Stochastic scanning events on the GCN4 mRNA 5' untranslated region generate cell-to-cell heterogeneity in the yeast nutritional stress response.

Gene expression stochasticity is inherent in the functional properties and evolution of biological systems, creating non-genetic cellular individuality and influencing multiple processes, including differentiation and stress responses. In a distinct form of non-transcriptional noise, we find that interactions of the yeast translation machinery with the GCN4 mRNA 5'UTR, which underpins starvation-induced regulation of this transcriptional activator gene, manifest stochastic variation across cellular populations. We use flow cytometry, fluorescence-activated cell sorting and microfluidics coupled to fluorescence microscopy to characterize the cell-to-cell heterogeneity of GCN4-5'UTR-mediated translation initiation. GCN4-5'UTR-mediated translation is generally not de-repressed under non-starvation conditions; however, a sub-population of cells consistently manifests a stochastically enhanced GCN4 translation (SETGCN4) state that depends on the integrity of the GCN4 uORFs. This sub-population is eliminated upon deletion of the Gcn2 kinase that phosphorylates eIF2α under nutrient-limitation conditions, or upon mutation to Ala of the Gcn2 kinase target site, eIF2α-Ser51. SETGCN4 cells isolated using cell sorting spontaneously regenerate the full bimodal population distribution upon further growth. Analysis of ADE8::ymRuby3/ GCN4::yEGFP cells reveals enhanced Gcn4-activated biosynthetic pathway activity in SETGCN4 cells under non-starvation conditions. Computational modeling interprets our experimental observations in terms of a novel translational noise mechanism underpinned by natural variations in Gcn2 kinase activity.

"Brain age" predicts disability accumulation in multiple sclerosis.

OBJECTIVE: Neurodegenerative conditions often manifest radiologically with the appearance of premature aging. Multiple sclerosis (MS) biomarkers related to lesion burden are well developed, but measures of neurodegeneration are less well-developed. The appearance of premature aging quantified by machine learning applied to structural MRI assesses neurodegenerative pathology. We assess the explanatory and predictive power of "brain age" analysis on disability in MS using a large, real-world dataset. METHODS: Brain age analysis is predicated on the over-estimation of predicted brain age in patients with more advanced pathology. We compared the performance of three brain age algorithms in a large, longitudinal dataset (>13,000 imaging sessions from >6,000 individual MS patients). Effects of MS, MS disease course, disability, lesion burden, and DMT efficacy were assessed using linear mixed effects models. RESULTS: MS was associated with advanced predicted brain age cross-sectionally and accelerated brain aging longitudinally in all techniques. While MS disease course (relapsing vs. progressive) did contribute to advanced brain age, disability was the primary correlate of advanced brain age. We found that advanced brain age at study enrollment predicted more disability accumulation longitudinally. Lastly, a more youthful appearing brain (predicted brain age less than actual age) was associated with decreased disability. INTERPRETATION: Brain age is a technically tractable and clinically relevant biomarker of disease pathology that correlates with and predicts increasing disability in MS. Advanced brain age predicts future disability accumulation.

Covariance-based vs. correlation-based functional connectivity dissociates healthy aging from Alzheimer disease.

Prior studies of aging and Alzheimer disease have evaluated resting state functional connectivity (FC) using either seed-based correlation (SBC) or independent component analysis (ICA), with a focus on particular functional systems. SBC and ICA both are insensitive to differences in signal amplitude. At the same time, accumulating evidence indicates that the amplitude of spontaneous BOLD signal fluctuations is physiologically meaningful. We systematically compared covariance-based FC, which is sensitive to amplitude, vs. correlation-based FC, which is not, in affected individuals and controls drawn from two cohorts of participants including autosomal dominant Alzheimer disease (ADAD), late onset Alzheimer disease (LOAD), and age-matched controls. Functional connectivity was computed over 222 regions of interest and group differences were evaluated in terms of components projected onto a space of lower dimension. Our principal observations are: (1) Aging is associated with global loss of resting state fMRI signal amplitude that is approximately uniform across resting state networks. (2) Thus, covariance FC measures decrease with age whereas correlation FC is relatively preserved in healthy aging. (3) In contrast, symptomatic ADAD and LOAD both lead to loss of spontaneous activity amplitude as well as severely degraded correlation structure. These results demonstrate a double dissociation between age vs. Alzheimer disease and the amplitude vs. correlation structure of resting state BOLD signals. Modeling results suggest that the AD-associated loss of correlation structure is attributable to a relative increase in the fraction of locally restricted as opposed to widely shared variance.

