DIMet: an open-source tool for differential analysis of targeted isotope-labeled metabolomics data.

MOTIVATION: Many diseases, such as cancer, are characterized by an alteration of cellular metabolism allowing cells to adapt to changes in the microenvironment. Stable isotope-resolved metabolomics (SIRM) and downstream data analyses are widely used techniques for unraveling cells' metabolic activity to understand the altered functioning of metabolic pathways in the diseased state. While a number of bioinformatic solutions exist for the differential analysis of SIRM data, there is currently no available resource providing a comprehensive toolbox. RESULTS: In this work, we present DIMet, a one-stop comprehensive tool for differential analysis of targeted tracer data. DIMet accepts metabolite total abundances, isotopologue contributions, and isotopic mean enrichment, and supports differential comparison (pairwise and multi-group), time-series analyses, and labeling profile comparison. Moreover, it integrates transcriptomics and targeted metabolomics data through network-based metabolograms. We illustrate the use of DIMet in real SIRM datasets obtained from Glioblastoma P3 cell-line samples. DIMet is open-source, and is readily available for routine downstream analysis of isotope-labeled targeted metabolomics data, as it can be used both in the command line interface or as a complete toolkit in the public Galaxy Europe and Workfow4Metabolomics web platforms. AVAILABILITY AND IMPLEMENTATION: DIMet is freely available at https://github.com/cbib/DIMet, and through https://usegalaxy.eu and https://workflow4metabolomics.usegalaxy.fr. All the datasets are available at Zenodo https://zenodo.org/records/10925786.

Synthesis of Flavonol-Bearing Probes for Chemoproteomic and Bioinformatic Analyses of Asteraceae Petals in Search of Novel Flavonoid Enzymes.

This study aimed at searching for the enzymes that are responsible for the higher hydroxylation of flavonols serving as UV-honey guides for pollinating insects on the petals of Asteraceae flowers. To achieve this aim, an affinity-based chemical proteomic approach was developed by relying on the use of quercetin-bearing biotinylated probes, which were thus designed and synthesized to selectively and covalently capture relevant flavonoid enzymes. Proteomic and bioinformatic analyses of proteins captured from petal microsomes of two Asteraceae species (Rudbeckia hirta and Tagetes erecta) revealed the presence of two flavonol 6-hydroxylases and several additional not fully characterized proteins as candidates for the identification of novel flavonol 8-hydroxylases, as well as relevant flavonol methyl- and glycosyltransferases. Generally speaking, this substrate-based proteome profiling methodology constitutes a powerful tool for the search for unknown (flavonoid) enzymes in plant protein extracts.

Experimental and computational modeling for signature and biomarker discovery of renal cell carcinoma progression.

BACKGROUND: Renal Cell Carcinoma (RCC) is difficult to treat with 5-year survival rate of 10% in metastatic patients. Main reasons of therapy failure are lack of validated biomarkers and scarce knowledge of the biological processes occurring during RCC progression. Thus, the investigation of mechanisms regulating RCC progression is fundamental to improve RCC therapy. METHODS: In order to identify molecular markers and gene processes involved in the steps of RCC progression, we generated several cell lines of higher aggressiveness by serially passaging mouse renal cancer RENCA cells in mice and, concomitantly, performed functional genomics analysis of the cells. Multiple cell lines depicting the major steps of tumor progression (including primary tumor growth, survival in the blood circulation and metastatic spread) were generated and analyzed by large-scale transcriptome, genome and methylome analyses. Furthermore, we performed clinical correlations of our datasets. Finally we conducted a computational analysis for predicting the time to relapse based on our molecular data. RESULTS: Through in vivo passaging, RENCA cells showed increased aggressiveness by reducing mice survival, enhancing primary tumor growth and lung metastases formation. In addition, transcriptome and methylome analyses showed distinct clustering of the cell lines without genomic variation. Distinct signatures of tumor aggressiveness were revealed and validated in different patient cohorts. In particular, we identified SAA2 and CFB as soluble prognostic and predictive biomarkers of the therapeutic response. Machine learning and mathematical modeling confirmed the importance of CFB and SAA2 together, which had the highest impact on distant metastasis-free survival. From these data sets, a computational model predicting tumor progression and relapse was developed and validated. These results are of great translational significance. CONCLUSION: A combination of experimental and mathematical modeling was able to generate meaningful data for the prediction of the clinical evolution of RCC.

