The accuracy of translation.
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Chromosome Aberrations↗
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Circadian clock control of ribosome composition promotes rhythmic translation and termination fidelity.
Ribosome composition is dynamic, shifting with cell state and stress, but whether it varies with circadian time is unknown. Here, we uncover circadian clock-driven changes in ribosome composition in Neurospora crassa. Mass spectrometry of ribosomes across circadian time identified six ribosomal proteins and one associated factor under clock control. Rhythms in eL31 abundance were validated in purified ribosomes, and deletion of el31 disrupted translation rhythms in nearly half of rhythmically translated mRNAs. N. crassa eL31 promotes circadian control of translation termination and impacts elongation fidelity while maintaining Mg homeostasis, a key determinant of translational accuracy. These findings reveal that the circadian clock reprograms ribosome composition to orchestrate rhythmic translation and fidelity, temporally expanding the proteome beyond the static genome to align cellular function with time of day.
Neurospora crassa↗
OGFOD1 enables AML chemo- and nutrient stress resistance by regulating protein synthesis.
Acute myeloid leukemia (AML) commonly relapses after initial chemotherapy response. We assessed metabolic adaptations in chemoresistant cells in vivo before overt relapse, identifying altered branched-chain amino acid (BCAA) levels in patient-derived xenografts (PDXs) and immunophenotypically identified leukemia stem cells from AML patients. Notably, this was associated with increased BCAA transporter expression with low BCAA catabolism. Restricting BCAAs further reduced chemoresistant AML cells, but relapse still occurred. Among the persisting cells, we found an unexpected increase in protein production. This was accompanied by elevated translation of 2-oxoglutarate- and iron-dependent oxygenase 1 (OGFOD1), a known ribosomal dioxygenase that adjusts the fidelity of tRNA anticodon pairing with coding mRNA. We found that OGFOD1 upregulates protein synthesis in AML, driving disease aggressiveness. Inhibiting OGFOD1 impaired translation processing, decreased protein synthesis and improved animal survival even with chemoresistant AML while sparing normal hematopoiesis. Leukemic cells can therefore persist despite the stress of chemotherapy and nutrient deprivation through adaptive control of translation. Targeting OGFOD1 may offer a distinctive, translation-modifying means of reducing the chemopersisting cells that drive relapse.
Leukemia, Myeloid, Acute↗
The measurement of bacterial translation by photon correlation spectroscopy.
Photon correlation spectroscopy is shown to be a practical technique for the accurate determination of translational speeds of bacteria. Though other attempts have been made to use light scattering as a probe of various aspects of bacterial motility, no other comprehensive studies to establish firmly the basic capabilities and limitations of the technique have been published. The intrinsic accuracy of the assay of translational speeds by photon correlation spectroscopy is investigated by analysis of synthetic autocorrelation data; consistently accurate estimates of the mean and second moment of the speed distribution can be calculated. Extensive analyses of experimental preparations of Salmonella typhimurium examine the possible sources of experimental difficulty with the assay. Cinematography confirms the bacterial speed estimates obtained by photon correlation techniques.
Light↗
A framework for delivering real-time, instrument-relative navigation in transoral robotic surgery.
