RNA-seq-based copy number inference for retrospective genomic characterization of lung cancer transcriptomes.
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Journal Article↗
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Building phenotypic character matrices for phylogenetic inference: exploration of 35 years of practice.
Recent methodological development in phylogenetic inference has focused predominantly on molecular data. However, renewed interest in other data types, particularly morphological data, has followed from the increased recognition of the power of total evidence and tip-dating approaches, including fossil data, for inference of time-scaled trees and rates of evolution. However, attention has largely focused on the improvement of models of morphological evolution and other analytical tools with much less discussion about data acquisition itself. Here we review past and current practice for describing and collecting morphological data for phylogenetic inference. We present a systematic review of 164 phylogenetic analyses conducted over the last 35 years and focused on a diverse group of extinct arthropods: trilobites. Trends in increasing matrix size, data type, and coding strategy are evident. Where present, polymorphic characters have been predominantly derived from discretized continuous characters, although increasingly practitioners are utilizing alternative approaches for the treatment of quantitative characters. Not surprisingly, traditional indices that describe character consistency are highly correlated with matrix size but show surprising variation at different taxonomic scales. More recent attempts to describe data quality using information theory imply that characters can have high information content even if data are missing for many tips, providing support against the exclusion of characters because of missing data. In consideration of this, as well as advances in the study of developmental biology and variational complexity, we identify several avenues for increasing the quality and quantity of morphological data going forward.
Phylogeny↗
Endocrine-disrupting chemical-induced gene networks confer coronary heart disease risk revealed by causal inference and single-cell analyses.
BACKGROUND: Endocrine-disrupting chemicals (EDCs) are linked to coronary heart disease (CHD), but underlying mechanisms remain unclear. We aimed to identify EDC-related genes and evaluate their causal roles in CHD. METHODS: We curated EDC-related genes from a compound-gene interaction database and integrated them with CHD genome-wide association study (GWAS) summary statistics and tissue-specific expression quantitative trait loci (eQTL) data. Two-sample Mendelian randomization (MR) and Bayesian colocalization were applied to infer causality. Functional enrichment, single-cell RNA sequencing of human coronary arteries, and EDC-gene networks were further analyzed. RESULTS: After FDR correction, 39 genes were significantly associated with CHD risk via MR. Four genes-ZNF827, FCHO1, IPO9 (protective), and RPL13 (risk-increasing)-showed strong colocalization (PPH4 > 0.9). Pathway and single-cell analyses of coronary artery tissue indicated that vascular and immune pathways mediate these effects. An interaction network highlighted associations between specific EDCs and candidate genes implicated in CHD susceptibility. CONCLUSION: This integrative genomic study provides evidence that EDCs influence CHD susceptibility through distinct gene networks, revealing potential mechanisms and molecular targets for prevention and therapy.
Humans↗
A contextual activity score (CAS) for inferring ADAR-associated transcriptional activity across RNA-seq, single-cell, and spatial transcriptomics.
BACKGROUND AND OBJECTIVE: Adenosine-to-inosine RNA editing, catalyzed by Adenosine Deaminases Acting on RNA (ADARs), is a widespread modification involved in neural function, immune regulation, and cancer. The Alu Editing Index (AEI) is the standard metric to estimate ADAR activity but requires raw sequencing reads and is poorly suited for single-cell and spatial transcriptomic data. This study aimed to develop an alternative framework for inferring ADAR-associated transcriptional activity from gene expression data across diverse transcriptomic technologies. METHODS: We developed the Contextual Activity Score (CAS), a framework based on transcriptional signatures from ADAR perturbation experiments. Context-specific signatures were generated for human neurons, mouse neurons, and cancer models to infer ADAR1 and ADAR2 activity. CAS was computed from normalized gene expression matrices using regulon-based enrichment analysis. Performance was evaluated by comparing with the Alu Editing Index across bulk RNA sequencing datasets, simulated sequencing depths, and library preparation protocols. RESULTS: CAS showed strong concordance with the Alu Editing Index across multiple datasets, while remaining robust to reduced sequencing depth and different library protocols. Unlike the Alu Editing Index, CAS can be applied to single-cell and spatial transcriptomic data and enables the independent assessment of ADAR2 activity. In cancer and neuronal contexts, CAS captured biologically meaningful variations in ADAR-associated transcriptional activity at sample, cell-type, and spatial levels. CONCLUSION: CAS provides a scalable approach applicable across multiple RNA-seq protocols for estimating ADAR-associated transcriptional activity using gene expression data. This method, implemented in an open-source R package for broad adoption, expands the ability to study ADAR-associated transcriptional activity across transcriptomic modalities where direct editing quantification is challenging, such as single-cell and spatial transcriptomics.
