The persistent dynamic secrets of senescence

While the beneficial versus detrimental implications of the senescence-associated secretome remain an issue of debate, time-resolved analyses of its composition, regulatory mechanisms and functional consequences have been largely missing. The dynamic activity of NOTCH is now shown to direct two distinct senescence phenotypes, by first promoting a pro-senescent TGF-{beta}1-dependent secretome, followed by a second wave of pro-inflammatory, senescence-clearing cytokines

Natural polymorphisms in C. elegans HECW-1 E3 ligase affect pathogen avoidance behaviour

available in PMC 2012 June 22.Heritable variation in behavioural traits generally has a complex genetic basis1, and thus naturally occurring polymorphisms that influence behaviour have been defined in only rare instances2,3. The isolation of wild strains of Caenorhabditis elegans has facilitated the study of natural genetic variation in this species4 and provided insights into its diverse microbial ecology5. C. elegans responds to bacterial infection with conserved innate immune responses6-8 and, while lacking the immunological memory of vertebrate adaptive immunity, exhibits an aversive learning response to pathogenic bacteria9. Here, we report the molecular characterization of naturally occurring coding polymorphisms in a C. elegans gene encoding a conserved HECT domain-containing E3 ubiquitin ligase, HECW-1. We show that two distinct polymorphisms in neighbouring residues of HECW-1 each affect C. elegans behavioural avoidance of a lawn of Pseudomonas aeruginosa. Neuronspecific rescue and ablation experiments, and genetic interaction analysis suggest that HECW-1 functions in a pair of sensory neurons to inhibit P. aeruginosa lawn avoidance behaviour through inhibition of the neuropeptide receptor NPR-110, which we have previously shown promotes P. aeruginosa lawn avoidance behaviour11. Our data establish a molecular basis for natural variation in a C. elegans behaviour that may undergo adaptive changes in response to microbial pathogens.National Institutes of Health (U.S.) (NIH Grant GM084477

Genetic determinants of co-accessible chromatin regions in activated T cells across humans.

Over 90% of genetic variants associated with complex human traits map to non-coding regions, but little is understood about how they modulate gene regulation in health and disease. One possible mechanism is that genetic variants affect the activity of one or more cis-regulatory elements leading to gene expression variation in specific cell types. To identify such cases, we analyzed ATAC-seq and RNA-seq profiles from stimulated primary CD4+ T cells in up to 105 healthy donors. We found that regions of accessible chromatin (ATAC-peaks) are co-accessible at kilobase and megabase resolution, consistent with the three-dimensional chromatin organization measured by in situ Hi-C in T cells. Fifteen percent of genetic variants located within ATAC-peaks affected the accessibility of the corresponding peak (local-ATAC-QTLs). Local-ATAC-QTLs have the largest effects on co-accessible peaks, are associated with gene expression and are enriched for autoimmune disease variants. Our results provide insights into how natural genetic variants modulate cis-regulatory elements, in isolation or in concert, to influence gene expression

Root-Knot Nematodes Exhibit Strain-Specific Clumping Behavior That Is Inherited as a Simple Genetic Trait

Root-knot nematodes are obligate parasites of a wide range of plant species and can feed only on the cytoplasm of living plant cells. In the absence of a suitable plant host, infective juveniles of strain VW9 of the Northern root-knot nematode, Meloidogyne hapla, when dispersed in Pluronic F-127 gel, aggregate into tight, spherical clumps containing thousands of worms. Aggregation or clumping behavior has been observed in diverse genera in the phylum Nematoda spanning free-living species such as Caenorhabditis elegans as well as both plant and animal parasites. Clumping behavior differs between strains of M. hapla and occurs with other species within this genus where strain-specific differences in clumping ability are also apparent. Exposure of M. hapla juveniles to a gradient formed using low levels of cyanide promotes formation of clumps at a preferred cyanide level. Analysis of F2 lines from a cross of M. hapla strains that differ in clump-forming behavior reveals that the behavior segregates as a single, major locus that can be positioned on the genetic map of this nematode. Clumping behavior may be a survival strategy whose importance and function depend on the niche of the nematode strain or species

Clustering of small - sample single - cell RNA - seq data via feature clustering and selection

We present FeatClust, a software tool for clustering small sample size single-cell RNA-Seq datasets. The FeatClust approach is based on feature selection. It divides features into several groups by performing agglomerative hierarchical clustering and then iteratively clustering the samples and removing features belonging to groups with the least variance across samples. The optimal number of feature groups is selected based on silhouette analysis on the clustered data, i.e., selecting the clustering with the highest average silhouette coefficient. FeatClust also allows one to visually choose the number of clusters if it is not known, by generating silhouette plot for a chosen number of groupings of the dataset. We cluster five small sample single-cell RNA-seq datasets and use the adjusted rand index metric to compare the results with other clustering packages. The results are promising and show the effectiveness of FeatClust on small sample size datasets

Normalizing single-cell RNA sequencing data: challenges and opportunities

Single-cell transcriptomics is becoming an important component of the molecular biologist's toolkit. A critical step when analyzing data generated using this technology is normalization. However, normalization is typically performed using methods developed for bulk RNA sequencing or even microarray data, and the suitability of these methods for single-cell transcriptomics has not been assessed. We here discuss commonly used normalization approaches and illustrate how these can produce misleading results. Finally, we present alternative approaches and provide recommendations for single-cell RNA sequencing users

Electron Transfer Function versus Oxygen Delivery: A Comparative Study for Several Hexacoordinated Globins Across the Animal Kingdom

Caenorhabditis elegans globin GLB-26 (expressed from gene T22C1.2) has been studied in comparison with human neuroglobin (Ngb) and cytoglobin (Cygb) for its electron transfer properties. GLB-26 exhibits no reversible binding for O2 and a relatively low CO affinity compared to myoglobin-like globins. These differences arise from its mechanism of gaseous ligand binding since the heme iron of GLB-26 is strongly hexacoordinated in the absence of external ligands; the replacement of this internal ligand, probably the E7 distal histidine, is required before binding of CO or O2 as for Ngb and Cygb. Interestingly the ferrous bis-histidyl GLB-26 and Ngb, another strongly hexacoordinated globin, can transfer an electron to cytochrome c (Cyt-c) at a high bimolecular rate, comparable to those of inter-protein electron transfer in mitochondria. In addition, GLB-26 displays an unexpectedly rapid oxidation of the ferrous His-Fe-His complex without O2 actually binding to the iron atom, since the heme is oxidized by O2 faster than the time for distal histidine dissociation. These efficient mechanisms for electron transfer could indicate a family of hexacoordinated globin which are functionally different from that of pentacoordinated globins

Beyond comparisons of means: understanding changes in gene expression at the single-cell level

Traditional differential expression tools are limited to detecting changes in overall expression, and fail to uncover the rich information provided by single-cell level data sets. We present a Bayesian hierarchical model that builds upon BASiCS to study changes that lie beyond comparisons of means, incorporating built-in normalization and quantifying technical artifacts by borrowing information from spike-in genes. Using a probabilistic approach, we highlight genes undergoing changes in cell-to-cell heterogeneity but whose overall expression remains unchanged. Control experiments validate our method’s performance and a case study suggests that novel biological insights can be revealed. Our method is implemented in R and available at https://github.com/catavallejos/BASiCS