Transcriptional profiling of mouse peripheral nerves to the single-cell level to build a sciatic nerve ATlas (SNAT).

Peripheral nerves are organ-like structures containing diverse cell types to optimize function. This interactive assembly includes mostly axon-associated Schwann cells, but also endothelial cells of supporting blood vessels, immune system-associated cells, barrier-forming cells of the perineurium surrounding and protecting nerve fascicles, and connective tissue-resident cells within the intra-fascicular endoneurium and inter-fascicular epineurium. We have established transcriptional profiles of mouse sciatic nerve-inhabitant cells to foster the fundamental understanding of peripheral nerves. To achieve this goal, we have combined bulk RNA sequencing of developing sciatic nerves up to the adult with focused bulk and single-cell RNA sequencing of Schwann cells throughout postnatal development, extended by single-cell transcriptome analysis of the full sciatic nerve both perinatally and in the adult. The results were merged in the transcriptome resource Sciatic Nerve ATlas (SNAT: https://www.snat.ethz.ch). We anticipate that insights gained from our multi-layered analysis will serve as valuable interactive reference point to guide future studies.

FASN-Dependent Lipid Metabolism Links Neurogenic Stem/Progenitor Cell Activity to Learning and Memory Deficits.

Altered neural stem/progenitor cell (NSPC) activity and neurodevelopmental defects are linked to intellectual disability. However, it remains unclear whether altered metabolism, a key regulator of NSPC activity, disrupts human neurogenesis and potentially contributes to cognitive defects. We investigated links between lipid metabolism and cognitive function in mice and human embryonic stem cells (hESCs) expressing mutant fatty acid synthase (FASN; R1819W), a metabolic regulator of rodent NSPC activity recently identified in humans with intellectual disability. Mice homozygous for the FASN R1812W variant have impaired adult hippocampal NSPC activity and cognitive defects because of lipid accumulation in NSPCs and subsequent lipogenic ER stress. Homozygous FASN R1819W hESC-derived NSPCs show reduced rates of proliferation in embryonic 2D cultures and 3D forebrain regionalized organoids, consistent with a developmental phenotype. These data from adult mouse models and in vitro models of human brain development suggest that altered lipid metabolism contributes to intellectual disability.

Sensitive Quantitative Proteomics of Human Hematopoietic Stem and Progenitor Cells by Data-independent Acquisition Mass Spectrometry.

Physiological processes in multicellular organisms depend on the function and interactions of specialized cell types operating in context. Some of these cell types are rare and thus obtainable only in minute quantities. For example, tissue-specific stem and progenitor cells are numerically scarce, but functionally highly relevant, and fulfill critical roles in development, tissue maintenance, and disease. Whereas low numbers of cells are routinely analyzed by genomics and transcriptomics, corresponding proteomic analyses have so far not been possible due to methodological limitations. Here we describe a sensitive and robust quantitative technique based on data-independent acquisition mass spectrometry. We quantified the proteome of sets of 25,000 human hematopoietic stem/multipotent progenitor cells (HSC/MPP) and three committed progenitor cell subpopulations of the myeloid differentiation pathway (common myeloid progenitors, megakaryocyte-erythrocyte progenitors, and granulocyte-macrophage progenitors), isolated by fluorescence-activated cell sorting from five healthy donors. On average, 5,851 protein groups were identified per sample. A subset of 4,131 stringently filtered protein groups was quantitatively compared across the 20 samples, defining unique signatures for each subpopulation. A comparison of proteomic and transcriptomic profiles indicated HSC/MPP-specific divergent regulation of biochemical functions such as telomerase maintenance and quiescence-inducing enzymes, including isocitrate dehydrogenases. These are essential for maintaining stemness and were detected at proteome, but not transcriptome, level. The method is equally applicable to almost any rare cell type, including healthy and cancer stem cells or physiologically and pathologically infiltrating cell populations. It thus provides essential new information toward the detailed biochemical understanding of cell development and functionality in health and disease.

GLI1-expressing mesenchymal cells form the essential Wnt-secreting niche for colon stem cells.