Paternal mitochondria from an rmd-2, rmd-3, rmd-6 triple mutant are properly positioned in the C. elegans zygote.

RMD-1,2,3,6 (regulator of microtubule dynamics) is a family of homologous proteins conserved between humans and C. elegans. Human RMD-3/PTPIP51 is a mitochondrial protein that tethers mitochondria to the endoplasmic reticulum. C. elegans RMD-2, 3, and 6 are expressed in sperm. To test whether paternal RMD-2, 3, 6 might redundantly tether paternal mitochondria to maternal ER at fertilization, we generated an rmd-2, rmd-3, rmd-6 triple mutant. Paternal mitochondria derived from control or triple mutant worms were concentrated in a cloud around the paternal DNA at the future posterior end of zygotes during meiosis. No significant difference was detected in the position of paternal mitochondria within the zygote nor in the position of paternal mitochondria relative to paternal DNA within the zygote. There was also no reduction in progeny viability between control and triple mutant worms.

Modeling the relative risk of SARS-CoV-2 infection to inform risk-cost-benefit analyses of activities during the SARS-CoV-2 pandemic.

Risk-cost-benefit analysis requires the enumeration of decision alternatives, their associated outcomes, and the quantification of uncertainty. Public and private decision-making surrounding the COVID-19 pandemic must contend with uncertainty about the probability of infection during activities involving groups of people, in order to decide whether that activity is worth undertaking. We propose a model of SARS-CoV-2 infection probability that can produce estimates of relative risk of infection for diverse activities, so long as those activities meet a list of assumptions, including that they do not last longer than one day (e.g., sporting events, flights, concerts), and that the probability of infection among possible routes of infection (i.e., droplet, aerosol, fomite, and direct contact) are independent. We show how the model can be used to inform decisions facing governments and industry, such as opening stadiums or flying on airplanes; in particular, it allows for estimating the ranking of the constituent components of activities (e.g., going through a turnstile, sitting in one's seat) by their relative risk of infection, even when the probability of infection is unknown or uncertain. We prove that the model is a good approximation of a more refined model in which we assume infections come from a series of independent risks. A linearity assumption governing several potentially modifiable risks factors-such as duration of the activity, density of participants, and infectiousness of the attendees-makes interpreting and using the model straightforward, and we argue that it does so without significantly diminishing the reliability of the model.

A deterministic linear infection model to inform Risk-Cost-Benefit Analysis of activities during the SARS-CoV-2 pandemic.

Risk-cost-benefit analysis requires the enumeration of decision alternatives, their associated outcomes, and the quantification of uncertainty. Public and private decision-making surrounding the COVID-19 pandemic must contend with uncertainty about the probability of infection during activities involving groups of people, in order to decide whether that activity is worth undertaking. We propose a deterministic linear model of SARS-CoV-2 infection probability that can produce estimates of relative risk for diverse activities, so long as those activities meet a list of assumptions, including that they do not last longer than one day. We show how the model can be used to inform decisions facing governments and industry, such as opening stadiums or flying on airplanes. We prove that the model is a good approximation of a more refined model in which we assume infections come from a series of independent risks. The linearity assumption makes interpreting and using the model straightforward, and we argue that it does so without significantly diminishing the reliability of the model.

Policy Implications of an Approximate Linear Infection Model for SARS-CoV-2.

We propose a linear model of infection probability, and prove that this is a good approximation to a more refined model in which we assume infections come from a series of independent risks. We argue that the linearity assumption makes interpreting and using the model much easier, without significantly diminishing the reliability of the model.

Multisite rate control analysis identifies ribosomal scanning as the sole high-capacity/low-flux-control step in mRNA translation.

