory context. To address these limitations, we developed Epi-PRS, a novel framework that leverages large language models (LLMs) to impute cell-type-specific epigenomic signals from personal diploid genotypes. These imputed signals act as informative intermediates between genotype and phenotype, allowing for more accurate modeling of variant impact. Our simulation studies demonstrate that Epi-PRS improves predictive accuracy by incorporating nonlinear relationships, rare variant effects, and regulatory information across large genomic regions. When applied to real data from the UK Biobank, Epi-PRS significantly outperforms existing PRS approaches in predicting risk for both breast cancer and type 2 diabetes. These results underscore the advantages of integrating WGS data, epigenomic context, and advanced LLMs framework to enhance both the predictive power and interpretability of PRS. Overall, Epi-PRS represents a promising step toward more precise and biologically informed disease risk prediction, with broad implications for advancing personalized medicine and understanding complex genetic architectures.

Generative prediction of causal gene sets responsible for complex traits

The relationship between genotype and phenotype remains an outstanding question for organism-level traits because these traits are generally complex . The challenge arises from complex traits being determined by a combination of multiple genes (or loci), which leads to an explosion of possible genotype–phenotype mappings. The primary techniques to resolve these mappings are genome/transcriptome-wide association studies, which are limited by their lack of causal inference and statistical power. Here, we develop an approach that combines transcriptional data endowed with causal information and a generative machine learning model designed to strengthen statistical power. Our implementation of the approach—dubbed transcriptome-wide conditional variational autoencoder (TWAVE)—includes a variational autoencoder trained on human transcriptional data, which is incorporated into an optimization framework. Given a trait phenotype, TWAVE generates expression profiles, which we dimensionally reduce by identifying independently varying generalized pathways (eigengenes). We then conduct constrained optimization to find causal gene sets that are the gene perturbations whose measured transcriptomic responses best explain trait phenotype differences. By considering several complex traits, we show that the approach identifies causal genes that cannot be detected by the primary existing techniques. Moreover, the approach identifies complex diseases caused by distinct sets of genes, meaning that the disease is polygenic and exhibits distinct subtypes driven by different genotype–phenotype mappings. We suggest that the approach will enable the design of tailored experiments to identify multigenic targets to address complex diseases.

Synthetic models of the nitrogenase FeMo cofactor

The FeMo cofactor (FeMoco), the key active site in the Mo-based nitrogenase, is one of the most complicated metalloenzyme molecules. Synthesis of the FeMoco model cluster ([MoFe 7 S 9 C]) is essential to understanding its function in dinitrogen binding, activation, and conversion. However, the complex framework of the FeMoco cluster, which features a unique trigonal prismatic [Fe 6 C] moiety comprising a µ 6 -bridging carbide, has made the synthesis of the cluster a persistent challenge. In this work, two analogous mimics of FeMoco have been synthesized, using a cluster-coupling synthetic strategy facilitated by the fabrication of unsaturated ligand/metal coordination. The incorporation of a µ 6 -X (X = C 4− or N 3− ) to construct the characteristic triangular prismatic [Fe 6 ( µ 6 -X)] moiety, replicating that in FeMoco, has been achieved synthetically. The two mimics have similar key structural parameters to FeMoco in natural nitrogenase, but differ from the FeMoco structure in two major aspects: the µ 2 -bridging ligands and the metal atoms capping the [Fe 6 S 9 C] cores (Mo/Fe in FeMoco vs. Mo/Mo or W/W in the synthetic models). Quantum chemical studies indicate that the electronic ground states of these clusters resemble those observed for FeMoco, with maximized antiferromagnetic coupling among the iron centers. A future systematic study on the physical and chemical properties of a family of mimics with programmed variations of key structural elements can provide a valuable comparison and facilitate a better understanding of the structure and function of FeMoco.

Comparative evaluation of behavioral epidemic models using COVID-19 data

Characterizing the feedback linking human behavior and the transmission of infectious diseases (i.e., behavioral changes) remains a significant challenge in computational and mathematical epidemiology. Existing behavioral epidemic models often lack real-world data calibration and cross-model performance evaluation in both retrospective analysis and forecasting. In this study, we systematically compare the performance of three mechanistic behavioral epidemic models across nine geographies and two modeling tasks during the first wave of COVID-19, using various metrics. The first model, a Data-Driven Behavioral Feedback Model, incorporates behavioral changes by leveraging mobility data to capture variations in contact patterns. The second and third models are Analytical Behavioral Feedback Models, which simulate the feedback loop either through the explicit representation of different behavioral compartments within the population or by utilizing an effective nonlinear force of infection. Our results do not identify a single best model overall, as performance varies based on factors such as data availability, data quality, and the choice of performance metrics. While the Data-Driven Behavioral Feedback Model incorporates substantial real-time behavioral information, the Analytical Compartmental Behavioral Feedback Model often demonstrates superior or equivalent performance in both retrospective fitting and out-of-sample forecasts. Overall, our work offers guidance for future approaches and methodologies to better integrate behavioral changes into the modeling and projection of epidemic dynamics.

