Neural mechanism underlying depressive-like state associated with social status loss.

Downward social mobility is a well-known mental risk factor for depression, but its neural mechanism remains elusive. Here, by forcing mice to lose against their subordinates in a non-violent social contest, we lower their social ranks stably and induce depressive-like behaviors. These rank-decline-associated depressive-like behaviors can be reversed by regaining social status. In vivo fiber photometry and single-unit electrophysiological recording show that forced loss, but not natural loss, generates negative reward prediction error (RPE). Through the lateral hypothalamus, the RPE strongly activates the brain's anti-reward center, the lateral habenula (LHb). LHb activation inhibits the medial prefrontal cortex (mPFC) that controls social competitiveness and reinforces retreats in contests. These results reveal the core neural mechanisms mutually promoting social status loss and depressive behaviors. The intertwined neuronal signaling controlling mPFC and LHb activities provides a mechanistic foundation for the crosstalk between social mobility and psychological disorder, unveiling a promising target for intervention.

Human MCTS1-dependent translation of JAK2 is essential for IFN-γ immunity to mycobacteria.

Human inherited disorders of interferon-gamma (IFN-γ) immunity underlie severe mycobacterial diseases. We report X-linked recessive MCTS1 deficiency in men with mycobacterial disease from kindreds of different ancestries (from China, Finland, Iran, and Saudi Arabia). Complete deficiency of this translation re-initiation factor impairs the translation of a subset of proteins, including the kinase JAK2 in all cell types tested, including T lymphocytes and phagocytes. JAK2 expression is sufficiently low to impair cellular responses to interleukin-23 (IL-23) and partially IL-12, but not other JAK2-dependent cytokines. Defective responses to IL-23 preferentially impair the production of IFN-γ by innate-like adaptive mucosal-associated invariant T cells (MAIT) and γδ T lymphocytes upon mycobacterial challenge. Surprisingly, the lack of MCTS1-dependent translation re-initiation and ribosome recycling seems to be otherwise physiologically redundant in these patients. These findings suggest that X-linked recessive human MCTS1 deficiency underlies isolated mycobacterial disease by impairing JAK2 translation in innate-like adaptive T lymphocytes, thereby impairing the IL-23-dependent induction of IFN-γ.

Error-Prone Replication through UV Lesions by DNA Polymerase θ Protects against Skin Cancers.

Cancers from sun-exposed skin accumulate "driver" mutations, causally implicated in oncogenesis. Because errors incorporated during translesion synthesis (TLS) opposite UV lesions would generate these mutations, TLS mechanisms are presumed to underlie cancer development. To address the role of TLS in skin cancer formation, we determined which DNA polymerase is responsible for generating UV mutations, analyzed the relative contributions of error-free TLS by Polη and error-prone TLS by Polθ to the replication of UV-damaged DNA and to genome stability, and examined the incidence of UV-induced skin cancers in Polθ-/-, Polη-/-, and Polθ-/- Polη-/- mice. Our findings that the incidence of skin cancers rises in Polθ-/- mice and is further exacerbated in Polθ-/- Polη-/- mice compared with Polη-/- mice support the conclusion that error-prone TLS by Polθ provides a safeguard against tumorigenesis and suggest that cancer formation can ensue in the absence of somatic point mutations.

Scalable, Continuous Evolution of Genes at Mutation Rates above Genomic Error Thresholds.

Directed evolution is a powerful approach for engineering biomolecules and understanding adaptation. However, experimental strategies for directed evolution are notoriously labor intensive and low throughput, limiting access to demanding functions, multiple functions in parallel, and the study of molecular evolution in replicate. We report OrthoRep, an orthogonal DNA polymerase-plasmid pair in yeast that stably mutates ∼100,000-fold faster than the host genome in vivo, exceeding the error threshold of genomic replication that causes single-generation extinction. User-defined genes in OrthoRep continuously and rapidly evolve through serial passaging, a highly straightforward and scalable process. Using OrthoRep, we evolved drug-resistant malarial dihydrofolate reductases (DHFRs) in 90 independent replicates. We uncovered a more complex fitness landscape than previously realized, including common adaptive trajectories constrained by epistasis, rare outcomes that avoid a frequent early adaptive mutation, and a suboptimal fitness peak that occasionally traps evolving populations. OrthoRep enables a new paradigm of routine, high-throughput evolution of biomolecular and cellular function.

Basic statistics in cell biology.

