A note on the calculation of empirical P values from Monte Carlo procedures

Hepatic WDR23 proteostasis mediates insulin homeostasis by regulating insulin-degrading enzyme capacity

Maintaining insulin homeostasis is critical for cellular and organismal metabolism. In the liver, insulin is degraded by the activity of the insulin-degrading enzyme (IDE). Here, we establish a hepatic regulatory axis for IDE through WDR23-proteostasis. Wdr23KO mice have increased IDE expression, reduced circulating insulin, and defective insulin responses. Genetically engineered human cell models lacking WDR23 also increase IDE expression and display dysregulated phosphorylation of insulin signaling cascade proteins, IRS-1, AKT2, MAPK, FoxO, and mTOR, similar to cells treated with insulin, which can be mitigated by chemical inhibition of IDE. Mechanistically, the cytoprotective transcription factor NRF2, a direct target of WDR23-Cul4 proteostasis, mediates the enhanced transcriptional expression of IDE when WDR23 is ablated. Moreover, an analysis of human genetic variation in WDR23 across a large naturally aging human cohort in the US Health and Retirement Study reveals a significant association of WDR23 with altered hemoglobin A1C (HbA1c) levels in older adults, supporting the use of WDR23 as a new molecular determinant of metabolic health in humans.

Proper network randomization is key to assessing social balance

Social ties, either positive or negative, lead to signed network patterns, the subject of balance theory. For example, strong balance introduces cycles with even numbers of negative edges. The statistical significance of such patterns is routinely assessed by comparisons to null models. Yet, results in signed networks remain controversial. Here, we show that even if a network exhibits strong balance by construction, current null models can fail to identify it. Our results indicate that matching the signed degree preferences of the nodes is a critical step and so is the preservation of network topology in the null model. As a solution, we propose the STP null model, which integrates both constraints within a maximum entropy framework. STP randomization leads to qualitatively different results, with most social networks consistently demonstrating strong balance in three- and four-node patterns. On the basis our results, we present a potential wiring mechanism behind the observed signed patterns and outline further applications of STP randomization.

Links between melanoma germline risk loci, driver genes and comorbidities: insight from a tissue-specific multi-omic analysis

Genome-wide association studies (GWAS) have associated 76 loci with the risk of developing melanoma. However, understanding the molecular basis of such associations has remained a challenge because most of these loci are in non-coding regions of the genome. Here, we integrated data on epigenomic markers, three-dimensional (3D) genome organization, and expression quantitative trait loci (eQTL) from melanoma-relevant tissues and cell types to gain novel insights into the mechanisms underlying melanoma risk. This integrative approach revealed a total of 151 target genes, both near and far away from the risk loci in linear sequence, with known and novel roles in the etiology of melanoma. Using protein-protein interaction networks, we identified proteins that interact-directly or indirectly-with the products of the target genes. The interacting proteins were enriched for known melanoma driver genes. Further integration of these target genes into tissue-specific gene regulatory networks revealed patterns of gene regulation that connect melanoma to its comorbidities. Our study provides novel insights into the biological implications of genetic variants associated with melanoma risk.

Integrated annotation prioritizes metabolites with bioactivity in inflammatory bowel disease

Microbial biochemistry is central to the pathophysiology of inflammatory bowel diseases (IBD). Improved knowledge of microbial metabolites and their immunomodulatory roles is thus necessary for diagnosis and management. Here, we systematically analyzed the chemical, ecological, and epidemiological properties of ~82k metabolic features in 546 Integrative Human Microbiome Project (iHMP/HMP2) metabolomes, using a newly developed methodology for bioactive compound prioritization from microbial communities. This suggested >1000 metabolic features as potentially bioactive in IBD and associated ~43% of prevalent, unannotated features with at least one well-characterized metabolite, thereby providing initial information for further characterization of a significant portion of the fecal metabolome. Prioritized features included known IBD-linked chemical families such as bile acids and short-chain fatty acids, and less-explored bilirubin, polyamine, and vitamin derivatives, and other microbial products. One of these, nicotinamide riboside, reduced colitis scores in DSS-treated mice. The method, MACARRoN, is generalizable with the potential to improve microbial community characterization and provide therapeutic candidates.

County-Level Cardiac Rehabilitation and Broadband Availability: Opportunities for Hybrid Care in the United States

PURPOSE

Cardiac rehabilitation (CR) improves patient outcomes and quality of life and can be provided virtually through hybrid CR. However, little is known about CR availability in conjunction with broadband access, a requirement for hybrid CR. This study examined the intersection of CR and broadband availability at the county level, nationwide.

METHODS

Data were gathered and analyzed in 2022 from the 2019 American Community Survey, the Centers for Medicare & Medicaid Services, and the Federal Communications Commission. Spatially adaptive floating catchments were used to calculate county-level percent CR availability among Medicare fee-for-service beneficiaries. Counties were categorized: by CR availability, whether lowest (ie, CR deserts), medium, or highest; and by broadband availability, whether CR deserts with majority-available broadband, or dual deserts. Results were stratified by state. County-level characteristics were examined for statistical significance by CR availability category.

RESULTS

Almost half of US adults (n = 116 325 976, 47.2%) lived in CR desert counties (1691 counties). Among adults in CR desert counties, 96.8% were in CR deserts with majority-available broadband (112 626 906). By state, the percentage of the adult population living in CR desert counties ranged from 3.2% (New Hampshire) to 100% (Hawaii and Washington, DC). Statistically significant differences in county CR availability existed by race/ethnicity, education, and income.

CONCLUSIONS

Almost half of US adults live in CR deserts. Given that up to 97% of adults living in CR deserts may have broadband access, implementation of hybrid CR programs that include a telehealth component could expand CR availability to as many as 113 million US adults.

Patterns of recombination in snakes reveal a tug-of-war between PRDM9 and promoter-like features

In some mammals, notably humans, recombination occurs almost exclusively where the protein PRDM9 binds, whereas in vertebrates lacking an intact PRDM9, such as birds and canids, recombination rates are elevated near promoter-like features. To determine whether PRDM9 directs recombination in nonmammalian vertebrates, we focused on an exemplar species with a single, intact PRDM9 ortholog, the corn snake (Pantherophis guttatus). Analyzing historical recombination rates along the genome and crossovers in pedigrees, we found evidence that PRDM9 specifies the location of recombination events, but we also detected a separable effect of promoter-like features. These findings reveal that the uses of PRDM9 and promoter-like features need not be mutually exclusive and instead reflect a tug-of-war that is more even in some species than others.

Socioeconomic inequalities in early adulthood disrupt the immune transcriptomic landscape via upstream regulators

Disparities in socio-economic status (SES) predict many immune system-related diseases, and previous research documents relationships between SES and the immune cell transcriptome. Drawing on a bioinformatically-informed network approach, we situate these findings in a broader molecular framework by examining the upstream regulators of SES-associated transcriptional alterations. Data come from the National Longitudinal Study of Adolescent to Adult Health (Add Health), a nationally representative sample of 4543 adults in the United States. Results reveal a network-of differentially expressed genes, transcription factors, and protein neighbors of transcription factors-that shows widespread SES-related dysregulation of the immune system. Mediational models suggest that body mass index (BMI) plays a key role in accounting for many of these associations. Overall, the results reveal the central role of upstream regulators in socioeconomic differences in the molecular basis of immunity, which propagate to increase risk of chronic health conditions in later-life.

MTL neurons phase-lock to human hippocampal theta

Memory formation depends on neural activity across a network of regions, including the hippocampus and broader medial temporal lobe (MTL). Interactions between these regions have been studied indirectly using functional MRI, but the bases for interregional communication at a cellular level remain poorly understood. Here, we evaluate the hypothesis that oscillatory currents in the hippocampus synchronize the firing of neurons both within and outside the hippocampus. We recorded extracellular spikes from 1854 single- and multi-units simultaneously with hippocampal local field potentials (LFPs) in 28 neurosurgical patients who completed virtual navigation experiments. A majority of hippocampal neurons phase-locked to oscillations in the slow (2-4 Hz) or fast (6-10 Hz) theta bands, with a significant subset exhibiting nested slow theta × beta frequency (13-20 Hz) phase-locking. Outside of the hippocampus, phase-locking to hippocampal oscillations occurred only at theta frequencies and primarily among neurons in the entorhinal cortex and amygdala. Moreover, extrahippocampal neurons phase-locked to hippocampal theta even when theta did not appear locally. These results indicate that spike-time synchronization with hippocampal theta is a defining feature of neuronal activity in the hippocampus and structurally connected MTL regions. Theta phase-locking could mediate flexible communication with the hippocampus to influence the content and quality of memories.