Wnt-ß-catenin signalling plays a pivotal role in the homeostasis of the intestinal epithelium by promoting stem cell renewal1,2. In the small intestine, epithelial Paneth cells secrete Wnt ligands and thus adopt the function of the stem cell niche to maintain epithelial homeostasis3,4. It is unclear which cells comprise the stem cell niche in the colon. Here we show that subepithelial mesenchymal GLI1-expressing cells form this essential niche. Blocking Wnt secretion from GLI1-expressing cells prevents colonic stem cell renewal in mice: the stem cells are lost and, as a consequence, the integrity of the colonic epithelium is corrupted, leading to death. GLI1-expressing cells also play an important role in the maintenance of the small intestine, where they serve as a reserve Wnt source that becomes critical when Wnt secretion from epithelial cells is prevented. Our data suggest a mechanism by which the stem cell niche is adjusted to meet the needs of the intestine via adaptive changes in the number of mesenchymal GLI1-expressing cells.

Prognostic value of cross-omics screening for kidney clear cell renal cancer survival.

BACKGROUND: Kidney renal clear cell carcinoma (KIRC) is a type of cancer that is resistant to chemotherapy and radiotherapy and has limited treatment possibilities. Large-scale molecular profiling of KIRC tumors offers a great potential to uncover the genetic and epigenetic changes underlying this disease and to improve the clinical management of KIRC patients. However, in practice the clinicians and researchers typically focus on single-platform molecular data or on a small set of genes. Using molecular and clinical data of over 500 patients, we have systematically studied which type of molecular data is the most informative in predicting the clinical outcome of KIRC patients, as a standalone platform and integrated with clinical data. RESULTS: We applied different computational approaches to preselect on survival-predictive genomic markers and evaluated the usability of mRNA/miRNA/protein expression data, copy number variation (CNV) data and DNA methylation data in predicting survival of KIRC patients. Our analyses show that expression and methylation data have statistically significant predictive powers compared to a random guess, but do not perform better than predictions on clinical data alone. However, the integration of molecular data with clinical variables resulted in improved predictions. We present a set of survival associated genomic loci that could potentially be employed as clinically useful biomarkers. CONCLUSIONS: Our study evaluates the survival prediction of different large-scale molecular data of KIRC patients and describes the prognostic relevance of such data over clinical-variable-only models. It also demonstrates the survival prognostic importance of methylation alterations in KIRC tumors and points to the potential of epigenetic modulators in KIRC treatment. REVIEWERS: An extended abstract of this research paper was selected for the CAMDA Satellite Meeting to ISMB 2015 by the CAMDA Programme Committee. The full research paper then underwent one round of Open Peer Review under a responsible CAMDA Programme Committee member, Djork-Arné Clevert, PhD (Bayer AG, Germany). Open Peer Review was provided by Martin Otava, PhD (Janssen Pharmaceutica, Belgium) and Hendrik Luuk, PhD (The Centre for Disease Models and Biomedical Imaging, University of Tartu, Estonia). The Reviewer comments section shows the full reviews and author responses.

Classification of selectively constrained DNA elements using feature vectors and rule-based classifiers.

Scarce work has been done in the analysis of the composition of conserved non-coding elements (CNEs) that are identified by comparisons of two or more genomes and are found to exist in all metazoan genomes. Here we present the analysis of CNEs with a methodology that takes into account word occurrence at various lengths scales in the form of feature vector representation and rule based classifiers. We implement our approach on both protein-coding exons and CNEs, originating from human, insect (Drosophila melanogaster) and worm (Caenorhabditis elegans) genomes, that are either identified in the present study or obtained from the literature. Alignment free feature vector representation of sequences combined with rule-based classification methods leads to successful classification of the different CNEs classes. Biologically meaningful results are derived by comparison with the genomic signatures approach, and classification rates for a variety of functional elements of the genomes along with surrogates are presented.

Unraveling patterns of site-to-site synonymous rates variation and associated gene properties of protein domains and families.

