Smart pooling: AI-powered COVID-19 informative group testing.

Massive molecular testing for COVID-19 has been pointed out as fundamental to moderate the spread of the pandemic. Pooling methods can enhance testing efficiency, but they are viable only at low incidences of the disease. We propose Smart Pooling, a machine learning method that uses clinical and sociodemographic data from patients to increase the efficiency of informed Dorfman testing for COVID-19 by arranging samples into all-negative pools. To do this, we ran an automated method to train numerous machine learning models on a retrospective dataset from more than 8000 patients tested for SARS-CoV-2 from April to July 2020 in Bogotá, Colombia. We estimated the efficiency gains of using the predictor to support Dorfman testing by simulating the outcome of tests. We also computed the attainable efficiency gains of non-adaptive pooling schemes mathematically. Moreover, we measured the false-negative error rates in detecting the ORF1ab and N genes of the virus in RT-qPCR dilutions. Finally, we presented the efficiency gains of using our proposed pooling scheme on proof-of-concept pooled tests. We believe Smart Pooling will be efficient for optimizing massive testing of SARS-CoV-2.

Continuous rate modeling of bacterial stochastic size dynamics.

Bacterial division is an inherently stochastic process with effects on fluctuations of protein concentration and phenotype variability. Current modeling tools for the stochastic short-term cell-size dynamics are scarce and mainly phenomenological. Here we present a general theoretical approach based on the Chapman-Kolmogorov equation incorporating continuous growth and division events as jump processes. This approach allows us to include different division strategies, noisy growth, and noisy cell splitting. Considering bacteria synchronized from their last division, we predict oscillations in both the central moments of the size distribution and its autocorrelation function. These oscillations, barely discussed in past studies, can arise as a consequence of the discrete time displacement invariance of the system with a period of one doubling time, and they do not disappear when including stochasticity on either division times or size heterogeneity on the starting population but only after inclusion of noise in either growth rate or septum position. This result illustrates the usefulness of having a solid mathematical description that explicitly incorporates the inherent stochasticity in various biological processes, both to understand the process in detail and to evaluate the effect of various sources of variability when creating simplified descriptions.

Slipstreaming Mother Machine: A Microfluidic Device for Single-Cell Dynamic Imaging of Yeast.

The yeast Saccharomyces cerevisiae is one of the most basic model organisms for studies of aging and other phenomena such as division strategies. These organisms have been typically studied with the use of microfluidic devices to keep cells trapped while under a flow of fresh media. However, all of the existing devices trap cells mechanically, subjecting them to pressures that may affect cell physiology. There is evidence mechanical pressure affects growth rate and the movement of intracellular components, so it is quite possible that it affects other physiological aspects such as aging. To allow studies with the lowest influence of mechanical pressure, we designed and fabricated a device that takes advantage of the slipstreaming effect. In slipstreaming, moving fluids that encounter a barrier flow around it forming a pressure gradient behind it. We trap mother cells in this region and force daughter cells to be in the negative pressure gradient region so that they are taken away by the flow. Additionally, this device can be fabricated using low resolution lithography techniques, which makes it less expensive than devices that require photolithography masks with resolution under 5 µm. With this device, it is possible to measure some of the most interesting aspects of yeast dynamics such as growth rates and Replicative Life Span. This device should allow future studies to eliminate pressure bias as well as extending the range of labs that can do these types of measurements.

Characterization of Neopestalotiopsis Species Associated with Mango Grey Leaf Spot Disease in Sinaloa, Mexico.

Mango is one of the most popular and nutritious fruits in the world and Mexico is the world's largest exporter. There are many diseases that directly affect fruit yield and quality. During the period 2016-2017, leaves with grey leaf spots were collected from 28 commercial mango orchards distributed in two main production areas in Sinaloa State of Mexico, and 50 Neopestalotiopsis isolates were obtained. Fungal identification of 20 representative isolates was performed using morphological characterization and phylogenetic analysis based on the internal transcribed spacer (ITS) region of ribosomal DNA, part of the translation elongation factor 1-alpha (TEF) and the ß-tubulin (TUB) genes. Phylogenetic analysis indicated that the 20 isolates from this study formed four consistent groups, however, overall tree topologies do not consistently provide a stable and sufficient resolution. Therefore, even though morphological and phylogenetic separation is evident, these isolates were not assigned to any new taxa and were tentatively placed into four clades (clades A-D). Pathogenicity tests on detached mango leaves of cv. Kent showed that the 20 isolates that belong to the four Neopestalotiopsis clades from this study and induce lesions on mango leaves. This is the first report of species of Neopestalotiopsis causing mango grey leaf spot disease in Mexico.