Transoral robotic surgery (TORS) is a minimally invasive, inside-out technique that, compared with traditional open approaches, provides fewer post-operative complications, shorter hospital stays, and improved survival for early-stage head and neck cancer. However, TORS is limited by its steep learning curve and poor visualization of deep tumor margins. This randomized crossover study evaluated a surgical navigation system's potential to enhance accuracy and user experience with real-time, instrument-relative feedback. Seven Teflon beads (d = 2.381 mm) were embedded at the tongue base of a porcine pharynx-and-larynx model. Tongue blade compression and retraction were applied to the model to mimic intraoperative tissue deformation, reproducing the anatomical shifts that occur relative to preoperative imaging. Eight participants used the da Vinci Surgical system to localize the beads by placing pins under two conditions: (a) preoperative computed tomography with no navigation; (b) model-based visual navigation with quantitative instrument-to-target metrics. Surgical accuracy was determined by calculating the target localization error (TLE, pin-to-bead Euclidean distance) and the angular error (AE, pin axis trajectory to bead). Accounting for training level and bead depth, surgical navigation reduced TLE by 5.44 mm (95% CI, 4.02-6.86 mm; p = 2.00e-11) and AE by 8.47 degrees (95% CI, 6.21-10.72 degrees; p = 5.17e-11). Impressions of the system were generally favorable using a 5-point Likert survey and task duration (p = 0.26) or cognitive workload via the NASA-Task Load Index (p = 0.22) were not significantly affected. The navigation system demonstrated translational promise, offering improved target localization accuracy and more consistent performance across experience levels, two critical determinants of surgical quality in TORS.
Robotic Surgical Procedures↗
AISP position statement: Standardising biological sample collection and handling for advanced diagnostics and multi-omic analyses in pancreatic cancer.
The quality of biological samples is a major determinant of analytical reliability and translational relevance in patients with pancreatic ductal adenocarcinoma (PDAC). However, variability in specimen procurement, handling, transport, processing, and storage can substantially affect tissue integrity and the robustness of downstream analyses. This paper, promoted by the Pathology and Basic Science Task Force of the Italian Association for the Study of the Pancreas (AISP), brings together experts in pathology, molecular biology, translational research, medical oncology, and gastroenterology to provide practical recommendations for the collection, handling, and pre-analytical management of biological samples. Draft recommendations were discussed during dedicated working group meetings and approved by consensus among all authors, supported by key literature. The document identifies the biological specimen as the critical link between patient care, pathology, and research, and provides guidance for clinicians and professionals involved in sample procurement and processing. By addressing the requirements of different analytical platforms, including genomics, organoid generation, immunophenotyping, pharmacogenomics, and multiplex/spatial analyses, this paper aims to reduce pre-analytical variability, improve diagnostic accuracy, and enhance the clinical and translational value of molecular investigations in pancreatic cancer. Standardised procedures across centres may facilitate comparable data collection, support multicentre studies, and strengthen collaboration between clinicians, pathologists, and research laboratories.
Biobanking↗
Benchmarking urinary cell transcriptomes for noninvasive differentiation of BK polyomavirus-associated nephropathy from T cell-mediated rejection.
BK polyomavirus-associated nephropathy (BKVN) adversely impacts kidney allograft survival and often mimics acute T cell-mediated rejection (TCMR), confounding diagnosis and management. To address this conundrum, we performed unbiased RNA sequencing of urinary cells matched to biopsies classified as BKVN with intragraft inflammation (BKVN-P), BKVN without inflammation (BKVN-N), TCMR, or no rejection (NR). BKVN-N displayed dominant host DNA replication, cell cycle, and repair programs, while BKVN-P samples exhibited expansive innate immune activation, antigen presentation, chemokine upregulation, and epithelial injury. Both BKVN subtypes shared signatures of T cell exhaustion and mature and tolerogenic dendritic cell activation but differed in immune orientation - Th1 predominance in BKVN-N versus Treg and CD8 enrichment in BKVN-P. Compared with TCMR samples, BKVN-P lacked robust TCR/CD28 signaling and was enriched for viral and innate modules; BKVN-N lacked alloimmune activation. B cell exhaustion characterized BKVN-N, while BKVN-P displayed robust B cell activation with metabolic downregulation. A ratiometric urinary cell biomarker, CXCL10 mRNA/CD3E mRNA, distinguished both BKVN subtypes from TCMR with diagnostic accuracy, replicated by quantitative reverse transcription PCR for clinical translation, and confirmed in an independent cohort. These findings demonstrate the utility of urinary cell transcriptomics for resolving viral injury from alloimmunity, enabling precision diagnostics and targeted immunomodulation in kidney transplantation.
Humans↗
Selphi, a tool for improving genotype imputation accuracy.
