A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics

Nonfrontal Control of Working Memory

Items held in visual working memory can be quickly updated, replaced, removed, and even manipulated in accordance with current behavioral goals. Here, we use multivariate pattern analyses to identify the patterns of neuronal activity that realize the executive control processes supervising these flexible stores. We find that portions of the middle temporal gyrus and the intraparietal sulcus represent what item is cued for continued memorization independently of representations of the item itself. Importantly, this selection-specific activity could not be explained by sensory representations of the cue and is only present when control is exerted. Our results suggest that the selection of memorized items might be controlled in a distributed and decentralized fashion. This evidence provides an alternative perspective to the notion of "domain general" central executive control over memory function.

Fast Bayesian Inference of Phylogenies from Multiple Continuous Characters

Time-scaled phylogenetic trees are an ultimate goal of evolutionary biology and a necessary ingredient in comparative studies. The accumulation of genomic data has resolved the tree of life to a great extent, yet timing evolutionary events remain challenging if not impossible without external information such as fossil ages and morphological characters. Methods for incorporating morphology in tree estimation have lagged behind their molecular counterparts, especially in the case of continuous characters. Despite recent advances, such tools are still direly needed as we approach the limits of what molecules can teach us. Here, we implement a suite of state-of-the-art methods for leveraging continuous morphology in phylogenetics, and by conducting extensive simulation studies we thoroughly validate and explore our methods' properties. While retaining model generality and scalability, we make it possible to estimate absolute and relative divergence times from multiple continuous characters while accounting for uncertainty. We compile and analyze one of the most data-type diverse data sets to date, comprised of contemporaneous and ancient molecular sequences, and discrete and continuous morphological characters from living and extinct Carnivora taxa. We conclude by synthesizing lessons about our method's behavior, and suggest future research venues.

Disentangling signal and noise in neural responses through generative modeling

Measurements of neural responses to identically repeated experimental events often exhibit large amounts of variability. This noise is distinct from signal , operationally defined as the average expected response across repeated trials for each given event. Accurately distinguishing signal from noise is important, as each is a target that is worthy of study (many believe noise reflects important aspects of brain function) and it is important not to confuse one for the other. Here, we introduce a principled modeling approach in which response measurements are explicitly modeled as the sum of samples from multivariate signal and noise distributions. In our proposed method-termed Generative Modeling of Signal and Noise (GSN)-the signal distribution is estimated by subtracting the estimated noise distribution from the estimated data distribution. We validate GSN using ground-truth simulations and demonstrate the application of GSN to empirical fMRI data. In doing so, we illustrate a simple consequence of GSN: by disentangling signal and noise components in neural responses, GSN denoises principal components analysis and improves estimates of dimensionality. We end by discussing other situations that may benefit from GSN's characterization of signal and noise, such as estimation of noise ceilings for computational models of neural activity. A code toolbox for GSN is provided with both MATLAB and Python implementations.

Reassessing the modularity of gene co-expression networks using the Stochastic Block Model

Finding communities in gene co-expression networks is a common first step toward extracting biological insight from these complex datasets. Most community detection algorithms expect genes to be organized into assortative modules, that is, groups of genes that are more associated with each other than with genes in other groups. While it is reasonable to expect that these modules exist, using methods that assume they exist a priori is risky, as it guarantees that alternative organizations of gene interactions will be ignored. Here, we ask: can we find meaningful communities without imposing a modular organization on gene co-expression networks, and how modular are these communities? For this, we use a recently developed community detection method, the weighted degree corrected stochastic block model (SBM), that does not assume that assortative modules exist. Instead, the SBM attempts to efficiently use all information contained in the co-expression network to separate the genes into hierarchically organized blocks of genes. Using RNA-seq gene expression data measured in two tissues derived from an outbred population of Drosophila melanogaster, we show that (a) the SBM is able to find ten times as many groups as competing methods, that (b) several of those gene groups are not modular, and that (c) the functional enrichment for non-modular groups is as strong as for modular communities. These results show that the transcriptome is structured in more complex ways than traditionally thought and that we should revisit the long-standing assumption that modularity is the main driver of the structuring of gene co-expression networks.

On Low-Rank Directed Acyclic Graphs and Causal Structure Learning

Despite several advances in recent years, learning causal structures represented by directed acyclic graphs (DAGs) remains a challenging task in high-dimensional settings when the graphs to be learned are not sparse. In this article, we propose to exploit a low-rank assumption regarding the (weighted) adjacency matrix of a DAG causal model to help address this problem. We utilize existing low-rank techniques to adapt causal structure learning methods to take advantage of this assumption and establish several useful results relating interpretable graphical conditions to the low-rank assumption. Specifically, we show that the maximum rank is highly related to hubs, suggesting that scale-free (SF) networks, which are frequently encountered in practice, tend to be low rank. Our experiments demonstrate the utility of the low-rank adaptations for a variety of data models, especially with relatively large and dense graphs. Moreover, with a validation procedure, the adaptations maintain a superior or comparable performance even when graphs are not restricted to be low rank.

Information-incorporated gene network construction with FDR control

MOTIVATION

Large-scale gene expression studies allow gene network construction to uncover associations among genes. To study direct associations among genes, partial correlation-based networks are preferred over marginal correlations. However, FDR control for partial correlation-based network construction is not well-studied. In addition, currently available partial correlation-based methods cannot take existing biological knowledge to help network construction while controlling FDR.

RESULTS

In this paper, we propose a method called Partial Correlation Graph with Information Incorporation (PCGII). PCGII estimates partial correlations between each pair of genes by regularized node-wise regression that can incorporate prior knowledge while controlling the effects of all other genes. It handles high-dimensional data where the number of genes can be much larger than the sample size and controls FDR at the same time. We compare PCGII with several existing approaches through extensive simulation studies and demonstrate that PCGII has better FDR control and higher power. We apply PCGII to a plant gene expression dataset where it recovers confirmed regulatory relationships and a hub node, as well as several direct associations that shed light on potential functional relationships in the system. We also introduce a method to supplement observed data with a pseudogene to apply PCGII when no prior information is available, which also allows checking FDR control and power for real data analysis.

AVAILABILITY AND IMPLEMENTATION

R package is freely available for download at https://cran.r-project.org/package=PCGII.

Generalization of auditory expertise in audio engineers and instrumental musicians

From auditory perception to general cognition, the ability to play a musical instrument has been associated with skills both related and unrelated to music. However, it is unclear if these effects are bound to the specific characteristics of musical instrument training, as little attention has been paid to other populations such as audio engineers and designers whose auditory expertise may match or surpass that of musicians in specific auditory tasks or more naturalistic acoustic scenarios. We explored this possibility by comparing students of audio engineering (n = 20) to matched conservatory-trained instrumentalists (n = 24) and to naive controls (n = 20) on measures of auditory discrimination, auditory scene analysis, and speech in noise perception. We found that audio engineers and performing musicians had generally lower psychophysical thresholds than controls, with pitch perception showing the largest effect size. Compared to controls, audio engineers could better memorise and recall auditory scenes composed of non-musical sounds, whereas instrumental musicians performed best in a sustained selective attention task with two competing streams of tones. Finally, in a diotic speech-in-babble task, musicians showed lower signal-to-noise-ratio thresholds than both controls and engineers; however, a follow-up online study did not replicate this musician advantage. We also observed differences in personality that might account for group-based self-selection biases. Overall, we showed that investigating a wider range of forms of auditory expertise can help us corroborate (or challenge) the specificity of the advantages previously associated with musical instrument training.