The physicist Ernest Rutherford said, "If your experiment needs statistics, you ought to have done a better experiment." Although this aphorism remains true for much of today's research in cell biology, a basic understanding of statistics can be useful to cell biologists to help in monitoring the conduct of their experiments, in interpreting the results, in presenting them in publications, and when critically evaluating research by others. However, training in statistics is often focused on the sophisticated needs of clinical researchers, psychologists, and epidemiologists, whose conclusions depend wholly on statistics, rather than the practical needs of cell biologists, whose experiments often provide evidence that is not statistical in nature. This review describes some of the basic statistical principles that may be of use to experimental biologists, but it does not cover the sophisticated statistics needed for papers that contain evidence of no other kind.

A bifunctional kinase-phosphatase module balances mitotic checkpoint strength and kinetochore-microtubule attachment stability.

Two major mechanisms safeguard genome stability during mitosis: the mitotic checkpoint delays mitosis until all chromosomes have attached to microtubules, and the kinetochore-microtubule error-correction pathway keeps this attachment process free from errors. We demonstrate here that the optimal strength and dynamics of these processes are set by a kinase-phosphatase pair (PLK1-PP2A) that engage in negative feedback from adjacent phospho-binding motifs on the BUB complex. Uncoupling this feedback to skew the balance towards PLK1 produces a strong checkpoint, hypostable microtubule attachments and mitotic delays. Conversely, skewing the balance towards PP2A causes a weak checkpoint, hyperstable microtubule attachments and chromosome segregation errors. These phenotypes are associated with altered BUB complex recruitment to KNL1-MELT motifs, implicating PLK1-PP2A in controlling auto-amplification of MELT phosphorylation. In support, KNL1-BUB disassembly becomes contingent on PLK1 inhibition when KNL1 is engineered to contain excess MELT motifs. This elevates BUB-PLK1/PP2A complex levels on metaphase kinetochores, stabilises kinetochore-microtubule attachments, induces chromosome segregation defects and prevents KNL1-BUB disassembly at anaphase. Together, these data demonstrate how a bifunctional PLK1/PP2A module has evolved together with the MELT motifs to optimise BUB complex dynamics and ensure accurate chromosome segregation.

Human genetic errors of immunity illuminate an adaptive arsenal model of rapid defenses.

New discoveries in the field of human monogenic immune diseases highlight critical genes and pathways governing immune responses. Here, I describe how the ~500 currently defined human inborn errors of immunity help shape what I propose is an 'adaptive arsenal model of rapid defenses', emphasizing the set of immunological defenses poised for rapid responses in the natural environment. This arsenal blurs the lines between innate and adaptive immunity and is established through molecular relays between cell types, often traversing from sensors (pathogen detection) to intermediates to executioners (pathogen clearance) via soluble factors. Predictions and missing information based on the adaptive arsenal model are discussed, as are emergent and outstanding questions fundamental to advances in the field.

Human germline gain-of-function in STAT6: from severe allergic disease to lymphoma and beyond.

Signal transducer and activator of transcription (STAT)-6 is a transcription factor central to pro-allergic immune responses, although the function of human STAT6 at the whole-organism level has long remained unknown. Germline heterozygous gain-of-function (GOF) rare variants in STAT6 have been recently recognized to cause a broad and severe clinical phenotype of early-onset, multi-system allergic disease. Here, we provide an overview of the clinical presentation of STAT6-GOF disease, discussing how dysregulation of the STAT6 pathway causes severe allergic disease, and identifying possible targeted treatment approaches. Finally, we explore the mechanistic overlap between STAT6-GOF disease and other monogenic atopic disorders, and how this group of inborn errors of immunity (IEIs) powerfully inform our fundamental understanding of common human allergic disease.

Gamblers: An Antibiotic-Induced Evolvable Cell Subpopulation Differentiated by Reactive-Oxygen-Induced General Stress Response.

