Time-aligned hourglass gastrulation models in rabbit and mouse.

The hourglass model describes the convergence of species within the same phylum to a similar body plan during development; however, the molecular mechanisms underlying this phenomenon in mammals remain poorly described. Here, we compare rabbit and mouse time-resolved differentiation trajectories to revisit this model at single-cell resolution. We modeled gastrulation dynamics using hundreds of embryos sampled between gestation days 6.0 and 8.5 and compared the species using a framework for time-resolved single-cell differentiation-flows analysis. We find convergence toward similar cell-state compositions at E7.5, supported by the quantitatively conserved expression of 76 transcription factors, despite divergence in surrounding trophoblast and hypoblast signaling. However, we observed noticeable changes in specification timing of some lineages and divergence of primordial germ cell programs, which in the rabbit do not activate mesoderm genes. Comparative analysis of temporal differentiation models provides a basis for studying the evolution of gastrulation dynamics across mammals.

Single-Cell Transcriptomics of Human and Mouse Lung Cancers Reveals Conserved Myeloid Populations across Individuals and Species.

Tumor-infiltrating myeloid cells (TIMs) comprise monocytes, macrophages, dendritic cells, and neutrophils, and have emerged as key regulators of cancer growth. These cells can diversify into a spectrum of states, which might promote or limit tumor outgrowth but remain poorly understood. Here, we used single-cell RNA sequencing (scRNA-seq) to map TIMs in non-small-cell lung cancer patients. We uncovered 25 TIM states, most of which were reproducibly found across patients. To facilitate translational research of these populations, we also profiled TIMs in mice. In comparing TIMs across species, we identified a near-complete congruence of population structures among dendritic cells and monocytes; conserved neutrophil subsets; and species differences among macrophages. By contrast, myeloid cell population structures in patients' blood showed limited overlap with those of TIMs. This study determines the lung TIM landscape and sets the stage for future investigations into the potential of TIMs as immunotherapy targets.

Comparative Analysis of Droplet-Based Ultra-High-Throughput Single-Cell RNA-Seq Systems.

Since its establishment in 2009, single-cell RNA sequencing (RNA-seq) has been a major driver behind progress in biomedical research. In developmental biology and stem cell studies, the ability to profile single cells confers particular benefits. Although most studies still focus on individual tissues or organs, the recent development of ultra-high-throughput single-cell RNA-seq has demonstrated potential power in characterizing more complex systems or even the entire body. However, although multiple ultra-high-throughput single-cell RNA-seq systems have attracted attention, no systematic comparison of these systems has been performed. Here, with the same cell line and bioinformatics pipeline, we developed directly comparable datasets for each of three widely used droplet-based ultra-high-throughput single-cell RNA-seq systems, inDrop, Drop-seq, and 10X Genomics Chromium. Although each system is capable of profiling single-cell transcriptomes, their detailed comparison revealed the distinguishing features and suitable applications for each system.

Speed and navigation control of thymocyte development by the fetal T-cell gene regulatory network.

T-cell differentiation is a tightly regulated developmental program governed by interactions between transcription factors (TFs) and chromatin landscapes and affected by signals received from the thymic stroma. This process is marked by a series of checkpoints: T-lineage commitment, T-cell receptor (TCR)ß selection, and positive and negative selection. Dynamically changing combinations of TFs drive differentiation along the T-lineage trajectory, through mechanisms that have been most extensively dissected in adult mouse T-lineage cells. However, fetal T-cell development differs from adult in ways that suggest that these TF mechanisms are not fully deterministic. The first wave of fetal T-cell differentiation occurs during a unique developmental window during thymic morphogenesis, shows more rapid kinetics of differentiation with fewer rounds of cell division, and gives rise to unique populations of innate lymphoid cells (ILCs) and invariant γδT cells that are not generated in the adult thymus. As the characteristic kinetics and progeny biases are cell-intrinsic properties of thymic progenitors, the differences could be based on distinct TF network circuitry within the progenitors themselves. Here, we review recent single-cell transcriptome data that illuminate the TF networks involved in T-cell differentiation in the fetal and adult mouse thymus.

Significance tests for R2 of out-of-sample prediction using polygenic scores.