Adenosine Deaminase↗
Can't see the forest for the trees: The influence of marker type on inferred phylogenetic relationships in a cosmopolitan bat genus.
Fine-resolution information on species relationships and biological diversity is critically needed to guide conservation efforts amidst rapid environmental changes. Systematics, which forms the foundation of this knowledge, has been revolutionized by phylogenomics, utilizing genome-scale datasets. However, the use of diverse marker types, non-comparable taxon sampling, and outgroup selection can lead to conflicting phylogenetic hypotheses. These inconsistencies complicate study comparisons and hinder our ability to assess marker-specific impacts on phylogenetic resolution. The phylogenetic reconstruction of the bat genus Myotis, encompassing over 140 species and characterized by a rapid radiation in the last 20 million years, has been particularly influenced by these challenges. Achieving phylogenetic resolution in Myotis is particularly complex due to subtle interspecific differences in both morphological and molecular traits. Mitochondrial and nuclear markers often produce discordant trees, influenced by hybridization, introgression, and methodological variations. In this study, we employed a consistent taxonomic sample set of 44 Myotis taxa to evaluate the impact of five different genetic marker types on phylogenetic reconstruction. We observed significant discordance between topologies derived from conserved nuclear and mitochondrial markers and found that transposable elements were inadequate for resolving relationships across the entire genus. Our results also clarify the placement of previously problematic taxa within the genus. These findings emphasize the importance of aligning genetic marker choice with specific phylogenetic questions and highlight the influence of taxonomic and methodological variation on phylogenomic outcomes. This work provides a framework for improving phylogenetic inference in rapidly radiating groups and enhances our understanding of evolutionary history in Myotis.
Animals↗
Quo vadis, BGA? A collaborative EDNAP exercise on the challenges and progress in forensic biogeographical ancestry inference.
There is a broad consensus that forensic tests for the prediction of externally visible characteristics (EVC) and analysis of biogeographic ancestry (BGA) of an individual are technically reliable. However, interpretation of the results and population-specific genotype distribution patterns remains challenging. EVC and BGA analyses provide valuable information for population genetics studies and as investigative leads for criminal cases, as well as for historical and contemporary identification tests. However, inaccurate or incorrect predictions, for example, from subjective bias in the interpretations made, have the potential to misdirect police investigations. The legal situation regarding EVC and BGA testing varies by country: ranging from countries where it is explicitly prohibited, to those without specific regulations on biogeographic ancestry prediction, and others that have already enacted laws governing its use. The reluctance to utilize these analyses is not only due to legal restrictions and data protection concerns, but also to initial limited sets of sufficiently comprehensive forensic DNA assays. Forensic BGA marker panels typically contain up to ∼300 SNPs. This relatively small number of genetic markers, along with limited reference population data, complicates the interpretation of results from donors of unknown origin. This paper presents the results of a collaborative EDNAP study, which, for the first time, evaluated the approach to reporting EVC and BGA data between international laboratories. For the study, DNA from nine individuals with self-reported ancestry was collected and analysed using various forensic panels differing in the number and composition of ancestry-informative markers genotyped, comprising: the Precision ID mtDNA Whole Genome Panel, the VISAGE Basic Tool and the VISAGE Enhanced Tool for Appearance and Ancestry Prediction, and the Ion AmpliSeq™ PhenoTrivium Panel. To ensure full data protection, all SNP genotypes and uniparental marker haplotypes obtained were not shared with third parties. Instead, the genetic data were analysed using a range of commonly used population analysis software packages. These analysis outcomes were then distributed to twelve European forensic laboratories (both academic and law enforcement institutions), who were asked to prepare reports based on their interpretation of the phenotypes and ancestry they inferred from the analysis data. A questionnaire sent alongside the genetic information, aimed to evaluate which difficulties were encountered by the participants in processing the BGA analysis data they were given.