Control of complex intracellular pathways such as protein synthesis is critical to organism survival, but is poorly understood. Translation of a reading frame in eukaryotic mRNA is preceded by a scanning process in which a subset of translation factors helps guide ribosomes to the start codon. Here, we perform comparative analysis of the control status of this scanning step that sits between recruitment of the small ribosomal subunit to the m7 GpppG-capped 5'end of mRNA and of the control exerted by downstream phases of polypeptide initiation, elongation and termination. We have utilized a detailed predictive model as guidance for designing quantitative experimental interrogation of control in the yeast translation initiation pathway. We have built a synthetic orthogonal copper-responsive regulatory promoter (PCuR3 ) that is used here together with the tet07 regulatory system in a novel dual-site in vivo rate control analysis strategy. Combining this two-site strategy with calibrated mass spectrometry to determine translation factor abundance values, we have tested model-based predictions of rate control properties of the in vivo system. We conclude from the results that the components of the translation machinery that promote scanning collectively function as a low-flux-control system with a capacity to transfer ribosomes into the core process of polypeptide production that exceeds the respective capacities of the steps of polypeptide initiation, elongation and termination. In contrast, the step immediately prior to scanning, that is, ribosome recruitment via the mRNA 5' cap-binding complex, is a high-flux-control step.

Resolution of Murine Toxic Hepatic Injury Quantified With Ultrasound Entropy Metrics.

Image-based classification of liver disease generally lacks specificity for distinguishing between acute, resolvable injury and chronic irreversible injury. We propose that ultrasound radiofrequency data acquired in vivo from livers subjected to toxic drug injury can be analyzed with information theoretic detectors to derive entropy metrics, which classify a statistical distribution of pathologic scatterers that dissipate over time as livers heal. Here we exposed 38 C57BL/6 mice to carbon tetrachloride to cause liver damage, and imaged livers in vivo 1, 4, 8, 12 and 18 d after exposure with a broadband 15-MHz probe. Selected entropy metrics manifested monotonic recovery to normal values over time as livers healed, and were correlated directly with progressive restoration of liver architecture by histologic assessment (r2 ≥ 0.95, p < 0.004). Thus, recovery of normal liver microarchitecture after toxic exposure can be delineated sensitively with entropy metrics.

Network residues: The enduring impact of intra-organizational dormant ties.

It is often seen as axiomatic in the social capital literature that relationships require ongoing maintenance to remain valuable. As a result, nearly all social network research has only considered relationships that are active (or recently restored to activity after a period of dormancy). Seldom considered is the impact of still-dormant, unmaintained ties that remain dormant. Using two bounded-network studies (one in a school district, one in a private company), we find consistently that still-dormant ties increase employees' organizational commitment. However, we also find that active ties can "dilute" this effect, rendering dormant ties less relevant and meaningful to individuals, or act as "preservatives" keeping dormant ties relevant and valued. In a third, vignette-based study, we find two mechanisms for the effect of dormant ties on organizational commitment: perceptions of the past (fond recollections) and future (information access). Thus, we offer a more time-oriented, layered theory of social network ties that can be activated in people's minds even when not active in practice. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

The Leishmania PABP1-eIF4E4 interface: a novel 5'-3' interaction architecture for trans-spliced mRNAs.

Trans-splicing of trypanosomatid polycistronic transcripts produces polyadenylated monocistronic mRNAs modified to form the 5' cap4 structure (m7Gpppm36,6,2'Apm2'Apm2'Cpm23,2'U). NMR and X-ray crystallography reveal that Leishmania has a unique type of N-terminally-extended cap-binding protein (eIF4E4) that binds via a PAM2 motif to PABP1. This relies on the interactions of a combination of polar and charged amino acid side-chains together with multiple hydrophobic interactions, and underpins a novel architecture in the Leishmania cap4-binding translation factor complex. Measurements using microscale thermophoresis, fluorescence anisotropy and surface plasmon resonance characterize the key interactions driving assembly of the Leishmania translation initiation complex. We demonstrate that this complex can accommodate Leishmania eIF4G3 which, unlike the standard eukaryotic initiation complex paradigm, binds tightly to eIF4E4, but not to PABP1. Thus, in Leishmania, the chain of interactions 5'cap4-eIF4E4-PABP1-poly(A) bridges the mRNA 5' and 3' ends. Exceptionally, therefore, by binding tightly to two protein ligands and to the mRNA 5' cap4 structure, the trypanosomatid N-terminally extended form of eIF4E acts as the core molecular scaffold for the mRNA-cap-binding complex. Finally, the eIF4E4 N-terminal extension is an intrinsically disordered region that transitions to a partly folded form upon binding to PABP1, whereby this interaction is not modulated by poly(A) binding to PABP1.

