Search

Mendelian randomization (MR) is a method of studying the causal effects of modifiable exposures (i.e., potential risk factors) on health, social, and economic outcomes using genetic variants associated with the specific exposures of interest. MR provides a more robust understanding of the influence of these exposures on outcomes because germline genetic variants are randomly inherited from parents to offspring and, as a result, should not be related to potential confounding factors that influence exposure-outcome associations. The genetic variant can therefore be used as a tool to link the proposed risk factor and outcome, and to estimate this effect with less confounding and bias than conventional epidemiological approaches. We describe the scope of MR, highlighting the range of applications being made possible as genetic data sets and resources become larger and more freely available. We outline the MR approach in detail, covering concepts, assumptions, and estimation methods. We cover some common misconceptions, provide strategies for overcoming violation of assumptions, and discuss future prospects for extending the clinical applicability, methodological innovations, robustness, and generalizability of MR findings.

Contemporary evolution has the potential to significantly alter biotic responses to global change, including range expansion dynamics and biological invasions. However, predictive models often make highly simplifying assumptions about the genetic architecture underlying relevant traits. This can be problematic since genetic architecture defines evolvability, that is, evolutionary rates, and higher order evolutionary processes, which determine whether evolution will be able to keep up with environmental change or not. Therefore, we here study the impact of the genetic architecture of dispersal and local adaptation, two central traits of high relevance for range expansion dynamics, on the speed and variability of range expansions into an environmental gradient, such as temperature. In our theoretical model we assume that dispersal and local adaptation traits result from the products of two non-interacting gene-regulatory networks (GRNs). We compare our model to simpler quantitative genetics models and show that in the GRN model, range expansions are accelerated, faster and more variable. Increased variability implies that these evolutionary changes reduce predictability. We further find that acceleration in the GRN model is primarily driven by an increase in the rate of local adaptation to novel habitats which results from greater sensitivity to mutation (decreased robustness) and increased gene expression. Our results highlight how processes at microscopic scales, here, within genomes, can impact the predictions of large scale, macroscopic phenomena, such as range expansions, by modulating the rate of evolution.

Over the past two decades, the advances in high throughput sequencing (HTS) enabled the characterisation of biological processes at an unprecedented level of detail; as a result the vast majority of hypotheses in molecular biology rely on analyses of HTS data. However, achieving increased robustness and reproducibility of results remains one of the main challenges across analyses. Although variability in results may be introduced at various stages, such as alignment, summarisation or detection of differences in expression, one source of variability has been systematically omitted: the consequences of choices that influence the sequencing design which propagate through analyses and introduce an additional layer of technical variation. In this study, we illustrate qualitative and quantitative differences in results arising from the splitting of samples across lanes, on bulk and single cell sequencing outputs. For bulk mRNAseq data, we focus on differential expression and enrichment analyses; for bulk ChIPseq data, we investigate the effect on peak calling, and the peaks’ properties. At single cell level, we concentrate on the identification of cell subpopulations (cells clustered based on their expression profiles). We rely on the identity of markers used for assigning cell identities; both smartSeq and 10x data are presented. We conclude that the observed reduction in the number of unique sequenced fragments reduces the level of detail on which the different prediction approaches depend. Further, the sequencing stochasticity adds in a weighting bias corroborated with variable sequencing depths.

Microorganisms have provided an attractive route for biosynthesis of various chemicals from renewable resources. CRISPR-Cas systems have served as powerful mechanisms for generating cell factories with desirable properties by manipulating nucleic acids quickly and efficiently. The CRISPR-Cas system provides a toolbox with excellent opportunities for identifying better biocatalysts, multiplexed fine-tuning of metabolic flux, efficient utilization of low-cost substrates, and improvement of metabolic robustness. The overall goal of this review highlights recent advances in the development of microbial cell factories for chemical production using various CRISPR-Cas systems. The perspectives for further development or applications of CRISPR-Cas systems for strain improvement are also discussed.