Argininosuccinate synthase 1 (ASS1): A marker of unclassified hepatocellular adenoma and high bleeding risk.

Hepatocellular adenomas (HCAs) are rare benign tumors divided into three main subgroups defined by pathomolecular features, HNF1A (H-HCA), mutated ß-catenin (b-HCA), and inflammatory (IHCA). In the case of unclassified HCAs (UHCAs), which are currently identified by default, a high risk of bleeding remains a clinical issue. The objective of this study was to explore UHCA proteome with the aim to identify specific biomarkers. Following dissection of the tumoral (T) and nontumoral (NT) tissue on formalin-fixed, paraffin-embedded HCA tissue sections using laser capture methodology, we performed mass spectrometry analysis to compare T and NT protein expression levels in H-HCA, IHCA, b-HCA, UHCA, and focal nodular hyperplasia. Using this methodology, we searched for proteins which are specifically deregulated in UHCA. We demonstrate that proteomic profiles allow for discriminating known HCA subtypes through identification of classical biomarkers in each HCA subgroup. We observed specific up-regulation of the arginine synthesis pathway associated with overexpression of argininosuccinate synthase (ASS1) and arginosuccinate lyase in UHCA. ASS1 immunohistochemistry identified all the UHCA, of which 64.7% presented clinical bleeding manifestations. Interestingly, we demonstrated that the significance of ASS1 was not restricted to UHCA, but also encompassed certain hemorrhagic cases in other HCA subtypes, particularly IHCA. CONCLUSION: ASS1 + HCA combined with a typical hematoxylin and eosin stain aspect defined a new HCA subgroup at a high risk of bleeding. (Hepatology 2017;66:2016-2028).

xHeinz: an algorithm for mining cross-species network modules under a flexible conservation model.

MOTIVATION: Integrative network analysis methods provide robust interpretations of differential high-throughput molecular profile measurements. They are often used in a biomedical context-to generate novel hypotheses about the underlying cellular processes or to derive biomarkers for classification and subtyping. The underlying molecular profiles are frequently measured and validated on animal or cellular models. Therefore the results are not immediately transferable to human. In particular, this is also the case in a study of the recently discovered interleukin-17 producing helper T cells (Th17), which are fundamental for anti-microbial immunity but also known to contribute to autoimmune diseases. RESULTS: We propose a mathematical model for finding active subnetwork modules that are conserved between two species. These are sets of genes, one for each species, which (i) induce a connected subnetwork in a species-specific interaction network, (ii) show overall differential behavior and (iii) contain a large number of orthologous genes. We propose a flexible notion of conservation, which turns out to be crucial for the quality of the resulting modules in terms of biological interpretability. We propose an algorithm that finds provably optimal or near-optimal conserved active modules in our model. We apply our algorithm to understand the mechanisms underlying Th17 T cell differentiation in both mouse and human. As a main biological result, we find that the key regulation of Th17 differentiation is conserved between human and mouse. AVAILABILITY AND IMPLEMENTATION: xHeinz, an implementation of our algorithm, as well as all input data and results, are available at http://software.cwi.nl/xheinz and as a Galaxy service at http://services.cbib.u-bordeaux2.fr/galaxy in CBiB Tools. CONTACT: gunnar.klau@cwi.nl SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome-wide association links candidate genes to resistance to Plum Pox Virus in apricot (Prunus armeniaca).

In fruit tree species, many important traits have been characterized genetically by using single-family descent mapping in progenies segregating for the traits. However, most mapped loci have not been sufficiently resolved to the individual genes due to insufficient progeny sizes for high resolution mapping and the previous lack of whole-genome sequence resources of the study species. To address this problem for Plum Pox Virus (PPV) candidate resistance gene identification in Prunus species, we implemented a genome-wide association (GWA) approach in apricot. This study exploited the broad genetic diversity of the apricot (Prunus armeniaca) germplasm containing resistance to PPV, next-generation sequence-based genotyping, and the high-quality peach (Prunus persica) genome reference sequence for single nucleotide polymorphism (SNP) identification. The results of this GWA study validated previously reported PPV resistance quantitative trait loci (QTL) intervals, highlighted other potential resistance loci, and resolved each to a limited set of candidate genes for further study. This work substantiates the association genetics approach for resolution of QTL to candidate genes in apricot and suggests that this approach could simplify identification of other candidate genes for other marked trait intervals in this germplasm.

