Simulations of Sequence Evolution: How (Un)realistic They Are and Why.

MOTIVATION: Simulating multiple sequence alignments (MSAs) using probabilistic models of sequence evolution plays an important role in the evaluation of phylogenetic inference tools and is crucial to the development of novel learning-based approaches for phylogenetic reconstruction, for instance, neural networks. These models and the resulting simulated data need to be as realistic as possible to be indicative of the performance of the developed tools on empirical data and to ensure that neural networks trained on simulations perform well on empirical data. Over the years, numerous models of evolution have been published with the goal to represent as faithfully as possible the sequence evolution process and thus simulate empirical-like data. In this study, we simulated DNA and protein MSAs under increasingly complex models of evolution with and without insertion/deletion (indel) events using a state-of-the-art sequence simulator. We assessed their realism by quantifying how accurately supervised learning methods are able to predict whether a given MSA is simulated or empirical. RESULTS: Our results show that we can distinguish between empirical and simulated MSAs with high accuracy using two distinct and independently developed classification approaches across all tested models of sequence evolution. Our findings suggest that the current state-of-the-art models fail to accurately replicate several aspects of empirical MSAs, including site-wise rates as well as amino acid and nucleotide composition.

CALDERA: finding all significant de Bruijn subgraphs for bacterial GWAS.

MOTIVATION: Genome-wide association studies (GWAS), aiming to find genetic variants associated with a trait, have widely been used on bacteria to identify genetic determinants of drug resistance or hypervirulence. Recent bacterial GWAS methods usually rely on k-mers, whose presence in a genome can denote variants ranging from single-nucleotide polymorphisms to mobile genetic elements. This approach does not require a reference genome, making it easier to account for accessory genes. However, a same gene can exist in slightly different versions across different strains, leading to diluted effects. RESULTS: Here, we overcome this issue by testing covariates built from closed connected subgraphs (CCSs) of the de Bruijn graph defined over genomic k-mers. These covariates capture polymorphic genes as a single entity, improving k-mer-based GWAS both in terms of power and interpretability. However, a method naively testing all possible subgraphs would be powerless due to multiple testing corrections, and the mere exploration of these subgraphs would quickly become computationally intractable. The concept of testable hypothesis has successfully been used to address both problems in similar contexts. We leverage this concept to test all CCSs by proposing a novel enumeration scheme for these objects which fully exploits the pruning opportunity offered by testability, resulting in drastic improvements in computational efficiency. Our method integrates with existing visual tools to facilitate interpretation. AVAILABILITY AND IMPLEMENTATION: We provide an implementation of our method, as well as code to reproduce all results at https://github.com/HectorRDB/Caldera_ISMB. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A fast and agnostic method for bacterial genome-wide association studies: Bridging the gap between k-mers and genetic events.

Genome-wide association study (GWAS) methods applied to bacterial genomes have shown promising results for genetic marker discovery or detailed assessment of marker effect. Recently, alignment-free methods based on k-mer composition have proven their ability to explore the accessory genome. However, they lead to redundant descriptions and results which are sometimes hard to interpret. Here we introduce DBGWAS, an extended k-mer-based GWAS method producing interpretable genetic variants associated with distinct phenotypes. Relying on compacted De Bruijn graphs (cDBG), our method gathers cDBG nodes, identified by the association model, into subgraphs defined from their neighbourhood in the initial cDBG. DBGWAS is alignment-free and only requires a set of contigs and phenotypes. In particular, it does not require prior annotation or reference genomes. It produces subgraphs representing phenotype-associated genetic variants such as local polymorphisms and mobile genetic elements (MGE). It offers a graphical framework which helps interpret GWAS results. Importantly it is also computationally efficient-experiments took one hour and a half on average. We validated our method using antibiotic resistance phenotypes for three bacterial species. DBGWAS recovered known resistance determinants such as mutations in core genes in Mycobacterium tuberculosis, and genes acquired by horizontal transfer in Staphylococcus aureus and Pseudomonas aeruginosa-along with their MGE context. It also enabled us to formulate new hypotheses involving genetic variants not yet described in the antibiotic resistance literature. An open-source tool implementing DBGWAS is available at https://gitlab.com/leoisl/dbgwas.