Humans↗
The landscape of pruning for large language models: A systematic review and unified taxonomy.
Confronting the inherent tension between the exceptional capabilities and the immense computational costs of Large Language Models (LLMs), pruning has become a crucial technique for achieving efficient deployment. However, a systematic analytical framework dedicated specifically to LLM pruning remains absent. In this paper, we aim to bridge this gap. We first elucidate the theoretical foundations that underpin the effectiveness of pruning, namely overparameterization and redundancy, and then propose a multidimensional taxonomy that organizes existing approaches along the axes of granularity, timing, and criteria. Building upon this unified perspective, we further analyze performance recovery mechanisms and the broader evaluation ecosystem, while also exploring forward-looking challenges such as interpretability, automation, and hardware-algorithm co-design. Through this comprehensive synthesis, we seek to provide an integrated and coherent analytical lens for advancing both research and practice in LLM pruning.
Large Language Models↗
Comparison of paralog identification methods and their impact on species tree topologies in target capture phylogenomics within the Sindora clade (Detarioideae: Leguminosae).
Target capture is a common method of generating high throughput DNA sequencing data for phylogenetic reconstruction of species relationships, for which single copy genes are usually most informative. However, a pervasive problem with target capture is that putatively single copy genes may in fact be paralogs resulting from gene duplication, which are problematic for phylogenetic inference because their evolutionary history may differ from the divergence history of species. Here, we use as a case study a target enrichment dataset of 88 species of Detarioideae (Leguminosae) with a focus on the Sindora clade to examine approaches for handling paralogs, including the built-in paralog handling functions in HybPiper and CAPTUS, plus subsequent steps using Putative Paralog Detection and the tree-based Yang & Smith orthology inference approach. We compare the paralogs flagged using these methods and verify their performance with BLAST mapping against a reference genome sequence of Sindora glabra, and then subsequently compare the species tree topologies produced across these methods. Our comparisons of paralogs flagged across the Sindora clade show that the Putative Paralog Detection pipeline was the most accurate in identifying paralogs in terms of its similarity to the BLAST mapping, followed by the built-in paralog identification function of CAPTUS. However, the results we recovered for the Detarioideae subfamily suggest that the largest differences in species tree topology resulted from the use of paralog-filtered alignments (such as with the Putative Paralog Detection pipeline and the Yang & Smith orthology inference approaches) rather than just by removing the sequences of identified paralogous genes. This was the true for HybPiper-assembled datasets but was not seen in CAPTUS-assembled datasets. In all comparisons, the topological differences caused by different paralog handling methods tended to be confined to clades where processes such as hybridisation and introgression are prevalent. Our study provides a roadmap to establish the best approach to identify, eliminate or separate paralogs in the absence of a chromosomally contiguous reference genome for a study group, and highlights the importance of careful data inspection and processing in addition to understanding the extent of paralogy and paralog characteristics (e.g. sequence divergence between copies) for their study group.
Phylogeny↗
Modelling the effects of biological intervention in a dynamical gene network.
Cellular response to environmental and internal signals can be modeled by dynamical gene regulatory networks (GRN). In the literature, three main classes of gene network models can be distinguished: (1) non-quantitative (or data-based) models which do not describe the probability distribution of gene expressions; (2) quantitative models which fully describe the probability distribution of all genes co-expression; and (3) mechanistic models which allow for a causal interpretation of gene interactions. We propose two rigorous frameworks to model gene alteration in a dynamical GRN, depending on whether the network model is quantitative or mechanistic. We explain how these models can be used for design of experiment, or, if additional alteration data are available, for validation purposes or to improve the parameter estimation of the original model. We apply these methods to the Gaussian graphical model, which is quantitative but non-mechanistic, and to mechanistic models of Bayesian networks and penalized linear regression.
Gene Regulatory Networks↗
Exploring China's Clean Air Act and associated cardiovascular disease risk: a prospective, quasi-experimental, and causal inference modelling study.