Clinical and genomic analysis of a randomised phase II study evaluating anastrozole and fulvestrant in postmenopausal patients treated for large operable or locally advanced hormone-receptor-positive breast cancer.

BACKGROUND: The aim of this study was to assess the efficacy of neoadjuvant anastrozole and fulvestrant treatment of large operable or locally advanced hormone-receptor-positive breast cancer not eligible for initial breast-conserving surgery, and to identify genomic changes occurring after treatment. METHODS: One hundred and twenty post-menopausal patients were randomised to receive 1 mg anastrozole (61 patients) or 500 mg fulvestrant (59 patients) for 6 months. Genomic DNA copy number profiles were generated for a subgroup of 20 patients before and after treatment. RESULTS: A total of 108 patients were evaluable for efficacy and 118 for toxicity. The objective response rate determined by clinical palpation was 58.9% (95% CI=45.0-71.9) in the anastrozole arm and 53.8% (95% CI=39.5-67.8) in the fulvestrant arm. The breast-conserving surgery rate was 58.9% (95% CI=45.0-71.9) in the anastrozole arm and 50.0% (95% CI=35.8-64.2) in the fulvestrant arm. Pathological responses >50% occurred in 24 patients (42.9%) in the anastrozole arm and 13 (25.0%) in the fulvestrant arm. The Ki-67 score fell after treatment but there was no significant difference between the reduction in the two arms (anastrozole 16.7% (95% CI=13.3-21.0) before, 3.2% (95% CI=1.9-5.5) after, n=43; fulvestrant 17.1% (95%CI=13.1-22.5) before, 3.2% (95% CI=1.8-5.7) after, n=38) or between the reduction in Ki-67 in clinical responders and non-responders. Genomic analysis appeared to show a reduction of clonal diversity following treatment with selection of some clones with simpler copy number profiles. CONCLUSIONS: Both anastrozole and fulvestrant were effective and well-tolerated, enabling breast-conserving surgery in over 50% of patients. Clonal changes consistent with clonal selection by the treatment were seen in a subgroup of patients.

Further steps in TANGO: improved taxonomic assignment in metagenomics.

MOTIVATION: TANGO is one of the most accurate tools for the taxonomic assignment of sequence reads. However, because of the differences in the taxonomy structures, performing a taxonomic assignment on different reference taxonomies will produce divergent results. RESULTS: We have improved the TANGO pipeline to be able to perform the taxonomic assignment of a metagenomic sample using alternative reference taxonomies, coming from different sources. We highlight the novel pre-processing step, necessary to accomplish this task, and describe the improvements in the assignment process. We present the new TANGO pipeline in details, and, finally, we show its performance on four real metagenomic datasets and also on synthetic datasets. AVAILABILITY: The new version of TANGO, including implementation improvements and novel developments to perform the assignment on different reference taxonomies, is freely available at http://sourceforge.net/projects/taxoassignment/.

THBS1+ myeloid cells expand in SLD hepatocellular carcinoma and contribute to immunosuppression and unfavorable prognosis through TREM1.

Hepatocellular carcinoma (HCC) is an inflammation-associated cancer arising from viral or non-viral etiologies including steatotic liver diseases (SLDs). Expansion of immunosuppressive myeloid cells is a hallmark of inflammation and cancer, but their heterogeneity in HCC is not fully resolved and might underlie immunotherapy resistance. Here, we present a high-resolution atlas of innate immune cells from patients with HCC that unravels an SLD-associated contexture characterized by influx of inflammatory and immunosuppressive myeloid cells, including a discrete population of THBS1+ regulatory myeloid (Mreg) cells expressing monocyte- and neutrophil-affiliated genes. THBS1+ Mreg cells expand in SLD-associated HCC, populate fibrotic lesions, and are associated with poor prognosis. THBS1+ Mreg cells are CD163+ but distinguished from macrophages by high expression of triggering receptor expressed on myeloid cells 1 (TREM1), which contributes to their immunosuppressive activity and promotes HCC tumor growth in vivo. Our data support myeloid subset-targeted immunotherapies to treat HCC.