A non-commutative Julia Inequality.

We prove a Julia inequality for bounded non-commutative functions on polynomial polyhedra. We use this to deduce a Julia inequality for holomorphic functions on classical domains in ℂ d . We look at differentiability at a boundary point for functions that have a certain regularity there.

Translation initiation events on structured eukaryotic mRNAs generate gene expression noise.

Gene expression stochasticity plays a major role in biology, creating non-genetic cellular individuality and influencing multiple processes, including differentiation and stress responses. We have addressed the lack of knowledge about posttranscriptional contributions to noise by determining cell-to-cell variations in the abundance of mRNA and reporter protein in yeast. Two types of structural element, a stem-loop and a poly(G) motif, not only inhibit translation initiation when inserted into an mRNA 5΄ untranslated region, but also generate noise. The noise-enhancing effect of the stem-loop structure also remains operational when combined with an upstream open reading frame. This has broad significance, since these elements are known to modulate the expression of a diversity of eukaryotic genes. Our findings suggest a mechanism for posttranscriptional noise generation that will contribute to understanding of the generally poor correlation between protein-level stochasticity and transcriptional bursting. We propose that posttranscriptional stochasticity can be linked to cycles of folding/unfolding of a stem-loop structure, or to interconversion between higher-order structural conformations of a G-rich motif, and have created a correspondingly configured computational model that generates fits to the experimental data. Stochastic events occurring during the ribosomal scanning process can therefore feature alongside transcriptional bursting as a source of noise.

On the Stability of BOLD fMRI Correlations.

Measurement of correlations between brain regions (functional connectivity) using blood oxygen level dependent (BOLD) fMRI has proven to be a powerful tool for studying the functional organization of the brain. Recently, dynamic functional connectivity has emerged as a major topic in the resting-state BOLD fMRI literature. Here, using simulations and multiple sets of empirical observations, we confirm that imposed task states can alter the correlation structure of BOLD activity. However, we find that observations of "dynamic" BOLD correlations during the resting state are largely explained by sampling variability. Beyond sampling variability, the largest part of observed "dynamics" during rest is attributable to head motion. An additional component of dynamic variability during rest is attributable to fluctuating sleep state. Thus, aside from the preceding explanatory factors, a single correlation structure-as opposed to a sequence of distinct correlation structures-may adequately describe the resting state as measured by BOLD fMRI. These results suggest that resting-state BOLD correlations do not primarily reflect moment-to-moment changes in cognitive content. Rather, resting-state BOLD correlations may predominantly reflect processes concerned with the maintenance of the long-term stability of the brain's functional organization.

Minimum-noise production of translation factor eIF4G maps to a mechanistically determined optimal rate control window for protein synthesis.

Gene expression noise influences organism evolution and fitness. The mechanisms determining the relationship between stochasticity and the functional role of translation machinery components are critical to viability. eIF4G is an essential translation factor that exerts strong control over protein synthesis. We observe an asymmetric, approximately bell-shaped, relationship between the average intracellular abundance of eIF4G and rates of cell population growth and global mRNA translation, with peak rates occurring at normal physiological abundance. This relationship fits a computational model in which eIF4G is at the core of a multi-component-complex assembly pathway. This model also correctly predicts a plateau-like response of translation to super-physiological increases in abundance of the other cap-complex factors, eIF4E and eIF4A. Engineered changes in eIF4G abundance amplify noise, demonstrating that minimum stochasticity coincides with physiological abundance of this factor. Noise is not increased when eIF4E is overproduced. Plasmid-mediated synthesis of eIF4G imposes increased global gene expression stochasticity and reduced viability because the intrinsic noise for this factor influences total cellular gene noise. The naturally evolved eIF4G gene expression noise minimum maps within the optimal activity zone dictated by eIF4G's mechanistic role. Rate control and noise are therefore interdependent and have co-evolved to share an optimal physiological abundance point.

