A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques.

BACKGROUND: The growing abundance of in vitro omics data, coupled with the necessity to reduce animal testing in the safety assessment of chemical compounds and even eliminate it in the evaluation of cosmetics, highlights the need for adequate computational methodologies. Data from omics technologies allow the exploration of a wide range of biological processes, therefore providing a better understanding of mechanisms of action (MoA) related to chemical exposure in biological systems. However, the analysis of these large datasets remains difficult due to the complexity of modulations spanning multiple biological processes. RESULTS: To address this, we propose a strategy to reduce information overload by computing, based on transcriptomics data, a comprehensive metabolic sub-network reflecting the metabolic impact of a chemical. The proposed strategy integrates transcriptomic data to a genome scale metabolic network through enumeration of condition-specific metabolic models hence translating transcriptomics data into reaction activity probabilities. Based on these results, a graph algorithm is applied to retrieve user readable sub-networks reflecting the possible metabolic MoA (mMoA) of chemicals. This strategy has been implemented as a three-step workflow. The first step consists in building cell condition-specific models reflecting the metabolic impact of each exposure condition while taking into account the diversity of possible optimal solutions with a partial enumeration algorithm. In a second step, we address the challenge of analyzing thousands of enumerated condition-specific networks by computing differentially activated reactions (DARs) between the two sets of enumerated possible condition-specific models. Finally, in the third step, DARs are grouped into clusters of functionally interconnected metabolic reactions, representing possible mMoA, using the distance-based clustering and subnetwork extraction method. The first part of the workflow was exemplified on eight molecules selected for their known human hepatotoxic outcomes associated with specific MoAs well described in the literature and for which we retrieved primary human hepatocytes transcriptomic data in Open TG-GATEs. Then, we further applied this strategy to more precisely model and visualize associated mMoA for two of these eight molecules (amiodarone and valproic acid). The approach proved to go beyond gene-based analysis by identifying mMoA when few genes are significantly differentially expressed (2 differentially expressed genes (DEGs) for amiodarone), bringing additional information from the network topology, or when very large number of genes were differentially expressed (5709 DEGs for valproic acid). In both cases, the results of our strategy well fitted evidence from the literature regarding known MoA. Beyond these confirmations, the workflow highlighted potential other unexplored mMoA. CONCLUSION: The proposed strategy allows toxicology experts to decipher which part of cellular metabolism is expected to be affected by the exposition to a given chemical. The approach originality resides in the combination of different metabolic modelling approaches (constraint based and graph modelling). The application to two model molecules shows the strong potential of the approach for interpretation and visual mining of complex omics in vitro data. The presented strategy is freely available as a python module ( https://pypi.org/project/manamodeller/ ) and jupyter notebooks ( https://github.com/LouisonF/MANA ).

An assessment of bacterial small RNA target prediction programs.

Most bacterial regulatory RNAs exert their function through base-pairing with target RNAs. Computational prediction of targets is a busy research field that offers biologists a variety of web sites and software. However, it is difficult for a non-expert to evaluate how reliable those programs are. Here, we provide a simple benchmark for bacterial sRNA target prediction based on trusted E. coli sRNA/target pairs. We use this benchmark to assess the most recent RNA target predictors as well as earlier programs for RNA-RNA hybrid prediction. Moreover, we consider how the definition of mRNA boundaries can impact overall predictions. Recent algorithms that exploit both conservation of targets and accessibility information offer improved accuracy over previous software. However, even with the best predictors, the number of true biological targets with low scores and non-targets with high scores remains puzzling.

Transcriptomic profiling of the oyster pathogen Vibrio splendidus opens a window on the evolutionary dynamics of the small RNA repertoire in the Vibrio genus.

Work in recent years has led to the recognition of the importance of small regulatory RNAs (sRNAs) in bacterial regulation networks. New high-throughput sequencing technologies are paving the way to the exploration of an expanding sRNA world in nonmodel bacteria. In the Vibrio genus, compared to the enterobacteriaceae, still a limited number of sRNAs have been characterized, mostly in Vibrio cholerae, where they have been shown to be important for virulence, as well as in Vibrio harveyi. In addition, genome-wide approaches in V. cholerae have led to the discovery of hundreds of potential new sRNAs. Vibrio splendidus is an oyster pathogen that has been recently associated with massive mortality episodes in the French oyster growing industry. Here, we report the first RNA-seq study in a Vibrio outside of the V. cholerae species. We have uncovered hundreds of candidate regulatory RNAs, be it cis-regulatory elements, antisense RNAs, and trans-encoded sRNAs. Conservation studies showed the majority of them to be specific to V. splendidus. However, several novel sRNAs, previously unidentified, are also present in V. cholerae. Finally, we identified 28 trans sRNAs that are conserved in all the Vibrio genus species for which a complete genome sequence is available, possibly forming a Vibrio "sRNA core."