Genotype imputation is a powerful tool for inferring missing genotype data in large-scale genetic studies. Over the last two decades, multiple imputation algorithms have been developed, steadily improving in speed and overall accuracy. However, accurate imputation of rare and infrequent variants remains a challenge, largely because existing methods rely on local haplotype matching within genomic windows and do not fully exploit the extended patterns of haplotype sharing that span entire chromosomes. Here we present Selphi, a new genotype imputation algorithm that combines the Positional Burrows-Wheeler Transform (PBWT) with a multi-stage haplotype selection heuristic operating across entire chromosomes. When compared to state-of-the-art methods Beagle 5.4, IMPUTE5, and Minimac4, Selphi showed higher accuracy on the 1000 Genomes Project and TOPMed datasets, across all super-populations and allele frequencies. Similarly, Selphi achieved higher accuracy than Beagle 5.4 on the UK Biobank dataset, which translated into improved concordance with hc-WGS GWAS summary statistics at known trait-associated loci and more accurate polygenic risk scores (PRS). Selphi outputs standard VCF files with genotype dosages (DS), haplotype-specific allele probabilities (AP1, AP2), and a per-variant dosage R-squared quality score (DR2), enabling direct integration with downstream analytical pipelines including standard post-imputation quality filtering.
Genome-Wide Association Study↗
Research progress and application prospects of multi-omics integration strategies in precision risk stratification of type 1 diabetes mellitus.
Type 1 diabetes (T1D) is a chronic metabolic disease mediated by autoimmunity. Its pathogenesis involves complex interactions between genetic susceptibility and environmental factors. Conventional T1D risk stratification primarily relies on genetic markers, islet autoantibodies, and glycemic indicators. Although these biomarkers remain indispensable in current clinical practice, they are often insufficient when used alone to accurately identify ultra-early high-risk individuals, predict disease progression rates, or support individualized preventive strategies. Consequently, more comprehensive molecular approaches are needed to improve precision risk stratification. In recent years, the rapid development of multi-omics technologies has provided new strategies for precise risk stratification of T1D. This narrative review critically evaluates how multi-omics integration strategies can improve precision risk stratification throughout the T1D disease continuum by integrating complementary molecular information from genomics, transcriptomics, proteomics, metabolomics, epigenomics, and the microbiome. Particular emphasis is placed on stage-specific biomarker discovery, multi-omics data integration frameworks, artificial intelligence-assisted prediction models, biomarker validation, and the opportunities and challenges associated with clinical translation. Current evidence suggests that integrated multi-omics approaches have the potential to improve risk prediction accuracy, distinguish heterogeneous disease trajectories, identify individuals at imminent risk of progression, and provide biologically informed targets for precision intervention. However, important challenges remain, including data harmonization, external validation, model interpretability, cost-effectiveness, and integration into routine clinical screening programs. Future research should prioritize prospective multicenter cohorts, standardized analytical pipelines, externally validated prediction models, and clinically interpretable multi-omics frameworks to facilitate the translation of precision risk stratification into routine T1D prevention and management.
Humans↗
Epigenetic clues: Predicting maternal depression through DNA methylation.
Perinatal depression (PND) is a prevalent and multifactorial mood disorder affecting approximately 10-20 % of women globally, with higher burdens reported in low- and middle-income countries. Despite the availability of screening tools such as the Edinburgh Postnatal Depression Scale, these approaches primarily identify risk without elucidating underlying biological mechanisms. Emerging evidence highlights the role of epigenetic regulation particularly DNA methylation as a key mediator linking genetic susceptibility and environmental exposures during the perinatal period. This review synthesizes current knowledge on DNA methylation dynamics in maternal depression, emphasizing both candidate gene and epigenome-wide association study (EWAS) approaches. Candidate gene studies have identified differential methylation in stress-related pathways, including HPA axis genes (NR3C1, FKBP5), serotonergic signalling (SLC6A4), and oxytocin pathways (OXTR), though findings remain limited by poor reproducibility and small sample sizes. In contrast, EWAS provides a hypothesis-free framework, identifying novel differentially methylated positions and regions associated with PND, including predictive CpG panels with potential diagnostic utility. The review also highlights the importance of tissue specificity, temporal epigenetic remodeling across pregnancy, and the interplay between maternal and fetal epigenomes. Furthermore, methodological challenges such as heterogeneity in study design, lack of replication, and analytical inconsistencies remain barriers to clinical translation. Integrating genetic, epigenetic, and environmental data through multi-omics approaches may enhance predictive accuracy and improve early intervention strategies. Overall, DNA methylation represents a promising avenue for understanding the biological underpinnings of PND and developing robust biomarkers for risk prediction and personalized care.