Biparental inheritance of germline-specific chromosomes in the sea lamprey and their roles in oocytes

Many eukaryotic species undergo programmed elimination of specific chromosomes during embryogenesis, typically retaining these chromosomes only in their germ cells. In some species, programmatic elimination of GRCs, or sex chromosomes, also occurs in a sex-specific manner, with specific chromosomes being transmitted or eliminated by only one sex. As such, these chromosomes provide a unique perspective on the evolution of gene functions that are advantageous to the germline and genetic tradeoffs between somatic vs germline or oocyte vs sperm biology. While GRCs have been extensively characterized in male sea lampreys ( Petromyzon marinus ), the status of GRCs in females has not yet been resolved, though it has been hypothesized that male-specific expression/transmission of these chromosomes might provide a solution to resolving the long-standing mystery of lamprey sex determining mechanisms. To gain insight into the roles of GRCs in female lampreys, we performed several karyological, transcriptomic, and genomic analyses, which demonstrate that GRCs are present in the female lamprey germline, transmitted by oocytes and somatically eliminated in both sexes. These analyses also show that GRCs play important roles in the maintenance and development of female germline but provide no evidence for sex-specific variation in the elimination and transmission of lamprey GRCs. These findings underscore the diversity of germline functions that are carried out by GRCs in both male and female lampreys and highlight the fact that sex-specific transmission/retention of GRCs likely follows no universal rules across the diverse lineages that have independently evolved to undergo developmentally programmed DNA elimination.

Whole-genome duplication leads to significant but inconsistent changes in climatic niche

Polyploidization (whole-genome duplication, WGD) is a widespread large-effect macromutation with far-reaching genomic, phenotypic, and evolutionary consequences. Yet, we do not know whether the consistent phenotypic changes that are associated with polyploidization translate into predictable changes in ecological preferences. Niche modeling studies in mixed-ploidy species provide an opportunity to compare recently originated polyploids with their lower-ploidy ancestors. However, the available isolated studies provide contrasting results and the diverse methodologies used limit generalization. Based on 25,857 georeferenced ploidy-verified occurrence data for 129 mixed-ploidy flowering plant species, we tested in a unified statistical framework whether WGD is associated with consistent changes in climatic niche and in past, current, and predicted future range size. We found that 74% of species exhibited significant niche shifts associated with ploidy transition. However, there was no consistent environmental parameter underlying ploidy differentiation across species, nor was there consistent support for polyploid range or niche expansion in a subset of 75 densely sampled species with sufficient data for modeling. Our results demonstrate that polyploidization is an important factor affecting niche evolution of a species, but the environmental parameters underlying the ploidy-related niche shifts vary from species to species, demonstrating limited predictability of the outcomes of WGD in ecological space.

SARS-CoV-2 nsp15 enhances viral virulence by subverting host antiviral defenses

SARS-CoV-2 encodes numerous virulence factors, yet their precise mechanisms of action remain unknown. We provide evidence that the SARS-CoV-2 nonstructural protein 15 (nsp15) enhances viral virulence by suppressing the production of viral double-stranded (dsRNA), a potent inducer of antiviral signaling. The viral variants lacking nsp15 endoribonuclease activity elicited higher innate immune responses and exhibited reduced replication in human stem cell–derived lung alveolar type II epithelial cells, as well as in the lungs of infected hamsters. Consistently, these variants caused significantly less weight loss and mortality compared to wild-type (WT) virus in K18-hACE2 mice. Mechanistically, the cells infected with nsp15 mutants accumulated more viral dsRNA, causing enhanced stimulation of the interferon pathway. Chemical inhibition of interferon signaling dampened immune responses to nsp15 mutants and restored their replication to levels similar to the WT virus. These findings indicate that the endoribonuclease activity of nsp15 contributes to viral virulence by limiting the accumulation of viral dsRNA, thereby allowing robust replication with reduced activation of the host innate immune response.