CSF concentrations of adipsin and adiponectin in patients with amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is described as a neurodegenerative disorder. However, neuroinflammation and chemokine expression are prominent pathological finding at sites of injury. Adipsin and adiponectin are molecules that are implicated in the pathogenesis of neurodegenerative and neuroimmune disorders. Adipsin and adiponectin concentrations were determined in the CSF of ALS patients and controls and the relationship of these chemokines with clinical severity and disease duration in ALS was determined. Seventy-seven ALS patients (mean age 49.5 ± 10.4 years) (mean body mass index 23.5 ± 4.5) were included. Twenty patients had bulbar, 53 spinal, and four bulbospinal onset ALS. Median adipsin CSF level was 12,650.94 pg/ml in ALS patients and 3290.98 pg/ml in controls (p < 0.001). Median adiponectin CSF level was 4608 pg/ml in ALS patients and 3453 pg/ml in controls (p = 0.1). No differences were observed in disease duration, progression rate or disease severity. There was a significant positive correlation between adipsin and adiponectin concentrations (r = 0.379, p = 0.01). No correlation with age, body mass index or ALFRS-R score was found. Adipsin was significantly elevated in CSF, suggesting that this chemokine might have a role in ALS pathogenesis. Adiponectin showed a trend towards higher concentrations, but failed to reach statistical significance. Due to the clinical heterogeneity in our cohort, these chemokines do not appear to be associated with disease duration or severity.

Caracterización morfológica y molecular de aislados de Macrophomina phaseolina asociados a caña de azúcar en México / Morphological and molecular characterization of isolates of Macrophomina phaseolina associated with sugarcane in Mexico

La pudrición carbonosa, causada por Macrophomina phaseolina, es una enfermedad importante de la caña de azúcar en México. Este estudio se realizó con el objetivo de caracterizar aislados de M. phaseolina obtenidos de caña de azúcar mediante análisis morfológicos y moleculares. La caracterización morfológica de 10 aislados se llevó a cabo con el uso de microscopia electrónica de barrido y microscopia de luz. Para confirmar la identificación, se extrajo el ADNr de 2 aislados representativos, y la región del espaciador interno transcrito (ITS) se amplificó mediante la reacción en cadena de la polimerasa y se secuenció usando los iniciadores específicos MpKF1 y MpKR1. Los aislados se identificaron como M. phaseolina con base en la morfología. El análisis de secuencias ITS mostró 100% de similitud con las secuencias de M. phaseolina depositadas en el GenBank. Para nuestro conocimiento, este es el primer estudio del mundo enfocado a caracterizar aislados de M. phaseolina obtenidos de caña de azúcar

Charcoal rot caused by Macrophomina phaseolina is an important disease of sugarcane in Mexico. This study was carried out to characterize isolates of M. phaseolina obtained from sugarcane by the combination of morphological and molecular analyses. The morphological characterization of 10 isolates was performed using scanning electron microscopy and light microscopy. To confirm the morphological identification, rDNA from two representative isolates was extracted, and the internal transcribed spacer (ITS) region was amplified by polymerase chain reaction and sequenced using specific primers MpKF1 and MpKR1. Based on their morphological characteristics, all isolates were identified as M. phaseolina. Moreover, the analysis of two ITS sequences showed 100% similarity with the M. phaseolina sequences deposited in the GenBank. To our knowledge, this is the first study in the world aimed at characterizing isolates of M. phaseolina obtained from sugarcane

[Morphological and molecular characterization of isolates of Macrophomina phaseolina associated with sugarcane in Mexico]. / Caracterización morfológica y molecular de aislados de Macrophomina phaseolina asociados a caña de azúcar en México.

Charcoal rot caused by Macrophomina phaseolina is an important disease of sugarcane in Mexico. This study was carried out to characterize isolates of M. phaseolina obtained from sugarcane by the combination of morphological and molecular analyses. The morphological characterization of 10 isolates was performed using scanning electron microscopy and light microscopy. To confirm the morphological identification, rDNA from two representative isolates was extracted, and the internal transcribed spacer (ITS) region was amplified by polymerase chain reaction and sequenced using specific primers MpKF1 and MpKR1. Based on their morphological characteristics, all isolates were identified as M. phaseolina. Moreover, the analysis of two ITS sequences showed 100% similarity with the M. phaseolina sequences deposited in the GenBank. To our knowledge, this is the first study in the world aimed at characterizing isolates of M. phaseolina obtained from sugarcane.

Tuning the range and stability of multiple phenotypic states with coupled positive-negative feedback loops.

Positive feedback loops can produce multistability, resulting in different phenotypic states. However, many transcription networks contain counteracting positive and negative feedbacks. Here we explore the dynamics of an interlinked positive and negative feedback motif based on the galactose-uptake control system of Saccharomyces cerevisiae modified to make the strength of each feedback externally controllable. Our results show that although the positive feedback loop determines the range of bistability and the width of the regions where intermediate activation is possible, the transition rates between states are mostly sensitive to the negative feedback strength. Thus, our results suggest that the function of the negative loop in this motif is to allow separate tuning of the range and transition rates between phenotypic states. This could enhance fitness by allowing improved matching of the stochastic switching to the frequency of environmental changes.