PEPA test: fast and powerful differential analysis from relative quantitative proteomics data using shared peptides.

We propose a new hypothesis test for the differential abundance of proteins in mass-spectrometry based relative quantification. An important feature of this type of high-throughput analyses is that it involves an enzymatic digestion of the sample proteins into peptides prior to identification and quantification. Due to numerous homology sequences, different proteins can lead to peptides with identical amino acid chains, so that their parent protein is ambiguous. These so-called shared peptides make the protein-level statistical analysis a challenge and are often not accounted for. In this article, we use a linear model describing peptide-protein relationships to build a likelihood ratio test of differential abundance for proteins. We show that the likelihood ratio statistic can be computed in linear time with the number of peptides. We also provide the asymptotic null distribution of a regularized version of our statistic. Experiments on both real and simulated datasets show that our procedures outperforms state-of-the-art methods. The procedures are available via the pepa.test function of the DAPAR Bioconductor R package.

Biological sequence modeling with convolutional kernel networks.

MOTIVATION: The growing number of annotated biological sequences available makes it possible to learn genotype-phenotype relationships from data with increasingly high accuracy. When large quantities of labeled samples are available for training a model, convolutional neural networks can be used to predict the phenotype of unannotated sequences with good accuracy. Unfortunately, their performance with medium- or small-scale datasets is mitigated, which requires inventing new data-efficient approaches. RESULTS: We introduce a hybrid approach between convolutional neural networks and kernel methods to model biological sequences. Our method enjoys the ability of convolutional neural networks to learn data representations that are adapted to a specific task, while the kernel point of view yields algorithms that perform significantly better when the amount of training data is small. We illustrate these advantages for transcription factor binding prediction and protein homology detection, and we demonstrate that our model is also simple to interpret, which is crucial for discovering predictive motifs in sequences. AVAILABILITY AND IMPLEMENTATION: Source code is freely available at https://gitlab.inria.fr/dchen/CKN-seq. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Thoracic Epidural Analgesia and Mortality in Acute Pancreatitis: A Multicenter Propensity Analysis.

OBJECTIVE: Recent preclinical and clinical data suggest that thoracic epidural analgesia, a technique primarily aimed at decreasing pain, might exert anti-inflammatory effects, enhance splanchnic and pancreatic blood flow during acute pancreatitis; however, the influence of epidural analgesia on mortality remains under investigated in this setting. This study was therefore designed to assess the impact of epidural analgesia on mortality in ICU patients with acute pancreatitis. DESIGN: Multicenter retrospective, observational, cohort study. SETTING: Seventeen French and Belgian ICUs. PATIENTS: All patients admitted to with acute pancreatitis between June 2009 and March 2014. INTERVENTIONS: The primary exposure was thoracic epidural analgesia versus standard care without epidural analgesia. The primary outcome was 30-day mortality. Propensity analyses were used to control for bias in treatment assignment and prognostic imbalances. MEASUREMENTS AND MAIN RESULTS: One thousand three ICU patients with acute pancreatitis were enrolled, of whom 212 died within 30 days. Epidural analgesia was used in 46 patients and was associated with reduced mortality in unadjusted analyses (4% vs. 22%; p = 0.003). After adjustment for baseline variables associated with mortality, epidural analgesia was still an independent predictor of 30-day mortality (adjusted odds ratio, 0.10; [95% CI, 0.02-0.49]; p = 0.004). Using propensity score analysis, the risk of all-cause 30-day mortality in patients with acute pancreatitis receiving epidural analgesia was significantly lower than that in matched patients who did not receive epidural analgesia (2% vs. 17%; p = 0.01). CONCLUSIONS: Among critically ill patients with acute pancreatitis, mortality at 30 days was lower in patients who received epidural analgesia than in comparable patients who did not. These findings support ongoing research on the use of epidural analgesia as a therapeutic intervention in acute pancreatitis.

Correcting gene expression data when neither the unwanted variation nor the factor of interest are observed.