Janus kinase inhibitors vs. abatacept about safety and efficacy for patients with rheumatoid arthritis-associated interstitial lung disease: a retrospective nested case-control study

BACKGROUND

Interstitial lung disease (ILD) related to rheumatoid arthritis (RA) is among the leading causes of death and an essential prognostic factor. There is only limited evidence for the safety of anti-rheumatic drugs for patients with RA-ILD. The aim of this study is to investigate the safety and efficacy of Janus kinase inhibitors (JAKis) by comparing it with abatacept (ABT) in patients with RA-ILD.

METHODS

This single centre, retrospective nested case-control study enrolled patients with RA-ILD treated with JAKi or ABT. To determine the safety of the two drugs for existing ILD, we compared their drug persistency, incidence rates of pulmonary complications, and change of chest computed tomography (CT) image. For their efficacy as RA treatment, disease activity scores and prednisolone (PSL)-sparing effect were compared. We performed propensity score matching to match the groups' patient characteristics.

RESULTS

We studied 71 patients with RA-ILD (ABT, n = 45; JAKi, n = 26). At baseline, the JAKi group had longer disease duration, longer duration of past bDMARD or JAKi use and higher usual interstitial pneumonia rate. After propensity score matching, no significant differences in patient characteristics were found between the two groups. No significant difference in the drug persistency rate for the first 2 years (ABT, 61.9%; JAKi, 42.8%; P = 0.256) was observed between the two matched groups. The incidence rate of pulmonary complications did not differ significantly between the two groups (P = 0.683). The CT score did not change after the treatment for the ABT group (Ground-glass opacities (GGO): P = 0.87; fibrosis: P = 0.78), while the GGO score significantly improved for the JAKi group (P = 0.03), although the number was limited (ABT: n = 7; JAKi: n = 8). The fibrosis score of the JAKi group did not change significantly.(P = 0.82). Regarding the efficacy for RA, a significant decrease in disease activity scores after the 1-year treatment was observed in both groups, and PSL dose was successfully tapered, although no significant differences were observed between the two drugs.

CONCLUSIONS

JAKi is as safe and effective as ABT for patients with RA-ILD. JAKi can be a good treatment option for such patients.

Entropy Estimators for Markovian Sequences: A Comparative Analysis

Entropy estimation is a fundamental problem in information theory that has applications in various fields, including physics, biology, and computer science. Estimating the entropy of discrete sequences can be challenging due to limited data and the lack of unbiased estimators. Most existing entropy estimators are designed for sequences of independent events and their performances vary depending on the system being studied and the available data size. In this work, we compare different entropy estimators and their performance when applied to Markovian sequences. Specifically, we analyze both binary Markovian sequences and Markovian systems in the undersampled regime. We calculate the bias, standard deviation, and mean squared error for some of the most widely employed estimators. We discuss the limitations of entropy estimation as a function of the transition probabilities of the Markov processes and the sample size. Overall, this paper provides a comprehensive comparison of entropy estimators and their performance in estimating entropy for systems with memory, which can be useful for researchers and practitioners in various fields.

Effect of motion state variability on error-related potentials during continuous feedback paradigms and their consequences for classification

BACKGROUND

An erroneous motion would elicit the error-related potential (ErrP) when humans monitor the behavior of the external devices. This EEG modality has been largely applied to brain-computer interface in an active or passive manner with discrete visual feedback. However, the effect of variable motion state on ErrP morphology and classification performance raises concerns when the interaction is conducted with continuous visual feedback.

NEW METHOD

In the present study, we designed a cursor control experiment. Participants monitored a continuously moving cursor to reach the target on one side of the screen. Motion state varied multiple times with two factors: (1) motion direction and (2) motion speed. The effects of these two factors on the morphological characteristics and classification performance of ErrP were analyzed. Furthermore, an offline simulation was performed to evaluate the effectiveness of the proposed extended ErrP-decoder in resolving the interference by motion direction changes.

RESULTS

The statistical analyses revealed that motion direction and motion speed significantly influenced the amplitude of feedback-ERN and frontal-Pe components, while only motion direction significantly affected the classification performance.

COMPARISON WITH EXISTING METHODS

Significant deviation was found in ErrP detection utilizing classical correct-versus-erroneous event training. However, this bias can be alleviated by 16% by the extended ErrP-decoder.

CONCLUSION

The morphology and classification performance of ErrP signal can be affected by motion state variability during continuous feedback paradigms. The results enhance the comprehension of ErrP morphological components and shed light on the detection of BCI's error behavior in practical continuous control.

Gene communities in co-expression networks across different tissues

With the recent availability of tissue-specific gene expression data, e.g., provided by the GTEx Consortium, there is interest in comparing gene co-expression patterns across tissues. One promising approach to this problem is to use a multilayer network analysis framework and perform multilayer community detection. Communities in gene co-expression networks reveal groups of genes similarly expressed across individuals, potentially involved in related biological processes responding to specific environmental stimuli or sharing common regulatory variations. We construct a multilayer network in which each of the four layers is an exocrine gland tissue-specific gene co-expression network. We develop methods for multilayer community detection with correlation matrix input and an appropriate null model. Our correlation matrix input method identifies five groups of genes that are similarly co-expressed in multiple tissues (a community that spans multiple layers, which we call a generalist community) and two groups of genes that are co-expressed in just one tissue (a community that lies primarily within just one layer, which we call a specialist community). We further found gene co-expression communities where the genes physically cluster across the genome significantly more than expected by chance (on chromosomes 1 and 11). This clustering hints at underlying regulatory elements determining similar expression patterns across individuals and cell types. We suggest that KRTAP3-1, KRTAP3-3, and KRTAP3-5 share regulatory elements in skin and pancreas. Furthermore, we find that CELA3A and CELA3B share associated expression quantitative trait loci in the pancreas. The results indicate that our multilayer community detection method for correlation matrix input extracts biologically interesting communities of genes.

A disordered region controls cBAF activity via condensation and partner recruitment

Intrinsically disordered regions (IDRs) represent a large percentage of overall nuclear protein content. The prevailing dogma is that IDRs engage in non-specific interactions because they are poorly constrained by evolutionary selection. Here, we demonstrate that condensate formation and heterotypic interactions are distinct and separable features of an IDR within the ARID1A/B subunits of the mSWI/SNF chromatin remodeler, cBAF, and establish distinct "sequence grammars" underlying each contribution. Condensation is driven by uniformly distributed tyrosine residues, and partner interactions are mediated by non-random blocks rich in alanine, glycine, and glutamine residues. These features concentrate a specific cBAF protein-protein interaction network and are essential for chromatin localization and activity. Importantly, human disease-associated perturbations in ARID1B IDR sequence grammars disrupt cBAF function in cells. Together, these data identify IDR contributions to chromatin remodeling and explain how phase separation provides a mechanism through which both genomic localization and functional partner recruitment are achieved.

Statistical inference on representational geometries

Neuroscience has recently made much progress, expanding the complexity of both neural activity measurements and brain-computational models. However, we lack robust methods for connecting theory and experiment by evaluating our new big models with our new big data. Here, we introduce new inference methods enabling researchers to evaluate and compare models based on the accuracy of their predictions of representational geometries: A good model should accurately predict the distances among the neural population representations (e.g. of a set of stimuli). Our inference methods combine novel 2-factor extensions of crossvalidation (to prevent overfitting to either subjects or conditions from inflating our estimates of model accuracy) and bootstrapping (to enable inferential model comparison with simultaneous generalization to both new subjects and new conditions). We validate the inference methods on data where the ground-truth model is known, by simulating data with deep neural networks and by resampling of calcium-imaging and functional MRI data. Results demonstrate that the methods are valid and conclusions generalize correctly. These data analysis methods are available in an open-source Python toolbox (rsatoolbox.readthedocs.io).