Canonical transcriptional gene silencing may contribute to long-term heat response and recovery through MOM1

Plant canonical transcriptional gene silencing (TGS) is involved in epigenetic mechanisms that mediate genomic imprinting and the suppression of transposable elements (TEs). It has been recognised that long-term heat disrupts epigenetic silencing, with the ensuing activation of TEs. However, the physiological involvement of the TGS machinery under prolonged high temperatures has not yet been established. Here, we performed non-lethal extended periodic heat stress and recovery treatments on Arabidopsis thaliana lines mutated on key TGS factors, analysing transcriptomic changes of coding-protein genes and TEs. Plants bearing MET1, DRM2 and CMT3, and MOM1 mutated alleles showed novel transcriptional properties compatible with functionalities concerning the induction/repression of partially shared or private heat-triggered transcriptome networks. Certain observations supported the idea that some responses are based on thermal de-silencing. TEs transcriptional activation uncovered the interaction with specific epigenetic layers, which may play dedicated suppressing roles under determinate physiological conditions such as heat. Furthermore, physiological experimentation suggested that MOM1 is required to resume growth after stress. Our data thus provide initial evidence that at least one canonical TGS factor may contribute to plant acclimation and recovery from non-lethal long-term heat despite the stress-induced epigenetic disturbance.

Sporadic cases of chronic wasting disease in old moose - an epidemiological study

Transmissible spongiform encephalopathies or prion diseases comprise diseases with different levels of contagiousness under natural conditions. The hypothesis has been raised that the chronic wasting disease (CWD) cases detected in Nordic moose (Alces alces) may be less contagious, or not contagious between live animals under field conditions. This study aims to investigate the epidemiology of CWD cases detected in moose in Norway, Sweden and Finland using surveillance data from 2016 to 2022.In total, 18 CWD cases were detected in Nordic moose. All moose were positive for prion (PrPres) detection in the brain, but negative in lymph nodes, all were old (mean 16 years; range 12-20) and all except one, were female. Age appeared to be a strong risk factor, and the sex difference may be explained by few males reaching high age due to hunting targeting calves, yearlings and males.The cases were geographically scattered, distributed over 15 municipalities. However, three cases were detected in each of two areas, Selbu in Norway and Arjeplog-Arvidsjaur in Sweden. A Monte Carlo simulation approach was applied to investigate the likelihood of such clustering occurring by chance, given the assumption of a non-contagious disease. The empirical P-value for obtaining three cases in one Norwegian municipality was less than 0.05, indicating clustering. However, the moose in Selbu were affected by different CWD strains, and over a 6 year period with intensive surveillance, the apparent prevalence decreased, which would not be expected for an ongoing outbreak of CWD. Likewise, the three cases in Arjeplog-Arvidsjaur could also indicate clustering, but management practices promotes a larger proportion of old females and the detection of the first CWD case contributed to increased awareness and sampling.The results of our study show that the CWD cases detected so far in Nordic moose have a different epidemiology compared to CWD cases reported from North America and in Norwegian reindeer (Rangifer tarandus tarandus). The results support the hypothesis that these cases are less contagious or not contagious between live animals under field conditions. To enable differentiation from other types of CWD, we support the use of sporadic CWD (sCWD) among the names already in use.

Circular extrachromosomal DNA promotes tumor heterogeneity in high-risk medulloblastoma

Circular extrachromosomal DNA (ecDNA) in patient tumors is an important driver of oncogenic gene expression, evolution of drug resistance and poor patient outcomes. Applying computational methods for the detection and reconstruction of ecDNA across a retrospective cohort of 481 medulloblastoma tumors from 465 patients, we identify circular ecDNA in 82 patients (18%). Patients with ecDNA-positive medulloblastoma were more than twice as likely to relapse and three times as likely to die within 5 years of diagnosis. A subset of tumors harbored multiple ecDNA lineages, each containing distinct amplified oncogenes. Multimodal sequencing, imaging and CRISPR inhibition experiments in medulloblastoma models reveal intratumoral heterogeneity of ecDNA copy number per cell and frequent putative 'enhancer rewiring' events on ecDNA. This study reveals the frequency and diversity of ecDNA in medulloblastoma, stratified into molecular subgroups, and suggests copy number heterogeneity and enhancer rewiring as oncogenic features of ecDNA.

A neural code for time and space in the human brain

Time and space are primary dimensions of human experience. Separate lines of investigation have identified neural correlates of time and space, yet little is known about how these representations converge during self-guided experience. Here, 10 subjects with intracranially implanted microelectrodes play a timed, virtual navigation game featuring object search and retrieval tasks separated by fixed delays. Time cells and place cells activate in parallel during timed navigation intervals, whereas a separate time cell sequence spans inter-task delays. The prevalence, firing rates, and behavioral coding strengths of time cells and place cells are indistinguishable-yet time cells selectively remap between search and retrieval tasks, while place cell responses remain stable. Thus, the brain can represent time and space as overlapping but dissociable dimensions. Time cells and place cells may constitute a biological basis for the cognitive map of spatiotemporal context onto which memories are written.

Spatial metatranscriptomics resolves host-bacteria-fungi interactomes

The interactions of microorganisms among themselves and with their multicellular host take place at the microscale, forming complex networks and spatial patterns. Existing technology does not allow the simultaneous investigation of spatial interactions between a host and the multitude of its colonizing microorganisms, which limits our understanding of host-microorganism interactions within a plant or animal tissue. Here we present spatial metatranscriptomics (SmT), a sequencing-based approach that leverages 16S/18S/ITS/poly-d(T) multimodal arrays for simultaneous host transcriptome- and microbiome-wide characterization of tissues at 55-µm resolution. We showcase SmT in outdoor-grown Arabidopsis thaliana leaves as a model system, and find tissue-scale bacterial and fungal hotspots. By network analysis, we study inter- and intrakingdom spatial interactions among microorganisms, as well as the host response to microbial hotspots. SmT provides an approach for answering fundamental questions on host-microbiome interplay.

Exploring pathway interactions to detect molecular mechanisms of disease: 22q11.2 deletion syndrome

BACKGROUND

22q11.2 Deletion Syndrome (22q11DS) is a genetic disorder characterized by the deletion of adjacent genes at a location specified as q11.2 of chromosome 22, resulting in an array of clinical phenotypes including autistic spectrum disorder, schizophrenia, congenital heart defects, and immune deficiency. Many characteristics of the disorder are known, such as the phenotypic variability of the disease and the biological processes associated with it; however, the exact and systemic molecular mechanisms between the deleted area and its resulting clinical phenotypic expression, for example that of neuropsychiatric diseases, are not yet fully understood.

RESULTS

Using previously published transcriptomics data (GEO:GSE59216), we constructed two datasets: one set compares 22q11DS patients experiencing neuropsychiatric diseases versus healthy controls, and the other set 22q11DS patients without neuropsychiatric diseases versus healthy controls. We modified and applied the pathway interaction method, originally proposed by Kelder et al. (2011), on a network created using the WikiPathways pathway repository and the STRING protein-protein interaction database. We identified genes and biological processes that were exclusively associated with the development of neuropsychiatric diseases among the 22q11DS patients. Compared with the 22q11DS patients without neuropsychiatric diseases, patients experiencing neuropsychiatric diseases showed significant overrepresentation of regulated genes involving the natural killer cell function and the PI3K/Akt signalling pathway, with affected genes being closely associated with downregulation of CRK like proto-oncogene adaptor protein. Both the pathway interaction and the pathway overrepresentation analysis observed the disruption of the same biological processes, even though the exact lists of genes collected by the two methods were different.

CONCLUSIONS

Using the pathway interaction method, we were able to detect a molecular network that could possibly explain the development of neuropsychiatric diseases among the 22q11DS patients. This way, our method was able to complement the pathway overrepresentation analysis, by filling the knowledge gaps on how the affected pathways are linked to the original deletion on chromosome 22. We expect our pathway interaction method could be used for problems with similar contexts, where complex genetic mechanisms need to be identified to explain the resulting phenotypic plasticity.

Socioeconomic inequalities in young adulthood disrupt the immune transcriptomic landscape via upstream regulators

Disparities in socio-economic status (SES) predict many immune system-related diseases, and previous research documents relationships between SES and the immune cell transcriptome. Drawing on a bioinformatically-informed network approach, we situate these findings in a broader molecular framework by examining the upstream regulators of SES-associated transcriptional alterations. Data come from the National Longitudinal Study of Adolescent to Adult Health (Add Health), a nationally representative sample of 4,543 adults in the United States. Results reveal a network-of differentially-expressed genes, transcription factors, and protein neighbors of transcription factors- that shows widespread SES-related dysregulation of the immune system. Mediational models suggest that body mass index plays a key role in accounting for many of these associations. Overall, the results reveal the central role of upstream regulators in socioeconomic differences in the molecular basis of immunity, which propagate to increase risk of chronic health conditions in later-life.

