Clear amniotic fluid aspiration syndrome: A novel description of an old entity.

BACKGROUND: Clear amniotic fluid aspiration syndrome (CAF-AS) is a very rare event occurring in 0.25% of our term clear amniotic fluids deliveries. The study's aims were: 1. to characterize the risk factors and outcomes associated with Clear Amniotic Fluid Aspiration Syndrome and 2. to compare the outcomes of Clear Amniotic Fluid Aspiration to Meconium Aspiration. METHODS: This was an observational study over a 22-year period in a single level-3 medical center. Compared were parturient/labor characteristics and neonatal outcomes in cases with suspected Clear Amniotic Fluid Aspiration to cases suspected for Meconium Aspiration. RESULTS: Out of 79,620 term deliveries there were 66,705 (83.8%) clear amniotic fluids and 12,915 (16.2%) meconium stained amniotic fluid (MSAF). Of neonates born with clear amniotic fluid, 166 (0.25%) were diagnosed with Clear Amniotic Fluid Aspiration Syndrome (CAF-AS), while 202 (15.7%) of those born with MSAF, were diagnosed with aspiration syndrome (MSAF-AS). Both conditions had comparable rates of mild manifestation (67.5% vs 69.2%, p = 0.63). Persistent pulmonary hypertension (PPH) occurred 5 times less in CAF-AS than MSAF-AS (4% vs 20%, OR 0.17, P< 0.0001) Both conditions presented similar rates of surfactant without PPH (11.1% vs 13.4%, p = 0.87). There was 1 postnatal death in CAF-AS vs 10 in MSAF. CONCLUSION: CAF-AS were quantitatively quite similar in terms of need of actual active intervention of the neonatologists in the delivery room (166 vs 202, i.e. in terms of numbers of cases and not prevalence) to MSAF-AS.We identified in these cases two major specific causes: hyperkinetic explosive deliveries in multiparas and long-lasting episodes of maternal hypotension due to epidural/spinal anaesthesia during labor. Out of 140 million births per year in the world, it should be of concern that 3 million cases are neglected nowadays. Future studies should evaluate if this CAF-AS should benefit from a more active intervention such as immediate endotracheal suction at birth, this clear fluid being very easy to suction.

Pacpaint: a histology-based deep learning model uncovers the extensive intratumor molecular heterogeneity of pancreatic adenocarcinoma.

Two tumor (Classical/Basal) and stroma (Inactive/active) subtypes of Pancreatic adenocarcinoma (PDAC) with prognostic and theragnostic implications have been described. These molecular subtypes were defined by RNAseq, a costly technique sensitive to sample quality and cellularity, not used in routine practice. To allow rapid PDAC molecular subtyping and study PDAC heterogeneity, we develop PACpAInt, a multi-step deep learning model. PACpAInt is trained on a multicentric cohort (n = 202) and validated on 4 independent cohorts including biopsies (surgical cohorts n = 148; 97; 126 / biopsy cohort n = 25), all with transcriptomic data (n = 598) to predict tumor tissue, tumor cells from stroma, and their transcriptomic molecular subtypes, either at the whole slide or tile level (112 µm squares). PACpAInt correctly predicts tumor subtypes at the whole slide level on surgical and biopsies specimens and independently predicts survival. PACpAInt highlights the presence of a minor aggressive Basal contingent that negatively impacts survival in 39% of RNA-defined classical cases. Tile-level analysis ( > 6 millions) redefines PDAC microheterogeneity showing codependencies in the distribution of tumor and stroma subtypes, and demonstrates that, in addition to the Classical and Basal tumors, there are Hybrid tumors that combine the latter subtypes, and Intermediate tumors that may represent a transition state during PDAC evolution.

Paraben exposure through drugs in the neonatal intensive care unit: a regional cohort study.

