Multiomics of GCN4-Dependent Replicative Lifespan Extension Models Reveals Gcn4 as a Regulator of Protein Turnover in Yeast.

We have shown that multiple tRNA synthetase inhibitors can increase lifespan in both the nematode C. elegans and the budding yeast S. cerevisiae by acting through the conserved transcription factor Gcn4 (yeast)/ATF-4 (worms). To further understand the biology downstream from this conserved transcription factor in the yeast model system, we looked at two different yeast models known to have upregulated Gcn4 and GCN4-dependent increased replicative lifespan. These two models were rpl31aΔ yeast and yeast treated with the tRNA synthetase inhibitor borrelidin. We used both proteomic and RNAseq analysis of a block experimental design that included both of these models to identify GCN4-dependent changes in these two long-lived strains of yeast. Proteomic analysis of these yeast indicate that the long-lived yeast have increased abundances of proteins involved in amino acid biosynthesis. The RNAseq of these same yeast uncovered further regulation of protein degradation, identifying the differential expression of genes associated with autophagy and the ubiquitin-proteasome system (UPS). The data presented here further underscore the important role that GCN4 plays in the maintenance of protein homeostasis, which itself is an important hallmark of aging. In particular, the changes in autophagy and UPS-related gene expression that we have observed could also have wide-ranging implications for the understanding and treatment of diseases of aging that are associated with protein aggregation.

Easy C. elegans Worm Lifespan Plotting and Statistics with WLSplot.

WLSplot is an R package used to easily analyze lifespan survival data, and display results graphically as a survival curve with useful labels and statistical information auto-generated from the data and added to the graph, within a single function. It is designed primarily with Caenorhabditis elegans lifespan data in mind initially but can easily be used for other types of survival data. The WLSplot GitHub repository provides a blank template spreadsheet to be used for collecting lifespan data, instructions on how to install and run WLSplot, and examples covering RNAi, Genotype, or Drug lifespan experimental set-ups. WLSplot can analyze and plot multiple experiments in bulk while correctly italicizing worm gene names and adding asterisks and p-values to the plot legend when a significantly different lifespan from the designated control lifespan is seen. This is returned as an editable scalable vector graphics (svg) file for each output, and WLSplot can also return the summary of the directly plotted data so that the researcher can do their own further manipulation, in addition to being able to edit the output svg files.

ALKBH4 Stabilization Is Required for Arsenic-Induced 6mA DNA Methylation Inhibition, Keratinocyte Malignant Transformation, and Tumorigenicity.

Inorganic arsenic is one of the well-known human skin carcinogens. However, the molecular mechanism by which arsenic promotes carcinogenesis remains unclear. Previous studies have established that epigenetic changes, including changes in DNA methylation, are among the critical mechanisms that drive carcinogenesis. N6-methyladenine (6mA) methylation on DNA is a widespread epigenetic modification that was initially found on bacterial and phage DNA. Only recently has 6mA been identified in mammalian genomes. However, the function of 6mA in gene expression and cancer development is not well understood. Here, we show that chronic low doses of arsenic induce malignant transformation and tumorigenesis in keratinocytes and lead to the upregulation of ALKBH4 and downregulation of 6mA on DNA. We found that reduced 6mA levels in response to low levels of arsenic were mediated by the upregulation of the 6mA DNA demethylase ALKBH4. Moreover, we found that arsenic increased ALKBH4 protein levels and that ALKBH4 deletion impaired arsenic-induced tumorigenicity in vitro and in mice. Mechanistically, we found that arsenic promoted ALKBH4 protein stability through reduced autophagy. Together, our findings reveal that the DNA 6mA demethylaseALKBH4 promotes arsenic tumorigenicity and establishes ALKBH4 as a promising target for arsenic-induced tumorigenesis.

Bartonella henselae Detected in Malignant Melanoma, a Preliminary Study.

Bartonella bacilliformis (B. bacilliformis), Bartonella henselae (B. henselae), and Bartonella quintana (B. quintana) are bacteria known to cause verruga peruana or bacillary angiomatosis, vascular endothelial growth factor (VEGF)-dependent cutaneous lesions in humans. Given the bacteria's association with the dermal niche and clinical suspicion of occult infection by a dermatologist, we determined if patients with melanoma had evidence of Bartonella spp. infection. Within a one-month period, eight patients previously diagnosed with melanoma volunteered to be tested for evidence of Bartonella spp. exposure/infection. Subsequently, confocal immunohistochemistry and PCR for Bartonella spp. were used to study melanoma tissues from two patients. Blood from seven of the eight patients was either seroreactive, PCR positive, or positive by both modalities for Bartonella spp. exposure. Subsequently, Bartonella organisms that co-localized with VEGFC immunoreactivity were visualized using multi-immunostaining confocal microscopy of thick skin sections from two patients. Using a co-culture model, B. henselae was observed to enter melanoma cell cytoplasm and resulted in increased vascular endothelial growth factor C (VEGFC) and interleukin 8 (IL-8) production. Findings from this small number of patients support the need for future investigations to determine the extent to which Bartonella spp. are a component of the melanoma pathobiome.

Single molecule conformational memory extraction: p5ab RNA hairpin.

Extracting kinetic models from single molecule data is an important route to mechanistic insight in biophysics, chemistry, and biology. Data collected from force spectroscopy can probe discrete hops of a single molecule between different conformational states. Model extraction from such data is a challenging inverse problem because single molecule data are noisy and rich in structure. Standard modeling methods normally assume (i) a prespecified number of discrete states and (ii) that transitions between states are Markovian. The data set is then fit to this predetermined model to find a handful of rates describing the transitions between states. We show that it is unnecessary to assume either (i) or (ii) and focus our analysis on the zipping/unzipping transitions of an RNA hairpin. The key is in starting with a very broad class of non-Markov models in order to let the data guide us toward the best model from this very broad class. Our method suggests that there exists a folding intermediate for the P5ab RNA hairpin whose zipping/unzipping is monitored by force spectroscopy experiments. This intermediate would not have been resolved if a Markov model had been assumed from the onset. We compare the merits of our method with those of others.

A maximum entropy framework for nonexponential distributions.

Probability distributions having power-law tails are observed in a broad range of social, economic, and biological systems. We describe here a potentially useful common framework. We derive distribution functions for situations in which a "joiner particle" k pays some form of price to enter a community of size , where costs are subject to economies of scale. Maximizing the Boltzmann-Gibbs-Shannon entropy subject to this energy-like constraint predicts a distribution having a power-law tail; it reduces to the Boltzmann distribution in the absence of economies of scale. We show that the predicted function gives excellent fits to 13 different distribution functions, ranging from friendship links in social networks, to protein-protein interactions, to the severity of terrorist attacks. This approach may give useful insights into when to expect power-law distributions in the natural and social sciences.