Trends and symptoms of SARS-CoV-2 infection: a longitudinal study on an Alpine population representative sample

OBJECTIVES

The continuous monitoring of SARS-CoV-2 infection waves and the emergence of novel pathogens pose a challenge for effective public health surveillance strategies based on diagnostics. Longitudinal population representative studies on incident events and symptoms of SARS-CoV-2 infection are scarce. We aimed at describing the evolution of the COVID-19 pandemic during 2020 and 2021 through regular monitoring of self-reported symptoms in an Alpine community sample.

DESIGN

To this purpose, we designed a longitudinal population representative study, the Cooperative Health Research in South Tyrol COVID-19 study.

PARTICIPANTS AND OUTCOME MEASURES

A sample of 845 participants was retrospectively investigated for active and past infections with swab and blood tests, by August 2020, allowing adjusted cumulative incidence estimation. Of them, 700 participants without previous infection or vaccination were followed up monthly until July 2021 for first-time infection and symptom self-reporting: COVID-19 anamnesis, social contacts, lifestyle and sociodemographic data were assessed remotely through digital questionnaires. Temporal symptom trajectories and infection rates were modelled through longitudinal clustering and dynamic correlation analysis. Negative binomial regression and random forest analysis assessed the relative importance of symptoms.

RESULTS

At baseline, the cumulative incidence of SARS-CoV-2 infection was 1.10% (95% CI 0.51%, 2.10%). Symptom trajectories mimicked both self-reported and confirmed cases of incident infections. Cluster analysis identified two groups of high-frequency and low-frequency symptoms. Symptoms like fever and loss of smell fell in the low-frequency cluster. Symptoms most discriminative of test positivity (loss of smell, fatigue and joint-muscle aches) confirmed prior evidence.

CONCLUSIONS

Regular symptom tracking from population representative samples is an effective screening tool auxiliary to laboratory diagnostics for novel pathogens at critical times, as manifested in this study of COVID-19 patterns. Integrated surveillance systems might benefit from more direct involvement of citizens' active symptom tracking.

Seiðr: Efficient calculation of robust ensemble gene networks

Gene regulatory and gene co-expression networks are powerful research tools for identifying biological signal within high-dimensional gene expression data. In recent years, research has focused on addressing shortcomings of these techniques with regard to the low signal-to-noise ratio, non-linear interactions and dataset dependent biases of published methods. Furthermore, it has been shown that aggregating networks from multiple methods provides improved results. Despite this, few useable and scalable software tools have been implemented to perform such best-practice analyses. Here, we present Seidr (stylized Seiðr), a software toolkit designed to assist scientists in gene regulatory and gene co-expression network inference. Seidr creates community networks to reduce algorithmic bias and utilizes noise corrected network backboning to prune noisy edges in the networks. Using benchmarks in real-world conditions across three eukaryotic model organisms, Saccharomyces cerevisiae, Drosophila melanogaster, and Arabidopsis thaliana, we show that individual algorithms are biased toward functional evidence for certain gene-gene interactions. We further demonstrate that the community network is less biased, providing robust performance across different standards and comparisons for the model organisms. Finally, we apply Seidr to a network of drought stress in Norway spruce (Picea abies (L.) H. Krast) as an example application in a non-model species. We demonstrate the use of a network inferred using Seidr for identifying key components, communities and suggesting gene function for non-annotated genes.

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.

Stepwise activities of mSWI/SNF family chromatin remodeling complexes direct T cell activation and exhaustion

Highly coordinated changes in gene expression underlie T cell activation and exhaustion. However, the mechanisms by which such programs are regulated and how these may be targeted for therapeutic benefit remain poorly understood. Here, we comprehensively profile the genomic occupancy of mSWI/SNF chromatin remodeling complexes throughout acute and chronic T cell stimulation, finding that stepwise changes in localization over transcription factor binding sites direct site-specific chromatin accessibility and gene activation leading to distinct phenotypes. Notably, perturbation of mSWI/SNF complexes using genetic and clinically relevant chemical strategies enhances the persistence of T cells with attenuated exhaustion hallmarks and increased memory features in vitro and in vivo. Finally, pharmacologic mSWI/SNF inhibition improves CAR-T expansion and results in improved anti-tumor control in vivo. These findings reveal the central role of mSWI/SNF complexes in the coordination of T cell activation and exhaustion and nominate small-molecule-based strategies for the improvement of current immunotherapy protocols.

Childhood exposure to non-persistent endocrine disrupting chemicals and multi-omic profiles: A panel study

BACKGROUND

Individuals are exposed to environmental pollutants with endocrine disrupting activity (endocrine disruptors, EDCs) and the early stages of life are particularly susceptible to these exposures. Previous studies have focused on identifying molecular signatures associated with EDCs, but none have used repeated sampling strategy and integrated multiple omics. We aimed to identify multi-omic signatures associated with childhood exposure to non-persistent EDCs.

METHODS

We used data from the HELIX Child Panel Study, which included 156 children aged 6 to 11. Children were followed for one week, in two time periods. Twenty-two non-persistent EDCs (10 phthalate, 7 phenol, and 5 organophosphate pesticide metabolites) were measured in two weekly pools of 15 urine samples each. Multi-omic profiles (methylome, serum and urinary metabolome, proteome) were measured in blood and in a pool urine samples. We developed visit-specific Gaussian Graphical Models based on pairwise partial correlations. The visit-specific networks were then merged to identify reproducible associations. Independent biological evidence was systematically sought to confirm some of these associations and assess their potential health implications.

RESULTS

950 reproducible associations were found among which 23 were direct associations between EDCs and omics. For 9 of them, we were able to find corroborating evidence from previous literature: DEP - serotonin, OXBE - cg27466129, OXBE - dimethylamine, triclosan - leptin, triclosan - serotonin, MBzP - Neu5AC, MEHP - cg20080548, oh-MiNP - kynurenine, oxo-MiNP - 5-oxoproline. We used these associations to explore possible mechanisms between EDCs and health outcomes, and found links to health outcomes for 3 analytes: serotonin and kynurenine in relation to neuro-behavioural development, and leptin in relation to obesity and insulin resistance.

CONCLUSIONS

This multi-omics network analysis at two time points identified biologically relevant molecular signatures related to non-persistent EDC exposure in childhood, suggesting pathways related to neurological and metabolic outcomes.

Pharmacological disruption of mSWI/SNF complex activity restricts SARS-CoV-2 infection

Identification of host determinants of coronavirus infection informs mechanisms of viral pathogenesis and can provide new drug targets. Here we demonstrate that mammalian SWItch/Sucrose Non-Fermentable (mSWI/SNF) chromatin remodeling complexes, specifically canonical BRG1/BRM-associated factor (cBAF) complexes, promote severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and represent host-directed therapeutic targets. The catalytic activity of SMARCA4 is required for mSWI/SNF-driven chromatin accessibility at the ACE2 locus, ACE2 expression and virus susceptibility. The transcription factors HNF1A/B interact with and recruit mSWI/SNF complexes to ACE2 enhancers, which contain high HNF1A motif density. Notably, small-molecule mSWI/SNF ATPase inhibitors or degraders abrogate angiotensin-converting enzyme 2 (ACE2) expression and confer resistance to SARS-CoV-2 variants and a remdesivir-resistant virus in three cell lines and three primary human cell types, including airway epithelial cells, by up to 5 logs. These data highlight the role of mSWI/SNF complex activities in conferring SARS-CoV-2 susceptibility and identify a potential class of broad-acting antivirals to combat emerging coronaviruses and drug-resistant variants.