The coefficient of determination (R2) is a well-established measure to indicate the predictive ability of polygenic scores (PGSs). However, the sampling variance of R2 is rarely considered so that 95% confidence intervals (CI) are not usually reported. Moreover, when comparisons are made between PGSs based on different discovery samples, the sampling covariance of R2 is required to test the difference between them. Here, we show how to estimate the variance and covariance of R2 values to assess the 95% CI and p value of the R2 difference. We apply this approach to real data calculating PGSs in 28,880 European participants derived from UK Biobank (UKBB) and Biobank Japan (BBJ) GWAS summary statistics for cholesterol and BMI. We quantify the significantly higher predictive ability of UKBB PGSs compared to BBJ PGSs (p value 7.6e-31 for cholesterol and 1.4e-50 for BMI). A joint model of UKBB and BBJ PGSs significantly improves the predictive ability, compared to a model of UKBB PGS only (p value 3.5e-05 for cholesterol and 1.3e-28 for BMI). We also show that the predictive ability of regulatory SNPs is significantly enriched over non-regulatory SNPs for cholesterol (p value 8.9e-26 for UKBB and 3.8e-17 for BBJ). We suggest that the proposed approach (available in R package r2redux) should be used to test the statistical significance of difference between pairs of PGSs, which may help to draw a correct conclusion about the comparative predictive ability of PGSs.

Improved functions for nonlinear sequence comparison using SEEKR.

SEquence Evaluation through k-mer Representation (SEEKR) is a method of sequence comparison that uses sequence substrings called k-mers to quantify the nonlinear similarity between nucleic acid species. We describe the development of new functions within SEEKR that enable end-users to estimate P-values that ascribe statistical significance to SEEKR-derived similarities, as well as visualize different aspects of k-mer similarity. We apply the new functions to identify chromatin-enriched lncRNAs that contain XIST-like sequence features, and we demonstrate the utility of applying SEEKR on lncRNA fragments to identify potential RNA-protein interaction domains. We also highlight ways in which SEEKR can be applied to augment studies of lncRNA conservation, and we outline the best practice of visualizing RNA-seq read density to evaluate support for lncRNA annotations before their in-depth study in cell types of interest.

Computational deconvolution of DNA methylation data from mixed DNA samples.

In this review, we provide a comprehensive overview of the different computational tools that have been published for the deconvolution of bulk DNA methylation (DNAm) data. Here, deconvolution refers to the estimation of cell-type proportions that constitute a mixed sample. The paper reviews and compares 25 deconvolution methods (supervised, unsupervised or hybrid) developed between 2012 and 2023 and compares the strengths and limitations of each approach. Moreover, in this study, we describe the impact of the platform used for the generation of methylation data (including microarrays and sequencing), the applied data pre-processing steps and the used reference dataset on the deconvolution performance. Next to reference-based methods, we also examine methods that require only partial reference datasets or require no reference set at all. In this review, we provide guidelines for the use of specific methods dependent on the DNA methylation data type and data availability.

Science, evidence, law, and justice.

For nearly 25 y, the Committee on Science, Technology, and Law (CSTL), of the National Academies of Sciences, Engineering, and Medicine, has brought together distinguished members of the science and law communities to stimulate discussions that would lead to a better understanding of the role of science in legal decisions and government policies and to a better understanding of the legal and regulatory frameworks that govern the conduct of science. Under the leadership of recent CSTL co-chairs David Baltimore and David Tatel, and CSTL director Anne-Marie Mazza, the committee has overseen many interdisciplinary discussions and workshops, such as the international summits on human genome editing and the science of implicit bias, and has delivered advisory consensus reports focusing on topics of broad societal importance, such as dual use research in the life sciences, voting systems, and advances in neural science research using organoids and chimeras. One of the most influential CSTL activities concerns the use of forensic evidence by law enforcement and the courts, with emphasis on the scientific validity of forensic methods and the role of forensic testimony in bringing about justice. As coeditors of this Special Feature, CSTL alumni Tom Albright and Jennifer Mnookin have recruited articles at the intersection of science and law that reveal an emerging scientific revolution of forensic practice, which we hope will engage a broad community of scientists, legal scholars, and members of the public with interest in science-based legal policy and justice reform.

Characterization of alternate encounter assemblies of SARS-CoV-2 main protease.