When dealing with large scale gene expression studies, observations are commonly contaminated by sources of unwanted variation such as platforms or batches. Not taking this unwanted variation into account when analyzing the data can lead to spurious associations and to missing important signals. When the analysis is unsupervised, e.g. when the goal is to cluster the samples or to build a corrected version of the dataset--as opposed to the study of an observed factor of interest--taking unwanted variation into account can become a difficult task. The factors driving unwanted variation may be correlated with the unobserved factor of interest, so that correcting for the former can remove the latter if not done carefully. We show how negative control genes and replicate samples can be used to estimate unwanted variation in gene expression, and discuss how this information can be used to correct the expression data. The proposed methods are then evaluated on synthetic data and three gene expression datasets. They generally manage to remove unwanted variation without losing the signal of interest and compare favorably to state-of-the-art corrections. All proposed methods are implemented in the bioconductor package RUVnormalize.

Early Identification of Patients With Out-of-Hospital Cardiac Arrest With No Chance of Survival and Consideration for Organ Donation.

BACKGROUND: In patients with out-of-hospital cardiac arrest (OHCA), care requirements can conflict with the need to promptly focus efforts on organ donation in patients who are pronounced dead. OBJECTIVE: To evaluate objective criteria for identifying patients with OHCA with no chance of survival during the first minutes of cardiopulmonary resuscitation to enable prompt orientation toward organ donation. DESIGN: Retrospective assessment using OHCA data from 2 registries and 1 trial. SETTING: France (Paris Sudden Death Expertise Center [SDEC] prospective cohort [2011 to 2014] and PRESENCE multicenter cluster randomized trial [ClinicalTrials.gov: NCT01009606] [2009 to 2011]) and the United States (King County, Washington, prospective cohort [2006 to 2011]). PATIENTS: 1771 patients from the Paris SDEC 1-year cohort (2011 to 2012) and 5192 from the validation cohorts. MEASUREMENTS: Evaluation of 3 objective criteria (OHCA not witnessed by emergency medical services personnel, nonshockable initial cardiac rhythm, and no return of spontaneous circulation before receipt of a third 1-mg dose of epinephrine), survival rate at hospital discharge among patients meeting these criteria, performance of the criteria, and number of patients eligible for organ donation. RESULTS: In the Paris SDEC 1-year cohort, the survival rate among the 772 patients with OHCA who met the objective criteria was 0% (95% CI, 0.0% to 0.5%), with a specificity of 100% (CI, 97% to 100%) and a positive predictive value of 100% (CI, 99% to 100%). These results were verified in the validation cohorts. Ninety-five (12%) patients in the Paris SDEC 1-year cohort may have been eligible for organ donation. LIMITATION: Several patients had unknown outcomes. CONCLUSION: Three objective criteria enable the early identification of patients with OHCA with essentially no chance of survival and may help in decision making about the organ donation process. PRIMARY FUNDING SOURCE: French Ministry of Health.

A convex formulation for joint RNA isoform detection and quantification from multiple RNA-seq samples.

BACKGROUND: Detecting and quantifying isoforms from RNA-seq data is an important but challenging task. The problem is often ill-posed, particularly at low coverage. One promising direction is to exploit several samples simultaneously. RESULTS: We propose a new method for solving the isoform deconvolution problem jointly across several samples. We formulate a convex optimization problem that allows to share information between samples and that we solve efficiently. We demonstrate the benefits of combining several samples on simulated and real data, and show that our approach outperforms pooling strategies and methods based on integer programming. CONCLUSION: Our convex formulation to jointly detect and quantify isoforms from RNA-seq data of multiple related samples is a computationally efficient approach to leverage the hypotheses that some isoforms are likely to be present in several samples. The software and source code are available at http://cbio.ensmp.fr/flipflop.

Statistical methods for handling unwanted variation in metabolomics data.

Metabolomics experiments are inevitably subject to a component of unwanted variation, due to factors such as batch effects, long runs of samples, and confounding biological variation. Although the removal of this unwanted variation is a vital step in the analysis of metabolomics data, it is considered a gray area in which there is a recognized need to develop a better understanding of the procedures and statistical methods required to achieve statistically relevant optimal biological outcomes. In this paper, we discuss the causes of unwanted variation in metabolomics experiments, review commonly used metabolomics approaches for handling this unwanted variation, and present a statistical approach for the removal of unwanted variation to obtain normalized metabolomics data. The advantages and performance of the approach relative to several widely used metabolomics normalization approaches are illustrated through two metabolomics studies, and recommendations are provided for choosing and assessing the most suitable normalization method for a given metabolomics experiment. Software for the approach is made freely available.

