Table_1_A Leveraged Signal-to-Noise Ratio (LSTNR) Method to Extract Differentially Expressed Genes and Multivariate Patterns of Expression From Noisy and Low-Replication RNAseq Data.xlsx

<p>To life scientists, one important feature offered by RNAseq, a next-generation sequencing tool used to estimate changes in gene expression levels, lies in its unprecedented resolution. It can score countable differences in transcript numbers among thousands of genes and between experimental groups, all at once. However, its high cost limits experimental designs to very small sample sizes, usually N = 3, which often results in statistically underpowered analysis and poor reproducibility. All these issues are compounded by the presence of experimental noise, which is harder to distinguish from instrumental error when sample sizes are limiting (e.g., small-budget pilot tests), experimental populations exhibit biologically heterogeneous or diffuse expression phenotypes (e.g., patient samples), or when discriminating among transcriptional signatures of closely related experimental conditions (e.g., toxicological modes of action, or MOAs). Here, we present a leveraged signal-to-noise ratio (LSTNR) thresholding method, founded on generalized linear modeling (GLM) of aligned read detection limits to extract differentially expressed genes (DEGs) from noisy low-replication RNAseq data. The LSTNR method uses an agnostic independent filtering strategy to define the dynamic range of detected aggregate read counts per gene, and assigns statistical weights that prioritize genes with better sequencing resolution in differential expression analyses. To assess its performance, we implemented the LSTNR method to analyze three separate datasets: first, using a systematically noisy in silico dataset, we demonstrated that LSTNR can extract pre-designed patterns of expression and discriminate between “noise” and “true” differentially expressed pseudogenes at a 100% success rate; then, we illustrated how the LSTNR method can assign patient-derived breast cancer specimens correctly to one out of their four reported molecular subtypes (luminal A, luminal B, Her2-enriched and basal-like); and last, we showed the ability to retrieve five different modes of action (MOA) elicited in livers of rats exposed to three toxicants under three nutritional routes by using the LSTNR method. By combining differential measurements with resolving power to detect DEGs, the LSTNR method offers an alternative approach to interrogate noisy and low-replication RNAseq datasets, which handles multiple biological conditions at once, and defines benchmarks to validate RNAseq experiments with standard benchtop assays.</p

Estimates of reflux velocity and pressure.

<p>A. Partial occlusion moving distally pushes fluid proximally (reflux), and creates a pressure gradient across the stenosis. B. At the stenosis, average reflux velocity </p><p></p><p></p><p></p><p></p><p><mi>v</mi><mo>¯</mo></p><p><mi>r</mi><mi>e</mi><mi>f</mi></p><p></p><mo>=</mo><p><mi>v</mi></p><p><mi>p</mi><mi>e</mi><mi>r</mi></p><p></p><mo>⋅</mo><p></p><p></p><p><mo>(</mo></p><p><mn>1</mn><mo>−</mo></p><p></p><p></p><p><mo>(</mo></p><p><mn>1</mn><mo>−</mo><mi>O</mi></p><mo>)</mo><p></p><p></p><mn>2</mn><p></p><p></p><mo>)</mo><p></p><p></p><mo>/</mo><p></p><p></p><p></p><p><mo>(</mo></p><p><mn>1</mn><mo>−</mo><mi>O</mi></p><mo>)</mo><p></p><p></p><mn>2</mn><p></p><p></p><p></p><p></p><p></p><p></p> is proportional to velocity of peristaltic wave <i>v</i>_<i>per</i>, but increases rapidly with occlusion (dashed curve). Pressure gradient across the stenosis is proportional to fluid viscosity <i>μ</i> and strongly depends on occlusion <i>O</i>: <p></p><p></p><p></p><p></p><p><mi>d</mi><mi>p</mi></p><p><mi>d</mi><mi>x</mi></p><p></p><mo>=</mo><p></p><p><mn>8</mn><mi>μ</mi></p><p><mi>v</mi></p><p><mi>p</mi><mi>e</mi><mi>r</mi></p><p></p><p></p><p></p><p><mi>a</mi><mn>2</mn></p><p></p><p></p><mo>⋅</mo><p></p><p><mn>1</mn><mo>−</mo></p><p></p><p></p><p><mo>(</mo></p><p><mn>1</mn><mo>−</mo><mi>O</mi></p><mo>)</mo><p></p><p></p><mn>2</mn><p></p><p></p><p></p><p></p><p></p><p><mo>(</mo></p><p><mn>1</mn><mo>−</mo><mi>O</mi></p><mo>)</mo><p></p><p></p><mn>4</mn><p></p><p></p><p></p><p></p><p></p><p></p>, where <i>a</i> is the relaxed lumen radius (solid curve).<p></p

Parameters and variables used in estimates and computational model.