NAPP: the Nucleic Acid Phylogenetic Profile Database.

Nucleic acid phylogenetic profiling (NAPP) classifies coding and non-coding sequences in a genome according to their pattern of conservation across other genomes. This procedure efficiently distinguishes clusters of functional non-coding elements in bacteria, particularly small RNAs and cis-regulatory RNAs, from other conserved sequences. In contrast to other non-coding RNA detection pipelines, NAPP does not require the presence of conserved RNA secondary structure and therefore is likely to identify previously undetected RNA genes or elements. Furthermore, as NAPP clusters contain both coding and non-coding sequences with similar occurrence profiles, they can be analyzed under a functional perspective. We recently improved the NAPP pipeline and applied it to a collection of 949 bacterial and 68 archaeal species. The database and web interface available at http://napp.u-psud.fr/ enable detailed analysis of NAPP clusters enriched in non-coding RNAs, graphical display of phylogenetic profiles, visualization of predicted RNAs in their genome context and extraction of predicted RNAs for use with genome browsers or other software.

RNA at 92 °C: the non-coding transcriptome of the hyperthermophilic archaeon Pyrococcus abyssi.

The non-coding transcriptome of the hyperthermophilic archaeon Pyrococcus abyssi is investigated using the RNA-seq technology. A dedicated computational pipeline analyzes RNA-seq reads and prior genome annotation to identify small RNAs, untranslated regions of mRNAs, and cis-encoded antisense transcripts. Unlike other archaea, such as Sulfolobus and Halobacteriales, P. abyssi produces few leaderless mRNA transcripts. Antisense transcription is widespread (215 transcripts) and targets protein-coding genes that are less conserved than average genes. We identify at least three novel H/ACA-like guide RNAs among the newly characterized non-coding RNAs. Long 5' UTRs in mRNAs of ribosomal proteins and amino-acid biosynthesis genes strongly suggest the presence of cis-regulatory leaders in these mRNAs. We selected a high-interest subset of non-coding RNAs based on their strong promoters, high GC-content, phylogenetic conservation, or abundance. Some of the novel small RNAs and long 5' UTRs display high GC contents, suggesting unknown structural RNA functions. However, we were surprised to observe that most of the high-interest RNAs are AU-rich, which suggests an absence of stable secondary structure in the high-temperature environment of P. abyssi. Yet, these transcripts display other hallmarks of functionality, such as high expression or high conservation, which leads us to consider possible RNA functions that do not require extensive secondary structure.

A multi-study analysis enables identification of potential microbial features associated with skin aging signs.

Introduction: During adulthood, the skin microbiota can be relatively stable if environmental conditions are also stable, yet physiological changes of the skin with age may affect the skin microbiome and its function. The microbiome is an important factor to consider in aging since it constitutes most of the genes that are expressed on the human body. However, severity of specific aging signs (one of the parameters used to measure "apparent" age) and skin surface quality (e.g., texture, hydration, pH, sebum, etc.) may not be indicative of chronological age. For example, older individuals can have young looking skin (young apparent age) and young individuals can be of older apparent age. Methods: Here we aim to identify microbial taxa of interest associated to skin quality/aging signs using a multi-study analysis of 13 microbiome datasets consisting of 16S rRNA amplicon sequence data and paired skin clinical data from the face. Results: We show that there is a negative relationship between microbiome diversity and transepidermal water loss, and a positive association between microbiome diversity and age. Aligned with a tight link between age and wrinkles, we report a global positive association between microbiome diversity and Crow's feet wrinkles, but with this relationship varying significantly by sub-study. Finally, we identify taxa potentially associated with wrinkles, TEWL and corneometer measures. Discussion: These findings represent a key step towards understanding the implication of the skin microbiota in skin aging signs.