The Subgroup Imperative: Chest Radiograph Classifier Generalization Gaps in Patient, Setting, and Pathology Subgroups

Purpose

To externally test four chest radiograph classifiers on a large, diverse, real-world dataset with robust subgroup analysis.

Materials and Methods

In this retrospective study, adult posteroanterior chest radiographs (January 2016-December 2020) and associated radiology reports from Trillium Health Partners in Ontario, Canada, were extracted and de-identified. An open-source natural language processing tool was locally validated and used to generate ground truth labels for the 197 540-image dataset based on the associated radiology report. Four classifiers generated predictions on each chest radiograph. Performance was evaluated using accuracy, positive predictive value, negative predictive value, sensitivity, specificity, F1 score, and Matthews correlation coefficient for the overall dataset and for patient, setting, and pathology subgroups.

Results

Classifiers demonstrated 68%-77% accuracy, 64%-75% sensitivity, and 82%-94% specificity on the external testing dataset. Algorithms showed decreased sensitivity for solitary findings (43%-65%), patients younger than 40 years (27%-39%), and patients in the emergency department (38%-60%) and decreased specificity on normal chest radiographs with support devices (59%-85%). Differences in sex and ancestry represented movements along an algorithm's receiver operating characteristic curve.

Conclusion

Performance of deep learning chest radiograph classifiers was subject to patient, setting, and pathology factors, demonstrating that subgroup analysis is necessary to inform implementation and monitor ongoing performance to ensure optimal quality, safety, and equity.Keywords: Conventional Radiography, Thorax, Ethics, Supervised Learning, Convolutional Neural Network (CNN), Machine Learning Algorithms Supplemental material is available for this article. © RSNA, 2023See also the commentary by Huisman and Hannink in this issue.

Maternal and neonatal blood vitamin D status and neurodevelopment at 24 months of age: a prospective birth cohort study

BACKGROUND

This study aimed to explore the relationship of 25-hydroxyvitamin D [25(OH)D] in three trimesters and at birth with neurodevelopment at 24 months of age.

METHODS

From 2013 to 2016, pregnant women from the Shanghai Birth Cohort in China were recruited for the study. Altogether, 649 mother-infant pairs were included. Serum 25(OH)D was measured with mass spectrometry in three trimesters, and cord blood was divided into deficiency (< 20 and < 12 ng/mL, respectively), insufficiency (20-30 and 12-20 ng/mL, respectively), and sufficiency (≥ 30 and ≥ 20 ng/mL, respectively). Bayley-III scale was used to assess cognitive, language, motor, social-emotional, and adaptive behavior development at 24 months of age. The Bayley-III scores were grouped into quartiles, and scores within the lowest quartile were defined as suboptimal development.

RESULTS

After adjusting for confounding factors, cord blood 25(OH)D in the sufficient group was positively correlated with cognitive [β = 11.43, 95% confidence interval (CI) = 5.65-17.22], language (β = 6.01, 95% CI = 1.67-10.3), and motor scores (β = 6.43, 95% CI = 1.73-11.1); cord blood 25(OH)D in the insufficient group was also positively correlated with cognitive scores (β = 9.42, 95% CI = 3.74-15.11). Additionally, sufficient vitamin D status in the four periods and persistent 25(OH)D ≥ 30 ng/mL throughout pregnancy were associated with a lower risk of suboptimal cognitive development in adjusted models, although the effects were attenuated after applying the false discovery rate adjustment.

CONCLUSIONS

Cord blood 25(OH)D ≥ 12 ng/mL has a significant positive association with cognitive, language, and motor development at 24 months of age. Sufficient vitamin D status in pregnancy might be a protective factor for suboptimal neurocognition development at 24 months of age.

Social odor discrimination and its enhancement by associative learning in the hippocampal CA2 region

Although the hippocampus is crucial for social memory, how social sensory information is combined with contextual information to form episodic social memories remains unknown. Here, we investigated the mechanisms for social sensory information processing using two-photon calcium imaging from hippocampal CA2 pyramidal neurons (PNs)-which are crucial for social memory-in awake head-fixed mice exposed to social and non-social odors. We found that CA2 PNs represent social odors of individual conspecifics and that these representations are refined during associative social odor-reward learning to enhance the discrimination of rewarded compared with unrewarded odors. Moreover, the structure of the CA2 PN population activity enables CA2 to generalize along categories of rewarded versus unrewarded and social versus non-social odor stimuli. Finally, we found that CA2 is important for learning social but not non-social odor-reward associations. These properties of CA2 odor representations provide a likely substrate for the encoding of episodic social memory.

Single-cell transcriptomic profiling of human pancreatic islets reveals genes responsive to glucose exposure over 24 hours

Disruption of pancreatic islet function and glucose homeostasis can lead to the development of sustained hyperglycemia, beta cell glucotoxicity, and ultimately type 2 diabetes (T2D). In this study, we sought to explore the effects of hyperglycemia on human pancreatic islet (HPI) gene expression by exposing HPIs from two donors to low (2.8mM) and high (15.0mM) glucose concentrations over 24 hours, assaying the transcriptome at seven time points using single-cell RNA sequencing (scRNA-seq). We modeled time as both a discrete and continuous variable to determine momentary and longitudinal changes in transcription associated with islet time in culture or glucose exposure. Across all cell types, we identified 1,528 genes associated with time, 1,185 genes associated with glucose exposure, and 845 genes associated with interaction effects between time and glucose. We clustered differentially expressed genes across cell types and found 347 modules of genes with similar expression patterns across time and glucose conditions, including two beta cell modules enriched in genes associated with T2D. Finally, by integrating genomic features from this study and genetic summary statistics for T2D and related traits, we nominate 363 candidate effector genes that may underlie genetic associations for T2D and related traits.

Patterns of recombination in snakes reveal a tug of war between PRDM9 and promoter-like features

In vertebrates, there are two known mechanisms by which meiotic recombination is directed to the genome: in humans, mice, and other mammals, recombination occurs almost exclusively where the protein PRDM9 binds, while in species lacking an intact PRDM9, such as birds and canids, recombination rates are elevated near promoter-like features. To test if PRDM9 also directs recombination in non-mammalian vertebrates, we focused on an exemplar species, the corn snake (Pantherophis guttatus). Unlike birds, this species possesses a single, intact PRDM9 ortholog. By inferring historical recombination rates along the genome from patterns of linkage disequilibrium and identifying crossovers in pedigrees, we found that PRDM9 specifies the location of recombination events outside of mammals. However, we also detected an independent effect of promoter-like features on recombination, which is more pronounced on macro- than microchromosomes. Thus, our findings reveal that the uses of PRDM9 and promoter-like features are not mutually-exclusive, and instead reflect a tug of war, which varies in strength along the genome and is more lopsided in some species than others.

First-in-human prediction of chronic pain state using intracranial neural biomarkers

Chronic pain syndromes are often refractory to treatment and cause substantial suffering and disability. Pain severity is often measured through subjective report, while objective biomarkers that may guide diagnosis and treatment are lacking. Also, which brain activity underlies chronic pain on clinically relevant timescales, or how this relates to acute pain, remains unclear. Here four individuals with refractory neuropathic pain were implanted with chronic intracranial electrodes in the anterior cingulate cortex and orbitofrontal cortex (OFC). Participants reported pain metrics coincident with ambulatory, direct neural recordings obtained multiple times daily over months. We successfully predicted intraindividual chronic pain severity scores from neural activity with high sensitivity using machine learning methods. Chronic pain decoding relied on sustained power changes from the OFC, which tended to differ from transient patterns of activity associated with acute, evoked pain states during a task. Thus, intracranial OFC signals can be used to predict spontaneous, chronic pain state in patients.

Widening the landscape of transcriptional regulation of green algal photoprotection

Availability of light and CO₂, substrates of microalgae photosynthesis, is frequently far from optimal. Microalgae activate photoprotection under strong light, to prevent oxidative damage, and the CO₂ Concentrating Mechanism (CCM) under low CO₂, to raise intracellular CO₂ levels. The two processes are interconnected; yet, the underlying transcriptional regulators remain largely unknown. Employing a large transcriptomic data compendium of Chlamydomonas reinhardtii's responses to different light and carbon supply, we reconstruct a consensus genome-scale gene regulatory network from complementary inference approaches and use it to elucidate transcriptional regulators of photoprotection. We show that the CCM regulator LCR1 also controls photoprotection, and that QER7, a Squamosa Binding Protein, suppresses photoprotection- and CCM-gene expression under the control of the blue light photoreceptor Phototropin. By demonstrating the existence of regulatory hubs that channel light- and CO₂-mediated signals into a common response, our study provides an accessible resource to dissect gene expression regulation in this microalga.