Humans↗
Sparse polygenic risk score inference with the spike-and-slab LASSO.
MOTIVATION: Large-scale biobanks, with rich phenotypic and genomic data across hundreds of thousands of samples, provide ample opportunities to elucidate the genetics of complex traits and diseases. Consequently, there is growing demand for robust and scalable methods for disease risk prediction from genotype data. Inference in this setting is challenging due to the high-dimensionality of genomic data, especially when coupled with smaller sample sizes. Popular Polygenic Risk Score (PRS) inference methods address this challenge by adopting sparse Bayesian priors or penalized regression techniques, such as the Least Absolute Shrinkage and Selection Operator (LASSO). However, the former class of methods are not as scalable and do not produce exact sparsity, while the latter tends to over-shrink large coefficients. RESULTS: In this study, we present SSLPRS, a novel PRS method based on the Spike-and-Slab LASSO (SSL) prior, which offers a theoretical bridge between the two frameworks. We extend previous work to derive a coordinate-ascent inference algorithm that operates on GWAS summary statistics, which is orders-of-magnitude more efficient than corresponding individual-level-based implementations. To illustrate the statistical properties of the proposed model, we conducted experiments involving nine simulation configurations and nine quantitative phenotypes from the UK Biobank. Our results demonstrate that SSLPRS is competitive with state-of-the-art methods in terms of prediction accuracy and exhibits superior variable selection performance, especially in sparse genetic architectures. In simulations, this translates to upwards of 50% improvement in positive predictive value. In analysis of real phenotypes, we show that selected variants are highly enriched for meaningful genomic annotations and have better replication rates in larger meta-analyses. AVAILABILITY AND IMPLEMENTATION: SSLPRS is available in the open-source package https://github.com/li-lab-mcgill/penprs.
Multifactorial Inheritance↗
Innovative advances and clinical applications of cell-free DNA methylation detection technologies.
Advances in DNA methylation detection technologies have promoted disease-related cell-free DNA (cfDNA) analysis. CfDNA methylation profiling has the potential to serve as a promising clinical tool for early disease diagnosis. However, current detection technologies suffer from high costs, complex operational procedures, and insufficient sensitivity for low-input samples. Moreover, the definitive validation of its clinical value still awaits robust evidence from high-quality confirmatory studies. Therefore, this review begins by mapping the historical evolution of cfDNA methylation, followed by a comparison of the traditional approaches and recent breakthroughs in cfDNA methylation analysis. Specifically, this review systematically examines the two major strategies: the ones based on bisulfite-dependent DNA modification and the bisulfite-free methods, including the techniques for whole-genome methylation profiling and methods targeting specific genomic regions. Additionally, to evaluate the clinical application potential of these methods, this review comprehensively describes the details of these technologies, such as sample input requirements and sensing accuracy in detecting clinical samples. The future development of cfDNA methylation detection will focus on clinical translation, integrating technical innovations with the demands for efficient clinical diagnosis. We believe this review will help researchers select methods tailored to sample availability and clinical applicability.
Humans↗
The SMN locus in the T2T era: Structure, gene conversion, and clinical implications.