BACKGROUND: Substantial improvements in air quality have been recorded following the implementation of China's Clean Air Act (CCAA) in 2013. However, the association between CCAA implementation and individual-level cardiovascular disease (CVD) risk remains unclear. We aimed to examine the long-term association between CCAA implementation and individual-level predicted CVD risk. METHODS: In this prospective, quasi-experimental study, we used data from the China Kadoorie Biobank, a prospective cohort study that recruited participants from five urban and five rural areas across China between 2004 and 2008, with three resurveys conducted after the baseline survey (in 2008, 2013-14, and 2020-21). We included 34 862 individuals (mean age 51·3 years) who participated in at least one resurvey and had no history of CVD at baseline. Participants were classified into intervention (n=25 497) and control (n=9365) groups based on the local government's targets for particulate matter reduction. We estimated the 10-year risk of incident CVD morbidity or mortality using a validated risk prediction model. We used a difference-in-difference model to assess the long-term association between CCAA implementation and predicted risk, with adjustments made for regional confounders and individual-level characteristics, including demographics, lifestyle factors, medical history, and indoor air pollution exposure. The relationship between changes in long-term exposure to PM2·5, PM10, and O3 and predicted risk after CCAA implementation was analysed using a linear model. The estimated risk differences associated with air pollutant changes were estimated based on the magnitude of changes and their corresponding effect sizes. FINDINGS: After the CCAA was implemented, PM2·5 and PM10 concentrations declined in both groups, but O3 concentrations increased. The intervention group showed a 3·95% (95% CI 3·18-4·72%) lower increase in predicted risk than the control group, with larger estimated differences under stricter enforcement. Between 2013 and 2021, each 10 μg/m3 change in PM2·5 concentration was positively associated with a 1·80 (1·34-2·27) percentage point change in predicted CVD risk, whereas each 10 μg/m3 change in PM10 concentration was associated with a 1·24 (0·84-1·63) percentage point change and each 10 μg/m3 change in O3 concentration with a 0·58 (0·33-0·83) percentage point change. Overall, the observed changes in air pollutants during the study period were associated with an average 6·6 percentage point reduction in predicted CVD risk. INTERPRETATION: The CCAA and improved air quality were associated with a slower increase in predicted CVD risk, supporting the necessity for stricter, multipollutant air quality policies to maximise public health benefits. FUNDING: National Natural Science Foundation of China, Kadoorie Charitable Foundation, Noncommunicable Chronic Diseases-National Science and Technology Major Project, National Key R&D Program of China, Chinese Ministry of Science and Technology, and UK Wellcome Trust.
Journal Article↗
Stage-specific ROMO1 in rheumatoid arthritis: predictive immune insights into the MIF pathway and HLA-DR/IL2RA axis via integrated GWAS, transcriptomic, single-cell, and spatial profiling.
Emerging evidence links reactive oxygen species modulator 1 (ROMO1), a key mitochondrial ROS regulator, to rheumatoid arthritis (RA) pathogenesis. However, its exact mechanism remains elusive given the conflicting evidence about its specific function. We used a four-level integrative framework combining multi-omics data and literature‑supported mechanistic inference. At the genetic level, Mendelian randomization (MR) was performed to explore potential causal relationships between ROMO1, IL2RA, HLA-DR, MIF, and RA risk, followed by differential expression analysis and machine learning-based feature selection to identify key mROS genes. The temporal expression dynamics of ROMO1 were assessed in RA progression. At the cellular and tissue levels, we integrated single-cell RNA sequencing and spatial transcriptomics to map cell-type-specific expression and synovial localization of ROMO1-related immune cells and pathways. Finally, our multi-omics findings were contextualized with literature-supported mechanistic inference. (1) MR results were consistent with a potential protective effect of ROMO1 on RA (OR = 0.52) and its potential regulation of risk factors IL2RA (OR = 0.46) and HLA-DR (OR = 0.40). Conversely, IL2RA (OR = 1.42), HLA-DR (OR = 1.88), and MIF (OR = 1.17) were positively associated with RA risk. Additionally, ROMO1 was identified as a top candidate diagnostic predictor with stage-specific dynamics: downregulated in the early but upregulated in the late/remission stages. (2) Single-cell RNA sequencing showed ROMO1's cell-specific expression in CD14+ HLA-DR+ CD74+ monocytes and CD4+ IL2RA+ T cells. Cell communication analysis further suggested that these cells may participate in MIF pathway regulation. Spatial transcriptomics subsequently identified that ROMO1-related cells localized to synovial pathological regions, with MIF pathway changes correlated with RA progression. (3) Finally, literature-supported mechanistic inference suggests that ROMO1 may modulate mROS levels to promote anti-inflammatory M2 macrophage polarization, which could theoretically contribute to reduced systemic inflammation and the alleviation of multi-organ decline in RA. This integrated multi-omics investigation, supported by literature-based mechanistic inference, suggests ROMO1 as a stage-dependent biomarker candidate and potential immune regulator in RA.