Going digital in Germany: An exploration of physicians’ attitudes towards the introduction of electronic prescriptions – a mixed methods approach

PURPOSE

Digitalization of medical prescriptions is a core element for the digitalization of healthcare. While some countries have introduced electronic prescriptions over 20 years ago and nearly reached 100 % penetration, physicians in Germany have only been able to use electronic prescriptions since mid-2021 and currently only 0.1 % of prescriptions are transmitted electronically. This study investigates German physicians' viewpoint towards electronic prescriptions as a potential reason for the low penetration and investigates levers to drive adoption.

BASIC PROCEDURES

We conducted a two-stage sequential mixed methods study consisting of semi-structured interviews followed by an online survey among 1136 physicians testing the main dimensions of the Unified Theory of Acceptance and Use of Technology model.

MAIN FINDINGS

Our initial interviews suggested that there was a high technology acceptance by physicians, but due to technical barriers, they were not able to use the system, explaining the low penetration. However, with the larger sample size of the survey, we identified, that while physicians see barriers for introducing electronic prescriptions, such as unclear cost reimbursement or lack of time to deal with the implementation, the majority believes these can be overcome within twelve months. Furthermore, we found that only one third of physicians is in favor of replacing paper-based prescriptions with electronic prescriptions and most physicians considers it unlikely that they will issue more than half of their prescriptions electronically within the next twelve month. Additionally, respondents perceived limited usefulness and expect high effort for using electronic prescriptions.

PRINCIPAL CONCLUSION

Low electronic prescription penetration in Germany seems to be driven by low technology acceptance, not technical barriers. This can be linked to low perceived usefulness, high effort expectancy and low perceived patient demand. Improving technical stability, system functionality and increasing physicians' level of information were seen as main levers to drive electronic prescription adoption.

Evolutionary characterization of lung adenocarcinoma morphology in TRACERx

Lung adenocarcinomas (LUADs) display a broad histological spectrum from low-grade lepidic tumors through to mid-grade acinar and papillary and high-grade solid, cribriform and micropapillary tumors. How morphology reflects tumor evolution and disease progression is poorly understood. Whole-exome sequencing data generated from 805 primary tumor regions and 121 paired metastatic samples across 248 LUADs from the TRACERx 421 cohort, together with RNA-sequencing data from 463 primary tumor regions, were integrated with detailed whole-tumor and regional histopathological analysis. Tumors with predominantly high-grade patterns showed increased chromosomal complexity, with higher burden of loss of heterozygosity and subclonal somatic copy number alterations. Individual regions in predominantly high-grade pattern tumors exhibited higher proliferation and lower clonal diversity, potentially reflecting large recent subclonal expansions. Co-occurrence of truncal loss of chromosomes 3p and 3q was enriched in predominantly low-/mid-grade tumors, while purely undifferentiated solid-pattern tumors had a higher frequency of truncal arm or focal 3q gains and SMARCA4 gene alterations compared with mixed-pattern tumors with a solid component, suggesting distinct evolutionary trajectories. Clonal evolution analysis revealed that tumors tend to evolve toward higher-grade patterns. The presence of micropapillary pattern and 'tumor spread through air spaces' were associated with intrathoracic recurrence, in contrast to the presence of solid/cribriform patterns, necrosis and preoperative circulating tumor DNA detection, which were associated with extra-thoracic recurrence. These data provide insights into the relationship between LUAD morphology, the underlying evolutionary genomic landscape, and clinical and anatomical relapse risk.

Analysis of endometrial carcinoma TCGA reveals differences in DNA methylation in tumors from Black and White women

OBJECTIVE

Racial disparities exist in cancer patients both in incidence and death rates. In endometrial cancer, Black patients are reported to have higher incidence of aggressive endometrial cancer subtypes and higher death rates than White women. To date, diagnostic and prognostic biomarkers associated with race-specific methylation driven genes have yet to be identified. The objective of this study was to explore DNA methylation patterns in endometrial tumor samples from White and Black women.

METHODS

Differentially methylated CpGs (DMCs) and differentially methylated regions (DMRs) were identified in White tumor samples compared to Black tumor samples using Endometrial Carcinoma (EC) methylation and clinical data from The Cancer Genome Atlas (TCGA). Survival analysis was performed using survival R package and results were visualized using Kaplan-Meier plots. To access the correlation between changes in methylation and gene expression, we downloaded raw RNA-sequencing by Expectation-Maximization (RSEM) counts data from The Cancer Genome Atlas (TCGA) using TCGABiolinks package (v2.18.0).

RESULTS

Our analysis revealed 704 differentially methylated CpGs in tumors from Black and White women. These differentially methylated genes showed strong negative correlation with gene expression and statistically significant enrichment in regulatory regions such as DNase I hypersensitivity sites (DHSs) and transcription factor binding sites (TFBSs). Increased variability in methylation occurred in genes such as the insulin signaling pathway in Black tumor samples.

CONCLUSION

By using two independent statistical method based on means (DMR, DMCs) and variances (DVCs) on the endometrial carcinoma TCGA data, we showed that endometrial tumors from Black women are hypomethylated and more hypervariable than tumors from White women. In-depth investigation of these methylation driven markers can aid in successful management of endometrial cancer disparities and improved overall survival in Black and White populations.

Decreasing mutant ATXN1 nuclear localization improves a spectrum of SCA1-like phenotypes and brain region transcriptomic profiles

Spinocerebellar ataxia type 1 (SCA1) is a dominant trinucleotide repeat neurodegenerative disease characterized by motor dysfunction, cognitive impairment, and premature death. Degeneration of cerebellar Purkinje cells is a frequent and prominent pathological feature of SCA1. We previously showed that transport of ATXN1 to Purkinje cell nuclei is required for pathology, where mutant ATXN1 alters transcription. To examine the role of ATXN1 nuclear localization broadly in SCA1-like disease pathogenesis, CRISPR-Cas9 was used to develop a mouse with an amino acid alteration (K772T) in the nuclear localization sequence of the expanded ATXN1 protein. Characterization of these mice indicates that proper nuclear localization of mutant ATXN1 contributes to many disease-like phenotypes including motor dysfunction, cognitive deficits, and premature lethality. RNA sequencing analysis of genes with expression corrected to WT levels in Atxn1175QK772T/2Q mice indicates that transcriptomic aspects of SCA1 pathogenesis differ between the cerebellum, brainstem, cerebral cortex, hippocampus, and striatum.

Disruption of the ATXN1-CIC complex reveals the role of additional nuclear ATXN1 interactors in spinocerebellar ataxia type 1

Spinocerebellar ataxia type 1 (SCA1) is a paradigmatic neurodegenerative disease in that it is caused by a mutation in a broadly expressed protein, ATXN1; however, only select populations of cells degenerate. The interaction of polyglutamine-expanded ATXN1 with the transcriptional repressor CIC drives cerebellar Purkinje cell pathogenesis; however, the importance of this interaction in other vulnerable cells remains unknown. Here, we mutated the 154Q knockin allele of Atxn1^154Q/2Q mice to prevent the ATXN1-CIC interaction globally. This normalized genome-wide CIC binding; however, it only partially corrected transcriptional and behavioral phenotypes, suggesting the involvement of additional factors in disease pathogenesis. Using unbiased proteomics, we identified three ATXN1-interacting transcription factors: RFX1, ZBTB5, and ZKSCAN1. We observed altered expression of RFX1 and ZKSCAN1 target genes in SCA1 mice and patient-derived iNeurons, highlighting their potential contributions to disease. Together, these data underscore the complexity of mechanisms driving cellular vulnerability in SCA1.