Antibiotics can induce mutations that cause antibiotic resistance. Yet, despite their importance, mechanisms of antibiotic-promoted mutagenesis remain elusive. We report that the fluoroquinolone antibiotic ciprofloxacin (cipro) induces mutations by triggering transient differentiation of a mutant-generating cell subpopulation, using reactive oxygen species (ROS). Cipro-induced DNA breaks activate the Escherichia coli SOS DNA-damage response and error-prone DNA polymerases in all cells. However, mutagenesis is limited to a cell subpopulation in which electron transfer together with SOS induce ROS, which activate the sigma-S (σS) general-stress response, which allows mutagenic DNA-break repair. When sorted, this small σS-response-"on" subpopulation produces most antibiotic cross-resistant mutants. A U.S. Food and Drug Administration (FDA)-approved drug prevents σS induction, specifically inhibiting antibiotic-promoted mutagenesis. Further, SOS-inhibited cell division, which causes multi-chromosome cells, promotes mutagenesis. The data support a model in which within-cell chromosome cooperation together with development of a "gambler" cell subpopulation promote resistance evolution without risking most cells.

Hidden multiple comparisons increase forensic error rates.

When wires are cut, the tool produces striations on the cut surface; as in other forms of forensic analysis, these striation marks are used to connect the evidence to the source that created them. Here, we argue that the practice of comparing two wire cut surfaces introduces complexities not present in better-investigated forensic examination of toolmarks such as those observed on bullets, as wire comparisons inherently require multiple distinct comparisons, increasing the expected false discovery rate. We call attention to the multiple comparison problem in wire examination and relate it to other situations in forensics that involve multiple comparisons, such as database searches.

A ~40-kb flavi-like virus does not encode a known error-correcting mechanism.

It is commonly held that there is a fundamental relationship between genome size and error rate, manifest as a notional "error threshold" that sets an upper limit on genome sizes. The genome sizes of RNA viruses, which have intrinsically high mutation rates due to a lack of mechanisms for error correction, must therefore be small to avoid accumulating an excessive number of deleterious mutations that will ultimately lead to population extinction. The proposed exceptions to this evolutionary rule are RNA viruses from the order Nidovirales (such as coronaviruses) that encode error-correcting exonucleases, enabling them to reach genome lengths greater than 40 kb. The recent discovery of large-genome flavi-like viruses (Flaviviridae), which comprise genomes up to 27 kb in length yet seemingly do not encode exonuclease domains, has led to the proposal that a proofreading mechanism is required to facilitate the expansion of nonsegmented RNA virus genomes above 30 kb. Herein, we describe a ~40 kb flavi-like virus identified in a Haliclona sponge metatranscriptome that does not encode a known exonuclease. Structural analysis revealed that this virus may have instead captured cellular domains associated with nucleic acid metabolism that have not been previously found in RNA viruses. Phylogenetic inference placed this virus as a divergent pesti-like lineage, such that we have provisionally termed it "Maximus pesti-like virus." This virus represents an instance of a flavi-like virus achieving a genome size comparable to that of the Nidovirales and demonstrates that RNA viruses have evolved multiple solutions to overcome the error threshold.

A dopamine mechanism for reward maximization.

Individual survival and evolutionary selection require biological organisms to maximize reward. Economic choice theories define the necessary and sufficient conditions, and neuronal signals of decision variables provide mechanistic explanations. Reinforcement learning (RL) formalisms use predictions, actions, and policies to maximize reward. Midbrain dopamine neurons code reward prediction errors (RPE) of subjective reward value suitable for RL. Electrical and optogenetic self-stimulation experiments demonstrate that monkeys and rodents repeat behaviors that result in dopamine excitation. Dopamine excitations reflect positive RPEs that increase reward predictions via RL; against increasing predictions, obtaining similar dopamine RPE signals again requires better rewards than before. The positive RPEs drive predictions higher again and thus advance a recursive reward-RPE-prediction iteration toward better and better rewards. Agents also avoid dopamine inhibitions that lower reward prediction via RL, which allows smaller rewards than before to elicit positive dopamine RPE signals and resume the iteration toward better rewards. In this way, dopamine RPE signals serve a causal mechanism that attracts agents via RL to the best rewards. The mechanism improves daily life and benefits evolutionary selection but may also induce restlessness and greed.

The quantum house of cards.

Quantum computers have been proposed to solve a number of important problems such as discovering new drugs, new catalysts for fertilizer production, breaking encryption protocols, optimizing financial portfolios, or implementing new artificial intelligence applications. Yet, to date, a simple task such as multiplying 3 by 5 is beyond existing quantum hardware. This article examines the difficulties that would need to be solved for quantum computers to live up to their promises. I discuss the whole stack of technologies that has been envisioned to build a quantum computer from the top layers (the actual algorithms and associated applications) down to the very bottom ones (the quantum hardware, its control electronics, cryogeny, etc.) while not forgetting the crucial intermediate layer of quantum error correction.