Humans↗
Experimental evolution reveals contrasting adaptive landscapes in lab and field environments.
Experimental evolution is widely used to infer microbial responses to environmental change, yet most laboratory studies impose constant, well-mixed conditions that differ fundamentally from fluctuating, spatially structured field environments. We compared genomic evolution in the leaf litter-associated bacterium Curtobacterium strain MMLR14_002 under control and warming treatments in laboratory culture and in a complementary field experiment. Laboratory-derived isolates accumulated more mutations per genome and exhibited stronger locus-level parallelism, with mutations recurring in a small number of coding loci. Field-derived isolates accumulated fewer mutations per genome, and these mutations rarely occurred in the same coding loci across replicate populations. Instead, field isolates exhibited a higher proportion of intergenic mutations, with mutations recurring in the same intergenic regions across independent field deployments. When coding mutations were detected in the field, they were distributed across functionally diffuse targets and more often involved metabolic pathways than the core cellular processes repeatedly targeted during laboratory evolution. Warming itself did not consistently influence mutation accumulation or the genomic distribution of mutations; instead, laboratory and field contexts primarily shaped the accumulation, targets, and repeatability of genomic change. These results suggest that laboratory thermal evolution identifies adaptive routes favored under sustained selection but may overestimate coding-level parallelism under heterogeneous field conditions. Bridging laboratory and field evolution will likely require experimental designs that incorporate temporal variability and spatial heterogeneity characteristic of natural systems.IMPORTANCEA central goal of experimental evolution is to infer how microbes evolve in nature from laboratory studies. Here, we evaluate this assumption by comparing genomic evolution of a leaf litter-associated Curtobacterium strain in laboratory and field warming experiments to identify broad patterns rather than isolate the contribution of any single environmental factor. We find that the strong parallelism at coding loci observed under laboratory conditions is reduced in the field, while mutations recurring in the same intergenic regions across field deployments suggest that parallel evolution in nature may more often involve regulatory noncoding regions rather than coding targets. These results show that environmental context reshapes adaptive landscapes and may limit the parallelism of coding-level genomic responses inferred from homogeneous laboratory conditions.
experimental evolution↗
Mining Stored-Specimen Studies for Information about Cancer Natural History.
The advent of new multicancer early detection tests and publication of early diagnostic results have generated expectations of clinical benefit from multicancer screening. The clinical benefit of a cancer screening test depends critically on disease natural history, which is typically learned from prospective screening studies. Retrospective studies of stored blood specimens are important in learning about a test's preclinical diagnostic performance but have rarely been used to infer natural history. The extent to which these studies might be harnessed to also learn natural history is discussed in the context of an article in this issue that infers the combined natural history of a range of cancers targeted by a multicancer early detection test using a case-control subsample of specimens from a large cohort study. The critical question concerns the identifiability of key transition rates in multistate models of natural history alongside state-specific sensitivities. The article suggests that these parameters are estimable within a Bayesian framework that leverages prior information about test sensitivity from diagnostic studies. We offer a heuristic discussion of identifiability in this setting and encourage formal study to determine the extent to which models with varying degrees of complexity may be learned from stored-specimen studies. See related article by Dai et al., p. 1535.