PRSet: Pathway-based polygenic risk score analyses and software

Polygenic risk scores (PRSs) have been among the leading advances in biomedicine in recent years. As a proxy of genetic liability, PRSs are utilised across multiple fields and applications. While numerous statistical and machine learning methods have been developed to optimise their predictive accuracy, these typically distil genetic liability to a single number based on aggregation of an individual's genome-wide risk alleles. This results in a key loss of information about an individual's genetic profile, which could be critical given the functional sub-structure of the genome and the heterogeneity of complex disease. In this manuscript, we introduce a 'pathway polygenic' paradigm of disease risk, in which multiple genetic liabilities underlie complex diseases, rather than a single genome-wide liability. We describe a method and accompanying software, PRSet, for computing and analysing pathway-based PRSs, in which polygenic scores are calculated across genomic pathways for each individual. We evaluate the potential of pathway PRSs in two distinct ways, creating two major sections: (1) In the first section, we benchmark PRSet as a pathway enrichment tool, evaluating its capacity to capture GWAS signal in pathways. We find that for target sample sizes of >10,000 individuals, pathway PRSs have similar power for evaluating pathway enrichment as leading methods MAGMA and LD score regression, with the distinct advantage of providing individual-level estimates of genetic liability for each pathway -opening up a range of pathway-based PRS applications, (2) In the second section, we evaluate the performance of pathway PRSs for disease stratification. We show that using a supervised disease stratification approach, pathway PRSs (computed by PRSet) outperform two standard genome-wide PRSs (computed by C+T and lassosum) for classifying disease subtypes in 20 of 21 scenarios tested. As the definition and functional annotation of pathways becomes increasingly refined, we expect pathway PRSs to offer key insights into the heterogeneity of complex disease and treatment response, to generate biologically tractable therapeutic targets from polygenic signal, and, ultimately, to provide a powerful path to precision medicine.

Klarigi: Characteristic explanations for semantic biomedical data

Annotation of biomedical entities with ontology classes provides for formal semantic analysis and mobilisation of background knowledge in determining their relationships. To date, enrichment analysis has been routinely employed to identify classes that are over-represented in annotations across sets of groups, such as biosample gene expression profiles or patient phenotypes, and is useful for a range of tasks including differential diagnosis and causative variant prioritisation. These approaches, however, usually consider only univariate relationships, make limited use of the semantic features of ontologies, and provide limited information and evaluation of the explanatory power of both singular and grouped candidate classes. Moreover, they are not designed to solve the problem of deriving cohesive, characteristic, and discriminatory sets of classes for entity groups. We have developed a new tool, called Klarigi, which introduces multiple scoring heuristics for identification of classes that are both compositional and discriminatory for groups of entities annotated with ontology classes. The tool includes a novel algorithm for derivation of multivariable semantic explanations for entity groups, makes use of semantic inference through live use of an ontology reasoner, and includes a classification method for identifying the discriminatory power of candidate sets, in addition to significance testing apposite to traditional enrichment approaches. We describe the design and implementation of Klarigi, including its scoring and explanation determination methods, and evaluate its use in application to two test cases with clinical significance, comparing and contrasting methods and results with literature-based and enrichment analysis methods. We demonstrate that Klarigi produces characteristic and discriminatory explanations for groups of biomedical entities in two settings. We also show that these explanations recapitulate and extend the knowledge held in existing biomedical databases and literature for several diseases. We conclude that Klarigi provides a distinct and valuable perspective on biomedical datasets when compared with traditional enrichment methods, and therefore constitutes a new method by which biomedical datasets can be explored, contributing to improved insight into semantic data.

Ants combine systematic meandering and correlated random walks when searching for unknown resources

Animal search movements are typically assumed to be mostly random walks, although non-random elements may be widespread. We tracked ants (Temnothorax rugatulus) in a large empty arena, resulting in almost 5 km of trajectories. We tested for meandering by comparing the turn autocorrelations for empirical ant tracks and simulated, realistic Correlated Random Walks. We found that 78% of ants show significant negative autocorrelation around 10 mm (3 body lengths). This means that turns in one direction are likely followed by turns in the opposite direction after this distance. This meandering likely makes the search more efficient, as it allows ants to avoid crossing their own paths while staying close to the nest, avoiding return-travel time. Combining systematic search with stochastic elements may make the strategy less vulnerable to directional inaccuracies. This study is the first to find evidence for efficient search by regular meandering in a freely searching animal.

Selection of p53 pathway in adaptive evolution and reproductive isolation in incipient sympatric speciation of Drosophila at Evolution Canyon

Sympatric speciation (SS) refers to the origin of new species within a freely breeding population. The ‘Evolution Canyon’ (EC) in Israel is a natural microsite model for SS of species across phylogenies from viruses and bacteria to mammals, adapting to, and speciating in, interslope microclimates. The cosmopolitan Drosophila melanogaster at EC I, Mount Carmel, is undergoing incipient SS in response to sharply divergent interslope microclimate stresses, including solar radiation, temperature, humidity and pathogenicity. We demonstrated here a selective interslope divergence of single nucleotide polymorphism (SNP) distribution in the Drosophila p53 pathway. This involves a total of 71 genes, which are associated with DNA repair, heat response, and fungal and bacterial resistant pathways. This distribution pattern links the previously observed thermotolerance and ageing divergence of D. melanogaster between the opposite canyon slopes: the south-facing slope (SFS, or African slope: tropical, savannoid and dry) and the abutting north-facing slope (NFS, or European slope; temperate, forested, cool and humid). The genes with interslope-significant differential SNPs link the p53 pathway with pathways related to the responses to microclimates through protein-protein interaction. Moreover, for the first time we provide evidence that the p53 pathway is linked to reproductive isolation, and is thus actively participating in incipient SS of D. melanogaster. This is the first demonstration of a link between the p53 pathway and reproductive isolation, thereby contributing to adaptive incipient sympatric speciation.

Algebra, Geometry and Topology of ERK Kinetics

The MEK/ERK signalling pathway is involved in cell division, cell specialisation, survival and cell death (Shaul and Seger in Biochim Biophys Acta (BBA)-Mol Cell Res 1773(8):1213–1226, 2007). Here we study a polynomial dynamical system describing the dynamics of MEK/ERK proposed by Yeung et al. (Curr Biol 2019, 10.1016/j.cub.2019.12.052) with their experimental setup, data and known biological information. The experimental dataset is a time-course of ERK measurements in different phosphorylation states following activation of either wild-type MEK or MEK mutations associated with cancer or developmental defects. We demonstrate how methods from computational algebraic geometry, differential algebra, Bayesian statistics and computational algebraic topology can inform the model reduction, identification and parameter inference of MEK variants, respectively. Throughout, we show how this algebraic viewpoint offers a rigorous and systematic analysis of such models.

Identification of genetic mechanisms for tissue-specific genetic effects based on CRISPR screens

A major challenge in genetic studies of complex diseases is to determine how the action of risk genes is restricted to a tissue or cell type. Here, we investigate tissue specificity of gene action using CRISPR screens from 786 cancer cell lines originating from 24 tissues. We find that the expression pattern of the gene across tissues explains only a minority of cases of tissue-specificity (9%), while gene amplification and the expression levels of paralogs account for 39.5% and 15.5%, respectively. In addition, the transfer of small molecules to mutant cells explains tissue-specific gene action in blood. The tissue-specific genes we found are not specific just for human cancer cell lines: we found that the tissue-specific genes are intolerant to functional mutations in the human population and are associated with human diseases more than genes that are essential across all cell types. Our findings offer important insights into genetic mechanisms for tissue specificity of human diseases.

Frequency and Longitudinal Course of Motor Signs in Genetic Frontotemporal Dementia

BACKGROUND AND OBJECTIVES

Frontotemporal dementia (FTD) is a highly heritable disorder. The majority of genetic cases are caused by autosomal dominant pathogenic variants in the chromosome 9 open reading frame 72 (c9orf72), progranulin (GRN), and microtubule-associated protein tau (MAPT) gene. As motor disorders are increasingly recognized as part of the clinical spectrum, the current study aimed to describe motor phenotypes caused by genetic FTD, quantify their temporal association, and investigate their regional association with brain atrophy.

METHODS

We analyzed baseline visit data of known carriers of a pathogenic variant in the c9orf72, GRN, or MAPT gene from the Genetic Frontotemporal Dementia Initiative cohort study. Principal component analysis with varimax rotation was performed to identify motor sign clusters that were compared with respect to frequency and severity between groups. Associations with cross-sectional atrophy patterns were determined using voxel-wise regression. We applied linear mixed effects models to assess whether groups differed in the association between motor signs and estimated time to symptom onset.