Repeatability and Reproducibility Uncertainty in Magnetic Resonance-Based Electric Properties Tomography of a Homogeneous Phantom

Uncertainty assessment is a fundamental step in quantitative magnetic resonance imaging because it makes comparable, in a strict metrological sense, the results of different scans, for example during a longitudinal study. Magnetic resonance-based electric properties tomography (EPT) is a quantitative imaging technique that retrieves, non-invasively, a map of the electric properties inside a human body. Although EPT has been used in some early clinical studies, a rigorous experimental assessment of the associated uncertainty has not yet been performed. This paper aims at evaluating the repeatability and reproducibility uncertainties in phase-based Helmholtz-EPT applied on homogeneous phantom data acquired with a clinical 3 T scanner. The law of propagation of uncertainty is used to evaluate the uncertainty in the estimated conductivity values starting from the uncertainty in the acquired scans, which is quantified through a robust James-Stein shrinkage estimator to deal with the dimensionality of the problem. Repeatable errors are detected in the estimated conductivity maps and are quantified for various values of the tunable parameters of the EPT implementation. The spatial dispersion of the estimated electric conductivity maps is found to be a good approximation of the reproducibility uncertainty, evaluated by changing the position of the phantom after each scan. The results underpin the use of the average conductivity (calculated by weighting the local conductivity values by their uncertainty and taking into account the spatial correlation) as an estimate of the conductivity of the homogeneous phantom.

Urine-based multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS

BACKGROUND

Acute respiratory distress syndrome (ARDS), a life-threatening condition during critical illness, is a common complication of COVID-19. It can originate from various disease etiologies, including severe infections, major injury, or inhalation of irritants. ARDS poses substantial clinical challenges due to a lack of etiology-specific therapies, multisystem involvement, and heterogeneous, poor patient outcomes. A molecular comparison of ARDS groups holds the potential to reveal common and distinct mechanisms underlying ARDS pathogenesis.

METHODS

We performed a comparative analysis of urine-based metabolomics and proteomics profiles from COVID-19 ARDS patients (n = 42) and bacterial sepsis-induced ARDS patients (n = 17). To this end, we used two different approaches, first we compared the molecular omics profiles between ARDS groups, and second, we correlated clinical manifestations within each group with the omics profiles.

RESULTS

The comparison of the two ARDS etiologies identified 150 metabolites and 70 proteins that were differentially abundant between the two groups. Based on these findings, we interrogated the interplay of cell adhesion/extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis through a multi-omic network approach. Moreover, we identified a proteomic signature associated with mortality in COVID-19 ARDS patients, which contained several proteins that had previously been implicated in clinical manifestations frequently linked with ARDS pathogenesis.

CONCLUSION

In summary, our results provide evidence for significant molecular differences in ARDS patients from different etiologies and a potential synergy of extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis. The proteomic mortality signature should be further investigated in future studies to develop prediction models for COVID-19 patient outcomes.

Decoding transcriptional regulation via a human gene expression predictor

Transcription factors (TFs) regulate cellular activities by controlling gene expression, but a predictive model describing how TFs quantitatively modulate human transcriptomes is lacking. We construct a universal human gene expression predictor and utilize it to decode transcriptional regulation. Using the expression of 1613 TFs, the predictor reconstitutes highly accurate transcriptomes for samples derived from a wide range of tissues and conditions. The broad applicability of the predictor indicates that it recapitulates the quantitative relationships between TFs and target genes ubiquitous across tissues. Significant interacting TF-target gene pairs are extracted from the predictor and enable downstream inference of TF regulators for diverse pathways involved in development, immunity, metabolism, and stress response. A detailed analysis of the hematopoiesis process reveals an atlas of key TFs regulating the development of different hematopoietic cell lineages, and a portion of these TFs are conserved between humans and mice. The results demonstrate that our method is capable of delineating the TFs responsible for fate determination. Compared to other existing tools, our approach shows better performance in recovering the correct TF regulators. Thus, we present a novel approach that can be used to study human transcriptional regulation in general.

Potential of the prognostic nutritional index to determine the risk factor for severe infection in elderly patients with rheumatoid arthritis

OBJECTIVE

To investigate the influence of nutritional status on severe infection complications in patients with rheumatoid arthritis (RA).

METHODS

This retrospective cohort study on 2108 patients with RA evaluated the prognostic nutritional index (PNI) as an index of nutritional status. Patients were classified into the high or low PNI group according to the cutoff PNI value (45.0). Based on propensity score matching analysis, 360 patients in each group were selected for comparing the incidence of serious infection, clinical findings, and PNI scores.

RESULTS

The incidence of infection was significantly higher in the low PNI group than in the high PNI group (p < 0.001). The occurrence rate of infectious complication at 104 weeks was significantly higher in the low PNI (<45.0) group than in the high PNI group (p < 0.001). The incidence of infection was particularly high in elderly patients (≥65 years) with a low PNI, but the incidence in elderly patients with a high PNI was similar to that in nonelderly patients with a high PNI.

CONCLUSIONS

Patients with RA and malnutrition had a higher incidence of severe infection; thus, evaluating and managing nutritional status is necessary for the appropriate and safe treatment of elderly patients with RA.

DRAGON: Determining Regulatory Associations using Graphical models on multi-Omic Networks

The increasing quantity of multi-omic data, such as methylomic and transcriptomic profiles collected on the same specimen or even on the same cell, provides a unique opportunity to explore the complex interactions that define cell phenotype and govern cellular responses to perturbations. We propose a network approach based on Gaussian Graphical Models (GGMs) that facilitates the joint analysis of paired omics data. This method, called DRAGON (Determining Regulatory Associations using Graphical models on multi-Omic Networks), calibrates its parameters to achieve an optimal trade-off between the network's complexity and estimation accuracy, while explicitly accounting for the characteristics of each of the assessed omics 'layers.' In simulation studies, we show that DRAGON adapts to edge density and feature size differences between omics layers, improving model inference and edge recovery compared to state-of-the-art methods. We further demonstrate in an analysis of joint transcriptome - methylome data from TCGA breast cancer specimens that DRAGON can identify key molecular mechanisms such as gene regulation via promoter methylation. In particular, we identify Transcription Factor AP-2 Beta (TFAP2B) as a potential multi-omic biomarker for basal-type breast cancer. DRAGON is available as open-source code in Python through the Network Zoo package (netZooPy v0.8; netzoo.github.io).

Utility of the Fatigue, Resistance, Ambulation, Illness, and Loss of weight Scale in Older Patients with Cardiovascular Disease

OBJECTIVES

To compare the Fried criteria for frailty diagnosis with the Frailty Screening Index (FSI) and the fatigue, resistance, ambulation, illness, and loss of weight (FRAIL) scale in older patients with cardiovascular disease (CVD).

DESIGN

We conducted a retrospective 1-year follow-up cohort study of adult inpatients who participated in a cardiac rehabilitation program between June 2016 and September 2018.

SETTING AND PARTICIPANTS

We included 1472 Japanese patients age 65 years and older with CVD. After excluding 765 patients with incomplete frailty measurements, 707 patients were included in the analysis.

METHODS

Frailty and physical function were measured before hospital discharge according to each of the 3 definitions. Outcomes were all-cause mortality and physical dysfunction.