Flux balance analysis (FBA) and associated techniques operating on stoichiometric genome-scale metabolic models play a central role in quantifying metabolic flows and constraining feasible phenotypes. At the heart of these methods lie two important assumptions: (i) the biomass precursors and energy requirements neither change in response to growth conditions nor environmental/genetic perturbations, and (ii) metabolite production and consumption rates are equal at all times (i.e., steady-state). Despite the stringency of these two assumptions, FBA has been shown to be surprisingly robust at predicting cellular phenotypes. In this paper, we formally assess the impact of these two assumptions on FBA results by quantifying how uncertainty in biomass reaction coefficients, and departures from steady-state due to temporal fluctuations could propagate to FBA results. In the first case, conditional sampling of parameter space is required to re-weigh the biomass reaction so as the molecular weight remains equal to 1 g/mmol, and in the second case, metabolite (and elemental) pool conservation must be imposed under temporally varying conditions. Results confirm the importance of enforcing the aforementioned constraints and explain the robustness of FBA biomass yield predictions.

Identification of genetic structure within wildlife populations have implications in their conservation and management. Accurately inferring population genetic structure requires whole-genome data across the geographical range of the species, which can be resource-intensive. A cheaper strategy is to employ a subset of markers that can efficiently recapitulate the population genetic structure inferred by the whole genome data. Such ancestry informative markers (AIMs), have rarely been developed for endangered species such as tigers utilizing single nucleotide polymorphisms (SNPs). Here, we first identify the population structure of the Indian tiger using whole-genome sequences and then develop an AIMs panel with a minimum number of SNPs that can recapitulate this structure. We identified four population clusters of Indian tigers with North-East, North-West, and South Indian tigers forming three separate groups, and Terai and Central Indian tigers forming a single cluster. To evaluate the robustness of our AIMs, we applied it to a separate dataset of tigers from across India. Out of 92 SNPs present in our AIMs panel, 49 were present in the new dataset. These 49 SNPs were sufficient to recapitulate the population genetic structure obtained from the whole genome data. To the best of our knowledge, this is the first-ever SNP-based AIMs panel for big cats, which can be used as a cost-effective alternative to whole-genome sequencing for detecting the biogeographical origin of Indian tigers. Our study can be used as a guideline for developing an AIMs panel for the management of other endangered species where obtaining whole genome sequences are difficult.

In eukaryotes, general mRNA decay requires the decapping complex. The activity of this complex depends on its catalytic subunit, DECAPPING2 (DCP2), and its interaction with decapping enhancers, including its main partner DECAPPING1 (DCP1). Here, we report that in Arabidopsis thaliana, DCP1 also interacts with a NYN domain endoribonuclease, hence named DCP1-ASSOCIATED NYN ENDORIBONUCLEASE 1 (DNE1). Interestingly, we found DNE1 predominantly associated with DCP1, but not with DCP2, and reciprocally, suggesting the existence of two distinct protein complexes. We also showed that the catalytic residues of DNE1 are required to repress the expression of mRNAs in planta upon transient expression. The overexpression of DNE1 in transgenic lines led to growth defects and a similar gene deregulation signature than inactivation of the decapping complex. Finally, the combination of dne1 and dcp2 mutations revealed a functional redundancy between DNE1 and DCP2 in controlling phyllotactic pattern formation. Our work identifies DNE1, a hitherto unknown DCP1 protein partner highly conserved in the plant kingdom and identifies its importance for developmental robustness.

Volatile compounds play important roles in rhizosphere biological communications and interactions. The emission of plant and microbial volatiles is a dynamic phenomenon that is affected by several endogenous and exogenous signals. Diffusion of volatiles can be limited by their adsorption, degradation, and dissolution under specific environmental conditions. Therefore, rhizosphere volatiles need to be investigated on a micro and spatiotemporal scale. Plant and microbial volatiles can expand and specialize the rhizobacterial niche not only by improving the root system architecture such that it serves as a nutrient-rich shelter, but also by inhibiting or promoting the growth, chemotaxis, survival, and robustness of neighboring organisms. Root volatiles play an important role in engineering the belowground microbiome by shaping the microbial community structure and recruiting beneficial microbes. Microbial volatiles are appropriate candidates for improving plant growth and health during environmental challenges and climate change. However, some technical and experimental challenges limit the non-destructive monitoring of volatile emissions in the rhizosphere in real-time. In this review, we attempt to clarify the volatile-mediated intra- and inter-kingdom communications in the rhizosphere, and propose improvements in experimental design for future research.