RESULTS

A total of 322 pathogenic variant carriers were included in the analysis: 122 c9orf72 (79 presymptomatic), 143 GRN (112 presymptomatic), and 57 MAPT (43 presymptomatic) pathogenic variant carriers. Principal component analysis revealed 5 motor clusters, which we call progressive supranuclear palsy (PSP)-like, bulbar amyotrophic lateral sclerosis (ALS)-like, mixed/ALS-like, Parkinson disease (PD) like, and corticobasal syndrome-like motor phenotypes. There was no significant group difference in the frequency of signs of different motor phenotypes. However, mixed/ALS-like motor signs were most frequent, followed by PD-like motor signs. Although the PSP-like phenotype was associated with mesencephalic atrophy, the mixed/ALS-like phenotype was associated with motor cortex and corticospinal tract atrophy. The PD-like phenotype was associated with widespread cortical and subcortical atrophy. Estimated time to onset, genetic group and their interaction influenced motor signs. In c9orf72 pathogenic variant carriers, motor signs could be detected up to 25 years before expected symptom onset.

DISCUSSION

These results indicate the presence of multiple natural clusters of motor signs in genetic FTD, each correlated with specific atrophy patterns. Their motor severity depends on time and the affected gene. These clinicogenetic associations can guide diagnostic evaluations and the design of clinical trials for new disease-modifying and preventive treatments.

Detecting drug-drug interactions between therapies for COVID-19 and concomitant medications through the FDA adverse event reporting system

Background: COVID-19 patients with underlying medical conditions are vulnerable to drug-drug interactions (DDI) due to the use of multiple medications. We conducted a discovery-driven data analysis to identify potential DDIs and associated adverse events (AEs) in COVID-19 patients from the FDA Adverse Event Reporting System (FAERS), a source of post-market drug safety.

Materials and Methods: We investigated 18,589 COVID-19 AEs reported in the FAERS database between 2020 and 2021. We applied multivariate logistic regression to account for potential confounding factors, including age, gender, and the number of unique drug exposures. The significance of the DDIs was determined using both additive and multiplicative measures of interaction. We compared our findings with the Liverpool database and conducted a Monte Carlo simulation to validate the identified DDIs.

Results: Out of 11,337 COVID-19 drug-Co-medication-AE combinations investigated, our methods identified 424 signals statistically significant, covering 176 drug-drug pairs, composed of 13 COVID-19 drugs and 60 co-medications. Out of the 176 drug-drug pairs, 20 were found to exist in the Liverpool database. The empirical p-value obtained based on 1,000 Monte Carlo simulations was less than 0.001. Remdesivir was discovered to interact with the largest number of concomitant drugs (41). Hydroxychloroquine was detected to be associated with most AEs (39). Furthermore, we identified 323 gender- and 254 age-specific DDI signals.

Conclusion: The results, particularly those not found in the Liverpool database, suggest a subsequent need for further pharmacoepidemiology and/or pharmacology studies.

Looking for compensation at multiple scales in a wetland bird community

Compensatory dynamics, during which community composition shifts despite a near‐constant total community size, are usually rare: Synchronous dynamics prevail in natural communities. This is a puzzle for ecologists, because of the key role of compensation in explaining the relation between biodiversity and ecosystem functioning. However, most studies so far have considered compensation in either plants or planktonic organisms, so that evidence for the generality of such synchrony is limited. Here, we extend analyses of community‐level synchrony to wetland birds. We analyze a 35‐year monthly survey of a community where we suspected that compensation might occur due to potential competition and changes in water levels, favoring birds with different habitat preferences. We perform both year‐to‐year analyses by season, using a compensation/synchrony index, and multiscale analyses using a wavelet‐based measure, which allows for both scale‐ and time‐dependence. We analyze synchrony both within and between guilds, with guilds defined either as tightknit phylogenetic groups or as larger functional groups. We find that abundance and biomass compensation are rare, likely due to the synchronizing influence of climate (and other drivers) on birds, even after considering several temporal scales of covariation (during either cold or warm seasons, above or below the annual scale). Negative covariation in abundance at the guild or community level did only appear at the scale of a few months or several years. We also found that synchrony varies with taxonomic and functional scale: The rare cases where compensation appeared consistently in year‐to‐year analyses were between rather than within functional groups. Our results suggest that abundance compensation may have more potential to emerge between broad functional groups rather than between species, and at relatively long temporal scales (multiple years for vertebrates), above that of the dominant synchronizing driver.

Imaging versus electrographic connectivity in human mood-related fronto-temporal networks

BACKGROUND

The efficacy of psychiatric DBS is thought to be driven by the connectivity of stimulation targets with mood-relevant fronto-temporal networks, which is typically evaluated using diffusion-weighted tractography.

OBJECTIVE

Leverage intracranial electrophysiology recordings to better predict the circuit-wide effects of neuromodulation to white matter targets. We hypothesize strong convergence between tractography-predicted structural connectivity and stimulation-induced electrophysiological responses.

METHODS

Evoked potentials were elicited by single-pulse stimulation to two common DBS targets for treatment-resistant depression - the subcallosal cingulate (SCC) and ventral capsule/ventral striatum (VCVS) - in two patients undergoing DBS with stereo-electroencephalographic (sEEG) monitoring. Evoked potentials were compared with predicted structural connectivity between DBS leads and sEEG contacts using probabilistic, patient-specific diffusion-weighted tractography.

RESULTS

Evoked potentials and tractography showed strong convergence in both patients in orbitofrontal, ventromedial prefrontal, and lateral prefrontal cortices for both SCC and VCVS stimulation targets. Low convergence was found in anterior cingulate (ACC), where tractography predicted structural connectivity from SCC targets but produced no evoked potentials during SCC stimulation. Further, tractography predicted no connectivity to ACC from VCVS targets, but VCVS stimulation produced robust evoked potentials.

CONCLUSION

The two connectivity methods showed significant convergence, but important differences emerged with respect to the ability of tractography to predict electrophysiological connectivity between SCC and VCVS to regions of the mood-related network. This multimodal approach raises intriguing implications for the use of tractography in surgical targeting and provides new data to enhance our understanding of the network-wide effects of neuromodulation.

Elastic network modeling of cellular networks unveils sensor and effector genes that control information flow

The high-level organization of the cell is embedded in indirect relationships that connect distinct cellular processes. Existing computational approaches for detecting indirect relationships between genes typically consist of propagating abstract information through network representations of the cell. However, the selection of genes to serve as the source of propagation is inherently biased by prior knowledge. Here, we sought to derive an unbiased view of the high-level organization of the cell by identifying the genes that propagate and receive information most effectively in the cell, and the indirect relationships between these genes. To this aim, we adapted a perturbation-response scanning strategy initially developed for identifying allosteric interactions within proteins. We deployed this strategy onto an elastic network model of the yeast genetic interaction profile similarity network. This network revealed a superior propensity for information propagation relative to simulated networks with similar topology. Perturbation-response scanning identified the major distributors and receivers of information in the network, named effector and sensor genes, respectively. Effectors formed dense clusters centrally integrated into the network, whereas sensors formed loosely connected antenna-shaped clusters and contained genes with previously characterized involvement in signal transduction. We propose that indirect relationships between effector and sensor clusters represent major paths of information flow between distinct cellular processes. Genetic similarity networks for fission yeast and human displayed similarly strong propensities for information propagation and clusters of effector and sensor genes, suggesting that the global architecture enabling indirect relationships is evolutionarily conserved across species. Our results demonstrate that elastic network modeling of cellular networks constitutes a promising strategy to probe the high-level organization and cooperativity in the cell.

Genetic variation in ALDH4A1 is associated with muscle health over the lifespan and across species

The influence of genetic variation on the aging process, including the incidence and severity of age-related diseases, is complex. Here, we define the evolutionarily conserved mitochondrial enzyme ALH-6/ALDH4A1 as a predictive biomarker for age-related changes in muscle health by combining Caenorhabditis elegans genetics and a gene-wide association scanning (GeneWAS) from older human participants of the US Health and Retirement Study (HRS). In a screen for mutations that activate oxidative stress responses, specifically in the muscle of C. elegans, we identified 96 independent genetic mutants harboring loss-of-function alleles of alh-6, exclusively. Each of these genetic mutations mapped to the ALH-6 polypeptide and led to the age-dependent loss of muscle health. Intriguingly, genetic variants in ALDH4A1 show associations with age-related muscle-related function in humans. Taken together, our work uncovers mitochondrial alh-6/ALDH4A1 as a critical component to impact normal muscle aging across species and a predictive biomarker for muscle health over the lifespan.

Ageing is inevitable, but what makes one person ‘age well’ and another decline more quickly remains largely unknown. While many aspects of ageing are clearly linked to genetics, the specific genes involved often remain unidentified.

Sarcopenia is an age-related condition affecting the muscles. It involves a gradual loss of muscle mass that becomes faster with age, and is associated with loss of mobility, decreased quality of life, and increased risk of death. Around half of all people aged 80 and over suffer from sarcopenia. Several lifestyle factors, especially poor diet and lack of exercise, are associated with the condition, but genetics is also involved: the condition accelerates more quickly in some people than others, and even fit, physically active individuals can be affected.

