Multiomics Study of Bacterial Growth Arrest in a Synthetic Biology Application.

We investigated the scalability of a previously developed growth switch based on external control of RNA polymerase expression. Our results indicate that, in liter-scale bioreactors operating in fed-batch mode, growth-arrested Escherichia coli cells are able to convert glucose to glycerol at an increased yield. A multiomics quantification of the physiology of the cells shows that, apart from acetate production, few metabolic side effects occur. However, a number of specific responses to growth slow-down and growth arrest are launched at the transcriptional level. These notably include the downregulation of genes involved in growth-associated processes, such as amino acid and nucleotide metabolism and translation. Interestingly, the transcriptional responses are buffered at the proteome level, probably due to the strong decrease of the total mRNA concentration after the diminution of transcriptional activity and the absence of growth dilution of proteins. Growth arrest thus reduces the opportunities for dynamically adjusting the proteome composition, which poses constraints on the design of biotechnological production processes but may also avoid the initiation of deleterious stress responses.

Single-cell RNA sequencing unveils the shared and the distinct cytotoxic hallmarks of human TCRVδ1 and TCRVδ2 γδ T lymphocytes.

γδ T lymphocytes represent ∼1% of human peripheral blood mononuclear cells and even more cells in most tissues of vertebrates. Although they have important anticancer functions, most current single-cell RNA sequencing (scRNA-seq) studies do not identify γδ T lymphocytes because their transcriptomes at the single-cell level are unknown. Here we show that high-resolution clustering of large scRNA-seq datasets and a combination of gene signatures allow the specific detection of human γδ T lymphocytes and identification of their T cell receptor (TCR)Vδ1 and TCRVδ2 subsets in large datasets from complex cell mixtures. In t-distributed stochastic neighbor embedding plots from blood and tumor samples, the few γδ T lymphocytes appear collectively embedded between cytotoxic CD8 T and NK cells. Their TCRVδ1 and TCRVδ2 subsets form close yet distinct subclusters, respectively neighboring NK and CD8 T cells because of expression of shared and distinct cytotoxic maturation genes. Similar pseudotime maturation trajectories of TCRVδ1 and TCRVδ2 γδ T lymphocytes were discovered, unveiling in both subsets an unattended pool of terminally differentiated effector memory cells with preserved proliferative capacity, a finding confirmed by in vitro proliferation assays. Overall, the single-cell transcriptomes of thousands of individual γδ T lymphocytes from different CMV+ and CMV- donors reflect cytotoxic maturation stages driven by the immunological history of donors. This landmark study establishes the rationale for identification, subtyping, and deep characterization of human γδ T lymphocytes in further scRNA-seq studies of complex tissues in physiological and disease conditions.

miR-497 suppresses cycle progression through an axis involving CDK6 in ALK-positive cells.

Anaplastic large-cell lymphoma, a T-cell neoplasm, is primarily a pediatric disease. Seventy-five percent of pediatric anaplastic large-cell lymphoma cases harbor the chromosomal translocation t(2;5)(p23;q35) leading to the ectopic expression of NPM-ALK, a chimeric tyrosine kinase. NPM-ALK consists of an N-terminal nucleophosmin (NPM) domain fused to an anaplastic lymphoma kinase (ALK) cytoplasmic domain. Pediatric NPM-ALK+ anaplastic large-cell lymphoma is often a disseminated disease and young patients are prone to chemoresistance or relapse shortly after chemotherapeutic treatment. Furthermore, there is no gold standard protocol for the treatment of relapses. To the best of our knowledge, this is the first study on the potential role of the microRNA, miR-497, in NPM-ALK+ anaplastic large-cell lymphoma tumorigenesis. Our results show that miR-497 expression is repressed in NPM-ALK+ cell lines and patient samples through the hypermethylation of its promoter and the activity of NPM-ALK is responsible for this epigenetic repression. We demonstrate that overexpression of miR-497 in human NPM-ALK+ anaplastic large-cell lymphoma cells inhibits cellular growth and causes cell cycle arrest by targeting CDK6, E2F3 and CCNE1, the three regulators of the G1 phase of the cell cycle. Interestingly, we show that a scoring system based on CDK6, E2F3 and CCNE1 expression could help to identify relapsing pediatric patients. In addition, we demonstrate the sensitivity of NPM-ALK+ cells to CDK4/6 inhibition using for the first time a selective inhibitor, palbociclib. Together, our findings suggest that CDK6 could be a therapeutic target for the development of future treatments for NPM-ALK+ anaplastic large-cell lymphoma.

A conserved mechanism drives partition complex assembly on bacterial chromosomes and plasmids.

Chromosome and plasmid segregation in bacteria are mostly driven by ParABS systems. These DNA partitioning machineries rely on large nucleoprotein complexes assembled on centromere sites (parS). However, the mechanism of how a few parS-bound ParB proteins nucleate the formation of highly concentrated ParB clusters remains unclear despite several proposed physico-mathematical models. We discriminated between these different models by varying some key parameters in vivo using the F plasmid partition system. We found that "Nucleation & caging" is the only coherent model recapitulating in vivo data. We also showed that the stochastic self-assembly of partition complexes (i) is a robust mechanism, (ii) does not directly involve ParA ATPase, (iii) results in a dynamic structure of discrete size independent of ParB concentration, and (iv) is not perturbed by active transcription but is by protein complexes. We refined the "Nucleation & caging" model and successfully applied it to the chromosomally encoded Par system of Vibrio cholerae, indicating that this stochastic self-assembly mechanism is widely conserved from plasmids to chromosomes.