In protein-coding genes, synonymous mutations are often thought not to affect fitness and therefore are not subject to natural selection. Yet increasingly, cases of non-neutral evolution at certain synonymous sites were reported over the last decade. To evaluate the extent and the nature of site-specific selection on synonymous codons, we computed the site-to-site synonymous rate variation (SRV) and identified gene properties that make SRV more likely in a large database of protein-coding gene families and protein domains. To our knowledge, this is the first study that explores the determinants and patterns of the SRV in real data. We show that the SRV is widespread in the evolution of protein-coding sequences, putting in doubt the validity of the synonymous rate as a standard neutral proxy. While protein domains rarely undergo adaptive evolution, the SRV appears to play important role in optimizing the domain function at the level of DNA. In contrast, protein families are more likely to evolve by positive selection, but are less likely to exhibit SRV. Stronger SRV was detected in genes with stronger codon bias and tRNA reusage, those coding for proteins with larger number of interactions or forming larger number of structures, located in intracellular components and those involved in typically conserved complex processes and functions. Genes with extreme SRV show higher expression levels in nearly all tissues. This indicates that codon bias in a gene, which often correlates with gene expression, may often be a site-specific phenomenon regulating the speed of translation along the sequence, consistent with the co-translational folding hypothesis. Strikingly, genes with SRV were strongly overrepresented for metabolic pathways and those associated with several genetic diseases, particularly cancers and diabetes.

UCNEbase--a database of ultraconserved non-coding elements and genomic regulatory blocks.

UCNEbase (http://ccg.vital-it.ch/UCNEbase) is a free, web-accessible information resource on the evolution and genomic organization of ultra-conserved non-coding elements (UCNEs). It currently covers 4351 such elements in 18 different species. The majority of UCNEs are supposed to be transcriptional regulators of key developmental genes. As most of them occur as clusters near potential target genes, the database is organized along two hierarchical levels: individual UCNEs and ultra-conserved genomic regulatory blocks (UGRBs). UCNEbase introduces a coherent nomenclature for UCNEs reflecting their respective associations with likely target genes. Orthologous and paralogous UCNEs share components of their names and are systematically cross-linked. Detailed synteny maps between the human and other genomes are provided for all UGRBs. UCNEbase is managed by a relational database system and can be accessed by a variety of web-based query pages. As it relies on the UCSC genome browser as visualization platform, a large part of its data content is also available as browser viewable custom track files. UCNEbase is potentially useful to any computational, experimental or evolutionary biologist interested in conserved non-coding DNA elements in vertebrates.

Genomic context analysis reveals dense interaction network between vertebrate ultraconserved non-coding elements.

MOTIVATION: Genomic context analysis, also known as phylogenetic profiling, is widely used to infer functional interactions between proteins but rarely applied to non-coding cis-regulatory DNA elements. We were wondering whether this approach could provide insights about utlraconserved non-coding elements (UCNEs). These elements are organized as large clusters, so-called gene regulatory blocks (GRBs) around key developmental genes. Their molecular functions and the reasons for their high degree of conservation remain enigmatic. RESULTS: In a special setting of genomic context analysis, we analyzed the fate of GRBs after a whole-genome duplication event in five fish genomes. We found that in most cases all UCNEs were retained together as a single block, whereas the corresponding target genes were often retained in two copies, one completely devoid of UCNEs. This 'winner-takes-all' pattern suggests that UCNEs of a GRB function in a highly cooperative manner. We propose that the multitude of interactions between UCNEs is the reason for their extreme sequence conservation. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online and at http://ccg.vital-it.ch/ucne/

Practicality and time complexity of a sparsified RNA folding algorithm.

Commonly used RNA folding programs compute the minimum free energy structure of a sequence under the pseudoknot exclusion constraint. They are based on Zuker's algorithm which runs in time O(n(3)). Recently, it has been claimed that RNA folding can be achieved in average time O(n(2)) using a sparsification technique. A proof of quadratic time complexity was based on the assumption that computational RNA folding obeys the "polymer-zeta property". Several variants of sparse RNA folding algorithms were later developed. Here, we present our own version, which is readily applicable to existing RNA folding programs, as it is extremely simple and does not require any new data structure. We applied it to the widely used Vienna RNAfold program, to create sibRNAfold, the first public sparsified version of a standard RNA folding program. To gain a better understanding of the time complexity of sparsified RNA folding in general, we carried out a thorough run time analysis with synthetic random sequences, both in the context of energy minimization and base pairing maximization. Contrary to previous claims, the asymptotic time complexity of a sparsified RNA folding algorithm using standard energy parameters remains O(n(3)) under a wide variety of conditions. Consistent with our run-time analysis, we found that RNA folding does not obey the "polymer-zeta property" as claimed previously. Yet, a basic version of a sparsified RNA folding algorithm provides 15- to 50-fold speed gain. Surprisingly, the same sparsification technique has a different effect when applied to base pairing optimization. There, its asymptotic running time complexity appears to be either quadratic or cubic depending on the base composition. The code used in this work is available at: .