To study the genetics of conditions like sarcopenia, researchers often use animals like flies or worms, which have short generation times but share genetic similarities with humans. For example, the worm Caenorhabditis elegans has equivalents of several human muscle genes, including the gene alh-6. In worms, alh-6 is important for maintaining energy supply to the muscles, and mutating it not only leads to muscle damage but also to premature ageing. Given this insight, Villa, Stuhr, Yen et al. wanted to determine if variation in the human version of alh-6, ALDH4A1, also contributes to individual differences in muscle ageing and decline in humans.

Evaluating variation in this gene required a large amount of genetic data from older adults. These were taken from a continuous study that follows >35,000 older adults. Importantly, the study collects not only information on gene sequences but also measures of muscle health and performance over time for each individual. Analysis of these genetic data revealed specific small variations in the DNA of ALDH4A1, all of which associated with reduced muscle health.

Follow-up experiments in worms used genetic engineering techniques to test how variation in the worm alh-6 gene could influence age-related health. The resulting mutant worms developed muscle problems much earlier than their normal counterparts, supporting the role of alh-6/ALDH4A1 in determining muscle health across the lifespan of both worms and humans.

These results have identified a key influencer of muscle health during ageing in worms, and emphasize the importance of validating effects of genetic variation among humans during this process. Villa, Stuhr, Yen et al. hope that this study will help researchers find more genetic ‘markers’ of muscle health, and ultimately allow us to predict an individual’s risk of sarcopenia based on their genetic make-up.

Grammatical Gender Disambiguates Syntactically Similar Nouns

Recent research into grammatical gender from the perspective of information theory has shown how seemingly arbitrary gender systems can ease processing demands by guiding lexical prediction. When the gender of a noun is revealed in a preceding element, the list of possible candidates is reduced to the nouns assigned to that gender. This strategy can be particularly effective if it eliminates words that are likely to compete for activation against the intended word. We propose syntax as the crucial context within which words must be disambiguated, hypothesizing that syntactically similar words should be less likely to share a gender cross-linguistically. We draw on recent work on syntactic information in the lexicon to define the syntactic distribution of a word as a probability vector of its participation in various dependency relations, and we extract such relations for 32 languages from the Universal Dependencies Treebanks. Correlational and mixed-effects regression analyses reveal that syntactically similar nouns are less likely to share a gender, the opposite pattern that is found for semantically and orthographically similar words. We interpret this finding as a design feature of language, and this study adds to a growing body of research attesting to the ways in which functional pressures on learning, memory, production, and perception shape the lexicon in different ways.

A permutation-based approach to inference for weighted sum regression with correlated chemical mixtures

There is a growing demand for methods to determine the effects that chemical mixtures have on human health. One statistical challenge is identifying true "bad actors" from a mixture of highly correlated predictors, a setting in which standard approaches such as linear regression become highly variable. Weighted Quantile Sum regression has been proposed to address this problem, through a two-step process where mixture component weights are estimated using bootstrap aggregation in a training dataset and inference on the overall mixture effect occurs in a held-out test set. Weighted Quantile Sum regression is popular in applied papers, but the reliance on data splitting is suboptimal, and analysts who use the same data for both steps risk inflating the Type I error rate. We therefore propose a modification of Weighted Quantile Sum regression that uses a permutation test for inference, which allows for weight estimation using the entire dataset and preserves Type I error. To minimize computational burden, we propose replacing the bootstrap with L1 or L2 penalization and describe how to choose the appropriate penalty given expert knowledge about a mixture of interest. We apply our method to a national pregnancy cohort study of prenatal phthalate exposure and child health outcomes.

A greedy regression algorithm with coarse weights offers novel advantages

Regularized regression analysis is a mature analytic approach to identify weighted sums of variables predicting outcomes. We present a novel Coarse Approximation Linear Function (CALF) to frugally select important predictors and build simple but powerful predictive models. CALF is a linear regression strategy applied to normalized data that uses nonzero weights + 1 or - 1. Qualitative (linearly invariant) metrics to be optimized can be (for binary response) Welch (Student) t-test p-value or area under curve (AUC) of receiver operating characteristic, or (for real response) Pearson correlation. Predictor weighting is critically important when developing risk prediction models. While counterintuitive, it is a fact that qualitative metrics can favor CALF with ± 1 weights over algorithms producing real number weights. Moreover, while regression methods may be expected to change most or all weight values upon even small changes in input data (e.g., discarding a single subject of hundreds) CALF weights generally do not so change. Similarly, some regression methods applied to collinear or nearly collinear variables yield unpredictable magnitude or the direction (in p-space) of the weights as a vector. In contrast, with CALF if some predictors are linearly dependent or nearly so, CALF simply chooses at most one (the most informative, if any) and ignores the others, thus avoiding the inclusion of two or more collinear variables in the model.

Set Size of Information in Long-Term Memory Similarly Modulates Retrieval Dynamics in Young and Older Adults

Our ability to rapidly distinguish new from already stored (old) information is important for behavior and decision making, but the underlying processes remain unclear. Here, we tested the hypothesis that contextual cues lead to a preselection of information and, therefore, faster recognition. Specifically, on the basis of previous modeling work, we hypothesized that recognition time depends on the amount of relevant content stored in long-term memory, i.e., set size, and we explored possible age-related changes of this relationship in older humans. In our paradigm, subjects learned by heart four different word lists (24, 48, 72, and 96 words) written in different colors (green, red, orange, and blue). On the day of testing, a color cue (e.g., green) indicated with a probability of 50% that a subsequent word might be from the corresponding list or from a list of new words. The old/new status of the word had to be distinguished via button press. As a main finding, we can show in a sample of n = 49 subjects, including 26 younger and 23 older humans, that response times increased linearly and logarithmically as a function of set size in both age groups. Conversely, corrected hit rates decreased as a function of set size with no statistically significant differences between both age groups. As such, our findings provide empirical evidence that contextual information can lead to a preselection of relevant information stored in long-term memory to promote efficient recognition, possibly by cyclical top-down and bottom-up processing.

Leveraging a graft collection to develop metabolome-based trait prediction for the selection of tomato rootstocks with enhanced salt tolerance

Grafting has been demonstrated to significantly enhance the salt tolerance of crops. However, breeding efforts to develop enhanced graft combinations are hindered by knowledge-gaps as to how rootstocks mediate scion-response to salt stress. We grafted the scion of cultivated M82 onto rootstocks of 254 tomato accessions and explored the morphological and metabolic responses of grafts under saline conditions (EC = 20 dS m^-1) as compared to self-grafted M82 (SG-M82). Correlation analysis and Least Absolute Shrinkage and Selection Operator were performed to address the association between morphological diversification and metabolic perturbation. We demonstrate that grafting the same variety onto different rootstocks resulted in scion phenotypic heterogeneity and emphasized the productivity efficiency of M82 irrespective of the rootstock. Spectrophotometric analysis to test lipid oxidation showed largest variability of malondialdehyde (MDA) equivalents across the population, while the least responsive trait was the ratio of fruit fresh weight to total fresh weight (FFW/TFW). Generally, grafts showed greater values for the traits measured than SG-M82, except for branch number and wild race-originated rootstocks; the latter were associated with smaller scion growth parameters. Highly responsive and correlated metabolites were identified across the graft collection including malate, citrate, and aspartate, and their variance was partly related to rootstock origin. A group of six metabolites that consistently characterized exceptional graft response was observed, consisting of sorbose, galactose, sucrose, fructose, myo-inositol, and proline. The correlation analysis and predictive modelling, integrating phenotype- and leaf metabolite data, suggest a potential predictive relation between a set of leaf metabolites and yield-related traits.

Co-expression network analyses of anthocyanin biosynthesis genes in Ruellia (Wild Petunias; Acanthaceae)

Background

Anthocyanins are major pigments contributing to flower coloration and as such knowledge of molecular architecture underlying the anthocyanin biosynthetic pathway (ABP) is key to understanding flower color diversification. To identify ABP structural genes and associated regulatory networks, we sequenced 16 transcriptomes generated from 10 species of Ruellia and then conducted co-expression analyses among resulting data.