The assembly of two monomeric constructs spanning segments 1-199 (MPro1-199) and 10-306 (MPro10-306) of SARS-CoV-2 main protease (MPro) was examined to assess the existence of a transient heterodimer intermediate in the N-terminal autoprocessing pathway of MPro model precursor. Together, they form a heterodimer population accompanied by a 13-fold increase in catalytic activity. Addition of inhibitor GC373 to the proteins increases the activity further by ∼7-fold with a 1:1 complex and higher order assemblies approaching 1:2 and 2:2 molecules of MPro1-199 and MPro10-306 detectable by analytical ultracentrifugation and native mass estimation by light scattering. Assemblies larger than a heterodimer (1:1) are discussed in terms of alternate pathways of domain III association, either through switching the location of helix 201 to 214 onto a second helical domain of MPro10-306 and vice versa or direct interdomain III contacts like that of the native dimer, based on known structures and AlphaFold 3 prediction, respectively. At a constant concentration of MPro1-199 with molar excess of GC373, the rate of substrate hydrolysis displays first order dependency on the MPro10-306 concentration and vice versa. An equimolar composition of the two proteins with excess GC373 exhibits half-maximal activity at ∼6 µM MPro1-199. Catalytic activity arises primarily from MPro1-199 and is dependent on the interface interactions involving the N-finger residues 1 to 9 of MPro1-199 and E290 of MPro10-306. Importantly, our results confirm that a single N-finger region with its associated intersubunit contacts is sufficient to form a heterodimeric MPro intermediate with enhanced catalytic activity.

Connecting Species-Specific Extents of Genome Reduction in Mitochondria and Plastids.

Mitochondria and plastids have both dramatically reduced their genomes since the endosymbiotic events that created them. The similarities and differences in the evolution of the two organelle genome types have been the target of discussion and investigation for decades. Ongoing work has suggested that similar mechanisms may modulate the reductive evolution of the two organelles in a given species, but quantitative data and statistical analyses exploring this picture remain limited outside of some specific cases like parasitism. Here, we use cross-eukaryote organelle genome data to explore evidence for coevolution of mitochondrial and plastid genome reduction. Controlling for differences between clades and pseudoreplication due to relatedness, we find that extents of mtDNA and ptDNA gene retention are related to each other across taxa, in a generally positive correlation that appears to differ quantitatively across eukaryotes, for example, between algal and nonalgal species. We find limited evidence for coevolution of specific mtDNA and ptDNA gene pairs, suggesting that the similarities between the two organelle types may be due mainly to independent responses to consistent evolutionary drivers.

Fast and flexible inference for joint models of multivariate longitudinal and survival data using integrated nested Laplace approximations.

Modeling longitudinal and survival data jointly offers many advantages such as addressing measurement error and missing data in the longitudinal processes, understanding and quantifying the association between the longitudinal markers and the survival events, and predicting the risk of events based on the longitudinal markers. A joint model involves multiple submodels (one for each longitudinal/survival outcome) usually linked together through correlated or shared random effects. Their estimation is computationally expensive (particularly due to a multidimensional integration of the likelihood over the random effects distribution) so that inference methods become rapidly intractable, and restricts applications of joint models to a small number of longitudinal markers and/or random effects. We introduce a Bayesian approximation based on the integrated nested Laplace approximation algorithm implemented in the R package R-INLA to alleviate the computational burden and allow the estimation of multivariate joint models with fewer restrictions. Our simulation studies show that R-INLA substantially reduces the computation time and the variability of the parameter estimates compared with alternative estimation strategies. We further apply the methodology to analyze five longitudinal markers (3 continuous, 1 count, 1 binary, and 16 random effects) and competing risks of death and transplantation in a clinical trial on primary biliary cholangitis. R-INLA provides a fast and reliable inference technique for applying joint models to the complex multivariate data encountered in health research.

A comprehensive assessment and comparison of tools for HLA class I peptide-binding prediction.

Human leukocyte antigen class I (HLA-I) molecules bind intracellular peptides produced by protein hydrolysis and present them to the T cells for immune recognition and response. Prediction of peptides that bind HLA-I molecules is very important in immunotherapy. A growing number of computational predictors have been developed in recent years. We survey a comprehensive collection of 27 tools focusing on their input and output data characteristics, key aspects of the underlying predictive models and their availability. Moreover, we evaluate predictive performance for eight representative predictors. We consider a wide spectrum of relevant aspects including allele-specific analysis, influence of negative to positive data ratios and runtime. We also curate high-quality benchmark datasets based on analysis of the consistency of the data labels. Results reveal that each considered method provides accurate results, which can be explained by our analysis that finds that their predictive models capture meaningful binding motifs. Although some methods are overall more accurate than others, we find that none of them is universally superior. We provide a comprehensive comparison of the convenience as well as the accuracy of the methods under specific prediction scenarios, such as for specific alleles, metrics of predictive performance and constraints on runtime. Our systematic and broad analysis provides informative clues to the users to identify the most suitable tools for a given prediction scenario and for the developers to design future methods.

netANOVA: novel graph clustering technique with significance assessment via hierarchical ANOVA.