Humans↗
The Complete Mitochondrial Genome of a Newly Recorded Chinese Species of Diglyphus sabulosus (Hymenoptera: Eulophidae) and Insights into Its Phylogenetic Position.
Diglyphus Walker, 1844 is an economically important genus which many species acting as biocontrol agents against agromyzid leafminer pests, but there is a lack of mitogenomic data on the evolutionary relationships within this genus, hindering a comprehensive understanding of its evolutionary history. We used traditional morphological methods to identify species, and present the first complete mitochondrial genome sequence and characterization of features of Diglyphus sabulosus and further infer its phylogenetic position based on the amino acid sequences of 13 protein-coding genes (PCGs). The complete mitochondrial genome of D. sabulosus is 15,690 bp in length, including 13 PCGs, 22 transfer RNA genes, 2 ribosomal RNA genes and a control region. The AT content of the whole genome sequence was 81.0%, indicating a significant AT bias. All protein-coding genes have the typical ATN as the start codon and TAA as the stop codon. Phylogenetic analysis inferred from the amino acid sequences of 13 PCGs revealed that all species within the family Eulophidae constituted a monophyletic clade, supporting the monophyly of this family. D. sabulosus and D. poppoea form a well-supported sister group, representing the species with the closest phylogenetic relationship within the analyzed taxa. In this study, the mitogenome structure was analyzed and the taxonomic status of D. sabulosus was clarified, thus providing a theoretical basis for understanding the phylogenetic relationships of Diglyphus.
Animals↗
Getting to the Core of the Matter-Assessing the Role of Replication in Metabarcoding-Based sedaDNA.
Replication is central to most experimental and sampling designs, increasing inferential power and capturing fine-scale data heterogeneity. However, its importance remains poorly evaluated in some ecological and evolutionary settings. This is the case of metabarcoding studies using DNA recovered from sedimentary archives, in which biological signals integrate ecological information through depositional and burial processes, yet are commonly inferred from a single sediment core per site. Here, we evaluated the effect of different types of replication using sedimentary DNA metabarcoding data from two genetic markers (mitochondrial COI and nuclear 18S) using a nested sampling design. The design included three intertidal sites, three spatially separated sediment cores per site (biological replicates), two sediment horizons per core, and eight PCR (technical) replicates per sediment sample. Variance partitioning showed that site identity and sediment age group together explained > 70% of the variation in beta diversity, indicating that among-site spatial and stratigraphic differences were the dominant drivers of community composition. PERMANOVA likewise identified non-significant effects of biological replication. Among PCR replicates from the same sediment sample, richness varied substantially, whereas Shannon diversity was more consistent. Despite this variability, differences in community composition among technical replicates remained smaller than those associated with biological replication or site identity, indicating a limited influence on broader ecological patterns. Community composition was highly similar among replicate cores within sites, consistent with stratigraphic coherence. These results indicate limited within-site heterogeneity and suggest that, under stratigraphically coherent conditions, increasing biological replication may provide little additional information, whereas enhancing technical replication and stratigraphic resolution can improve ecological inference from sedimentary DNA metabarcoding datasets.
DNA Barcoding, Taxonomic↗
Proprioception Training and Surrogate Outcomes: A Systematic Review of Definitions, Measures, and Effectiveness Claims.