Long-read sequencing, paralog-aware variant calling, and telomere-to-telomere (T2T) human genome assemblies now enable the resolution of copy-, haplotype-, and nucleotide-level complexities in segmentally duplicated loci, which were previously inaccessible with short-read sequencing. In this review, we highlight how current technologies and analysis methods reveal extensive diversity in copy number (CN), structure, and gene conversion within the spinal muscular atrophy-associated survival motor neuron (SMN) locus. We summarize how understanding population-level structural variation could be translated into clinical practice, where a nucleotide-level view of the SMN locus may refine prognostic accuracy beyond SMN2 CN and explain variable treatment responses. Finally, we discuss how the approaches and methodologies required to study the SMN locus may be applied elsewhere, providing a scaffold to characterize other complex human genetic regions.
Humans↗
A consensus guide to preclinical indirect calorimetry experiments.
Understanding the complex factors influencing mammalian metabolism and body weight homeostasis is a long-standing challenge requiring knowledge of energy intake, absorption and expenditure. Using measurements of respiratory gas exchange, indirect calorimetry can provide non-invasive estimates of whole-body energy expenditure. However, inconsistent measurement units and flawed data normalization methods have slowed progress in this field. This guide aims to establish consensus standards to unify indirect calorimetry experiments and their analysis for more consistent, meaningful and reproducible results. By establishing community-driven standards, we hope to facilitate data comparison across research datasets. This advance will allow the creation of an in-depth, machine-readable data repository built on shared standards. This overdue initiative stands to markedly improve the accuracy and depth of efforts to interrogate mammalian metabolism. Data sharing according to established best practices will also accelerate the translation of basic findings into clinical applications for metabolic diseases afflicting global populations.
Calorimetry, Indirect↗
Real-world deployment of a fine-tuned pathology foundation model for lung cancer biomarker detection.
Artificial intelligence models using digital histopathology slides stained with hematoxylin and eosin offer promising, tissue-preserving diagnostic tools for patients with cancer. Despite their advantages, their clinical utility in real-world settings remains unproven. Assessing EGFR mutations in lung adenocarcinoma demands rapid, accurate and cost-effective tests that preserve tissue for genomic sequencing. PCR-based assays provide rapid results but with reduced accuracy compared with next-generation sequencing and require additional tissue. Computational biomarkers leveraging modern foundation models can address these limitations. Here we assembled a large international clinical dataset of digital lung adenocarcinoma slides (N = 8,461) to develop a computational EGFR biomarker. Our model fine-tunes an open-source foundation model, improving task-specific performance with out-of-center generalization and clinical-grade accuracy on primary and metastatic specimens (mean area under the curve: internal 0.847, external 0.870). To evaluate real-world clinical translation, we conducted a prospective silent trial of the biomarker on primary samples, achieving an area under the curve of 0.890. The artificial-intelligence-assisted workflow reduced the number of rapid molecular tests needed by up to 43% while maintaining the current clinical standard performance. Our retrospective and prospective analyses demonstrate the real-world clinical utility of a computational pathology biomarker.
Humans↗
Secobarbital and information processing.
The Sternberg fixed-set memory-search paradigm was used to assess the relative vulnerability of hypothetical stages of information processing to an oral dose of secobarbital (2.9 mg/kg). D-amphetamine (15 mg, oral dose) was intended to serve as an active placebo. However, since the amphetamine produced a slight, non-significant reduction in choice reaction time (RT), the principal analysis of secobarbital effects was conducted between drug and baseline conditions. Secobarbital showed choice RT by 60 msec. and did not increase errors significantly. The results, as interpreted within Sternberg's model, suggest that input processes, e.g., stimulus preprocessing-encoding, are particularly sensitive to the effects of the barbiturate. There was no evidence of a drug effect on cognitive processes associated with serial comparison, binary decision, or translation-response organization (response selection). In contrast, earlier studies have indicated that another CNS depressant, alcohol, interferes with both speed and accuracy of output processes, viz, the response selection stage.
Adult↗
Emerging techniques of CRISPR/Cas system in antiviral therapy and diagnostics: Applications, limitations, and translational perspectives.