<p>¹ No studies report stiffness of embryonic lung tissue. Range is an estimate. Lower bound: 20 Pa for amphibian embryos; upper bound 400 Pa for ASM cells in vitro.</p><p>² We assume that the viscosity of airway lumen fluid in the embryo is lower than that of neonatal airway mucus but higher than that of blood.</p><p>³ Fetal pig airway SM 1–20 kPa, highest in trachea, lowest in bronchioles. We assume this as an upper bound, and that embryonic SM will likely be weaker by 1–2 orders of magnitude. We assume a SM thickness of 15 microns.</p><p>⁴ Fetal pig, pseudoglandular stage</p><p>⁵ Fetal mouse (lowest value).</p><p>⁶ Rabbit fetus, static pressure.</p><p>⁷ Fetal sheep, static pressure.</p><p>Parameters and variables used in estimates and computational model.</p

Diffusion coefficients (μm²/s) of various molecules in various fluids.

<p>Diffusion coefficients in μm²/s. Viscosities (Pa-s) of water 0.001, mouse embryonic lung lumen fluid (this paper) 0.016, neonatal mucus 0.4, adult mucus 3000.</p><p>Diffusion coefficients (μm²/s) of various molecules in various fluids.</p

Velocity and shear rate.

<p>Lumen fluid velocity at the midline (solid curves) and shear rate at the lumen surface (dashed curves) track each other in time (horiz. axis). Curves correspond to locations on airway at left. Red dots indicate location, relative magnitude, and time of SM force peak. Each curve shows time series of fluid velocity and shear rate. Maximal flow at a position occurs slightly after maximal SM force at that position. Flow is fastest towards trachea, opposite the direction of peristaltic SM wave; refilling flows are slower. Flow distal to SM is negligible. Flow is dramatically reduced in the closed-end airway.</p

Peristaltic wave dramatically stretches fluid layers adjacent to the occlusion, while modestly affecting distal fluid.

<p>If the trachea is open, mixing is much more dramatic than if the trachea is closed. Even for the closed trachea, fluid markers do not return precisely to their original locations despite the low Reynolds number. The spatiotemporal asymmetry of the waveform results in mixing.</p

Frames from model simulations of AP with partial occlusion, for open and closed trachea.

<p>Each frame shows half the symmetric tubule. A. Closed trachea. Lumen pressure is spatially uniform and increases as soon as AP begins. B. Open trachea. Lumen pressure is negligible until occlusion is almost complete. Pressure is uniform everywhere in the lumen except at stenosis, where flow is fastest. Maximal occlusion shown ~ 90%. C. Detail of open-trachea AP. Maximal occlusion precedes maximal pressure. Pressure distal to pinch forces fluid leakage and reduces occlusion as wave moves distally. Identical parameters (stiffness, viscosity, force input). Frames every 1.0 sec (A, B) and 0.5 sec (C).</p

Geometry of embryonic lung and model.

<p>A. Explanted E11.5 mouse lung showing lumen (l), epithelium (e, green), and mesenchyme (m, red). Smooth muscle (sm) not visible. B. Embryonic lung idealized as unbranched, axisymmetric tubule, with three uniform tissue layers plus lumen. Smooth muscle undergoes active circumferential contraction wave (red), propagating distally, building lumen pressure ahead of it (blue).</p

Time scales of transport in the embryonic lung.

<p>In the absence of flow, solutes can only diffuse (dotted curve). In the absence of diffusion, solutes and particles advect with the flow (dashed curve). Advection-diffusion (solid curve) transports solutes rapidly relative to diffusion alone, and a small occlusion from weak airway peristalsis can yield a dramatic reduction in transport time. 100 kDa globular protein in lumen fluid of measured viscosity 0.016 Pa-s.</p

Mitochondrial nicotinamide adenine dinucleotide reduced (NADH) oxidation links the tricarboxylic acid (TCA) cycle with methionine metabolism and nuclear DNA methylation

<div><p>Mitochondrial function affects many aspects of cellular physiology, and, most recently, its role in epigenetics has been reported. Mechanistically, how mitochondrial function alters DNA methylation patterns in the nucleus remains ill defined. Using a cell culture model of induced mitochondrial DNA (mtDNA) depletion, in this study we show that progressive mitochondrial dysfunction leads to an early transcriptional and metabolic program centered on the metabolism of various amino acids, including those involved in the methionine cycle. We find that this program also increases DNA methylation, which occurs primarily in the genes that are differentially expressed. Maintenance of mitochondrial nicotinamide adenine dinucleotide reduced (NADH) oxidation in the context of mtDNA loss rescues methionine salvage and polyamine synthesis and prevents changes in DNA methylation and gene expression but does not affect serine/folate metabolism or transsulfuration. This work provides a novel mechanistic link between mitochondrial function and epigenetic regulation of gene expression that involves polyamine and methionine metabolism responding to changes in the tricarboxylic acid (TCA) cycle. Given the implications of these findings, future studies across different physiological contexts and in vivo are warranted.</p></div