Measuring and modeling the dynamics of mitotic error correction.

Error correction is central to many biological systems and is critical for protein function and cell health. During mitosis, error correction is required for the faithful inheritance of genetic material. When functioning properly, the mitotic spindle segregates an equal number of chromosomes to daughter cells with high fidelity. Over the course of spindle assembly, many initially erroneous attachments between kinetochores and microtubules are fixed through the process of error correction. Despite the importance of chromosome segregation errors in cancer and other diseases, there is a lack of methods to characterize the dynamics of error correction and how it can go wrong. Here, we present an experimental method and analysis framework to quantify chromosome segregation error correction in human tissue culture cells with live cell confocal imaging, timed premature anaphase, and automated counting of kinetochores after cell division. We find that errors decrease exponentially over time during spindle assembly. A coarse-grained model, in which errors are corrected in a chromosome-autonomous manner at a constant rate, can quantitatively explain both the measured error correction dynamics and the distribution of anaphase onset times. We further validated our model using perturbations that destabilized microtubules and changed the initial configuration of chromosomal attachments. Taken together, this work provides a quantitative framework for understanding the dynamics of mitotic error correction.

Genotype error due to low-coverage sequencing induces uncertainty in polygenic scoring.

Polygenic scores (PGSs) have emerged as a standard approach to predict phenotypes from genotype data in a wide array of applications from socio-genomics to personalized medicine. Traditional PGSs assume genotype data to be error-free, ignoring possible errors and uncertainties introduced from genotyping, sequencing, and/or imputation. In this work, we investigate the effects of genotyping error due to low coverage sequencing on PGS estimation. We leverage SNP array and low-coverage whole-genome sequencing data (lcWGS, median coverage 0.04×) of 802 individuals from the Dana-Farber PROFILE cohort to show that PGS error correlates with sequencing depth (p = 1.2 × 10-7). We develop a probabilistic approach that incorporates genotype error in PGS estimation to produce well-calibrated PGS credible intervals and show that the probabilistic approach increases classification accuracy by up to 6% as compared to traditional PGSs that ignore genotyping error. Finally, we use simulations to explore the combined effect of genotyping and effect size errors and their implication on PGS-based risk-stratification. Our results illustrate the importance of considering genotyping error as a source of PGS error especially for cohorts with varying genotyping technologies and/or low-coverage sequencing.

Efficient in vivo prime editing corrects the most frequent phenylketonuria variant, associated with high unmet medical need.

The c.1222C>T (p.Arg408Trp) variant in the phenylalanine hydroxylase gene (PAH) is the most frequent cause of phenylketonuria (PKU), the most common inborn error of metabolism. This autosomal-recessive disorder is characterized by accumulation of blood phenylalanine (Phe) to neurotoxic levels. Using real-world data, we observed that despite dietary and medical interventions, most PKU individuals harboring at least one c.1222C>T variant experience chronic, severe Phe elevations and do not comply with Phe monitoring guidelines. Motivated by these findings, we generated an edited c.1222C>T hepatocyte cell line and humanized c.1222C>T mouse models, with which we demonstrated efficient in vitro and in vivo correction of the variant with prime editing. Delivery via adeno-associated viral (AAV) vectors reproducibly achieved complete normalization of blood Phe levels in PKU mice, with up to 52% whole-liver corrective PAH editing. These studies validate a strategy involving prime editing as a potential treatment for a large proportion of individuals with PKU.

Estimating heritability explained by local ancestry and evaluating stratification bias in admixture mapping from summary statistics.

The heritability explained by local ancestry markers in an admixed population (hγ2) provides crucial insight into the genetic architecture of a complex disease or trait. Estimation of hγ2 can be susceptible to biases due to population structure in ancestral populations. Here, we present heritability estimation from admixture mapping summary statistics (HAMSTA), an approach that uses summary statistics from admixture mapping to infer heritability explained by local ancestry while adjusting for biases due to ancestral stratification. Through extensive simulations, we demonstrate that HAMSTA hγ2 estimates are approximately unbiased and are robust to ancestral stratification compared to existing approaches. In the presence of ancestral stratification, we show a HAMSTA-derived sampling scheme provides a calibrated family-wise error rate (FWER) of ∼5% for admixture mapping, unlike existing FWER estimation approaches. We apply HAMSTA to 20 quantitative phenotypes of up to 15,988 self-reported African American individuals in the Population Architecture using Genomics and Epidemiology (PAGE) study. We observe hˆγ2 in the 20 phenotypes range from 0.0025 to 0.033 (mean hˆγ2 = 0.012 ± 9.2 × 10-4), which translates to hˆ2 ranging from 0.062 to 0.85 (mean hˆ2 = 0.30 ± 0.023). Across these phenotypes we find little evidence of inflation due to ancestral population stratification in current admixture mapping studies (mean inflation factor of 0.99 ± 0.001). Overall, HAMSTA provides a fast and powerful approach to estimate genome-wide heritability and evaluate biases in test statistics of admixture mapping studies.