Background and objectives: Environmental factors influence the development of very preterm infants (VPIs, born at less than 32 weeks of gestation). It is important to identify all potential sources of paraben exposure in these vulnerable infants. We aimed to quantify paraben exposure via drug administration in a cohort of VPI cared for in neonatal intensive care units (NICUs). Methods: A prospective, observational study was carried out over a five-year period in a regional setting (two NICUs using the same computerized order-entry system). The main outcome was exposure to paraben-containing drugs. The secondary outcomes were: time of the first exposure, daily intake, number of infants exceeding paraben acceptable daily intake (ADI: 0-10 mg/kg/d), duration of exposure, and cumulative dose. Results: The cohort consisted of 1,315 VPIs [BW 1129.9 (±360.4) g]. Among them, 85.5% were exposed to paraben-containing drugs. In 40.4% of infants, the first exposure occurred during the second week of life. Mean paraben intake and duration of exposure were, respectively, 2.2 (±1.4) mg/kg/d and 33.1 (±22.3) days. The cumulative paraben intake was 80.3 (±84.6) mg/kg. The ADI was exceeded in 3.5% of exposed infants. Lower GA was associated with higher intake and longer exposure (p < 0.0001). The main molecules involved in paraben exposure were: sodium iron feredetate, paracetamol, furosemide, and sodium bicarbonate + sodium alginate. Conclusion: Commonly used drugs are potential source of parabens, and ADI can be easily exceeded in VPIs cared for in NICUs. Efforts are needed to identify paraben-free alternative formulations for these vulnerable infants.

Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform.

Obtaining a standardized benchmark of computational methods is a major issue in data-science communities. Dedicated frameworks enabling fair benchmarking in a unified environment are yet to be developed. Here, we introduce Codabench, a meta-benchmark platform that is open sourced and community driven for benchmarking algorithms or software agents versus datasets or tasks. A public instance of Codabench is open to everyone free of charge and allows benchmark organizers to fairly compare submissions under the same setting (software, hardware, data, algorithms), with custom protocols and data formats. Codabench has unique features facilitating easy organization of flexible and reproducible benchmarks, such as the possibility of reusing templates of benchmarks and supplying compute resources on demand. Codabench has been used internally and externally on various applications, receiving more than 130 users and 2,500 submissions. As illustrative use cases, we introduce four diverse benchmarks covering graph machine learning, cancer heterogeneity, clinical diagnosis, and reinforcement learning.

DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification.

BACKGROUND: Quantification of tumor heterogeneity is essential to better understand cancer progression and to adapt therapeutic treatments to patient specificities. Bioinformatic tools to assess the different cell populations from single-omic datasets as bulk transcriptome or methylome samples have been recently developed, including reference-based and reference-free methods. Improved methods using multi-omic datasets are yet to be developed in the future and the community would need systematic tools to perform a comparative evaluation of these algorithms on controlled data. RESULTS: We present DECONbench, a standardized unbiased benchmarking resource, applied to the evaluation of computational methods quantifying cell-type heterogeneity in cancer. DECONbench includes gold standard simulated benchmark datasets, consisting of transcriptome and methylome profiles mimicking pancreatic adenocarcinoma molecular heterogeneity, and a set of baseline deconvolution methods (reference-free algorithms inferring cell-type proportions). DECONbench performs a systematic performance evaluation of each new methodological contribution and provides the possibility to publicly share source code and scoring. CONCLUSION: DECONbench allows continuous submission of new methods in a user-friendly fashion, each novel contribution being automatically compared to the reference baseline methods, which enables crowdsourced benchmarking. DECONbench is designed to serve as a reference platform for the benchmarking of deconvolution methods in the evaluation of cancer heterogeneity. We believe it will contribute to leverage the benchmarking practices in the biomedical and life science communities. DECONbench is hosted on the open source Codalab competition platform. It is freely available at: https://competitions.codalab.org/competitions/27453 .

PenDA, a rank-based method for personalized differential analysis: Application to lung cancer.

The hopes of precision medicine rely on our capacity to measure various high-throughput genomic information of a patient and to integrate them for personalized diagnosis and adapted treatment. Reaching these ambitious objectives will require the development of efficient tools for the detection of molecular defects at the individual level. Here, we propose a novel method, PenDA, to perform Personalized Differential Analysis at the scale of a single sample. PenDA is based on the local ordering of gene expressions within individual cases and infers the deregulation status of genes in a sample of interest compared to a reference dataset. Based on realistic simulations of RNA-seq data of tumors, we showed that PenDA outcompetes existing approaches with very high specificity and sensitivity and is robust to normalization effects. Applying the method to lung cancer cohorts, we observed that deregulated genes in tumors exhibit a cancer-type-specific commitment towards up- or down-regulation. Based on the individual information of deregulation given by PenDA, we were able to define two new molecular histologies for lung adenocarcinoma cancers strongly correlated to survival. In particular, we identified 37 biomarkers whose up-regulation lead to bad prognosis and that we validated on two independent cohorts. PenDA provides a robust, generic tool to extract personalized deregulation patterns that can then be used for the discovery of therapeutic targets and for personalized diagnosis. An open-access, user-friendly R package is available at https://github.com/bcm-uga/penda.