Single cell tracing of Pomc neurons reveals recruitment of 'Ghost' subtypes with atypical identity in a mouse model of obesity.

The hypothalamus contains a remarkable diversity of neurons that orchestrate behavioural and metabolic outputs in a highly plastic manner. Neuronal diversity is key to enabling hypothalamic functions and, according to the neuroscience dogma, it is predetermined during embryonic life. Here, by combining lineage tracing of hypothalamic pro-opiomelanocortin (Pomc) neurons with single-cell profiling approaches in adult male mice, we uncovered subpopulations of 'Ghost' neurons endowed with atypical molecular and functional identity. Compared to 'classical' Pomc neurons, Ghost neurons exhibit negligible Pomc expression and are 'invisible' to available neuroanatomical approaches and promoter-based reporter mice for studying Pomc biology. Ghost neuron numbers augment in diet-induced obese mice, independent of neurogenesis or cell death, but weight loss can reverse this shift. Our work challenges the notion of fixed, developmentally programmed neuronal identities in the mature hypothalamus and highlight the ability of specialised neurons to reversibly adapt their functional identity to adult-onset obesogenic stimuli.

Finishing bacterial genome assemblies with Mix.

MOTIVATION: Among challenges that hamper reaping the benefits of genome assembly are both unfinished assemblies and the ensuing experimental costs. First, numerous software solutions for genome de novo assembly are available, each having its advantages and drawbacks, without clear guidelines as to how to choose among them. Second, these solutions produce draft assemblies that often require a resource intensive finishing phase. METHODS: In this paper we address these two aspects by developing Mix , a tool that mixes two or more draft assemblies, without relying on a reference genome and having the goal to reduce contig fragmentation and thus speed-up genome finishing. The proposed algorithm builds an extension graph where vertices represent extremities of contigs and edges represent existing alignments between these extremities. These alignment edges are used for contig extension. The resulting output assembly corresponds to a set of paths in the extension graph that maximizes the cumulative contig length. RESULTS: We evaluate the performance of Mix on bacterial NGS data from the GAGE-B study and apply it to newly sequenced Mycoplasma genomes. Resulting final assemblies demonstrate a significant improvement in the overall assembly quality. In particular, Mix is consistent by providing better overall quality results even when the choice is guided solely by standard assembly statistics, as is the case for de novo projects. AVAILABILITY: Mix is implemented in Python and is available at https://github.com/cbib/MIX, novel data for our Mycoplasma study is available at http://services.cbib.u-bordeaux2.fr/mix/.

Sex-specific DNA methylation and transcription of zbtb38 and effects of gene-environment interactions on its natural antisense transcript in zebrafish.

There is increasing evidence for the involvement of epigenetics in sex determination, maintenance, and plasticity, from plants to humans. In our previous work, we reported a transgenerational feminization of a zebrafish population for which the first generation was exposed to cadmium, a metal with endocrine disrupting effects. In this study, starting from the previously performed whole methylome analysis, we focused on the zbtb38 gene and hypothesized that it could be involved in sex differentiation and Cd-induced offspring feminization. We observed sex-specific patterns of both DNA methylation and RNA transcription levels of zbtb38. We also discovered that the non-coding exon 3 of zbtb38 encodes for a natural antisense transcript (NAT). The activity of this NAT was found to be influenced by both genetic and environmental factors. Furthermore, increasing transcription levels of this NAT in parental gametes was highly correlated with offspring sex ratios. Since zbtb38 itself encodes for a transcription factor that binds methylated DNA, our results support a non-negligible role of zbtb38 not only in orchestrating the sex-specific transcriptome (i.e., sex differentiation) but also, via its NAT, offspring sex ratios.

Transgenerational endocrine disruptor effects of cadmium in zebrafish and contribution of standing epigenetic variation to adaptation.