Conspectus The last decades have witnessed unprecedented scientific breakthroughs in all the fields of knowledge, from basic sciences to translational research, resulting in the drastic improvement of the lifespan and overall quality of life. However, despite these great advances, the treatment and diagnosis of some diseases remain a challenge. Inspired by nature, scientists have been exploring biomolecules and their derivatives as novel therapeutic/diagnostic agents. Among biomolecules, proteins raise much interest due to their high versatility, biocompatibility, and biodegradability. Protein binders (binders) are proteins that bind other proteins, in certain cases, inhibiting or modulating their action. Given their therapeutic potential, binders are emerging as the next generation of biopharmaceuticals. The most well-known example of binders are antibodies, and inspired by them researchers have developed alternative binders using protein design approaches. Protein design can be based on naturally occurring proteins in which, by means of rational design or combinatorial approaches, new binding interfaces can be engineered to obtain specific functions or based on de novo proteins emerging from state-of-the-art computational methodologies. Among the novel designed proteins, a class of engineered repeat proteins, the consensus tetratricopeptide repeat (CTPR) proteins, stand out due to their stability and robustness. The CTPR unit is a helix-turn-helix motif constituted of 34 amino acids, of which only 8 are essential to ensure correct folding of the structure. The small number of conserved residues of CTPR proteins leaves plenty of freedom for functional mutations, making them a base scaffold that can be easily and reproducibly tailored to endow desired functions to the protein. For example, the introduction of metal-binding residues (e.g., histidines, cysteines) drives the coordination of metal ions and the subsequent formation of nanomaterials. Additionally, the CTPR unit can be conjugated with other peptides/proteins or repeated in tandem to encode larger CTPR proteins with superhelical structures. These properties allow for the design of both binder and nanomaterial-coordination modules as well as their combination within the same molecule, making the CTPR proteins, as we have demonstrated in several recent examples, the ideal platform to develop protein–nanomaterial hybrids. Generally, the fusion of two distinct materials exploits the best properties of each; however, in protein–nanomaterial hybrids, the fusion takes on a new dimension as new properties arise. These hybrids have ushered the use of protein-based nanomaterials as biopharmaceuticals beyond their original therapeutic scope and paved the way for their use as theranostic agents. Despite several reports of protein-stabilized nanomaterials found in the literature, these systems offer limited control in the synthesis and properties of the grown nanomaterials, as the protein acts just as a stabilizing agent with no significant functional contribution. Therefore, the rational design of protein-based nanomaterials as true theranostic agents is still incipient. In this context, CTPR proteins have emerged as promising scaffolds to hold simultaneously therapeutic and diagnostic functions through protein engineering, as it has been recently demonstrated in pioneering in vitro and in vivo examples.

Evolutionary programming (EP) uses a mutation as a unique operator. Gaussian, Cauchy, Lévy, and double exponential probability distributions and single-point mutation were nominated as mutation operators. Many mutation strategies have been proposed over the last two decades. The most recent EP variant was proposed using a step-size-based self-adaptive mutation operator. In SSEP, the mutation type with its parameters is selected based on the step size, which differs from generation to generation. Several principles for choosing proper parameters have been proposed; however, SSEP still has limitations and does not display outstanding performance on some benchmark functions. In this work, we proposed a novel mutation strategy based on both the “step size” and “survival rate” for EP (SSMSEP). SSMSEP-1 and SSMSEP-2 are two variants of SSMSEP, which use “survival rate” or “step size” separately. Our proposed method can select appropriate mutation operators and update parameters for mutation operators according to diverse landscapes during the evolutionary process. Compared with SSMSEP-1, SSMSEP-2, SSEP, and other EP variants, the SSMSEP demonstrates its robustness and stable performance on most benchmark functions tested.