Guidelines for cell-type heterogeneity quantification based on a comparative analysis of reference-free DNA methylation deconvolution software.

BACKGROUND: Cell-type heterogeneity of tumors is a key factor in tumor progression and response to chemotherapy. Tumor cell-type heterogeneity, defined as the proportion of the various cell-types in a tumor, can be inferred from DNA methylation of surgical specimens. However, confounding factors known to associate with methylation values, such as age and sex, complicate accurate inference of cell-type proportions. While reference-free algorithms have been developed to infer cell-type proportions from DNA methylation, a comparative evaluation of the performance of these methods is still lacking. RESULTS: Here we use simulations to evaluate several computational pipelines based on the software packages MeDeCom, EDec, and RefFreeEWAS. We identify that accounting for confounders, feature selection, and the choice of the number of estimated cell types are critical steps for inferring cell-type proportions. We find that removal of methylation probes which are correlated with confounder variables reduces the error of inference by 30-35%, and that selection of cell-type informative probes has similar effect. We show that Cattell's rule based on the scree plot is a powerful tool to determine the number of cell-types. Once the pre-processing steps are achieved, the three deconvolution methods provide comparable results. We observe that all the algorithms' performance improves when inter-sample variation of cell-type proportions is large or when the number of available samples is large. We find that under specific circumstances the methods are sensitive to the initialization method, suggesting that averaging different solutions or optimizing initialization is an avenue for future research. CONCLUSION: Based on the lessons learned, to facilitate pipeline validation and catalyze further pipeline improvement by the community, we develop a benchmark pipeline for inference of cell-type proportions and implement it in the R package medepir.

CRELD1 is an evolutionarily-conserved maturational enhancer of ionotropic acetylcholine receptors.

The assembly of neurotransmitter receptors in the endoplasmic reticulum limits the number of receptors delivered to the plasma membrane, ultimately controlling neurotransmitter sensitivity and synaptic transfer function. In a forward genetic screen conducted in the nematode C. elegans, we identified crld-1 as a gene required for the synaptic expression of ionotropic acetylcholine receptors (AChR). We demonstrated that the CRLD-1A isoform is a membrane-associated ER-resident protein disulfide isomerase (PDI). It physically interacts with AChRs and promotes the assembly of AChR subunits in the ER. Mutations of Creld1, the human ortholog of crld-1a, are responsible for developmental cardiac defects. We showed that Creld1 knockdown in mouse muscle cells decreased surface expression of AChRs and that expression of mouse Creld1 in C. elegans rescued crld-1a mutant phenotypes. Altogether these results identify a novel and evolutionarily-conserved maturational enhancer of AChR biogenesis, which controls the abundance of functional receptors at the cell surface.

Genomics of cellular proliferation in periodic environmental fluctuations.

Living systems control cell growth dynamically by processing information from their environment. Although responses to a single environmental change have been intensively studied, little is known about how cells react to fluctuating conditions. Here, we address this question at the genomic scale by measuring the relative proliferation rate (fitness) of 3,568 yeast gene deletion mutants in out-of-equilibrium conditions: periodic oscillations between two environmental conditions. In periodic salt stress, fitness and its genetic variance largely depended on the oscillating period. Surprisingly, dozens of mutants displayed pronounced hyperproliferation under short stress periods, revealing unexpected controllers of growth under fast dynamics. We validated the implication of the high-affinity cAMP phosphodiesterase and of a regulator of protein translocation to mitochondria in this group. Periodic oscillations of extracellular methionine, a factor unrelated to salinity, also altered fitness but to a lesser extent and for different genes. The results illustrate how natural selection acts on mutations in a dynamic environment, highlighting unsuspected genetic vulnerabilities to periodic stress in molecular processes that are conserved across all eukaryotes.