Acoustic firearm discharge detection and classification in an enclosed environment.

Two different signal processing algorithms are described for detection and classification of acoustic signals generated by firearm discharges in small enclosed spaces. The first is based on the logarithm of the signal energy. The second is a joint entropy. The current study indicates that a system using both signal energy and joint entropy would be able to both detect weapon discharges and classify weapon type, in small spaces, with high statistical certainty.

Local and distributed PiB accumulation associated with development of preclinical Alzheimer's disease.

Amyloid-beta plaques are a hallmark of Alzheimer's disease (AD) that can be assessed by amyloid imaging (e.g., Pittsburgh B compound [PiB]) and summarized as a scalar value. Summary values may have clinical utility but are an average over many regions of interest, potentially obscuring important topography. This study investigates the longitudinal evolution of amyloid topographies in cognitively normal older adults who had normal (N = 131) or abnormal (N = 26) PiB scans at baseline. At 3 years follow-up, 16 participants with a previously normal PiB scan had conversion to PiB scans consistent with preclinical AD. We investigated the multivariate relationship (canonical correlation) between baseline and follow-up PiB topographies. Furthermore, we used penalized regression to investigate the added information derived from PiB topography compared to summary measures. PiB accumulation can be local, that is, a topography predicting the same topography in the future, and/or distributed, that is, one topography predicting another. Both local and distributed PiB accumulation was associated with conversion of PiB status. Additionally, elements of the multivariate topography, and not the commonly used summary scalar, correlated with future PiB changes. Consideration of the entire multivariate PiB topography provides additional information regarding the development of amyloid-beta pathology in very early preclinical AD.

Non-commutative holomorphic functions on operator domains.

We characterize functions of d-tuples of bounded operators on a Hilbert space that are uniformly approximable by free polynomials on balanced open sets.

Rate control in yeast protein synthesis at the population and single-cell levels.

Yeast commits approximately 76% of its energy budget to protein synthesis and the efficiency and control of this process are accordingly critical to organism growth and fitness. We now have detailed genetic, biochemical and biophysical knowledge of the components of the eukaryotic translation machinery. However, these kinds of information do not, in themselves, give us a satisfactory picture of how the overall system is controlled. This is where quantitative system analysis can enable a step-change in our understanding of biological resource management and how this relates to cell physiology and evolution. An important aspect of this more system-oriented approach to translational control is the inherent heterogeneity of cell populations that is generated by gene expression noise. In this short review, we address the fact that, although the vast majority of our knowledge of the translation machinery is based on experimental analysis of samples that each contain hundreds of millions of cells, in reality every cell is unique in terms of its composition and control properties. We have entered a new era in which research into the heterogeneity of cell systems promises to provide answers to many (previously unanswerable) questions about cell physiology and evolution.

Partial covariance based functional connectivity computation using Ledoit-Wolf covariance regularization.

Functional connectivity refers to shared signals among brain regions and is typically assessed in a task free state. Functional connectivity commonly is quantified between signal pairs using Pearson correlation. However, resting-state fMRI is a multivariate process exhibiting a complicated covariance structure. Partial covariance assesses the unique variance shared between two brain regions excluding any widely shared variance, hence is appropriate for the analysis of multivariate fMRI datasets. However, calculation of partial covariance requires inversion of the covariance matrix, which, in most functional connectivity studies, is not invertible owing to rank deficiency. Here we apply Ledoit-Wolf shrinkage (L2 regularization) to invert the high dimensional BOLD covariance matrix. We investigate the network organization and brain-state dependence of partial covariance-based functional connectivity. Although RSNs are conventionally defined in terms of shared variance, removal of widely shared variance, surprisingly, improved the separation of RSNs in a spring embedded graphical model. This result suggests that pair-wise unique shared variance plays a heretofore unrecognized role in RSN covariance organization. In addition, application of partial correlation to fMRI data acquired in the eyes open vs. eyes closed states revealed focal changes in uniquely shared variance between the thalamus and visual cortices. This result suggests that partial correlation of resting state BOLD time series reflect functional processes in addition to structural connectivity.