Continuous clinical improvement of mild-to-moderate seborrheic dermatitis and rebalancing of the scalp microbiome using a selenium disulfide-based shampoo after an initial treatment with ketoconazole.

OBJECTIVE: Scalp seborrheic dermatitis (SD) is a chronic, relapsing, and inflammatory scalp disease. Studies indicate a global bacterial and fungal microbiota shift of scalp SD, as compared to healthy scalp. Ketoconazole and selenium disulfide (SeS2 ) improve clinical signs and symptoms in both scalp dandruff and SD. AIM: The main objective of this study was to investigate the changes in the scalp microbiota diversity and counts in subjects with scalp SD during a two-phase treatment period. MATERIAL AND METHODS: The scalp microbiota and clinical efficacy were investigated in 68 subjects with mild-to-moderate scalp SD after an initial one-month treatment with 2% ketoconazole, and after a 2-month maintenance phase, either with a 1% SeS2 -based shampoo or its vehicle. RESULTS: Thirty one subjects in the active and 37 subjects in the vehicle group participated. Ketoconazole provided an improvement of clinical symptoms (adherent (-1.75 p < 0.05), non-adherent (-1.5, p < 0.05)) flakes and erythema (scores 1.67-0.93, p < 0.001), in an increased fungal diversity and in a significant (p < 0.005) decrease of Malassezia spp. SeS2 provided an additional clinical improvement (-0.8; p = 0.0002 and -0.7; p = 0.0081 for adherent and non-adherent flakes, respectively, at Day 84) compared to the vehicle associated with a low Malassezia spp. count and an additional significant (p < 0.001) decrease of the Staphylococcus spp. level. CONCLUSION: Selenium disulfide provides an additional benefit on the scalp microbiota and in clinical symptoms of SD and dandruff after treatment with ketoconazole. The results confirm the role of Staphylococcus spp. in scalp SD and open possible perspectives for preventing relapses.

HCK and ABAA: A Newly Designed Pipeline to Improve Fungi Metabarcoding Analysis.

INTRODUCTION: The fungi ITS sequence length dissimilarity, non-specific amplicons, including chimaera formed during Polymerase Chain Reaction (PCR), added to sequencing errors, create bias during similarity clustering and abundance estimation in the downstream analysis. To overcome these challenges, we present a novel approach, Hierarchical Clustering with Kraken (HCK), to classify ITS1 amplicons and Abundance-Base Alternative Approach (ABAA) pipeline to detect and filter non-specific amplicons in fungi metabarcoding sequencing datasets. MATERIALS AND METHODS: We compared the performances of both pipelines against QIIME, KRAKEN, and DADA2 using publicly available fungi ITS mock community datasets and using BLASTn as a reference. We calculated the Precision, Recall, F-score using the True-Positive, False-positive, and False-negative estimation. Alpha diversity (Chao1 and Shannon metrics) was also used to evaluate the diversity estimation of our method. RESULTS: The analysis shows that ABAA reduced the number of false-positive with all metabarcoding methods tested, and HCK increases precision and recall. HCK, coupled with ABAA, improves the F-score and bring alpha diversity metric value close to that of the BLASTn alpha diversity values when compared to QIIME, KRAKEN, and DADA2. CONCLUSION: The developed HCK-ABAA approach allows better identification of the fungi community structures while avoiding use of a reference database for non-specific amplicons filtration. It results in a more robust and stable methodology over time. The software can be downloaded on the following link: https://bitbucket.org/GottySG36/hck/src/master/.

Using massively parallel shotgun sequencing of maternal plasmatic cell-free DNA for cytomegalovirus DNA detection during pregnancy: a proof of concept study.

Human cytomegalovirus (HCMV) primary infections of pregnant women can lead to congenital infections of the fetus that could have severe impacts on the health of the newborn. Recent studies have shown that 10-100 billion DNA fragments per milliliter of plasma are circulating cell-free. The study of this DNA has rapidly expanding applications to non-invasive prenatal testing (NIPT). In this study, we have shown that we can detect viral specific reads in the massively parallel shotgun sequencing (MPSS) NIPT data. We have also observed a strong correlation between the viral load of calibration samples and the number of reads aligned on the reference genome. Based on these observations we have constructed a statistical model able to quantify the viral load of patient samples. We propose to use this new method to detect and quantify circulating DNA virus like HCMV during pregnancy using the same sequencing results as NIPT data. This method could be used to improve the NIPT diagnosis.