Assigning function to natural allelic variation via dynamic modeling of gene network induction.

More and more natural DNA variants are being linked to physiological traits. Yet, understanding what differences they make on molecular regulations remains challenging. Important properties of gene regulatory networks can be captured by computational models. If model parameters can be "personalized" according to the genotype, their variation may then reveal how DNA variants operate in the network. Here, we combined experiments and computations to visualize natural alleles of the yeast GAL3 gene in a space of model parameters describing the galactose response network. Alleles altering the activation of Gal3p by galactose were discriminated from those affecting its activity (production/degradation or efficiency of the activated protein). The approach allowed us to correctly predict that a non-synonymous SNP would change the binding affinity of Gal3p with the Gal80p transcriptional repressor. Our results illustrate how personalizing gene regulatory models can be used for the mechanistic interpretation of genetic variants.

Exploiting Single-Cell Quantitative Data to Map Genetic Variants Having Probabilistic Effects.

Despite the recent progress in sequencing technologies, genome-wide association studies (GWAS) remain limited by a statistical-power issue: many polymorphisms contribute little to common trait variation and therefore escape detection. The small contribution sometimes corresponds to incomplete penetrance, which may result from probabilistic effects on molecular regulations. In such cases, genetic mapping may benefit from the wealth of data produced by single-cell technologies. We present here the development of a novel genetic mapping method that allows to scan genomes for single-cell Probabilistic Trait Loci that modify the statistical properties of cellular-level quantitative traits. Phenotypic values are acquired on thousands of individual cells, and genetic association is obtained from a multivariate analysis of a matrix of Kantorovich distances. No prior assumption is required on the mode of action of the genetic loci involved and, by exploiting all single-cell values, the method can reveal non-deterministic effects. Using both simulations and yeast experimental datasets, we show that it can detect linkages that are missed by classical genetic mapping. A probabilistic effect of a single SNP on cell shape was detected and validated. The method also detected a novel locus associated with elevated gene expression noise of the yeast galactose regulon. Our results illustrate how single-cell technologies can be exploited to improve the genetic dissection of certain common traits. The method is available as an open source R package called ptlmapper.

How does evolution tune biological noise?

Part of molecular and phenotypic differences between individual cells, between body parts, or between individuals can result from biological noise. This source of variation is becoming more and more apparent thanks to the recent advances in dynamic imaging and single-cell analysis. Some of these studies showed that the link between genotype and phenotype is not strictly deterministic. Mutations can change various statistical properties of a biochemical reaction, and thereby the probability of a trait outcome. The fact that they can modulate phenotypic noise brings up an intriguing question: how may selection act on these mutations? In this review, we approach this question by first covering the evidence that biological noise is under genetic control and therefore a substrate for evolution. We then sequentially inspect the possibilities of negative, neutral, and positive selection for mutations increasing biological noise. Finally, we hypothesize on the specific case of H2A.Z, which was shown to both buffer phenotypic noise and modulate transcriptional efficiency.

Biosynthesis of ionotropic acetylcholine receptors requires the evolutionarily conserved ER membrane complex.

The number of nicotinic acetylcholine receptors (AChRs) present in the plasma membrane of muscle and neuronal cells is limited by the assembly of individual subunits into mature pentameric receptors. This process is usually inefficient, and a large number of the synthesized subunits are degraded by endoplasmic reticulum (ER)-associated degradation. To identify cellular factors required for the synthesis of AChRs, we performed a genetic screen in the nematode Caenorhabditis elegans for mutants with decreased sensitivity to the cholinergic agonist levamisole. We isolated a partial loss-of-function allele of ER membrane protein complex-6 (emc-6), a previously uncharacterized gene in C. elegans. emc-6 encodes an evolutionarily conserved 111-aa protein with two predicted transmembrane domains. EMC-6 is ubiquitously expressed and localizes to the ER. Partial inhibition of EMC-6 caused decreased expression of heteromeric levamisole-sensitive AChRs by destabilizing unassembled subunits in the ER. Inhibition of emc-6 also reduced the expression of homomeric nicotine-sensitive AChRs and GABAA receptors in C. elegans muscle cells. emc-6 is orthologous to the yeast and human EMC6 genes that code for a component of the recently identified ER membrane complex (EMC). Our data suggest this complex is required for protein folding and is connected to ER-associated degradation. We demonstrated that inactivation of additional EMC members in C. elegans also impaired AChR synthesis and induced the unfolded protein response. These results suggest that the EMC is a component of the ER folding machinery. AChRs might provide a valuable proxy to decipher the function of the EMC further.