Evidence has emerged that environmentally-induced epigenetic changes can have long-lasting effects on gene transcription across generations. These recent findings highlight the need to investigate the transgenerational impacts of pollutants to assess their long term effects on populations. In this study, we investigated the transgenerational effect of cadmium on zebrafish across 4 generations. A first whole methylome approach carried out on fish of the first two generations led us to focus our investigations on the estradiol receptor alpha gene (esr1). We observed a sex-dependent transgenerational inheritance of Cd-induced DNA methylation changes up to the last generation. These changes were associated with single nucleotide polymorphisms (SNPs) that were themselves at the origin of the creation or deletion of methylation sites. Thus, Cd-induced genetic selection gave rise to DNA methylation changes. We also analyzed the transcription level of various sections of esr1 as well as estrogen responsive genes. While Cd triggered transgenerational disorders, Cd-induced epigenetic changes in esr1 contributed to the rapid transgenerational adaptation of fish to Cd. Our results provide insight into the processes underpinning rapid adaptation and highlight the need to maintain genetic diversity within natural populations to bolster the resilience of species faced with the global environmental changes.

Effects of Lumacaftor-Ivacaftor on Airway Microbiota-Mycobiota and Inflammation in Patients with Cystic Fibrosis Appear To Be Linked to Pseudomonas aeruginosa Chronic Colonization.

Lumacaftor-ivacaftor is a cystic fibrosis transmembrane conductance regulator (CFTR) modulator combination approved for patients with cystic fibrosis (CF) who are homozygous for the F508del allele. This treatment showed significant clinical improvement; however, few studies have addressed the evolution of the airway microbiota-mycobiota and inflammation in patients receiving lumacaftor-ivacaftor treatment. Seventy-five patients with CF aged 12 years or older were enrolled at the initiation of lumacaftor-ivacaftor therapy. Among them, 41 had spontaneously produced sputa collected before and 6 months after treatment initiation. Airway microbiota and mycobiota analyses were performed via high-throughput sequencing. Airway inflammation was assessed by measuring the calprotectin levels in sputum; the microbial biomass was evaluated via quantitative PCR (qPCR). At baseline (n = 75), bacterial alpha-diversity was correlated with pulmonary function. After 6 months of lumacaftor-ivacaftor treatment, a significant improvement in the body mass index and a decreased number of intravenous antibiotic courses were noted. No significant changes in bacterial and fungal alpha- and beta-diversities, pathogen abundances, or calprotectin levels were observed. However, for patients not chronically colonized with Pseudomonas aeruginosa at treatment initiation, calprotectin levels were lower, and a significant increase in bacterial alpha-diversity was observed at 6 months. This study shows that the evolution of the airway microbiota-mycobiota in CF patients depends on the patient's characteristics at lumacaftor-ivacaftor treatment initiation, notably chronic colonization with P. aeruginosa. IMPORTANCE The management of cystic fibrosis has been transformed recently by the advent of CFTR modulators, including lumacaftor-ivacaftor. However, the effects of such therapies on the airway ecosystem, particularly on the microbiota-mycobiota and local inflammation, which are involved in the evolution of pulmonary damage, are unclear. This multicenter study of the evolution of the microbiota under protein therapy supports the notion that CFTR modulators should be started as soon as possible, ideally before the patient is chronically colonized with P. aeruginosa. (This study has been registered at ClinicalTrials.gov under identifier NCT03565692).

Systemic Convergent Multitarget Interactions of Plant Polyphenols Revealed by Affinity-Based Protein Profiling of Bone Cells Using C-Glucosidic Vescal(ag)in-Bearing Chemoproteomic Probes.

The ellagitannins vescalagin and vescalin, known as actin-dependent inhibitors of osteoclastic bone resorption, were mounted onto chemical probes to explore their interactions with bone cell proteins by means of affinity-based chemoproteomics and bioinformatics. The chemical reactivity of the pyrogallol units of these polyphenols toward oxidation into electrophilic ortho-quinones was exploited using NaIO4 to promote the covalent capture of target proteins, notably those expressed at lower abundance and those interacting with polyphenols at low-to-moderate levels of affinity. Different assays revealed the multitarget nature of both ellagitannins, with 100-370 statistically significant proteins captured by their corresponding probes. A much higher number of proteins were captured from osteoclasts than from osteoblasts. Bioinformatic analyses unveiled a preference for the capture of proteins having phosphorylated ligands and GTPase regulators and enabled the identification of 33 potential target proteins with systemic relevance to osteoclast differentiation and activity, as well as to the regulation of actin dynamics.