BACKGROUND: "Proprioception training" is widely advocated in rehabilitation and sports practice, yet the term encompasses heterogeneous constructs, interventions, and outcomes. Many trials infer proprioceptive benefits from surrogate outcomes (balance, strength, or pain) rather than direct psychophysical indices. OBJECTIVE: We aimed to examine how proprioception is defined and measured, and how improvement is claimed, in randomized controlled trials. METHODS: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020, PubMed, Scopus, and Web of Science were searched to October 2025. Eligible randomized controlled trials explicitly described interventions as "proprioceptive" or "sensorimotor training" and reported at least one proprioceptive outcome, either direct (e.g., joint position reproduction, threshold to detection of passive motion, active movement extent discrimination) or indirect (e.g., sway, balance). Methodological quality was appraised with the Physiotherapy Evidence Database (PEDro) scale and risk of bias using the Cochrane Risk of Bias 2 (RoB 2) tool. RESULTS: Fifty-one randomized controlled trials (n = 2319) were included. Comparative synthesis showed that improvements inferred from surrogate outcomes were more frequent and often larger than improvements observed in direct psychophysical measures. Directly targeted practice, angle specific, attentionally demanding, and aligned with the measured proprioceptive submodality and task construct, produced the most consistent benefits in position-reproduction accuracy/error, movement-detection sensitivity, or discrimination performance, depending on the outcome assessed. In contrast, multimodal regimens (balance, strengthening, taping, manual therapy) commonly improved balance, pain, strength, or function without comparably consistent evidence of enhanced direct psychophysical proprioceptive function. CONCLUSIONS: Specific psychophysical components of proprioceptive function appear modifiable, but only when training explicitly targets the sensory construct measured. The field remains conceptually diffuse, with frequent conflation of sensorimotor performance and proprioception. Progress depends on defining proprioceptive submodalities a priori, privileging validated psychophysical outcomes over surrogate outcomes, and aligning intervention content with measurement to substantiate true perceptual learning rather than generic motor adaptation.
Journal Article↗
Opposing kinase signaling may underlie the inverse relationship between cancer and Alzheimer's disease.
Cancer and Alzheimer's disease (AD) are leading causes of mortality and exhibit an inverse relationship, where AD patients have reduced cancer risk and vice versa. However, the molecular basis of this relationship remains poorly understood. We reanalyzed published proteomic and phosphoproteomic datasets to investigate this relationship. Differentially abundant proteins were identified in lung adenocarcinoma and glioblastoma samples relative to controls and compared with proteins altered in AD brains, revealing 37 proteins with opposing abundance patterns. Protein-protein interaction and pathway analyses revealed enrichment in kinase signaling and phosphorylation pathways. Phosphoproteomic analysis identified 52 differentially phosphorylated sites with opposing patterns, while kinase-substrate enrichment analysis identified 44 kinases with opposing inferred activity profiles. Integration of kinase activity and phosphosite data identified 29 kinase-phosphosite pairs, including 4 prioritized pairs with opposing patterns relevant to both diseases. Across seven independent cancer cohorts, 17 of 20 statistically significant phosphosite-cohort comparisons (85%) were concordant with the discovery findings, supporting reproducibility of the prioritized phosphosites. Together, these findings highlight opposing kinase signaling as a prominent feature of the inverse relationship and suggest potential biomarkers and therapeutic targets. This study provides a novel systems-level framework for investigating inverse relationships, supported by an R Shiny application for data exploration (https://advscancer.shinyapps.io/advscancer/). SIGNIFICANCE: This study presents an integrated proteomic and phosphoproteomic framework for investigating the inverse relationship between cancer and Alzheimer's disease (AD). By integrating differential protein abundance, phosphosite phosphorylation, inferred kinase activity, and curated kinase-substrate relationships, we identified opposing signaling patterns and prioritized four kinase-phosphosite pairs. Independent evaluation across seven CPTAC cancer cohorts supported the reproducibility of the prioritized phosphosite patterns. These findings provide insight into molecular processes potentially associated with the inverse relationship between cancer and AD, identify candidate biomarkers and therapeutic targets, and demonstrate the value of systems-level, data-driven approaches for investigating shared and opposing disease processes.
Humans↗
Alternative genetic codes in bacteria and archaea identified with a fast k-mer-based algorithm.
The genetic code is conserved across all domains of life and is often described as universal. Nevertheless, many exceptions to the "universal" code have now been documented, most of these through manual or semiautomated inspection of highly conserved genes. Modern bioinformatics tools improved our ability to find alternative genetic codes but remain computationally expensive, preventing widespread use on thousands of new species identified by sequencing environmental samples. Here, I report a >100-fold accelerated method for inferring the genetic code directly from assembled genomes and apply it to thousands of previously uncharacterized assemblies from archaea and bacteria. I describe three candidate genetic code variations, one of which, an alternative genetic code used by a family of Asgard archaea, is a unique example of sense codon reassignments for this domain. Identifying genetic code variations is important for understanding evolution of the standard code and improving accuracy of protein databases and open reading frame identification.
Genetic Code↗