Efficient RNA isoform identification and quantification from RNA-Seq data with network flows.

MOTIVATION: Several state-of-the-art methods for isoform identification and quantification are based on [Formula: see text]-regularized regression, such as the Lasso. However, explicitly listing the-possibly exponentially-large set of candidate transcripts is intractable for genes with many exons. For this reason, existing approaches using the [Formula: see text]-penalty are either restricted to genes with few exons or only run the regression algorithm on a small set of preselected isoforms. RESULTS: We introduce a new technique called FlipFlop, which can efficiently tackle the sparse estimation problem on the full set of candidate isoforms by using network flow optimization. Our technique removes the need of a preselection step, leading to better isoform identification while keeping a low computational cost. Experiments with synthetic and real RNA-Seq data confirm that our approach is more accurate than alternative methods and one of the fastest available. AVAILABILITY AND IMPLEMENTATION: Source code is freely available as an R package from the Bioconductor Web site (http://www.bioconductor.org/), and more information is available at http://cbio.ensmp.fr/flipflop. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Combining calls from multiple somatic mutation-callers.

BACKGROUND: Accurate somatic mutation-calling is essential for insightful mutation analyses in cancer studies. Several mutation-callers are publicly available and more are likely to appear. Nonetheless, mutation-calling is still challenging and there is unlikely to be one established caller that systematically outperforms all others. Therefore, fully utilizing multiple callers can be a powerful way to construct a list of final calls for one's research. RESULTS: Using a set of mutations from multiple callers that are impartially validated, we present a statistical approach for building a combined caller, which can be applied to combine calls in a wider dataset generated using a similar protocol. Using the mutation outputs and the validation data from The Cancer Genome Atlas endometrial study (6,746 sites), we demonstrate how to build a statistical model that predicts the probability of each call being a somatic mutation, based on the detection status of multiple callers and a few associated features. CONCLUSION: The approach allows us to build a combined caller across the full range of stringency levels, which outperforms all of the individual callers.

VEGF-C prophylaxis favors lymphatic drainage and modulates neuroinflammation in a stroke model.

Meningeal lymphatic vessels (MLVs) promote tissue clearance and immune surveillance in the central nervous system (CNS). Vascular endothelial growth factor-C (VEGF-C) regulates MLV development and maintenance and has therapeutic potential for treating neurological disorders. Herein, we investigated the effects of VEGF-C overexpression on brain fluid drainage and ischemic stroke outcomes in mice. Intracerebrospinal administration of an adeno-associated virus expressing mouse full-length VEGF-C (AAV-mVEGF-C) increased CSF drainage to the deep cervical lymph nodes (dCLNs) by enhancing lymphatic growth and upregulated neuroprotective signaling pathways identified by single nuclei RNA sequencing of brain cells. In a mouse model of ischemic stroke, AAV-mVEGF-C pretreatment reduced stroke injury and ameliorated motor performances in the subacute stage, associated with mitigated microglia-mediated inflammation and increased BDNF signaling in brain cells. Neuroprotective effects of VEGF-C were lost upon cauterization of the dCLN afferent lymphatics and not mimicked by acute post-stroke VEGF-C injection. We conclude that VEGF-C prophylaxis promotes multiple vascular, immune, and neural responses that culminate in a protection against neurological damage in acute ischemic stroke.

Correction: Impact of a Postintensive Care Unit Multidisciplinary Follow-up on the Quality of Life (SUIVI-REA): Protocol for a Multicenter Randomized Controlled Trial.

[This corrects the article DOI: 10.2196/30496.].

Preliminary results of transplantation with kidneys donated after cardiocirculatory determination of death: a French single-centre experience.