RESULTS

The prevalence of frailty according to the Fried criteria, the FRAIL scale, and the FSI was 213 (30.1%), 181 (25.6%), and 186 (26.3%), respectively. The FSI and the FRAIL scale showed moderate agreement with the Fried criteria [vs FSI: K = 0.52, 95% confidence interval (CI): 0.45-0.59; vs FRAIL scale: K = 0.45, 95% CI: 0.37-0.52; all P < .001]. We found a significant correlation between all-cause mortality and frailty assessed by all of the definitions, even after multivariate adjustment [FSI: hazard ratio (HR): 2.43, 95% CI: 1.30-4.58, P = .006; FRAIL scale: HR: 2.32, 95% CI: 1.21-4.45, P = .011; Fried criteria: HR: 1.99, 95% CI: 1.04-3.82, P = .038). However, the prediction accuracy of the FRAIL scale was higher than that of the FSI and comparable to that of the Fried criteria for physical dysfunction.

CONCLUSIONS AND IMPLICATIONS

The FSI and the FRAIL scale showed moderate agreement with the Fried criteria regarding frailty diagnostic performance and had comparable prognostic value. However, only the FRAIL scale was as accurate as the Fried criteria in screening for physical dysfunction.

Redheaded women are more sexually active than other women, but it is probably due to their suitors

Women with red hair color, i.e., 1–9% of female Europeans, tend to be the subject of various stereotypes about their sexually liberated behavior. The aim of the present case–control study was to explore whether a connection between red hair color and sexual behavior really exists using data from 110 women (34% redheaded) and 93 men (22% redheaded). Redheadedness in women, correlated with various traits related to sexual life, namely with higher sexual desire as measured by Revised Sociosexual Orientation Inventory, with higher sexual activity and more sexual partners of the preferred gender over the past year, earlier initiation of sexual life, and higher sexual submissiveness. Structural equation modelling, however, showed that sexual desire of redheaded women mediated neither their higher sexual activity nor their higher number of sexual partners. These results indirectly indicate that the apparently more liberated sexual behavior in redheaded women could be the consequence of potential mates’ frequent attempts to have sex with them. Our results contradicted the three other tested models, specifically the models based on the assumption of different physiology, faster life history strategy, and altered self-perception of redheaded women induced by stereotypes about them. Naturally, the present study cannot say anything about the validity of other potential models that were not subjects of testing.

The GR2D2 estimator for the precision matrices

Biological networks are important for the analysis of human diseases, which summarize the regulatory interactions and other relationships between different molecules. Understanding and constructing networks for molecules, such as DNA, RNA and proteins, can help elucidate the mechanisms of complex biological systems. The Gaussian Graphical Models (GGMs) are popular tools for the estimation of biological networks. Nonetheless, reconstructing GGMs from high-dimensional datasets is still challenging. The current methods cannot handle the sparsity and high-dimensionality issues arising from datasets very well. Here, we developed a new GGM, called the GR2D2 (Graphical $R^2$-induced Dirichlet Decomposition) model, based on the R2D2 priors for linear models. Besides, we provided a data-augmented block Gibbs sampler algorithm. The R code is available at https://github.com/RavenGan/GR2D2. The GR2D2 estimator shows superior performance in estimating the precision matrices compared with the existing techniques in various simulation settings. When the true precision matrix is sparse and of high dimension, the GR2D2 provides the estimates with smallest information divergence from the underlying truth. We also compare the GR2D2 estimator with the graphical horseshoe estimator in five cancer RNA-seq gene expression datasets grouped by three cancer types. Our results show that GR2D2 successfully identifies common cancer pathways and cancer-specific pathways for each dataset.

Simultaneous multiple-stimulus auditory brain-computer interface with semi-supervised learning and prior probability distribution tuning

Objective.Auditory brain-computer interfaces (BCIs) enable users to select commands based on the brain activity elicited by auditory stimuli. However, existing auditory BCI paradigms cannot increase the number of available commands without decreasing the selection speed, because each stimulus needs to be presented independently and sequentially under the standard oddball paradigm. To solve this problem, we propose a double-stimulus paradigm that simultaneously presents multiple auditory stimuli.Approach.For addition to an existing auditory BCI paradigm, the best discriminable sound was chosen following a subjective assessment. The new sound was located on the right-hand side and presented simultaneously with an existing sound from the left-hand side. A total of six sounds were used for implementing the auditory BCI with a 6 × 6 letter matrix. We employ semi-supervised learning (SSL) and prior probability distribution tuning to improve the accuracy of the paradigm. The SSL method involved updating of the classifier weights, and their prior probability distributions were adjusted using the following three types of distributions: uniform, empirical, and extended empirical (e-empirical). The performance was evaluated based on the BCI accuracy and information transfer rate (ITR).Main results.The double-stimulus paradigm resulted in a BCI accuracy of 67.89 ± 11.46% and an ITR of 2.67 ± 1.09 bits min^-1, in the absence of SSL and with uniform distribution. The proposed combination of SSL with e-empirical distribution improved the BCI accuracy and ITR to 74.59 ± 12.12% and 3.37 ± 1.27 bits min^-1, respectively. The event-related potential analysis revealed that contralateral and right-hemispheric dominances contributed to the BCI performance improvement.Significance.Our study demonstrated that a BCI based on multiple simultaneous auditory stimuli, incorporating SSL and e-empirical prior distribution, can increase the number of commands without sacrificing typing speed beyond the acceptable level of accuracy.

Mendelian randomization accounting for complex correlated horizontal pleiotropy while elucidating shared genetic etiology

Mendelian randomization (MR) harnesses genetic variants as instrumental variables (IVs) to study the causal effect of exposure on outcome using summary statistics from genome-wide association studies. Classic MR assumptions are violated when IVs are associated with unmeasured confounders, i.e., when correlated horizontal pleiotropy (CHP) arises. Such confounders could be a shared gene or inter-connected pathways underlying exposure and outcome. We propose MR-CUE (MR with Correlated horizontal pleiotropy Unraveling shared Etiology and confounding), for estimating causal effect while identifying IVs with CHP and accounting for estimation uncertainty. For those IVs, we map their cis-associated genes and enriched pathways to inform shared genetic etiology underlying exposure and outcome. We apply MR-CUE to study the effects of interleukin 6 on multiple traits/diseases and identify several S100 genes involved in shared genetic etiology. We assess the effects of multiple exposures on type 2 diabetes across European and East Asian populations.

Cross-Validated Loss-Based Covariance Matrix Estimator Selection in High Dimensions

The covariance matrix plays a fundamental role in many modern exploratory and inferential statistical procedures, including dimensionality reduction, hypothesis testing, and regression. In low-dimensional regimes, where the number of observations far exceeds the number of variables, the optimality of the sample covariance matrix as an estimator of this parameter is well-established. High-dimensional regimes do not admit such a convenience. Thus, a variety of estimators have been derived to overcome the shortcomings of the canonical estimator in such settings. Yet, selecting an optimal estimator from among the plethora available remains an open challenge. Using the framework of cross-validated loss-based estimation, we develop the theoretical underpinnings of just such an estimator selection procedure. We propose a general class of loss functions for covariance matrix estimation and establish accompanying finite-sample risk bounds and conditions for the asymptotic optimality of the cross-validation selector. In numerical experiments, we demonstrate the optimality of our proposed selector in moderate sample sizes and across diverse data-generating processes. The practical benefits of our procedure are highlighted in a dimension reduction application to single-cell transcriptome sequencing data.