Results

Complete coding sequences for 12 candidate structural loci representing eight genes plus nine candidate regulatory loci were assembled. Analysis of non-synonymous/synonymous (dn/ds) mutation rates indicated all identified loci are under purifying selection, suggesting overall selection to prevent the accumulation of deleterious mutations. Additionally, upstream enzymes have lower rates of molecular evolution compared to downstream enzymes. However, site-specific tests of selection yielded evidence for positive selection at several sites, including four in F3'H2 and five in DFR3, and these sites are located in protein binding regions. A species-level phylogenetic tree constructed using a newly implemented hybrid transcriptome–RADseq approach implicates several flower color transitions among the 10 species. We found evidence of both regulatory and structural mutations to F3′5'H in helping to explain the evolution of red flowers from purple-flowered ancestors.

Conclusions

Sequence comparisons and co-expression analyses of ABP loci revealed that mutations in regulatory loci are likely to play a greater role in flower color transitions in Ruellia compared to mutations in underlying structural genes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12862-021-01955-x.

OUP accepted manuscript

Conflicts between transcription and replication machinery are a potent source of replication stress and genome instability; however, no technique currently exists to identify endogenous genomic locations prone to transcription–replication interactions. Here, we report a novel method to identify genomic loci prone to transcription–replication interactions termed transcription–replication immunoprecipitation on nascent DNA sequencing, TRIPn-Seq. TRIPn-Seq employs the sequential immunoprecipitation of RNA polymerase 2 phosphorylated at serine 5 (RNAP2s5) followed by enrichment of nascent DNA previously labeled with bromodeoxyuridine. Using TRIPn-Seq, we mapped 1009 unique transcription–replication interactions (TRIs) in mouse primary B cells characterized by a bimodal pattern of RNAP2s5, bidirectional transcription, an enrichment of RNA:DNA hybrids, and a high probability of forming G-quadruplexes. TRIs are highly enriched at transcription start sites and map to early replicating regions. TRIs exhibit enhanced Replication Protein A association and TRI-associated genes exhibit higher replication fork termination than control transcription start sites, two marks of replication stress. TRIs colocalize with double-strand DNA breaks, are enriched for deletions, and accumulate mutations in tumors. We propose that replication stress at TRIs induces mutations potentially contributing to age-related disease, as well as tumor formation and development.

Chronos: a cell population dynamics model of CRISPR experiments that improves inference of gene fitness effects

CRISPR loss of function screens are powerful tools to interrogate biology but exhibit a number of biases and artifacts that can confound the results. Here, we introduce Chronos, an algorithm for inferring gene knockout fitness effects based on an explicit model of cell proliferation dynamics after CRISPR gene knockout. We test Chronos on two pan-cancer CRISPR datasets and one longitudinal CRISPR screen. Chronos generally outperforms competitors in separation of controls and strength of biomarker associations, particularly when longitudinal data is available. Additionally, Chronos exhibits the lowest copy number and screen quality bias of evaluated methods. Chronos is available at https://github.com/broadinstitute/chronos .

Disentangling environmental effects in microbial association networks

BACKGROUND

Ecological interactions among microorganisms are fundamental for ecosystem function, yet they are mostly unknown or poorly understood. High-throughput-omics can indicate microbial interactions through associations across time and space, which can be represented as association networks. Associations could result from either ecological interactions between microorganisms, or from environmental selection, where the association is environmentally driven. Therefore, before downstream analysis and interpretation, we need to distinguish the nature of the association, particularly if it is due to environmental selection or not.

RESULTS

We present EnDED (environmentally driven edge detection), an implementation of four approaches as well as their combination to predict which links between microorganisms in an association network are environmentally driven. The four approaches are sign pattern, overlap, interaction information, and data processing inequality. We tested EnDED on networks from simulated data of 50 microorganisms. The networks contained on average 50 nodes and 1087 edges, of which 60 were true interactions but 1026 false associations (i.e., environmentally driven or due to chance). Applying each method individually, we detected a moderate to high number of environmentally driven edges-87% sign pattern and overlap, 67% interaction information, and 44% data processing inequality. Combining these methods in an intersection approach resulted in retaining more interactions, both true and false (32% of environmentally driven associations). After validation with the simulated datasets, we applied EnDED on a marine microbial network inferred from 10 years of monthly observations of microbial-plankton abundance. The intersection combination predicted that 8.3% of the associations were environmentally driven, while individual methods predicted 24.8% (data processing inequality), 25.7% (interaction information), and up to 84.6% (sign pattern as well as overlap). The fraction of environmentally driven edges among negative microbial associations in the real network increased rapidly with the number of environmental factors.

CONCLUSIONS

To reach accurate hypotheses about ecological interactions, it is important to determine, quantify, and remove environmentally driven associations in marine microbial association networks. For that, EnDED offers up to four individual methods as well as their combination. However, especially for the intersection combination, we suggest using EnDED with other strategies to reduce the number of false associations and consequently the number of potential interaction hypotheses. Video abstract.

Physicians' Attitudes Toward Prescribable mHealth Apps and Implications for Adoption in Germany: Mixed Methods Study

Background

In October 2020, Germany became the first country, worldwide, to approve certain mobile health (mHealth) apps, referred to as DiGA (Digitale Gesundheitsanwendungen, in German, meaning digital health applications), for prescription with costs covered by standard statutory health insurance. Yet, this option has only been used to a limited extent so far.

Objective

The aim of this study was to investigate physicians’ and psychotherapists’ current attitudes toward mHealth apps, barriers to adoption, and potential remedies.

Methods

We conducted a two-stage sequential mixed methods study. In phase one, semistructured interviews were conducted with physicians and psychotherapists for questionnaire design. In phase two, an online survey was conducted among general practitioners, physicians, and psychotherapists.

Results

A total of 1308 survey responses by mostly outpatient-care general practitioners, physicians, and psychotherapists from across Germany who could prescribe DiGA were recorded, making this the largest study on mHealth prescriptions to date. A total of 62.1% (807/1299) of respondents supported the opportunity to prescribe DiGA. Improved adherence (997/1294, 77.0%), health literacy (842/1294, 65.1%), and disease management (783/1294, 60.5%) were most frequently seen as benefits of DiGA. However, only 30.3% (393/1299) of respondents planned to prescribe DiGA, varying greatly by medical specialty. Professionals are still facing substantial barriers, such as insufficient information (1135/1295, 87.6%), reimbursement for DiGA-related medical services (716/1299, 55.1%), medical evidence (712/1298, 54.9%), legal uncertainties (680/1299, 52.3%), and technological uncertainties (658/1299, 50.7%). To support professionals who are unsure of prescribing DiGA, extended information campaigns (1104/1297, 85.1%) as well as recommendations from medical associations (1041/1297, 80.3%) and medical colleagues (1024/1297, 79.0%) were seen as the most impactful remedies.

Conclusions

To realize the benefits from DiGA through increased adoption, additional information sharing about DiGA from trusted bodies, reimbursement for DiGA-related medical services, and further medical evidence are recommended.

Localization of anatomical changes in patients during proton therapy with in-beam PET monitoring: A voxel-based morphometry approach exploiting Monte Carlo simulations

Purpose

In‐beam positron emission tomography (PET) is one of the modalities that can be used for in vivo noninvasive treatment monitoring in proton therapy. Although PET monitoring has been frequently applied for this purpose, there is still no straightforward method to translate the information obtained from the PET images into easy‐to‐interpret information for clinical personnel. The purpose of this work is to propose a statistical method for analyzing in‐beam PET monitoring images that can be used to locate, quantify, and visualize regions with possible morphological changes occurring over the course of treatment.

Methods

We selected a patient treated for squamous cell carcinoma (SCC) with proton therapy, to perform multiple Monte Carlo (MC) simulations of the expected PET signal at the start of treatment, and to study how the PET signal may change along the treatment course due to morphological changes. We performed voxel‐wise two‐tailed statistical tests of the simulated PET images, resembling the voxel‐based morphometry (VBM) method commonly used in neuroimaging data analysis, to locate regions with significant morphological changes and to quantify the change.

Results

The VBM resembling method has been successfully applied to the simulated in‐beam PET images, despite the fact that such images suffer from image artifacts and limited statistics. Three dimensional probability maps were obtained, that allowed to identify interfractional morphological changes and to visualize them superimposed on the computed tomography (CT) scan. In particular, the characteristic color patterns resulting from the two‐tailed statistical tests lend themselves to trigger alarms in case of morphological changes along the course of treatment.

Conclusions

The statistical method presented in this work is a promising method to apply to PET monitoring data to reveal interfractional morphological changes in patients, occurring over the course of treatment. Based on simulated in‐beam PET treatment monitoring images, we showed that with our method it was possible to correctly identify the regions that changed. Moreover we could quantify the changes, and visualize them superimposed on the CT scan. The proposed method can possibly help clinical personnel in the replanning procedure in adaptive proton therapy treatments.