Many problems in life sciences can be brought back to a comparison of graphs. Even though a multitude of such techniques exist, often, these assume prior knowledge about the partitioning or the number of clusters and fail to provide statistical significance of observed between-network heterogeneity. Addressing these issues, we developed an unsupervised workflow to identify groups of graphs from reliable network-based statistics. In particular, we first compute the similarity between networks via appropriate distance measures between graphs and use them in an unsupervised hierarchical algorithm to identify classes of similar networks. Then, to determine the optimal number of clusters, we recursively test for distances between two groups of networks. The test itself finds its inspiration in distance-wise ANOVA algorithms. Finally, we assess significance via the permutation of between-object distance matrices. Notably, the approach, which we will call netANOVA, is flexible since users can choose multiple options to adapt to specific contexts and network types. We demonstrate the benefits and pitfalls of our approach via extensive simulations and an application to two real-life datasets. NetANOVA achieved high performance in many simulation scenarios while controlling type I error. On non-synthetic data, comparison against state-of-the-art methods showed that netANOVA is often among the top performers. There are many application fields, including precision medicine, for which identifying disease subtypes via individual-level biological networks improves prevention programs, diagnosis and disease monitoring.

Precision oncology: a review to assess interpretability in several explainable methods.

Great efforts have been made to develop precision medicine-based treatments using machine learning. In this field, where the goal is to provide the optimal treatment for each patient based on his/her medical history and genomic characteristics, it is not sufficient to make excellent predictions. The challenge is to understand and trust the model's decisions while also being able to easily implement it. However, one of the issues with machine learning algorithms-particularly deep learning-is their lack of interpretability. This review compares six different machine learning methods to provide guidance for defining interpretability by focusing on accuracy, multi-omics capability, explainability and implementability. Our selection of algorithms includes tree-, regression- and kernel-based methods, which we selected for their ease of interpretation for the clinician. We also included two novel explainable methods in the comparison. No significant differences in accuracy were observed when comparing the methods, but an improvement was observed when using gene expression instead of mutational status as input for these methods. We concentrated on the current intriguing challenge: model comprehension and ease of use. Our comparison suggests that the tree-based methods are the most interpretable of those tested.

Clinical characteristics and outcomes of patients with mpox during the 2022 mpox outbreak compared with those before the outbreak: A systematic review and meta-analysis.

On 23 July 2022, the World Health Organization declared the global mpox outbreak as a public health emergency of international significance. The mpox virus (MPXV) that caused the outbreak was classified as clade IIb, which belongs to the West African clade. However, the relationship between MPXV clades and symptoms, as well as the severity of mpox outcomes, is not fully understood. Thus, we aimed to investigate the global mpox prevalence and the differences in clinical manifestations and outcomes among patients with mpox between pre-outbreak (2003-2021) and the current mpox outbreak. In this systematic review and meta-analysis, PubMed/MEDLINE, Web of Science, Embase, Cumulative Index to Nursing and Allied Health Literature, and Google Scholar were searched using the keyword "monkeypox" and "mpox" up to 13 October 2022. A random effects model was used to obtain the pooled prevalence and 95% confidence intervals. This study included 27 articles, and 5698 patients with mpox with 19 distinctive features from 19 countries across five continents were assessed. Patients with mpox during the 2022 mpox outbreak showed mild clinical manifestations and outcomes compared with those before the 2022 mpox outbreak: mild rash (relative ratio [RR]: 5.09, 95% confidence interval [CI]: 1.52-17.08), fever (0.68, 0.49-0.94), pruritus (0.25, 0.19-0.32), myalgia (0.50, 0.31-0.81), headache (0.56, 0.35-0.88), skin ulcer (0.32, 0.17-0.59), abdominal symptom (0.29, 0.20-0.42), pharyngitis (0.32, 0.18-0.58), nausea or vomiting (0.15, 0.02-0.93), conjunctivitis (0.11, 0.03-0.38), concomitant infection with HIV (1.70, 0.95-3 0.04), and death (0.02, 0.001-0.31). MPXV clade IIb exhibited higher infectivity but may cause mild disease symptoms and low mortality rate. It is important to consider MPXV infection in patients with mpox-related features and/or a history of sexual transmission to prevent the spread of the disease and recognise the current pandemic threat.

High- and low-social-anxiety individuals process the outcomes of ability comparisons differently: an event-related potential study.