GeneNetTools: tests for Gaussian graphical models with shrinkage

MOTIVATION

Gaussian graphical models (GGMs) are network representations of random variables (as nodes) and their partial correlations (as edges). GGMs overcome the challenges of high-dimensional data analysis by using shrinkage methodologies. Therefore, they have become useful to reconstruct gene regulatory networks from gene-expression profiles. However, it is often ignored that the partial correlations are 'shrunk' and that they cannot be compared/assessed directly. Therefore, accurate (differential) network analyses need to account for the number of variables, the sample size, and also the shrinkage value, otherwise, the analysis and its biological interpretation would turn biased. To date, there are no appropriate methods to account for these factors and address these issues.

RESULTS

We derive the statistical properties of the partial correlation obtained with the Ledoit-Wolf shrinkage. Our result provides a toolbox for (differential) network analyses as (i) confidence intervals, (ii) a test for zero partial correlation (null-effects) and (iii) a test to compare partial correlations. Our novel (parametric) methods account for the number of variables, the sample size and the shrinkage values. Additionally, they are computationally fast, simple to implement and require only basic statistical knowledge. Our simulations show that the novel tests perform better than DiffNetFDR-a recently published alternative-in terms of the trade-off between true and false positives. The methods are demonstrated on synthetic data and two gene-expression datasets from Escherichia coli and Mus musculus.

AVAILABILITY AND IMPLEMENTATION

The R package with the methods and the R script with the analysis are available in https://github.com/V-Bernal/GeneNetTools.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Rice co-expression network analysis identifies gene modules associated with agronomic traits

Identifying trait-associated genes is critical for rice (Oryza sativa) improvement, which usually relies on map-based cloning, quantitative trait locus analysis, or genome-wide association studies. Here we show that trait-associated genes tend to form modules within rice gene co-expression networks, a feature that can be exploited to discover additional trait-associated genes using reverse genetics. We constructed a rice gene co-expression network based on the graphical Gaussian model using 8,456 RNA-seq transcriptomes, which assembled into 1,286 gene co-expression modules functioning in diverse pathways. A number of the modules were enriched with genes associated with agronomic traits, such as grain size, grain number, tiller number, grain quality, leaf angle, stem strength, and anthocyanin content, and these modules are considered to be trait-associated gene modules. These trait-associated gene modules can be used to dissect the genetic basis of rice agronomic traits and to facilitate the identification of trait genes. As an example, we identified a candidate gene, OCTOPUS-LIKE 1 (OsOPL1), a homolog of the Arabidopsis (Arabidopsis thaliana) OCTOPUS gene, from a grain size module and verified it as a regulator of grain size via functional studies. Thus, our network represents a valuable resource for studying trait-associated genes in rice.

Multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS

BACKGROUND

Acute respiratory distress syndrome (ARDS), a life-threatening condition characterized by hypoxemia and poor lung compliance, is associated with high mortality. ARDS induced by COVID-19 has similar clinical presentations and pathological manifestations as non-COVID-19 ARDS. However, COVID-19 ARDS is associated with a more protracted inflammatory respiratory failure compared to traditional ARDS. Therefore, a comprehensive molecular comparison of ARDS of different etiologies groups may pave the way for more specific clinical interventions.

METHODS AND FINDINGS

In this study, we compared COVID-19 ARDS (n = 43) and bacterial sepsis-induced (non-COVID-19) ARDS (n = 24) using multi-omic plasma profiles covering 663 metabolites, 1,051 lipids, and 266 proteins. To address both between- and within- ARDS group variabilities we followed two approaches. First, we identified 706 molecules differently abundant between the two ARDS etiologies, revealing more than 40 biological processes differently regulated between the two groups. From these processes, we assembled a cascade of therapeutically relevant pathways downstream of sphingosine metabolism. The analysis suggests a possible overactivation of arginine metabolism involved in long-term sequelae of ARDS and highlights the potential of JAK inhibitors to improve outcomes in bacterial sepsis-induced ARDS. The second part of our study involved the comparison of the two ARDS groups with respect to clinical manifestations. Using a data-driven multi-omic network, we identified signatures of acute kidney injury (AKI) and thrombocytosis within each ARDS group. The AKI-associated network implicated mitochondrial dysregulation which might lead to post-ARDS renal-sequalae. The thrombocytosis-associated network hinted at a synergy between prothrombotic processes, namely IL-17, MAPK, TNF signaling pathways, and cell adhesion molecules. Thus, we speculate that combination therapy targeting two or more of these processes may ameliorate thrombocytosis-mediated hypercoagulation.

CONCLUSION

We present a first comprehensive molecular characterization of differences between two ARDS etiologies-COVID-19 and bacterial sepsis. Further investigation into the identified pathways will lead to a better understanding of the pathophysiological processes, potentially enabling novel therapeutic interventions.

SEMgsa: topology-based pathway enrichment analysis with structural equation models

Background

Pathway enrichment analysis is extensively used in high-throughput experimental studies to gain insight into the functional roles of pre-defined subsets of genes, proteins and metabolites. Methods that leverages information on the topology of the underlying pathways outperform simpler methods that only consider pathway membership, leading to improved performance. Among all the proposed software tools, there’s the need to combine high statistical power together with a user-friendly framework, making it difficult to choose the best method for a particular experimental environment.

Results

We propose SEMgsa, a topology-based algorithm developed into the framework of structural equation models. SEMgsa combine the SEM p values regarding node-specific group effect estimates in terms of activation or inhibition, after statistically controlling biological relations among genes within pathways. We used SEMgsa to identify biologically relevant results in a Coronavirus disease (COVID-19) RNA-seq dataset (GEO accession: GSE172114) together with a frontotemporal dementia (FTD) DNA methylation dataset (GEO accession: GSE53740) and compared its performance with some existing methods. SEMgsa is highly sensitive to the pathways designed for the specific disease, showing low p values (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$< 0.001$$\end{document}<0.001) and ranking in high positions, outperforming existing software tools. Three pathway dysregulation mechanisms were used to generate simulated expression data and evaluate the performance of methods in terms of type I error followed by their statistical power. Simulation results confirm best overall performance of SEMgsa.

Conclusions

SEMgsa is a novel yet powerful method for identifying enrichment with regard to gene expression data. It takes into account topological information and exploits pathway perturbation statistics to reveal biological information. SEMgsa is implemented in the R package SEMgraph, easily available at https://CRAN.R-project.org/package=SEMgraph.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04884-8.

Multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS

Background

Acute respiratory distress syndrome (ARDS), a life-threatening condition characterized by hypoxemia and poor lung compliance, is associated with high mortality. ARDS induced by COVID-19 has similar clinical presentations and pathological manifestations as non-COVID-19 ARDS. However, COVID-19 ARDS is associated with a more protracted inflammatory respiratory failure compared to traditional ARDS. Therefore, a comprehensive molecular comparison of ARDS of different etiologies groups may pave the way for more specific clinical interventions.

Methods and Findings

In this study, we compared COVID-19 ARDS (n=43) and bacterial sepsis-induced (non-COVID-19) ARDS (n=24) using multi-omic plasma profiles covering 663 metabolites, 1,051 lipids, and 266 proteins. To address both between- and within-ARDS group variabilities we followed two approaches. First, we identified 706 molecules differently abundant between the two ARDS etiologies, revealing more than 40 biological processes differently regulated between the two groups. From these processes, we assembled a cascade of therapeutically relevant pathways downstream of sphingosine metabolism. The analysis suggests a possible overactivation of arginine metabolism involved in long-term sequelae of ARDS and highlights the potential of JAK inhibitors to improve outcomes in bacterial sepsis-induced ARDS. The second part of our study involved the comparison of the two ARDS groups with respect to clinical manifestations. Using a data-driven multi-omic network, we identified signatures of acute kidney injury (AKI) and thrombocytosis within each ARDS group. The AKI-associated network implicated mitochondrial dysregulation which might lead to post-ARDS renal-sequalae. The thrombocytosis-associated network hinted at a synergy between prothrombotic processes, namely IL-17, MAPK, TNF signaling pathways, and cell adhesion molecules. Thus, we speculate that combination therapy targeting two or more of these processes may ameliorate thrombocytosis-mediated hypercoagulation.

