Transcriptional Interference Regulates the Evolutionary Development of Speech

The human capacity to speak is fundamental to our advanced intellectual, technological and social development. Yet so very little is known regarding the evolutionary genetics of speech or its relationship with the broader aspects of evolutionary development in primates. In this study, we describe a large family with evolutionary retrograde development of the larynx and wrist. The family presented with severe speech impairment and incremental retrograde elongations of the pisiform in the wrist that limited wrist rotation from 180◦ to 90◦ as in primitive primates. To our surprise, we found that a previously unknown primate-specific gene TOSPEAK had been disrupted in the family. TOSPEAK emerged de novo in an ancestor of extant primates across a 540 kb region of the genome with a pre-existing highly conserved long-range laryngeal enhancer for a neighbouring bone morphogenetic protein gene GDF6. We used transgenic mouse modelling to identify two additional GDF6 long-range enhancers within TOSPEAK that regulate GDF6 expression in the wrist. Disruption of TOSPEAK in the affected family blocked the transcription of TOSPEAK across the 3 GDF6 enhancers in association with a reduction in GDF6 expression and retrograde development of the larynx and wrist. Furthermore, we describe how TOSPEAK developed a human-specific promoter through the expansion of a penta-nucleotide direct repeat that first emerged de novo in the promoter of TOSPEAK in gibbon. This repeat subsequently expanded incrementally in higher hominids to form an overlapping series of Sp1/KLF transcription factor consensus binding sites in human that correlated with incremental increases in the promoter strength of TOSPEAK with human having the strongest promoter. Our research indicates a dual evolutionary role for the incremental increases in TOSPEAK transcriptional interference of GDF6 enhancers in the incremental evolutionary development of the wrist and larynx in hominids and the human capacity to speak and their retrogression with the reduction of TOSPEAK transcription in the affected family

Progenitor-like cells derived from mouse kidney protect against renal fibrosis in a remnant kidney model via decreased endothelial mesenchymal transition

Showing A quantification of GFP-positive cells in the lung after intravenous injection of MKPCs in five-sixths nephrectomized mice (y axis shows the number of cells, while the x axis (FL1-H) shows the fluorescence intensity; M1 is the area of GFP-positive cells) and B immunohistochemistry of the lung after intravenous injection of MKPCs into a mouse that underwent five-sixths nephrectomy. Few GFP positive cells were found in the lung at the first day but there were no GFP-positive cells at week 14. (TIFF 2253 kb

Principled Selection of Baseline Covariates to Account for Censoring in Randomized Trials with a Survival Endpoint

The analysis of randomized trials with time-to-event endpoints is nearly always plagued by the problem of censoring. As the censoring mechanism is usually unknown, analyses typically employ the assumption of non-informative censoring. While this assumption usually becomes more plausible as more baseline covariates are being adjusted for, such adjustment also raises concerns. Pre-specification of which covariates will be adjusted for (and how) is difficult, thus prompting the use of data-driven variable selection procedures, which may impede valid inferences to be drawn. The adjustment for covariates moreover adds concerns about model misspecification, and the fact that each change in adjustment set, also changes the censoring assumption and the treatment effect estimand. In this paper, we discuss these concerns and propose a simple variable selection strategy that aims to produce a valid test of the null in large samples. The proposal can be implemented using off-the-shelf software for (penalized) Cox regression, and is empirically found to work well in simulation studies and real data analyses

Microbial fuel cells: a green and alternative source for bioenergy production

Microbial fuel cell (MFC) represents one of the green technologies for the production of bioenergy. MFCs using microalgae produce bioenergy by converting solar energy into electrical energy as a function of metabolic and anabolic pathways of the cells. In the MFCs with bacteria, bioenergy is generated as a result of the organic substrate oxidation. MFCs have received high attention from researchers in the last years due to the simplicity of the process, the absence in toxic by-products, and low requirements for the algae growth. Many studies have been conducted on MFC and investigated the factors affecting the MFC performance. In the current chapter, the performance of MFC in producing bioenergy as well as the factors which influence the efficacy of MFCs is discussed. It appears that the main factors affecting MFC’s performance include bacterial and algae species, pH, temperature, salinity, substrate, mechanism of electron transfer in an anodic chamber, electrodes materials, surface area, and electron acceptor in a cathodic chamber. These factors are becoming more influential and might lead to overproduction of bioenergy when they are optimized using response surface methodology (RSM)

Quantifying Inactive Lithium in Lithium Metal Batteries

Inactive lithium (Li) formation is the immediate cause of capacity loss and catastrophic failure of Li metal batteries. However, the chemical component and the atomic level structure of inactive Li have rarely been studied due to the lack of effective diagnosis tools to accurately differentiate and quantify Li+ in solid electrolyte interphase (SEI) components and the electrically isolated unreacted metallic Li0, which together comprise the inactive Li. Here, by introducing a new analytical method, Titration Gas Chromatography (TGC), we can accurately quantify the contribution from metallic Li0 to the total amount of inactive Li. We uncover that the Li0, rather than the electrochemically formed SEI, dominates the inactive Li and capacity loss. Using cryogenic electron microscopies to further study the microstructure and nanostructure of inactive Li, we find that the Li0 is surrounded by insulating SEI, losing the electronic conductive pathway to the bulk electrode. Coupling the measurements of the Li0 global content to observations of its local atomic structure, we reveal the formation mechanism of inactive Li in different types of electrolytes, and identify the true underlying cause of low Coulombic efficiency in Li metal deposition and stripping. We ultimately propose strategies to enable the highly efficient Li deposition and stripping to enable Li metal anode for next generation high energy batteries

TEAD and YAP regulate the enhancer network of human embryonic pancreatic progenitors.