Individuals engage in upward or downward comparisons with superiors or inferiors, respectively. Social comparison is associated with social anxiety. Utilizing event-related potentials, we investigated how individuals with high social anxiety (HSA) and low social anxiety (LSA) evaluate self- versus other-outcomes in upward and downward comparison contexts. We found significant valence effects of self- or other-outcomes on feedback-related negativity (FRN) and P300 for both groups, with loss inducing larger FRN and smaller P300 than gain. In the early stage, the valence effect of other-outcomes was significant when LSA participants gained money, but not when they lost money, revealing a social comparison effect on FRN. Conversely, this valence effect was significant whether HSA participants gained or lost money. At the late stage, the valence effect of other-outcomes was significant when HSA or LSA participants gained money but not when they lost, revealing social comparison effects on the P300. Notably, only the social comparison effect in the LSA group was further moderated by comparison direction. These findings suggest that LSA participants engaged in social comparison throughout all evaluation stages, whereas HSA participants started at the late stage. Moreover, LSA participants were more sensitive to different comparison directions in the late stage.

Neural mechanisms of social comparison in subthreshold depression.

Social comparison is a common phenomenon in our daily life, through which people get to know themselves, and plays an important role in depression. In this study, event-related potential (ERP) was used to explore the temporal course of social comparison processing in the subthreshold depression group. Electrophysiological recordings were acquired from 30 subthreshold depressed individuals and 31 healthy individuals while they conducted the adapted dot estimation task. The ERP results revealed that there was a significant difference of feedback-related negativity (FRN) in the process of social comparison. Especially only in the subthreshold depression, the FRN amplitudes of worse off than some, better off than many comparisons were larger than those of upward comparisons and downward comparisons. Our results suggested that the abnormal reward sensitivity for worse off than some, better off than many comparisons might be prodromal symptoms in the subthreshold depression.

Comparative Analysis of Single-Cell RNA Sequencing Methods.

Single-cell RNA sequencing (scRNA-seq) offers new possibilities to address biological and medical questions. However, systematic comparisons of the performance of diverse scRNA-seq protocols are lacking. We generated data from 583 mouse embryonic stem cells to evaluate six prominent scRNA-seq methods: CEL-seq2, Drop-seq, MARS-seq, SCRB-seq, Smart-seq, and Smart-seq2. While Smart-seq2 detected the most genes per cell and across cells, CEL-seq2, Drop-seq, MARS-seq, and SCRB-seq quantified mRNA levels with less amplification noise due to the use of unique molecular identifiers (UMIs). Power simulations at different sequencing depths showed that Drop-seq is more cost-efficient for transcriptome quantification of large numbers of cells, while MARS-seq, SCRB-seq, and Smart-seq2 are more efficient when analyzing fewer cells. Our quantitative comparison offers the basis for an informed choice among six prominent scRNA-seq methods, and it provides a framework for benchmarking further improvements of scRNA-seq protocols.

How to make better forensic decisions.

Much of forensic practice today involves human decisions about the origins of patterned sensory evidence, such as tool marks and fingerprints discovered at a crime scene. These decisions are made by trained observers who compare the evidential pattern to an exemplar pattern produced by the suspected source of the evidence. The decision consists of a determination as to whether the two patterns are similar enough to have come from the same source. Although forensic pattern comparison disciplines have for decades played a valued role in criminal investigation and prosecution, the extremely high personal and societal costs of failure-the conviction of innocent people-has elicited calls for caution and for the development of better practices. These calls have been heard by the scientific community involved in the study of human information processing, which has begun to offer much-needed perspectives on sensory measurement, discrimination, and classification in a forensic context. Here I draw from a well-established theoretical and empirical approach in sensory science to illustrate the vulnerabilities of contemporary pattern comparison disciplines and to suggest specific strategies for improvement.

When peer comparison information harms physician well-being.

Policymakers and business leaders often use peer comparison information-showing people how their behavior compares to that of their peers-to motivate a range of behaviors. Despite their widespread use, the potential impact of peer comparison interventions on recipients' well-being is largely unknown. We conducted a 5-mo field experiment involving 199 primary care physicians and 46,631 patients to examine the impact of a peer comparison intervention on physicians' job performance, job satisfaction, and burnout. We varied whether physicians received information about their preventive care performance compared to that of other physicians in the same health system. Our analyses reveal that our implementation of peer comparison did not significantly improve physicians' preventive care performance, but it did significantly decrease job satisfaction and increase burnout, with the effect on job satisfaction persisting for at least 4 mo after the intervention had been discontinued. Quantitative and qualitative evidence on the mechanisms underlying these unanticipated negative effects suggest that the intervention inadvertently signaled a lack of support from leadership. Consistent with this account, providing leaders with training on how to support physicians mitigated the negative effects on well-being. Our research uncovers a critical potential downside of peer comparison interventions, highlights the importance of evaluating the psychological costs of behavioral interventions, and points to how a complementary intervention-leadership support training-can mitigate these costs.