BACKGROUND: Donation after circulatory determination of death (DCDD), formerly non-heart-beating donation and donation after cardiac death, has been re-introduced into clinical practice in France since June 2006 as a potential solution to organ shortage, but this kidney transplantation programme is not popular yet, mainly because of logistical concerns and uncertainty about the long-term warm ischaemia impact on transplanted kidneys. METHODS: Our institution started the DCDD programme in January 2007, following the national 'BioMedicine Agency' protocol. We only considered uncontrolled donors with an initial no-flow period (i.e. delay between collapse and external cardiac massage start) <30 min. A 5-min stand-off period was observed before declaring the death and performing in situ cold perfusion, and since January 2010, normothermic subdiaphragmatic extracorporeal membrane oxygenation. All kidneys were machine-perfused using the hypothermic pulsatile preservation system before transplantation. Morphologic assessment and perfusion indexes were used to assess the suitability for transplantation. RESULTS: From January 2007 to December 2010, our team performed 58 kidney transplantations from uncontrolled Maastricht Category I and II donors. Mean recipient age was 47 ± 9 years. Male/female ratio was 45/13. Mean waiting time on transplantation registry was 30 months (4-180). Mean cold ischaemia time was 13 h 40 min (7-18) and pulsatile perfusion time 8 h (1-16). We had three cases (5%) of primary non-function (PNF) and 95% of delayed graft function. There was no increase in biopsy-proven acute rejection incidence (12.7%). Patient and graft survivals were 98 and 91.4%, respectively, at 1 year and 98 and 88%, respectively, at last follow-up. Estimated glomerular filtration rate ( Modification of Diet in Renal Disease formula) was 48 ± 16 mL/min/1.73 m(2) at 1 year and 48 ± 15 mL/min/1.73 m(2) at the last follow-up. CONCLUSIONS: DCDD kidneys are a valuable additional source of organs for transplantation. Our results show encouraging outcomes, which give rise to further interest in this donor pool. Respecting the national protocol is crucial to prevent PNF and deleterious warm ischaemia effect on transplanted kidney.

Endothelial Unc5B controls blood-brain barrier integrity.

Blood-brain barrier (BBB) integrity is critical for proper function of the central nervous system (CNS). Here, we show that the endothelial Unc5B receptor controls BBB integrity by maintaining Wnt/ß-catenin signaling. Inducible endothelial-specific deletion of Unc5B in adult mice leads to BBB leak from brain capillaries that convert to a barrier-incompetent state with reduced Claudin-5 and increased PLVAP expression. Loss of Unc5B decreases BBB Wnt/ß-catenin signaling, and ß-catenin overexpression rescues Unc5B mutant BBB defects. Mechanistically, the Unc5B ligand Netrin-1 enhances Unc5B interaction with the Wnt co-receptor LRP6, induces its phosphorylation and activates Wnt/ß-catenin downstream signaling. Intravenous delivery of antibodies blocking Netrin-1 binding to Unc5B causes a transient BBB breakdown and disruption of Wnt signaling, followed by neurovascular barrier resealing. These data identify Netrin-1-Unc5B signaling as a ligand-receptor pathway that regulates BBB integrity, with implications for CNS diseases.

Impact of a Postintensive Care Unit Multidisciplinary Follow-up on the Quality of Life (SUIVI-REA): Protocol for a Multicenter Randomized Controlled Trial.

BACKGROUND: Critically ill patients are at risk of developing a postintensive care syndrome (PICS), which is characterized by physical, psychological, and cognitive impairments and which dramatically impacts the patient's quality of life (QoL). No intervention has been shown to improve QoL. We hypothesized that a medical, psychological, and social follow-up would improve QoL by mitigating the PICS. OBJECTIVE: This multicenter, randomized controlled trial (SUIVI-REA) aims to compare a multidisciplinary follow-up with a standard postintensive care unit (ICU) follow-up. METHODS: Patients were randomized to the control or intervention arm. In the intervention arm, multidisciplinary follow-up involved medical, psychological, and social evaluation at ICU discharge and at 3, 6, and 12 months thereafter. In the placebo group, patients were seen only at 12 months by the multidisciplinary team. Baseline characteristics at ICU discharge were collected for all patients. The primary outcome was QoL at 1 year, assessed using the Euro Quality of Life-5 dimensions (EQ5D). Secondary outcomes were mortality, cognitive, psychological, and functional status; social and professional reintegration; and the rate of rehospitalization and outpatient consultations at 1 year. RESULTS: The study was funded by the Ministry of Health in June 2010. It was approved by the Ethics Committee on July 8, 2011. The first and last patient were randomized on December 20, 2012, and September 1, 2017, respectively. A total of 546 patients were enrolled across 11 ICUs. At present, data management is ongoing, and all parties involved in the trial remain blinded. CONCLUSIONS: The SUVI-REA multicenter randomized controlled trial aims to assess whether a post-ICU multidisciplinary follow-up improves QoL at 1 year. TRIAL REGISTRATION: Clinicaltrials.gov NCT01796509; https://clinicaltrials.gov/ct2/show/NCT01796509. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/30496.

