Memory effects and static disorder reduce information in single-molecule signals

A key theoretical challenge posed by single-molecule studies is the inverse problem of deducing the underlying molecular dynamics from the time evolution of low-dimensional experimental observables. Toward this goal, a variety of low-dimensional models have been proposed as descriptions of single-molecule signals, including random walks with or without conformational memory and/or with static or dynamics disorder. Differentiating among different models presents a challenge, as many distinct physical scenarios lead to similar experimentally observable behaviors such as anomalous diffusion and nonexponential relaxation. Here we show that information-theory-based analysis of single-molecule time series, inspired by Shannon's work studying the information content of printed English, can differentiate between Markov (memoryless) and non-Markov single-molecule signals and between static and dynamic disorder. In particular, non-Markov time series are more predictable and thus can be compressed and transmitted within shorter messages (i.e. have a lower entropy rate) than appropriately constructed Markov approximations, and we demonstrate that in practice the LZMA compression algorithm reliably differentiates between these entropy rates across several simulated dynamical models.

JOINT CONTRIBUTION OF MARKERS OF NEURODEGENERATION AND HYPERCHOLESTEROLEMIA TO DEMENTIA RISK

Background The examination of markers of neurodegeneration (glial fibrillary acidic protein; GFAP, neurofilament light chain; NfL, phosphorylated tau181; p-tau181) among individuals with high comorbidity of neurodegenerative and cerebrovascular disease and their interplay with vascular risk factors, particularly high cholesterol levels, might contribute to explaining the link between body and brain. The aim of this study was to assess whether the association of GFAP, NfL, and p-tau181 with dementia risk varies depending on levels of total cholesterol (TC) and APOE ε4 genotype. Methods Nested case-control study embedded within a population-based cohort and including 768 older adults (261 dementia cases and 508 randomly selected controls) followed for up to 17 years with regard to clinical diagnosis of various age-related diseases. GFAP, NfL, and p-tau181 were measured in baseline blood samples using the Single-Molecule Array (Simoa) Technology (Quanterix, USA) and categorized into high (quartile 4) versus low (quartiles 1-3). Logistic regression analyses and spline regression models for dose-response analyses were used. ROC curves by cholesterol levels were also calculated. Results The risk of a dementia diagnosis was significantly increased between participants with high vs. low levels of GFAP and NfL and the risk substantially varied by TC levels. For GFAP and NfL the ORs of a dementia diagnosis were 5.10 (2.45-10.60) and 2.96 (1.43-6.14) in participants with high and 2.44 (1.47-4.07) and 1.15 (0.69-1.92) in those with low TC. APOE ε4 genotype further modified the strength of the associations with different patterns, depending on specific marker and type of dementia. No significant association was seen with p-tau181. Conclusions These results suggest that in the general population blood GFAP and NfL are better predictors of dementia than p-tau181 and that their associations with dementia risk are highly amplified by hypercholesterolemia, also depending on APOE ε4 genotype.

CircFISH: A Novel Method for the Simultaneous Imaging of Linear and Circular RNAs

Circular RNAs (circRNAs) are regulatory RNAs which have recently been shown to have clinical significance in several diseases, including, but not limited to, various cancers, neurological diseases and cardiovascular diseases. The function of such regulatory RNAs is largely dependent on their subcellular localization. Several circRNAs have been shown to conduct antagonistic roles compared to the products of the linear isoforms, and thus need to be characterized distinctly from the linear RNAs. However, conventional fluorescent in situ hybridization (FISH) techniques cannot be employed directly to distinguish the signals from linear and circular isoforms because most circRNAs share the same sequence with the linear RNAs. In order to address this unmet need, we adapted the well-established method of single-molecule FISH by designing two sets of probes to differentiate the linear and circular RNA isoforms by virtue of signal colocalization. We call this method ‘circular fluorescent in situ hybridization’ (circFISH). Linear and circular RNAs were successfully visualized and quantified at a single-molecule resolution in fixed cells. RNase R treatment during the circFISH reduced the levels of linear RNAs while the circRNA levels remain unaltered. Furthermore, cells with shRNAs specific to circRNA showed the loss of circRNA levels, whereas the linear RNA levels were unaffected. The optimization of the in-situ RNase R treatment allowed the multiplexing of circFISH to combine it with organelle staining. CircFISH was found to be compatible with multiple sample types, including cultured cells and fresh-frozen and formalin-fixed tissue sections. Thus, we present circFISH as a versatile method for the simultaneous visualization and quantification of the distribution and localization of linear and circular RNA in fixed cells and tissue samples.

CtIP-dependent nascent RNA expression flanking DNA breaks guides the choice of DNA repair pathway

The RNA world is changing our views about sensing and resolution of DNA damage. Here, we developed single-molecule DNA/RNA analysis approaches to visualize how nascent RNA facilitates the repair of DNA double-strand breaks (DSBs). RNA polymerase II (RNAPII) is crucial for DSB resolution in human cells. DSB-flanking, RNAPII-generated nascent RNA forms RNA:DNA hybrids, guiding the upstream DNA repair steps towards favouring the error-free Homologous Recombination (HR) pathway over Non-Homologous End Joining. Specific RNAPII inhibitor, THZ1, impairs recruitment of essential HR proteins to DSBs, implicating nascent RNA in DNA end resection, initiation and execution of HR repair. We further propose that resection factor CtIP interacts with and re-activates RNAPII when paused by the RNA:DNA hybrids, collectively promoting faithful repair of chromosome breaks to maintain genomic integrity.

Characterizing the conformational free-energy landscape of RNA using single-molecule field-effect transistors

We have developed and used high-time-resolution, single-molecule field-effect transistors (smFETs) to characterize the con-formational free-energy landscape of RNA stem-loops. Stem-loops are some of the most common RNA structural motifs and serve as building blocks for the formation of more complex RNA structures. Given their prevalence and integral role in RNA folding, the kinetics of stem-loop (un)folding has been extensively characterized using both experimental and computational approaches. Interestingly, these studies have reported vastly disparate timescales of (un)folding, which has been recently in-terpreted as evidence that (un)folding of even simple stem-loops occurs on a highly rugged conformational energy landscape. Because smFETs do not rely on fluorophore reporters of conformation or on the application of mechanical (un)folding forces, they provide a unique and complementary approach that has allowed us to directly monitor tens of thousands of (un)folding events of individual stem-loops at a 200 μs time resolution. Our results show that under our experimental conditions, stem-loops fold and unfold over a 1-200 ms timescale during which they transition between ensembles of unfolded and folded conformations, the latter of which is composed of at least two sub-populations. The 1-200 ms timescale of (un)folding we observe here indicates that smFETs report on complete (un)folding trajectories in which relatively extended unfolded con-formations of the RNA spend long periods of time wandering the free-energy landscape before sampling one of several mis-folded conformations or, alternatively, the natively folded conformation. Our findings demonstrate how the combination of single-molecule sensitivity and high time resolution makes smFETs unique and powerful tools for characterizing the con-formational free-energy landscape of RNA and highlight the extremely rugged landscape on which even the simplest RNA structural elements fold.