A screen for X-linked mutations affecting Drosophila photoreceptor differentiation identifies Casein kinase 1α as an essential negative regulator of wingless signaling.

The Wnt and Hedgehog signaling pathways are essential for normal development and are misregulated in cancer. The casein kinase family of serine/threonine kinases regulates both pathways at multiple levels. However, it has been difficult to determine whether individual members of this family have distinct functions in vivo, due to their overlapping substrate specificities. In Drosophila melanogaster, photoreceptor differentiation is induced by Hedgehog and inhibited by Wingless, providing a sensitive system in which to identify regulators of each pathway. We used a mosaic genetic screen in the Drosophila eye to identify mutations in genes on the X chromosome required for signal transduction. We recovered mutations affecting the transcriptional regulator CREB binding protein, the small GTPase dynamin, the cytoskeletal regulator Actin-related protein 2, and the protein kinase Casein kinase 1α. Consistent with its reported function in the ß-Catenin degradation complex, Casein Kinase 1α mutant cells accumulate ß-Catenin and ectopically induce Wingless target genes. In contrast to previous studies based on RNA interference, we could not detect any effect of the same Casein Kinase 1α mutation on Hedgehog signaling. We thus propose that Casein kinase 1α is essential to allow ß-Catenin degradation and prevent inappropriate Wingless signaling, but its effects on the Hedgehog pathway are redundant with other Casein kinase 1 family members.

Neuroinflammatory and oxidative stress phenomena in MPS IIIA mouse model: the positive effect of long-term aspirin treatment.

Sanfilippo disease (MPS IIIA) is an autosomal recessive lysosomal storage disorder resulting from sulfamidase deficiency, which is characterized by severe neurological impairment. Various tissues of MPS IIIA mice accumulate undegraded glycosaminoglycans and mimic the human neurodegenerative disorder, and are an excellent tool to both delineate disease pathogenesis and test potential therapies. The relationship between abnormal glycosaminoglycan storage and neurodysfunction remains ill defined. Pathways such as inflammation or oxidative stress have been highlighted in many neurodegenerative disorders, including lysosomal storage diseases, as major components of the neuropathology. By using quantitative polymerase chain reaction, we have compared the expression of selected genes in normal and MPS IIIA mouse cerebral tissues, focusing on inflammation, apoptosis and oxidative stress-related genes. We have identified several genes strongly over-expressed in the central nervous system of a MPS IIIA mouse, reflecting a neurological deterioration state. We have used these genes as markers to follow-up a long-term aspirin treatment. Aspirin treatment led to the normalization of inflammation- and oxidative stress-related mRNA levels in treated MPS IIIA mouse brains. A biochemical correction of an oxidative stress phenomenon both in the brain and peripheral organs of treated MPS IIIA mice was also obtained. These results suggest that anti-inflammatory intervention may be of potential benefit in MPS IIIA disease.

pFARs, plasmids free of antibiotic resistance markers, display high-level transgene expression in muscle, skin and tumour cells.

BACKGROUND: Nonviral gene therapy requires a high yield and a low cost production of eukaryotic expression vectors that meet defined criteria such as biosafety and quality of pharmaceutical grade. To fulfil these objectives, we designed a novel antibiotic-free selection system. METHODS: The proposed strategy relies on the suppression of a chromosomal amber mutation by a plasmid-borne function. We first introduced a nonsense mutation into the essential Escherichia coli thyA gene, resulting in thymidine auxotrophy. The bacterial strain was optimized for the production of small and novel plasmids free of antibiotic resistance markers (pFARs) and encoding an amber suppressor t-RNA. Finally, the potentiality of pFARs as eukaryotic expression vectors was assessed by monitoring luciferase activities after electrotransfer of LUC-encoding plasmids into various tissues. RESULTS: The introduction of pFARs into the optimized bacterial strain restored normal growth to the auxotrophic mutant and allowed an efficient production of monomeric supercoiled plasmids. The electrotransfer of LUC-encoding pFAR into muscle led to high luciferase activities, demonstrating an efficient gene delivery. In transplanted tumours, transgene expression levels were superior after electrotransfer of the pFAR derivative compared to a plasmid carrying a kanamycin resistance gene. Finally, in skin, whereas luciferase activities decreased within 3 weeks after intradermal electrotransfer of a conventional expression vector, sustained luciferase expression was observed with the pFAR plasmid. CONCLUSIONS: Thus, we have designed a novel strategy for the efficient production of biosafe plasmids and demonstrated their potentiality for nonviral gene delivery and high-level transgene expression in several tissues.