Comparative genomics of protoploid Saccharomycetaceae.

Our knowledge of yeast genomes remains largely dominated by the extensive studies on Saccharomyces cerevisiae and the consequences of its ancestral duplication, leaving the evolution of the entire class of hemiascomycetes only partly explored. We concentrate here on five species of Saccharomycetaceae, a large subdivision of hemiascomycetes, that we call "protoploid" because they diverged from the S. cerevisiae lineage prior to its genome duplication. We determined the complete genome sequences of three of these species: Kluyveromyces (Lachancea) thermotolerans and Saccharomyces (Lachancea) kluyveri (two members of the newly described Lachancea clade), and Zygosaccharomyces rouxii. We included in our comparisons the previously available sequences of Kluyveromyces lactis and Ashbya (Eremothecium) gossypii. Despite their broad evolutionary range and significant individual variations in each lineage, the five protoploid Saccharomycetaceae share a core repertoire of approximately 3300 protein families and a high degree of conserved synteny. Synteny blocks were used to define gene orthology and to infer ancestors. Far from representing minimal genomes without redundancy, the five protoploid yeasts contain numerous copies of paralogous genes, either dispersed or in tandem arrays, that, altogether, constitute a third of each genome. Ancient, conserved paralogs as well as novel, lineage-specific paralogs were identified.

VirHunter: A Deep Learning-Based Method for Detection of Novel RNA Viruses in Plant Sequencing Data.

High-throughput sequencing has provided the capacity of broad virus detection for both known and unknown viruses in a variety of hosts and habitats. It has been successfully applied for novel virus discovery in many agricultural crops, leading to the current drive to apply this technology routinely for plant health diagnostics. For this, efficient and precise methods for sequencing-based virus detection and discovery are essential. However, both existing alignment-based methods relying on reference databases and even more recent machine learning approaches are not efficient enough in detecting unknown viruses in RNAseq datasets of plant viromes. We present VirHunter, a deep learning convolutional neural network approach, to detect novel and known viruses in assemblies of sequencing datasets. While our method is generally applicable to a variety of viruses, here, we trained and evaluated it specifically for RNA viruses by reinforcing the coding sequences' content in the training dataset. Trained on the NCBI plant viruses data for three different host species (peach, grapevine, and sugar beet), VirHunter outperformed the state-of-the-art method, DeepVirFinder, for the detection of novel viruses, both in the synthetic leave-out setting and on the 12 newly acquired RNAseq datasets. Compared with the traditional tBLASTx approach, VirHunter has consistently exhibited better results in the majority of leave-out experiments. In conclusion, we have shown that VirHunter can be used to streamline the analyses of plant HTS-acquired viromes and is particularly well suited for the detection of novel viral contigs, in RNAseq datasets.

DeepSpot: A deep neural network for RNA spot enhancement in single-molecule fluorescence in-situ hybridization microscopy images.

Detection of RNA spots in single-molecule fluorescence in-situ hybridization microscopy images remains a difficult task, especially when applied to large volumes of data. The variable intensity of RNA spots combined with the high noise level of the images often requires manual adjustment of the spot detection thresholds for each image. In this work, we introduce DeepSpot, a Deep Learning-based tool specifically designed for RNA spot enhancement that enables spot detection without the need to resort to image per image parameter tuning. We show how our method can enable downstream accurate spot detection. DeepSpot's architecture is inspired by small object detection approaches. It incorporates dilated convolutions into a module specifically designed for context aggregation for small object and uses Residual Convolutions to propagate this information along the network. This enables DeepSpot to enhance all RNA spots to the same intensity, and thus circumvents the need for parameter tuning. We evaluated how easily spots can be detected in images enhanced with our method by testing DeepSpot on 20 simulated and 3 experimental datasets, and showed that accuracy of more than 97% is achieved. Moreover, comparison with alternative deep learning approaches for mRNA spot detection (deepBlink) indicated that DeepSpot provides more precise mRNA detection. In addition, we generated single-molecule fluorescence in-situ hybridization images of mouse fibroblasts in a wound healing assay to evaluate whether DeepSpot enhancement can enable seamless mRNA spot detection and thus streamline studies of localized mRNA expression in cells.