ChIP-GPT: a managed large language model for robust data extraction from biomedical database records.

Increasing volumes of biomedical data are amassing in databases. Large-scale analyses of these data have wide-ranging applications in biology and medicine. Such analyses require tools to characterize and process entries at scale. However, existing tools, mainly centered on extracting predefined fields, often fail to comprehensively process database entries or correct evident errors-a task humans can easily perform. These tools also lack the ability to reason like domain experts, hindering their robustness and analytical depth. Recent advances with large language models (LLMs) provide a fundamentally new way to query databases. But while a tool such as ChatGPT is adept at answering questions about manually input records, challenges arise when scaling up this process. First, interactions with the LLM need to be automated. Second, limitations on input length may require a record pruning or summarization pre-processing step. Third, to behave reliably as desired, the LLM needs either well-designed, short, 'few-shot' examples, or fine-tuning based on a larger set of well-curated examples. Here, we report ChIP-GPT, based on fine-tuning of the generative pre-trained transformer (GPT) model Llama and on a program prompting the model iteratively and handling its generation of answer text. This model is designed to extract metadata from the Sequence Read Archive, emphasizing the identification of chromatin immunoprecipitation (ChIP) targets and cell lines. When trained with 100 examples, ChIP-GPT demonstrates 90-94% accuracy. Notably, it can seamlessly extract data from records with typos or absent field labels. Our proposed method is easily adaptable to customized questions and different databases.

Error modelled gene expression analysis (EMOGEA) provides a superior overview of time course RNA-seq measurements and low count gene expression.

Temporal RNA-sequencing (RNA-seq) studies of bulk samples provide an opportunity for improved understanding of gene regulation during dynamic phenomena such as development, tumor progression or response to an incremental dose of a pharmacotherapeutic. Moreover, single-cell RNA-seq (scRNA-seq) data implicitly exhibit temporal characteristics because gene expression values recapitulate dynamic processes such as cellular transitions. Unfortunately, temporal RNA-seq data continue to be analyzed by methods that ignore this ordinal structure and yield results that are often difficult to interpret. Here, we present Error Modelled Gene Expression Analysis (EMOGEA), a framework for analyzing RNA-seq data that incorporates measurement uncertainty, while introducing a special formulation for those acquired to monitor dynamic phenomena. This method is specifically suited for RNA-seq studies in which low-count transcripts with small-fold changes lead to significant biological effects. Such transcripts include genes involved in signaling and non-coding RNAs that inherently exhibit low levels of expression. Using simulation studies, we show that this framework down-weights samples that exhibit extreme responses such as batch effects allowing them to be modeled with the rest of the samples and maintain the degrees of freedom originally envisioned for a study. Using temporal experimental data, we demonstrate the framework by extracting a cascade of gene expression waves from a well-designed RNA-seq study of zebrafish embryogenesis and an scRNA-seq study of mouse pre-implantation and provide unique biological insights into the regulation of genes in each wave. For non-ordinal measurements, we show that EMOGEA has a much higher rate of true positive calls and a vanishingly small rate of false negative discoveries compared to common approaches. Finally, we provide two packages in Python and R that are self-contained and easy to use, including test data.

The scientific reinvention of forensic science.

Forensic science is undergoing an evolution in which a long-standing "trust the examiner" focus is being replaced by a "trust the scientific method" focus. This shift, which is in progress and still partial, is critical to ensure that the legal system uses forensic information in an accurate and valid way. In this Perspective, we discuss the ways in which the move to a more empirically grounded scientific culture for the forensic sciences impacts testing, error rate analyses, procedural safeguards, and the reporting of forensic results. However, we caution that the ultimate success of this scientific reinvention likely depends on whether the courts begin to engage with forensic science claims in a more rigorous way.