A data-driven estimation of the ribosome drop-off rate in S. cerevisiae reveals a correlation with the genes length.

Ribosomes are the molecular machinery that catalyse all the fundamental steps involved in the translation of mRNAs into proteins. Given the complexity of this process, the efficiency of protein synthesis depends on a large number of factors among which ribosome drop-off (i.e. the premature detachment of the ribosome from the mRNA template) plays an important role. However, an in vitro quantification of the extent to which ribosome drop-off occurs is not trivial due to difficulties in obtaining the needed experimental evidence. In this work we focus on the study of ribosome drop-off in Saccharomyces cerevisiae by using 'Ribofilio', a novel software tool that relies on a high sensitive strategy to estimate the ribosome drop-off rate from ribosome profiling data. Our results show that ribosome drop-off events occur at a significant rate also when S. cerevisiae is cultured in standard conditions. In this context, we also identified a correlation between the ribosome drop-off rate and the genes length: the longer the gene, the lower the drop-off rate.

Unraveling the Developmental Roadmap toward Human Brown Adipose Tissue.

Increasing brown adipose tissue (BAT) mass and activation is a therapeutic strategy to treat obesity and complications. Obese and diabetic patients possess low amounts of BAT, so an efficient way to expand their mass is necessary. There is limited knowledge about how human BAT develops, differentiates, and is optimally activated. Accessing human BAT is challenging, given its low volume and anatomical dispersion. These constraints make detailed BAT-related developmental and functional mechanistic studies in humans virtually impossible. We have developed and characterized functionally and molecularly a new chemically defined protocol for the differentiation of human pluripotent stem cells (hPSCs) into brown adipocytes (BAs) that overcomes current limitations. This protocol recapitulates step by step the physiological developmental path of human BAT. The BAs obtained express BA and thermogenic markers, are insulin sensitive, and responsive to ß-adrenergic stimuli. This new protocol is scalable, enabling the study of human BAs at early stages of development.

Mylk3 null C57BL/6N mice develop cardiomyopathy, whereas Nnt null C57BL/6J mice do not.

The C57BL/6J and C57BL/6N mice have well-documented phenotypic and genotypic differences, including the infamous nicotinamide nucleotide transhydrogenase (Nnt) null mutation in the C57BL/6J substrain, which has been linked to cardiovascular traits in mice and cardiomyopathy in humans. To assess whether Nnt loss alone causes a cardiovascular phenotype, we investigated the C57BL/6N, C57BL/6J mice and a C57BL/6J-BAC transgenic rescuing NNT expression, at 3, 12, and 18 mo. We identified a modest dilated cardiomyopathy in the C57BL/6N mice, absent in the two B6J substrains. Immunofluorescent staining of cardiomyocytes revealed eccentric hypertrophy in these mice, with defects in sarcomere organisation. RNAseq analysis identified differential expression of a number of cardiac remodelling genes commonly associated with cardiac disease segregating with the phenotype. Variant calling from RNAseq data identified a myosin light chain kinase 3 (Mylk3) mutation in C57BL/6N mice, which abolishes MYLK3 protein expression. These results indicate the C57BL/6J Nnt-null mice do not develop cardiomyopathy; however, we identified a null mutation in Mylk3 as a credible cause of the cardiomyopathy phenotype in the C57BL/6N.

Chemokines modulate the tumour microenvironment in pituitary neuroendocrine tumours.

Non-tumoural cells within the tumour microenvironment (TME) influence tumour proliferation, invasiveness and angiogenesis. Little is known about TME in pituitary neuroendocrine tumours (PitNETs). We aimed to characterise the role of TME in the aggressive behaviour of PitNETs, focusing on immune cells and cytokines. The cytokine secretome of 16 clinically non-functioning PitNETs (NF-PitNETs) and 8 somatotropinomas was assessed in primary culture using an immunoassay panel with 42 cytokines. This was correlated with macrophage (CD68, HLA-DR, CD163), T-lymphocyte (CD8, CD4, FOXP3), B-lymphocyte (CD20), neutrophil (neutrophil elastase) and endothelial cells (CD31) content, compared to normal pituitaries (NPs, n = 5). In vitro tumour-macrophage interactions were assessed by conditioned medium (CM) of GH3 (pituitary tumour) and RAW264.7 (macrophage) cell lines on morphology, migration/invasion, epithelial-to-mesenchymal transition and cytokine secretion. IL-8, CCL2, CCL3, CCL4, CXCL10, CCL22 and CXCL1 are the main PitNET-derived cytokines. PitNETs with increased macrophage and neutrophil content had higher IL-8, CCL2, CCL3, CCL4 and CXCL1 levels. CD8+ T-lymphocytes were associated to higher CCL2, CCL4 and VEGF-A levels. PitNETs had more macrophages than NPs (p < 0.001), with a 3-fold increased CD163:HLA-DR macrophage ratio. PitNETs contained more CD4+ T-lymphocytes (p = 0.005), but fewer neutrophils (p = 0.047) with a 2-fold decreased CD8:CD4 ratio. NF-PitNETs secreted more cytokines and had 9 times more neutrophils than somatotropinomas (p = 0.002). PitNETs with higher Ki-67 had more FOXP3+ T cells, as well as lower CD68:FOXP3, CD8:CD4 and CD8:FOXP3 ratios. PitNETs with "deleterious immune phenotype" (CD68hiCD4hiFOXP3hiCD20hi) had a Ki-67 ≥ 3%. CD163:HLA-DR macrophage ratio was positively correlated with microvessel density (p = 0.015) and area (p < 0.001). GH3 cell-CM increased macrophage chemotaxis, while macrophage-CM changed morphology, invasion, epithelial-to-mesenchymal transition and secreted cytokines of GH3 cells. PitNETs are characterised by increased CD163:HLA-DR macrophage and reduced CD8:CD4 and CD8:FOXP3 T cell ratios. PitNET-derived chemokines facilitate macrophage, neutrophil and T cell recruitment into the tumours which can determine aggressive behaviour.