Genetic and epigenetic interplay allows rapid transgenerational adaptation to metal pollution in zebrafish.

Despite still being a matter of debate, there is growing evidence that pollutant-induced epigenetic changes can be propagated across generations. Whereas such modifications could have long-lasting effects on organisms and even on population, environmentally relevant data from long-term exposure combined with follow-up through multiple generations remain scarce for non-mammalian species. We performed a transgenerational experiment comprising four successive generations of zebrafish. Only fish from the first generation were exposed to an environmentally realistic concentration of cadmium (Cd). Using a whole methylome analysis, we first identified the DNA regions that were differentially methylated in response to Cd exposure and common to fish of the first two generations. Among them, we then focused our investigations on the exon 3 (ex3) of the cep19 gene. We indeed recorded transgenerational growth disorders in Cd-exposed fish, and a mutation in this exon is known to cause morbid obesity in mammals. Its methylation level was thus determined in zebrafish from all the four generations by means of a targeted and base resolution method. We observed a transgenerational inheritance of Cd-induced DNA methylation changes up to the fourth generation. However, these changes were closely associated with genetic variations, mainly a single nucleotide polymorphism. This single nucleotide polymorphism was itself at the origin of the creation or deletion of a methylation site and deeply impacted the methylation level of neighboring methylation sites. Cd-induced epigenetic changes were associated with different mRNA transcripts and an improved condition of Cd fish. Our results emphasize a tight relationship between genetic and epigenetic mechanisms and suggest that their interplay and pre-existing diversity can allow rapid adaptation to anthropogenic environmental changes.

Deep learning model for automatic segmentation of lungs and pulmonary metastasis in small animal MR images.

Lungs are the most frequent site of metastases growth. The amount and size of pulmonary metastases acquired from MRI imaging data are the important criteria to assess the efficacy of new drugs in preclinical models. While efficient solutions both for MR imaging and the downstream automatic segmentation have been proposed for human patients, both MRI lung imaging and segmentation in preclinical animal models remains challenging due to the physiological motion (respiratory and cardiac movements), to the low amount of protons in this organ and to the particular challenge of precise segmentation of metastases. As a consequence post-mortem analysis is currently required to obtain information on metastatic volume. In this work, we have developed a complete methodological pipeline for automated analysis of lungs and metastases in mice, consisting of an MR sequence for image acquisition and a deep learning method for automatic segmentation of both lungs and metastases. On one hand, we optimized an MR sequence for mouse lung imaging with high contrast for high detection sensitivity. On the other hand we developed DeepMeta, a multiclass U-Net 3+ deep learning model to automatically segment the images. To assess if the proposed deep learning pipeline is able to provide an accurate segmentation of both lungs and pulmonary metastases, we have longitudinally imaged mice with fast- and slow-growing metastasis. Fifty-five balb/c mice were injected with two different derivatives of renal carcinoma cells. Mice were imaged with a SG-bSSFP (self-gated balanced steady state free precession) sequence at different time points after the injection of cancer cells. Both lung and metastases segmentations were manually performed by experts. DeepMeta was trained to perform lung and metastases segmentation based on the resulting ground truth annotations. Volumes of lungs and of pulmonary metastases as well as the number of metastases per mouse were measured on a separate test dataset of MR images. Thanks to the SG method, the 3D bSSFP images of lungs were artifact-free, enabling the downstream detection and serial follow-up of metastases. Moreover, both lungs and metastases segmentation was accurately performed by DeepMeta as soon as they reached the volume of ∼ 0.02 m m 3 . Thus we were able to distinguish two groups of mice in terms of number and volume of pulmonary metastases as well as in terms of the slow versus fast patterns of growth of metastases. We have shown that our methodology combining SG-bSSFP with deep learning, enables processing of the whole animal lungs and is thus a viable alternative to histology alone.