Dynamic transcriptomic analysis of Ischemic Injury in a Porcine Pre-Clinical Model mimicking Donors Deceased after Circulatory Death.

Due to organ shortage, clinicians are prone to consider alternative type of organ donors among them donors deceased after circulatory death (DCD). However, especially using these organs which are more prone to graft dysfunction, there is a need to better understand mechanistic events ocuring during ischemia phase and leading to ischemia/reperfusion injuries (IRI). The aim of this study is to provide a dynamic transcriptomic analysis of preclinical porcine model kidneys subjected to ischemic stress mimicking DCD donor. We compared cortex and corticomedullary junction (CMJ) tissues from porcine kidneys submitted to 60 min warm ischemia (WI) followed by 0, 6 or 24 hours of cold storage in University of Wisconsin solution versus control non-ischemic kidneys (n = 5 per group). 29 cortex genes and 113 CMJ genes were significantly up or down-regulated after WI versus healthy kidneys, and up to 400 genes were regulated after WI followed by 6 or 24 hours of cold storage (p < 0.05). Functionnal enrichment analysis (home selected gene kinetic classification, Gene-ontology-biological processes and Gene-ontology-molecular-function) revealed relevant genes implication during WI and cold storage. We uncovered targets which we will further validate as biomarkers and new therapeutic targets to optimize graft kidney quality before transplantation and improve whole transplantation outcome.

Optimization of an RNA-Seq Differential Gene Expression Analysis Depending on Biological Replicate Number and Library Size.

RNA-Seq is a widely used technology that allows an efficient genome-wide quantification of gene expressions for, for example, differential expression (DE) analysis. After a brief review of the main issues, methods and tools related to the DE analysis of RNA-Seq data, this article focuses on the impact of both the replicate number and library size in such analyses. While the main drawback of previous relevant studies is the lack of generality, we conducted both an analysis of a two-condition experiment (with eight biological replicates per condition) to compare the results with previous benchmark studies, and a meta-analysis of 17 experiments with up to 18 biological conditions, eight biological replicates and 100 million (M) reads per sample. As a global trend, we concluded that the replicate number has a larger impact than the library size on the power of the DE analysis, except for low-expressed genes, for which both parameters seem to have the same impact. Our study also provides new insights for practitioners aiming to enhance their experimental designs. For instance, by analyzing both the sensitivity and specificity of the DE analysis, we showed that the optimal threshold to control the false discovery rate (FDR) is approximately 2-r, where r is the replicate number. Furthermore, we showed that the false positive rate (FPR) is rather well controlled by all three studied R packages: DESeq, DESeq2, and edgeR. We also analyzed the impact of both the replicate number and library size on gene ontology (GO) enrichment analysis. Interestingly, we concluded that increases in the replicate number and library size tend to enhance the sensitivity and specificity, respectively, of the GO analysis. Finally, we recommend to RNA-Seq practitioners the production of a pilot data set to strictly analyze the power of their experimental design, or the use of a public data set, which should be similar to the data set they will obtain. For individuals working on tomato research, on the basis of the meta-analysis, we recommend at least four biological replicates per condition and 20 M reads per sample to be almost sure of obtaining about 1000 DE genes if they exist.

CAZyChip: dynamic assessment of exploration of glycoside hydrolases in microbial ecosystems.

BACKGROUND: Microorganisms constitute a reservoir of enzymes involved in environmental carbon cycling and degradation of plant polysaccharides through their production of a vast variety of Glycoside Hydrolases (GH). The CAZyChip was developed to allow a rapid characterization at transcriptomic level of these GHs and to identify enzymes acting on hydrolysis of polysaccharides or glycans. RESULTS: This DNA biochip contains the signature of 55,220 bacterial GHs available in the CAZy database. Probes were designed using two softwares, and microarrays were directly synthesized using the in situ ink-jet technology. CAZyChip specificity and reproducibility was validated by hybridization of known GHs RNA extracted from recombinant E. coli strains, which were previously identified by a functional metagenomic approach. The GHs arsenal was also studied in bioprocess conditions using rumen derived microbiota. CONCLUSIONS: The CAZyChip appears to be a user friendly tool for profiling the expression of a large variety of GHs. It can be used to study temporal variations of functional diversity, thereby facilitating the identification of new efficient candidates for enzymatic conversions from various ecosystems.

Fuzzy logic selection as a new reliable tool to identify molecular grade signatures in breast cancer--the INNODIAG study.