The genomic regulatory programmes that underlie human organogenesis are poorly understood. Pancreas development, in particular, has pivotal implications for pancreatic regeneration, cancer and diabetes. We have now characterized the regulatory landscape of embryonic multipotent progenitor cells that give rise to all pancreatic epithelial lineages. Using human embryonic pancreas and embryonic-stem-cell-derived progenitors we identify stage-specific transcripts and associated enhancers, many of which are co-occupied by transcription factors that are essential for pancreas development. We further show that TEAD1, a Hippo signalling effector, is an integral component of the transcription factor combinatorial code of pancreatic progenitor enhancers. TEAD and its coactivator YAP activate key pancreatic signalling mediators and transcription factors, and regulate the expansion of pancreatic progenitors. This work therefore uncovers a central role for TEAD and YAP as signal-responsive regulators of multipotent pancreatic progenitors, and provides a resource for the study of embryonic development of the human pancreas

The molecular characterisation of Escherichia coli K1 isolated from neonatal nasogastric feeding tubes

Background: The most common cause of Gram-negative bacterial neonatal meningitis is E. coli K1. It has a mortality rate of 10–15%, and neurological sequelae in 30– 50% of cases. Infections can be attributable to nosocomial sources, however the pre-colonisation of enteral feeding tubes has not been considered as a specific risk factor. Methods: Thirty E. coli strains, which had been isolated in an earlier study, from the residual lumen liquid and biofilms of neonatal nasogastric feeding tubes were genotyped using pulsed-field gel electrophoresis, and 7-loci multilocus sequence typing. Potential pathogenicity and biofilm associated traits were determined using specific PCR probes, genome analysis, and in vitro tissue culture assays. Results: The E. coli strains clustered into five pulsotypes, which were genotyped as sequence types (ST) 95, 73, 127, 394 and 2076 (Achman scheme). The extra-intestinal pathogenic E. coli (ExPEC) phylogenetic group B2 ST95 serotype O1:K1:NM strains had been isolated over a 2 week period from 11 neonates who were on different feeding regimes. The E. coli K1 ST95 strains encoded for various virulence traits associated with neonatal meningitis and extracellular matrix formation. These strains attached and invaded intestinal, and both human and rat brain cell lines, and persisted for 48 h in U937 macrophages. E. coli STs 73, 394 and 2076 also persisted in macrophages and invaded Caco-2 and human brain cells, but only ST394 invaded rat brain cells. E. coli ST127 was notable as it did not invade any cell lines. Conclusions: Routes by which E. coli K1 can be disseminated within a neonatal intensive care unit are uncertain, however the colonisation of neonatal enteral feeding tubes may be one reservoir source which could constitute a serious health risk to neonates following ingestion

Small RNA analysis in Sindbis virus infected human HEK293 cells

In contrast to the defence mechanism of RNA interference (RNAi) in plants and invertebrates, its role in the innate response to virus infection of mammals is a matter of debate. Since RNAi has a well-established role in controlling infection of the alphavirus Sindbis virus (SINV) in insects, we have used this virus to investigate the role of RNAi in SINV infection of human cells

The Escherichia coli transcriptome mostly consists of independently regulated modules

Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome

Historical changes in the stomatal limitation of photosynthesis: empirical support for an optimality principle

The ratio of leaf‐internal (ci) to ambient (ca) partial pressure of CO2, defined here as χ, is an index of adjustments in both leaf stomatal conductance and photosynthetic rate to environmental conditions. Measurements and proxies of this ratio can be used to constrain vegetation models uncertainties for predicting terrestrial carbon uptake and water use. We test a theory based on the least‐cost optimality hypothesis for modelling historical changes in χ over the 1951‐2014 period, across different tree species and environmental conditions, as reconstructed from stable carbon isotopic measurements across a global network of 103 absolutely‐dated tree‐ring chronologies. The theory predicts optimal χ as a function of air temperature, vapour pressure deficit, ca and atmospheric pressure. The theoretical model predicts 39% of the variance in χ values across sites and years, but underestimates the inter‐site variability in the reconstructed χ trends, resulting in only 8% of the variance in χ trends across years explained by the model. Overall, our results support theoretical predictions that variations in χ are tightly regulated by the four environmental drivers. They also suggest that explicitly accounting for the effects of plant‐available soil water and other site‐specific characteristics might improve the predictions