Conclusion

We present a first comprehensive molecular characterization of differences between two ARDS etiologies - COVID-19 and bacterial sepsis. Further investigation into the identified pathways will lead to a better understanding of the pathophysiological processes, potentially enabling novel therapeutic interventions.

Urine-based multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS

Acute respiratory distress syndrome (ARDS), a life-threatening condition during critical illness, is a common complication of COVID-19. It can originate from various disease etiologies, including severe infections, major injury, or inhalation of irritants. ARDS poses substantial clinical challenges due to a lack of etiology-specific therapies, multisystem involvement, and heterogeneous, poor patient outcomes. A molecular comparison of ARDS groups holds the potential to reveal common and distinct mechanisms underlying ARDS pathogenesis. In this study, we performed a comparative analysis of urine-based metabolomics and proteomics profiles from COVID-19 ARDS patients (n = 42) and bacterial sepsis-induced ARDS patients (n = 17). The comparison of these ARDS etiologies identified 150 metabolites and 70 proteins that were differentially abundant between the two groups. Based on these findings, we interrogated the interplay of cell adhesion/extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis through a multi-omic network approach. Moreover, we identified a proteomic signature associated with mortality in COVID-19 ARDS patients, which contained several proteins that had previously been implicated in clinical manifestations frequently linked with ARDS pathogenesis. In summary, our results provide evidence for significant molecular differences in ARDS patients from different etiologies and a potential synergy of extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis. The proteomic mortality signature should be further investigated in future studies to develop prediction models for COVID-19 patient outcomes.

A high-level overview of the Organisation for Economic Co-operation and Development Adverse Outcome Pathway Programme

Background The Organisation for Economic Co-operation and Development (OECD), through its Chemical Safety Programme, is delegated to ensure the safety of humans and wildlife from harmful toxicants. To support these needs, initiatives to increase the efficiency of hazard identification and risk management are under way. Amongst these, the adverse outcome pathway (AOP) approach integrates information on biological knowledge and test methodologies (both established and new) to support regulatory decision making. AOPs collate biological knowledge from different sources, assess lines of evidence through considerations of causality and undergo rigorous peer-review before being subsequently endorsed by the OECD. It is envisioned that the OECD AOP Development Programme will transform the toxicity testing paradigm by leveraging the strengths of mechanistic and modelling based approaches and enhance the utility of high throughput screening assays. Since its launch, in 2012, the AOP Development Programme has matured with a greater number of AOPs endorsed since inception, and the attraction of new scientific disciplines (e.g. the radiation field). Recently, a Radiation and Chemical (Rad/Chem) AOP Joint Topical Group has been formed by the OECD Nuclear Energy Agency High-Level Group on Low-Dose Research (HLG-LDR) under the auspices of the Committee on Radiological Protection and Public Health (CRPPH). The topical group will work to evolve the development and use of the AOP framework in radiation research and regulation. As part of these efforts, the group will bring awareness and understanding on the programme, as it has matured from the chemical perspective. In this context, this paper provides the radiation community with a high-level overview of the OECD AOP Development Programme, including examples of application using knowledge gleaned from the field of chemical toxicology, and their work towards regulatory implementation. Conclusion: Although the drivers for developing AOPs in chemical sector differ from that of the radiation field, the principles and transparency of the approach can benefit both scientific disciplines. By providing perspectives and an understanding of the evolution of the OECD AOP Development Programme including case examples and work towards quantitative AOP development, it may motivate the expansion and implementation of AOPs in the radiation field.

A multiomics disease progression signature of low-risk ccRCC

Clear cell renal cell carcinoma (ccRCC) is the most common renal cancer. Identification of ccRCC likely to progress, despite an apparent low risk at the time of surgery, represents a key clinical issue. From a cohort of adult ccRCC patients (n = 443), we selected low-risk tumors progressing within a 5-years average follow-up (progressors: P, n = 8) and non-progressing (NP) tumors (n = 16). Transcriptome sequencing, miRNA sequencing and proteomics were performed on tissues obtained at surgery. We identified 151 proteins, 1167 mRNAs and 63 miRNAs differentially expressed in P compared to NP low-risk tumors. Pathway analysis demonstrated overrepresentation of proteins related to “LXR/RXR and FXR/RXR Activation”, “Acute Phase Response Signaling” in NP compared to P samples. Integrating mRNA, miRNA and proteomic data, we developed a 10-component classifier including two proteins, three genes and five miRNAs, effectively differentiating P and NP ccRCC and capturing underlying biological differences, potentially useful to identify “low-risk” patients requiring closer surveillance and treatment adjustments. Key results were validated by immunohistochemistry, qPCR and data from publicly available databases. Our work suggests that LXR, FXR and macrophage activation pathways could be critically involved in the inhibition of the progression of low-risk ccRCC. Furthermore, a 10-component classifier could support an early identification of apparently low-risk ccRCC patients.

Covariance shrinkage can assess and improve functional connectomes

Connectomes derived from resting-state functional MRI scans have significantly benefited from the development of dedicated fMRI motion correction and denoising algorithms. But they are based on empirical correlations that can produce unreliable results in high dimension low sample size settings. A family of statistical estimators, the covariance shrinkage methods, could mitigate this issue. Unfortunately, these methods have rarely been used to correct functional connectomes and no extensive experiment has been conducted so far to compare the shrinkage methods available for this task. In this work, we propose to fix this issue by processing a benchmark dataset made of a thousand high-resolution resting-state fMRI scans provided by the Human Connectome Project to compare the ability of five prominent covariance shrinkage methods to produce reliable functional connectomes at different spatial resolutions and scans duration: the pioneer linear covariance shrinkage method introduced by Ledoit and Wolf, the Oracle Approximating Shrinkage, the QuEST method, the NERCOME method, and a recent analytical approximation of the QuEST approach. Our experiments establish that all covariance shrinkage methods significantly improve functional connectomes derived from short fMRI scans. The Oracle Approximating Shrinkage and the QuEST method produced the best results. Lastly, we present shrinkage intensity charts that can be used for designing and analyzing fMRI studies. These charts indicate that sparse connectomes are difficult to estimate from short fMRI scans, and they describe a range of settings where dynamic functional connectivity should not be computed.

Aggregative trans-eQTL analysis detects trait-specific target gene sets in whole blood

Large scale genetic association studies have identified many trait-associated variants and understanding the role of these variants in the downstream regulation of gene-expressions can uncover important mediating biological mechanisms. Here we propose ARCHIE, a summary statistic based sparse canonical correlation analysis method to identify sets of gene-expressions trans-regulated by sets of known trait-related genetic variants. Simulation studies show that compared to standard methods, ARCHIE is better suited to identify "core"-like genes through which effects of many other genes may be mediated and can capture disease-specific patterns of genetic associations. By applying ARCHIE to publicly available summary statistics from the eQTLGen consortium, we identify gene sets which have significant evidence of trans-association with groups of known genetic variants across 29 complex traits. Around half (50.7%) of the selected genes do not have any strong trans-associations and are not detected by standard methods. We provide further evidence for causal basis of the target genes through a series of follow-up analyses. These results show ARCHIE is a powerful tool for identifying sets of genes whose trans-regulation may be related to specific complex traits.