Building a morbidostat: an automated continuous-culture device for studying bacterial drug resistance under dynamically sustained drug inhibition.

We present a protocol for building and operating an automated fluidic system for continuous culture that we call the 'morbidostat'. The morbidostat is used to follow the evolution of microbial drug resistance in real time. Instead of exposing bacteria to predetermined drug environments, the morbidostat constantly measures the growth rates of evolving microbial populations and dynamically adjusts drug concentrations inside culture vials in order to maintain a constant drug-induced inhibition. The growth rate measurements are done using an optical detection system that is based on measuring the intensity of back-scattered light from bacterial cells suspended in the liquid culture. The morbidostat can additionally be used as a chemostat or a turbidostat. The whole system can be built from readily available components within 2-3 weeks by biologists with some electronics experience or engineers familiar with basic microbiology.

Effects of molecular memory and bursting on fluctuations in gene expression.

Many cellular components are present in such low numbers per cell that random births and deaths of individual molecules can cause substantial "noise" in concentrations. But biochemical events do not necessarily occur in single steps of individual molecules. Some processes are greatly randomized when synthesis or degradation occurs in large bursts of many molecules during a short time interval. Conversely, each birth or death of a macromolecule could involve several small steps, creating a memory between individual events. We present a generalized theory for stochastic gene expression, formulating the variance in protein abundance in terms of the randomness of the individual gene expression events. We show that common types of molecular mechanisms can produce gestation and senescence periods that reduce noise without requiring higher abundances, shorter lifetimes, or any concentration-dependent control loops. We also show that most single-cell experimental methods cannot distinguish between qualitatively different stochastic principles, although this in turn makes such methods better suited for identifying which components introduce fluctuations. Characterizing the random events that give rise to noise in concentrations instead requires dynamic measurements with single-molecule resolution.

Stochastic gene expression out-of-steady-state in the cyanobacterial circadian clock.

Recent advances in measuring gene expression at the single-cell level have highlighted the stochastic nature of messenger RNA and protein synthesis. Stochastic gene expression creates a source of variability in the abundance of cellular components, even among isogenic cells exposed to an identical environment. Recent integrated experimental and modelling studies have shed light on the molecular sources of this variability. However, many of these studies focus on systems that have reached a steady state and therefore do not address a large class of dynamic phenomena including oscillatory gene expression. Here we develop a general protocol for analysing and predicting stochastic gene expression in systems that never reach steady states. We use this framework to analyse experimentally stochastic expression of genes driven by the Synechococcus elongatus circadian clock. We find that, although the average expression at two points in the circadian cycle separated by 12 hours is identical, the variability at these two time points can be different. We show that this is a general feature of out-of-steady-state systems. We demonstrate how intrinsic noise sources, owing to random births and deaths of mRNAs and proteins, or extrinsic noise sources, which introduce fluctuations in rate constants, affect the cell-to-cell variability. To distinguish experimentally between these sources, we measured how the correlation between expression fluctuations of two identical genes is modulated during the circadian cycle. This quantitative framework is generally applicable to any out-of-steady-state system and will be necessary for understanding the fidelity of dynamic cellular systems.

Predicting stochastic gene expression dynamics in single cells.

Fluctuations in protein numbers (noise) due to inherent stochastic effects in single cells can have large effects on the dynamic behavior of gene regulatory networks. Although deterministic models can predict the average network behavior, they fail to incorporate the stochasticity characteristic of gene expression, thereby limiting their relevance when single cell behaviors deviate from the population average. Recently, stochastic models have been used to predict distributions of steady-state protein levels within a population but not to predict the dynamic, presteady-state distributions. In the present work, we experimentally examine a system whose dynamics are heavily influenced by stochastic effects. We measure population distributions of protein numbers as a function of time in the Escherichia coli lactose uptake network (lac operon). We then introduce a dynamic stochastic model and show that prediction of dynamic distributions requires only a few noise parameters in addition to the rates that characterize a deterministic model. Whereas the deterministic model cannot fully capture the observed behavior, our stochastic model correctly predicts the experimental dynamics without any fit parameters. Our results provide a proof of principle for the possibility of faithfully predicting dynamic population distributions from deterministic models supplemented by a stochastic component that captures the major noise sources.

Noise propagation in gene networks.

Accurately predicting noise propagation in gene networks is crucial for understanding signal fidelity in natural networks and designing noise-tolerant gene circuits. To quantify how noise propagates through gene networks, we measured expression correlations between genes in single cells. We found that noise in a gene was determined by its intrinsic fluctuations, transmitted noise from upstream genes, and global noise affecting all genes. A model was developed that explains the complex behavior exhibited by the correlations and reveals the dominant noise sources. The model successfully predicts the correlations as the network is systematically perturbed. This approach provides a step toward understanding and manipulating noise propagation in more complex gene networks.