The CRISPR/Cas (clustered regularly interspaced short palindromic repeats) system is a versatile technology for developing antiviral medicines and editing viral genomes in both diagnostics and vaccine synthesis. Emerging insights into class 2 effectors, such as Cas9, Cas12, and Cas13, which target viral DNA and RNA, have revolutionized vaccines against viruses such as HIV, HPV, HBV, and EBV. Innovative diagnostic techniques such as SHERLOCK, DETECTR, and FELUDA have demonstrated system's diversity and accuracy in detecting the virus markers, supporting clinical decision-making, indicating adaptability and precision of CRISPR. This review critically evaluates CRISPR's role in RNA editing, emphasizing its importance for functional genomics and development of recombinant vaccines. Translational challenges are critically discussed, including off-target effects, delivery limitations, and ethical issues, for which unique approaches such as high-fidelity Cas variants, non-viral delivery systems, and bioethical frameworks are evaluated to address these limitations. This review also covers other social implications, such as accessibility and biosecurity risks, associated with CRISPR technologies Collectively, these advances underscore the transformative potential of CRISPR technologies in shaping next-generation antiviral diagnostics and therapeutics.
CRISPR-Cas Systems↗
Predicting the First Onset of Suicidal Thoughts and Behaviors in Adolescents Using Multimodal Risk Factors: A 4-Year Longitudinal Study.
OBJECTIVE: Suicide is one of the leading causes of death among youth worldwide, yet existing studies that aimed to predict the first onset of suicidal thoughts and behaviors (STB) included a limited number of data modalities and/or focused on adult populations. This study aimed to prospectively predict first-onset STB across 4-year follow-ups in adolescents using an existing STB history classification model that was previously applied to baseline data and a new machine learning model with 195 biopsychosocial features. METHOD: Participants were 7,503 unrelated adolescents (54.5% female, ages 9-11 years at baseline) from the multisite, longitudinal Adolescent Brain Cognitive Development (ABCD) Study. An existing baseline STB history classification model was applied to predict longitudinal first-onset STB in adolescents compared with healthy controls and clinical controls (individuals with a mental health disorder but no STB). A new elastic net logistic regression model with 195 features was trained on data from 14 sites (n = 5,220), and the resulting top 15 features were validated at 7 independent sites (n = 2,283). RESULTS: The previously developed model to classify STB lifetime history also prospectively predicted first-onset STB in adolescents with an area under the curve (AUC) [95% CI] of 0.73 [0.70, 0.75], p < .001, compared with healthy controls and AUC [95% CI] of 0.63 [0.60, 0.66], p < .001, compared with clinical controls. The newly trained model with top 15 features performed similarly with AUC [95% CI] of 0.73 [0.71, 0.76], p < .001, and AUC [95% CI] of 0.64 [0.60, 0.66], p < .001, for the same comparison groups. The most consistent predictors across models included female sex, sleep disturbances, and maladaptive home and school environments. CONCLUSION: The models predicted first-onset STB in adolescents with moderate accuracy. This study also confirmed the roles of well-established psychological risk factors for STB and identified several novel neurocognitive and brain imaging risk factors. Future studies should validate these models in large-scale diverse samples before clinical translation. PLAIN LANGUAGE SUMMARY: This study followed over 7,500 adolescents for 4 years and tested 2 machine learning models using psychological, social, and brain data to identify those at risk of experiencing suicidal thoughts or behaviors. Both models predicted first-time suicidal thoughts or behaviors with moderate accuracy. Key risk factors that were identified included being female, experiencing sleep problems, and negative home and school environments. DIVERSITY & INCLUSION STATEMENT: We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. Diverse cell lines and/or genomic datasets were not available. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. We actively worked to promote sex and gender balance in our author group. One or more of the authors of this paper received support from a program designed to increase minority representation in science. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list. While citing references scientifically relevant for this work, we also actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our reference list. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work.
Adolescent↗