Conserved meningeal lymphatic drainage circuits in mice and humans.

Meningeal lymphatic vessels (MLVs) were identified in the dorsal and caudobasal regions of the dura mater, where they ensure waste product elimination and immune surveillance of brain tissues. Whether MLVs exist in the anterior part of the murine and human skull and how they connect with the glymphatic system and extracranial lymphatics remained unclear. Here, we used light-sheet fluorescence microscopy (LSFM) imaging of mouse whole-head preparations after OVA-A555 tracer injection into the cerebrospinal fluid (CSF) and performed real-time vessel-wall (VW) magnetic resonance imaging (VW-MRI) after systemic injection of gadobutrol in patients with neurological pathologies. We observed a conserved three-dimensional anatomy of MLVs in mice and humans that aligned with dural venous sinuses but not with nasal CSF outflow, and we discovered an extended anterior MLV network around the cavernous sinus, with exit routes through the foramina of emissary veins. VW-MRI may provide a diagnostic tool for patients with CSF drainage defects and neurological diseases.

Mycobacterium tuberculosis genetic features associated with pulmonary tuberculosis severity.

OBJECTIVES: Mycobacterium tuberculosis (Mtb) infections result in a wide spectrum of clinical presentations but without proven Mtb genetic determinants. Herein, we hypothesized that the genetic features of Mtb clinical isolates, such as specific polymorphisms or microdiversity, may be linked to tuberculosis (TB) severity. METHODS: A total of 234 patients with pulmonary TB (including 193 drug-susceptible and 14 monoresistant cases diagnosed between 2017 and 2020 and 27 multidrug-resistant cases diagnosed between 2010 and 2020) were stratified according to TB disease severity, and Mtb genetic features were explored using whole genome sequencing, including heterologous single-nucleotide polymorphism (SNP), calling to explore microdiversity. Finally, we performed a structural equation modeling analysis to relate TB severity to Mtb genetic features. RESULTS: The clinical isolates from patients with mild TB carried mutations in genes associated with host-pathogen interaction, whereas those from patients with moderate/severe TB carried mutations associated with regulatory mechanisms. Genome-wide association study identified an SNP in the promoter of the gene coding for the virulence regulator espR, statistically associated with moderate/severe disease. Structural equation modeling and model comparisons indicated that TB severity was associated with the detection of Mtb microdiversity within clinical isolates and to the espR SNP. CONCLUSION: Taken together, these results provide a new insight to better understand TB pathophysiology and could provide a new prognosis tool for pulmonary TB severity.

SLIT2/ROBO signaling in tumor-associated microglia and macrophages drives glioblastoma immunosuppression and vascular dysmorphia.

SLIT2 is a secreted polypeptide that guides migration of cells expressing Roundabout 1 and 2 (ROBO1 and ROBO2) receptors. Herein, we investigated SLIT2/ROBO signaling effects in gliomas. In patients with glioblastoma (GBM), SLIT2 expression increased with malignant progression and correlated with poor survival and immunosuppression. Knockdown of SLIT2 in mouse glioma cells and patient-derived GBM xenografts reduced tumor growth and rendered tumors sensitive to immunotherapy. Tumor cell SLIT2 knockdown inhibited macrophage invasion and promoted a cytotoxic gene expression profile, which improved tumor vessel function and enhanced efficacy of chemotherapy and immunotherapy. Mechanistically, SLIT2 promoted microglia/macrophage chemotaxis and tumor-supportive polarization via ROBO1- and ROBO2-mediated PI3K-γ activation. Macrophage Robo1 and Robo2 deletion and systemic SLIT2 trap delivery mimicked SLIT2 knockdown effects on tumor growth and the tumor microenvironment (TME), revealing SLIT2 signaling through macrophage ROBOs as a potentially novel regulator of the GBM microenvironment and immunotherapeutic target for brain tumors.