Integrative metabolomic and proteomic signatures define clinical outcomes in severe COVID-19

The coronavirus disease-19 (COVID-19) pandemic has ravaged global healthcare with previously unseen levels of morbidity and mortality. In this study, we performed large-scale integrative multi-omics analyses of serum obtained from COVID-19 patients with the goal of uncovering novel pathogenic complexities of this disease and identifying molecular signatures that predict clinical outcomes. We assembled a network of protein-metabolite interactions through targeted metabolomic and proteomic profiling in 330 COVID-19 patients compared to 97 non-COVID, hospitalized controls. Our network identified distinct protein-metabolite cross talk related to immune modulation, energy and nucleotide metabolism, vascular homeostasis, and collagen catabolism. Additionally, our data linked multiple proteins and metabolites to clinical indices associated with long-term mortality and morbidity. Finally, we developed a novel composite outcome measure for COVID-19 disease severity based on metabolomics data. The model predicts severe disease with a concordance index of around 0.69, and shows high predictive power of 0.83-0.93 in two independent datasets.

Multi-Cell-Type Openness-Weighted Association Studies for Trait-Associated Genomic Segments Prioritization

Openness-weighted association study (OWAS) is a method that leverages the in silico prediction of chromatin accessibility to prioritize genome-wide association studies (GWAS) signals, and can provide novel insights into the roles of non-coding variants in complex diseases. A prerequisite to apply OWAS is to choose a trait-related cell type beforehand. However, for most complex traits, the trait-relevant cell types remain elusive. In addition, many complex traits involve multiple related cell types. To address these issues, we develop OWAS-joint, an efficient framework that aggregates predicted chromatin accessibility across multiple cell types, to prioritize disease-associated genomic segments. In simulation studies, we demonstrate that OWAS-joint achieves a greater statistical power compared to OWAS. Moreover, the heritability explained by OWAS-joint segments is higher than or comparable to OWAS segments. OWAS-joint segments also have high replication rates in independent replication cohorts. Applying the method to six complex human traits, we demonstrate the advantages of OWAS-joint over a single-cell-type OWAS approach. We highlight that OWAS-joint enhances the biological interpretation of disease mechanisms, especially for non-coding regions.

Induction and inhibition of Drosophila X chromosome gene expression are both impeded by the dosage compensation complex

Sex chromosomes frequently differ from the autosomes in the frequencies of genes with sexually dimorphic or tissue-specific expression. Multiple hypotheses have been put forth to explain the unique gene content of the X chromosome, including selection against male-beneficial X-linked alleles, expression limits imposed by the haploid dosage of the X in males, and interference by the dosage compensation complex on expression in males. Here, we investigate these hypotheses by examining differential gene expression in Drosophila melanogaster following several treatments that have widespread transcriptomic effects: bacterial infection, viral infection, and abiotic stress. We found that genes that are induced (upregulated) by these biotic and abiotic treatments are frequently under-represented on the X chromosome, but so are those that are repressed (downregulated) following treatment. We further show that whether a gene is bound by the dosage compensation complex in males can largely explain the paucity of both up- and downregulated genes on the X chromosome. Specifically, genes that are bound by the dosage compensation complex, or close to a dosage compensation complex high-affinity site, are unlikely to be up- or downregulated after treatment. This relationship, however, could partially be explained by a correlation between differential expression and breadth of expression across tissues. Nonetheless, our results suggest that dosage compensation complex binding, or the associated chromatin modifications, inhibit both up- and downregulation of X chromosome gene expression within specific contexts, including tissue-specific expression. We propose multiple possible mechanisms of action for the effect, including a role of Males absent on the first, a component of the dosage compensation complex, as a dampener of gene expression variance in both males and females. This effect could explain why the Drosophila X chromosome is depauperate in genes with tissue-specific or induced expression, while the mammalian X has an excess of genes with tissue-specific expression.

Immunolipidomics Reveals a Globoside Network During the Resolution of Pro-Inflammatory Response in Human Macrophages

Toll-like receptor 4 (TLR4)-mediated changes in macrophages reshape intracellular lipid pools to coordinate an effective innate immune response. Although this has been previously well-studied in different model systems, it remains incompletely understood in primary human macrophages. Here we report time-dependent lipidomic and transcriptomic responses to lipopolysaccharide (LPS) in primary human macrophages from healthy donors. We grouped the variation of ~200 individual lipid species measured by LC-MS/MS into eight temporal clusters. Among all other lipids, glycosphingolipids (glycoSP) and cholesteryl esters (CE) showed a sharp increase during the resolution phase (between 8h or 16h post LPS). GlycoSP, belonging to the globoside family (Gb3 and Gb4), showed the greatest inter-individual variability among all lipids quantified. Integrative network analysis between GlycoSP/CE levels and genome-wide transcripts, identified Gb4 d18:1/16:0 and CE 20:4 association with subnetworks enriched for T cell receptor signaling (PDCD1, CD86, PTPRC, CD247, IFNG) and DC-SIGN signaling (RAF1, CD209), respectively. Our findings reveal Gb3 and Gb4 globosides as sphingolipids associated with the resolution phase of inflammatory response in human macrophages.

Riemannian geometry-based transfer learning for reducing training time in c-VEP BCIs

One of the main problems that a brain-computer interface (BCI) face is that a training stage is required for acquiring training data to calibrate its classification model just before every use. Transfer learning is a promising method for addressing the problem. In this paper, we propose a Riemannian geometry-based transfer learning algorithm for code modulated visual evoked potential (c-VEP)-based BCIs, which can effectively reduce the calibration time without sacrificing the classification accuracy. The algorithm includes the main procedures of log-Euclidean data alignment (LEDA), super-trial construction, covariance matrix estimation, training accuracy-based subject selection (TSS) and minimum distance to mean classification. Among them, the LEDA reduces the difference in data distribution between subjects, whereas the TSS promotes the similarity between a target subject and the source subjects. The resulting performance of transfer learning is improved significantly. Sixteen subjects participated in a c-VEP BCI experiment and the recorded data were used in offline analysis. Leave-one subject-out (LOSO) cross-validation was used to evaluate the proposed algorithm on the data set. The results showed that the algorithm achieved much higher classification accuracy than the subject-specific (baseline) algorithm with the same number of training trials. Equivalently, the algorithm reduces the training time of the BCI at the same performance level and thus facilitates its application in real world.

SMARCE1 deficiency generates a targetable mSWI/SNF dependency in clear cell meningioma

Mammalian SWI/SNF (mSWI/SNF) ATP-dependent chromatin remodeling complexes establish and maintain chromatin accessibility and gene expression, and are frequently perturbed in cancer. Clear cell meningioma (CCM), an aggressive tumor of the central nervous system, is uniformly driven by loss of SMARCE1, an integral subunit of the mSWI/SNF core. Here, we identify a structural role for SMARCE1 in selectively stabilizing the canonical BAF (cBAF) complex core-ATPase module interaction. In CCM, cBAF complexes fail to stabilize on chromatin, reducing enhancer accessibility, and residual core module components increase the formation of BRD9-containing non-canonical BAF (ncBAF) complexes. Combined attenuation of cBAF function and increased ncBAF complex activity generates the CCM-specific gene expression signature, which is distinct from that of NF2-mutated meningiomas. Importantly, SMARCE1-deficient cells exhibit heightened sensitivity to small-molecule inhibition of ncBAF complexes. These data inform the function of a previously elusive SWI/SNF subunit and suggest potential therapeutic approaches for intractable SMARCE1-deficient CCM tumors.