BACKGROUND: Personalized medicine has become a priority in breast cancer patient management. In addition to the routinely used clinicopathological characteristics, clinicians will have to face an increasing amount of data derived from tumor molecular profiling. The aims of this study were to develop a new gene selection method based on a fuzzy logic selection and classification algorithm, and to validate the gene signatures obtained on breast cancer patient cohorts. METHODS: We analyzed data from four published gene expression datasets for breast carcinomas. We identified the best discriminating genes by comparing molecular expression profiles between histologic grade 1 and 3 tumors for each of the training datasets. The most pertinent probes were selected and used to define fuzzy molecular grade 1-like (good prognosis) and fuzzy molecular grade 3-like (poor prognosis) profiles. To evaluate the prognostic performance of the fuzzy grade signatures in breast cancer tumors, a Kaplan-Meier analysis was conducted to compare the relapse-free survival deduced from histologic grade and fuzzy molecular grade classification. RESULTS: We applied the fuzzy logic selection on breast cancer databases and obtained four new gene signatures. Analysis in the training public sets showed good performance of these gene signatures for grade (sensitivity from 90% to 95%, specificity 67% to 93%). To validate these gene signatures, we designed probes on custom microarrays and tested them on 150 invasive breast carcinomas. Good performance was obtained with an error rate of less than 10%. For one gene signature, among 74 histologic grade 3 and 18 grade 1 tumors, 88 cases (96%) were correctly assigned. Interestingly histologic grade 2 tumors (n = 58) were split in these two molecular grade categories. CONCLUSION: We confirmed the use of fuzzy logic selection as a new tool to identify gene signatures with good reliability and increased classification power. This method based on artificial intelligence algorithms was successfully applied to breast cancers molecular grade classification allowing histologic grade 2 classification into grade 1 and grade 2 like to improve patients prognosis. It opens the way to further development for identification of new biomarker combinations in other applications such as prediction of treatment response.

Integration of transcriptomic and metabolic analyses for understanding the global responses of low-temperature winemaking fermentations.

Wine produced at low temperature is often considered to have improved sensory qualities. To investigate the effects of temperature on winemaking, the expression patterns during the industrial fermentation process carried out at 13 degrees C and 25 degrees C were compared, and correlated with physiological and biochemical data, including viability, fermentation byproducts and lipid content of the cells. From a total of 535 ORFs that were significantly differentially expressed between the 13 degrees C and 25 degrees C fermentations, two significant transcription programmes were identified. A cold-stress response was expressed at the initial stage of the fermentation, and this was followed by a transcription pattern of upregulated genes concerned with the cell cycle, growth control and maintenance in the middle and late stages of the process at 13 degrees C with respect to 25 degrees C. These expression patterns were correlated with higher cell viability at low temperature. The other relevant transcriptomic difference was that several genes implicated in cytosolic fatty acid synthesis were downregulated, while those involved in mitochondrial short-chain fatty acid synthesis were upregulated in the fermentation process conducted at 13 degrees C with respect to that at 25 degrees C. These transcriptional changes were qualitatively correlated with improved resistance to ethanol and increased production of short-chain (C(4)-C(8)) fatty acids and their corresponding esters at 13 degrees C as compared to 25 degrees C. While this increase of ethyl esters may account in part for the improved sensory quality of wine fermented at 13 degrees C, it is still unclear how the esterification of the short-chain fatty acids takes place. On the basis of its strong upregulation at 13 degrees C, we propose a possible role of IAH1 encoding an esterase/ester synthase in this process.

Genome-wide analysis of the response to cell wall mutations in the yeast Saccharomyces cerevisiae.

Perturbations of the yeast cell wall trigger a repair mechanism that reconfigures its molecular structure to preserve cell integrity. To investigate this mechanism, we compared the global gene expression in five mutant strains, each bearing a mutation (i.e. fks1, kre6, mnn9, gas1, and knr4 mutants) that affects in a different manner the cell wall construction. Altogether, 300 responsive genes were kept based on high stringency criteria during data processing. Functional classification of these differentially expressed genes showed a substantial subset of induced genes involved in cell wall construction and an enrichment of metabolic, energy generation, and cell defense categories, whereas families of genes belonging to transcription, protein synthesis, and cellular growth were underrepresented. Clustering methods isolated a single group of approximately 80 up-regulated genes that could be considered as the stereotypical transcriptional response of the cell wall compensatory mechanism. The in silico analysis of the DNA upstream region of these co-regulated genes revealed pairwise combinations of DNA-binding sites for transcriptional factors implicated in stress and heat shock responses (Msn2/4p and Hsf1p) with Rlm1p and Swi4p, two PKC1-regulated transcription factors involved in the activation genes related to cell wall biogenesis and G1/S transition. Moreover, this computational analysis also uncovered the 6-bp 5'-AGCCTC-3' CDRE (calcineurin-dependent response element) motif in 40% of the co-regulated genes. This motif was recently shown to be the DNA binding site for Crz1p, the major effector of calcineurin-regulated gene expression in yeast. Taken altogether, the data presented here lead to the conclusion that the cell wall compensatory mechanism, as triggered by cell wall mutations, integrates three major regulatory systems: namely the PKC1-SLT2 mitogen-activated protein kinase-signaling module, the "global stress" response mediated by Msn2/4p, and the Ca2+/calcineurin-dependent pathway. The relative importance of these regulatory systems in the cell wall compensatory mechanism is discussed.