<i>Hamiltonella</i> symbionts benefit whitefly fertilization by regulating the maternal protein Tudor–mediated piRNA pathway

Although it is widely recognized that nutritional symbionts can manipulate host reproduction, the underlying molecular and cellular mechanisms are largely unclear. The facultative symbiont Hamiltonella in bacteriocyte induces female-biased sex ratio of whiteflies. Here, we demonstrate that a maternal gene tudor ( tud ) and its encoded protein have lower expression levels in ovaries of Hamiltonella- cured whiteflies. Tud family proteins can interlink the various stages of biosynthesis of PIWI-interacting RNA (piRNA), a class of small noncoding RNAs. We find that Hamiltonella affects the abundance of a piRNA through the maternal gene tud , thereby regulating the expression of the vacuolar ( H+ ) -ATPase H subunit ( VATPH ), which is the switch of activity of the vacuolar (H+)-ATPase that plays a crucial role in maintaining the homeostasis of intracellular energy and supporting mitochondrial respiration. This regulation adjusts the ATP level in ovaries of whiteflies. The ATP level shapes the F-actin pattern in ovaries and eggs of whiteflies, ultimately manipulating whitefly fertilization. Silencing tud inhibited whitefly fertilization by impairing ATP levels and F-actin patterns in ovaries and eggs. This study reveals that symbiont and maternal protein associations can regulate host fertilization by piRNA biosynthesis.

QnAs with David B. Lobell

Acquired resistance to immunotherapy by physical barriers with cancer cell–expressing collagens in non–small cell lung cancer

Immunotherapy has become the standard treatment for many types of cancers, but an increasing number of patients who initially respond to these treatments develop acquired immunotherapy resistance (AIR). Here, we recapitulated the entire process of immunotherapy from response to AIR in mice with non–small cell lung cancer (NSCLC). With implanted tumor organoids derived from these models and serial transplants, we demonstrated that tumor cell–intrinsic mechanisms contributed significantly to AIR. Single-cell RNA sequencing and electron microscope assays revealed that resistant tumor cell–expressing collagens, including Col3a1 and Col6a1 , formed multiple physical barriers surrounding tumor cells. Disruption of these barriers by collagenase or knockout of both Col3a1 and Col6a1 in tumor cells could sensitize the tumors of AIR. Mechanistically, the TGFβ pathway was upregulated upon immunotherapy, and treatment with TGFβ significantly increased the expression levels of both Col3a1 and Col6a1 in tumor cells. COL3A1 formed a castle-like barrier for a cluster of tumor cells and prevented T cell infiltration, while COL6A1 formed an armor-like barrier surrounding individual tumor cells to protect them against direct T cell attack. Our data reveal a tumor cell–intrinsic mechanism of AIR, mediated by collagen-containing physical barriers, which immediately suggests a clinical treatment option.

Mycobacteriophage-mediated gene transfer enables in vitro drug screening and in vivo tracking of <i>Mycobacterium leprae</i>

Mycobacterium leprae , the causative agent of leprosy, has never been cultured in vitro, posing significant challenges for genetic manipulation and drug discovery. Current antileprosy drug screening methods relying on microscopic count, radiorespirometry, and qPCR are time consuming and require the use of radioactive elements. We demonstrate mycobacteriophage-mediated introduction of foreign DNA using the broad-host range mycobacteriophage TM4 and the application of the luciferase reporter mycobacteriophage (LRM) for drug screening. Mycobacteriophage infection of M. leprae was shown using TM4 expressing the highly sensitive BRET-nanoluciferase-based reporter, GeNL (TM4 ::GeNL ), which enables luminescence measurement for over 72 h. When M. leprae was exposed to rifampicin, dapsone, and Q203 for 24 and 48 h, followed by TM4 ::GeNL infection, the luminescence output decreased in a dose-dependent manner, establishing an in vitro two-day screening assay for drugs. We have also electroporated M. leprae with a ColE1 -integration proficient plasmid expressing GeNL and shown that the transformed leprosy bacilli could be propagated in mice footpads and detected using an in vivo imaging system (IVIS). These findings introduce powerful genetic tools for M. leprae research enabling in vivo tracking and in vitro viability testing.

Evolution of gene order in prokaryotes is driven primarily by gene gain and loss

Evolution of bacterial and archaeal genomes is highly dynamic, including extensive gene gain via horizontal gene transfer (HGT) and gene loss as well as different types of genome rearrangements, such as inversions and translocations, so that gene order is not highly conserved even among closely related organisms. We sought to quantify the contributions of different genome dynamics processes to the evolution of the gene order in prokaryote genomes, relying on the recently developed, simple, stochastic model of genome rearrangement through single gene translocations (“jump” model). The jump model was completely solved analytically in our previous work and provides the exact distribution of syntenic gene block lengths (SBL) in compared genomes based on gene translocations alone. Comparing the SBL distribution predicted by the jump model with the distributions empirically observed for multiple groups of closely related bacterial and archaeal genomes, we obtained robust estimates of the genome rearrangement to gene flux (gain and loss) ratio. In most groups of bacteria and archaea, this ratio was found to be on the order of 0.1 indicating that the loss of synteny in the evolution of bacteria and archaea is driven primarily by gene gain and loss rather than by gene translocation.