Pituitary tumour fibroblast-derived cytokines influence tumour aggressiveness.

Tumour-associated fibroblasts (TAFs) are key elements of the tumour microenvironment, but their role in pituitary neuroendocrine tumours (PitNETs) has been little explored. We hypothesised that TAF-derived cytokines may play a role in tumour aggressiveness and that their release can be inhibited by somatostatin analogues. TAFs were isolated and cultured from 16 PitNETs (11 clinically non-functioning tumours and 5 somatotropinomas). The fibroblast secretome was assessed with a 42-plex cytokine array before and after multiligand somatostatin receptor agonist pasireotide treatment. Angiogenesis and epithelial-to-mesenchymal transition pathway assessment included CD31, E-cadherin and ZEB1 expression. GH3 cells treated with TAF- or skin fibroblast-conditioned medium were assessed for migration, invasion and cell morphology changes. PitNET TAFs secreted significant amounts of cytokines including CCL2, CCL11, VEGF-A, CCL22, IL-6, FGF-2 and IL-8. TAFs from PitNETs with cavernous sinus invasion secreted higher IL-6 levels compared to fibroblasts from non-invasive tumours (P = 0.027). Higher CCL2 release from TAFs correlated with more capillaries (r = 0.672, P = 0.004), and TAFs from PitNETs with a higher Ki-67 tended to secrete more CCL2 (P = 0.058). SST1 is the predominant somatostatin receptor in TAFs, and pasireotide decreased TAF-derived IL-6 by 80% (P < 0.001) and CCL2 by 35% (P = 0.038). GH3 cells treated with TAF-conditioned medium showed increased migration and invasion compared to cells treated with skin fibroblast-conditioned medium, with morphological and E-cadherin and ZEB1 expression changes suggesting epithelial-to-mesenchymal transition. TAF-derived cytokines may increase PitNET aggressiveness, alter angiogenesis and induce epithelial-to-mesenchymal transition changes. Pasireotide's inhibitory effect on TAF-derived cytokines suggest that this effect may play a role in its anti-tumour effects.

The khmer software package: enabling efficient nucleotide sequence analysis.

The khmer package is a freely available software library for working efficiently with fixed length DNA words, or k-mers. khmer provides implementations of a probabilistic k-mer counting data structure, a compressible De Bruijn graph representation, De Bruijn graph partitioning, and digital normalization. khmer is implemented in C++ and Python, and is freely available under the BSD license at  https://github.com/dib-lab/khmer/.

Inferring transcription factor collaborations in gene regulatory networks.

BACKGROUND: Living cells are realized by complex gene expression programs that are moderated by regulatory proteins called transcription factors (TFs). The TFs control the differential expression of target genes in the context of transcriptional regulatory networks (TRNs), either individually or in groups. Deciphering the mechanisms of how the TFs control the expression of target genes is a challenging task, especially when multiple TFs collaboratively participate in the transcriptional regulation. RESULTS: We model the underlying regulatory interactions in terms of the directions (activation or repression) and their logical roles (necessary and/or sufficient) with a modified association rule mining approach, called mTRIM. The experiment on Yeast discovered 670 regulatory interactions, in which multiple TFs express their functions on common target genes collaboratively. The evaluation on yeast genetic interactions, TF knockouts and a synthetic dataset shows that our algorithm is significantly better than the existing ones. CONCLUSIONS: mTRIM is a novel method to infer TF collaborations in transcriptional regulation networks. mTRIM is available at http://www.msu.edu/~jinchen/mTRIM.

Inferring the regulatory interaction models of transcription factors in transcriptional regulatory networks.

Living cells are realized by complex gene expression programs that are moderated by regulatory proteins called transcription factors (TFs). The TFs control the differential expression of target genes in the context of transcriptional regulatory networks (TRNs), either individually or in groups. Deciphering the mechanisms of how the TFs control the differential expression of a target gene in a TRN is challenging, especially when multiple TFs collaboratively participate in the transcriptional regulation. To unravel the roles of the TFs in the regulatory networks, we model the underlying regulatory interactions in terms of the TF-target interactions' directions (activation or repression) and their corresponding logical roles (necessary and/or sufficient). We design a set of constraints that relate gene expression patterns to regulatory interaction models, and develop TRIM (Transcriptional Regulatory Interaction Model Inference), a new hidden Markov model, to infer the models of TF-target interactions in large-scale TRNs of complex organisms. Besides, by training TRIM with wild-type time-series gene expression data, the activation timepoints of each regulatory module can be obtained. To demonstrate the advantages of TRIM, we applied it on yeast TRN to infer the TF-target interaction models for individual TFs as well as pairs of TFs in collaborative regulatory modules. By comparing with TF knockout and other gene expression data, we were able to show that the performance of TRIM is clearly higher than DREM (the best existing algorithm). In addition, on an individual Arabidopsis binding network, we showed that the target genes' expression correlations can be significantly improved by incorporating the TF-target regulatory interaction models inferred by TRIM into the expression data analysis, which may introduce new knowledge in transcriptional dynamics and bioactivation.