Storage correction in cells of patients suffering from mucopolysaccharidoses types IIIA and VII after treatment with genistein and other isoflavones.

Mucopolysaccharidoses are autosomal and recessive lysosomal storage disorders caused by the deficiency of a lysosomal enzyme involved in glycosaminoglycan catabolism. The Sanfilippo type A disease (MPS III A) results from sulfamidase deficiency, which leads to accumulation of heparan sulfate, whereas Sly disease (MPS VII) results from beta-glucuronidase deficiency, leading to accumulation of heparan, dermatan, and chondroitin sulfates. These syndromes are characterized by severe central nervous system degeneration, resulting in progressive mental retardation, and fatality occurs in severely affected children. To date, no effective treatment is available except for bone marrow transplantation in specific cases. Recently, the use of genistein, an isoflavone that inhibits glycosaminoglycans synthesis, has been tested as substrate reduction therapy for neuronopathic forms of these diseases.We tested five natural analogs to genistein in human fibroblasts from both Sanfilippo A and Sly patients. Four molecules were as efficient as genistein in decreasing glycosaminoglycan accumulation. Moreover, a combination of several isoflavones was more efficient than one single isoflavone, suggesting a synergistic effect. These preliminary data may offer new perspectives for treating Sly and Sanfilippo A diseases and could be relevant to other neurological forms of mucopolysaccharidoses.

How to make siRNA lipoplexes efficient? Add a DNA cargo.

BACKGROUND: We recently reported an efficient formulation of siRNA targeting TNF-alpha, that was able to restore immunological balance in a mouse arthritis model following intravenous injection. METHOD: Since this efficient formulation included the pre association of siRNA with a DNA cargo, we decided to extensively characterise siRNA lipoplexes with or without DNA cargo, in order to better understand the DNA cargo enhancing effect. RESULTS: We showed that addition of DNA cargo to siRNA lipoplexes led to specific gene extinction in vitro, using reduced siRNA concentration. This procedure is also applicable to other lipid vectors, like Lipofectamine or DMRIE-C. No structural modification could be observed in siRNA lipoplexes upon addition of DNA cargo using dynamic light scattering or transmission electronic microscopy. Nevertheless, we observed some slight differences, in the amount of lipid required to obtain neutrality of the complex and in stability of the complex towards incubation with heparan sulfate. CONCLUSIONS: These results suggest that the addition of DNA cargo to siRNA complexes is an easy procedure that leads to more efficient complexes to transfer siRNA at low concentration and in the presence of serum.

Identification of new markers for neurodegeneration process in the mouse model of Sly disease as revealed by expression profiling of selected genes.

Sly disease (MPS VII) is an autosomal-recessive lysosomal storage disorder resulting from beta-glucuronidase deficiency, which is characterized by a severe neurological impairment. MPS VII mice accumulate undegraded glycosaminoglycans and mimic the human neurodegenerative disorder, thus appearing to be an excellent tool to delineate disease pathogenesis. The relationship between abnormal glycosaminoglycan storage and neurodysfunction is not yet well understood, but inflammatory components can be involved, as in several neurological lysosomal disorders. Inflammatory biomarkers are thus good candidates to evaluate the neurodegeneration state of the disease. By using quantitative polymerase chain reaction, we have compared the expression of selected genes of normal and MPS VII cerebral tissues, focusing on inflammation and apoptosis-related genes. The gene expression was evaluated in various brain regions throughout the lifetime of the animals. We have identified a specific expression profile for 27 genes, which was strongly marked in the central nervous system posterior region. Finally, new Sly disease markers were characterized that reflect neurological deterioration state, and that can be used in preclinical follow-up studies.