Motivation Metagenomes offer a glimpse into the total genomic diversity contained within a sample. Currently, however, there is no straightforward way to obtain a non-redundant list of all putative homologs of a set of reference sequences present in a metagenome. Results To address this problem, we developed a novel clustering approach called ‘metagenomic clustering by reference library’ (MCRL), where a reference library containing a set of reference genes is clustered with respect to an assembled metagenome. According to our proposed approach, reference genes homologous to similar sets of metagenomic sequences, termed ‘signatures’, are iteratively clustered in a greedy fashion, retaining at each step the reference genes yielding the lowest E values, and terminating when signatures of remaining reference genes have a minimal overlap. The outcome of this computation is a non-redundant list of reference genes homologous to minimally overlapping sets of contigs, representing potential candidates for gene families present in the metagenome. Unlike metagenomic clustering methods, there is no need for contigs to overlap to be associated with a cluster, enabling MCRL to draw on more information encoded in the metagenome when computing tentative gene families. We demonstrate how MCRL can be used to extract candidate viral gene families from an oral metagenome and an oral virome that otherwise could not be determined using standard approaches. We evaluate the sensitivity, accuracy and robustness of our proposed method for the viral case study and compare it with existing analysis approaches. Availability and implementation https://github.com/a-tadmor/MCRL. Supplementary information Supplementary data are available at Bioinformatics online.

Three-dimensional structures of SARS-CoV-2 and other coronaviral proteins archived in the Protein Data Bank were used to analyze viral proteome evolution during the first six months of the COVID-19 pandemic. Analyses of spatial locations, chemical properties, and structural and energetic impacts of the observed amino acid changes in >48,000 viral proteome sequences showed how each one of the 29 viral study proteins have undergone amino acid changes. Structural models computed for every unique sequence variant revealed that most substitutions map to protein surfaces and boundary layers with a minority affecting hydrophobic cores. Conservative changes were observed more frequently in cores versus boundary layers/surfaces. Active sites and protein-protein interfaces showed modest numbers of substitutions. Energetics calculations showed that the impact of substitutions on the thermodynamic stability of the proteome follows a universal bi-Gaussian distribution. Detailed results are presented for six drug discovery targets and four structural proteins comprising the virion, highlighting substitutions with the potential to impact protein structure, enzyme activity, and functional interfaces. Characterizing the evolution of the virus in three dimensions provides testable insights into viral protein function and should aid in structure-based drug discovery efforts as well as the prospective identification of amino acid substitutions with potential for drug resistance.

Many structural patterns have been found to be important for the stability and robustness of mutualistic plant–pollinator networks. These structural patterns are impacted by a suite of variables, including species traits, species abundances, their spatial configuration, and their phylogenetic history. Here, we consider a specific trait: phenology, or the timing of life history events. We expect that timing and duration of activity of pollinators, or of flowering in plants, could greatly affect the species' roles within networks in which they are embedded. Using plant–pollinator networks from 33 sites in southern British Columbia, Canada, we asked (a) how phenological species traits, specifically timing of first appearance in the network and duration of activity in a network, were related to species' roles within a network, and (b) how those traits affected network robustness to phenologically biased species loss. We found that long duration of activity increased connection within modules for both pollinators and plants and among modules for plants. We also found that date of first appearance was positively related to interaction strength asymmetry in plants but negatively related to pollinators. Networks were generally more robust to the loss of pollinators than plants, and robustness increased if the models allow new interactions to form when old ones are lost, constrained by overlapping phenology of plants and pollinators. Robustness declined with the loss of late‐flowering plants, which tended to have higher interaction strength asymmetry. In addition, robustness declined with loss of early‐flying or long‐duration pollinators. These pollinators tended to be among‐module connectors. Our results point to networks being limited by early‐flying pollinators. If plants flower earlier due to climate change, plant fitness may decline as they will depend on early emerging pollinators, unless pollinators also emerge earlier., In plant–pollinator networks of southern British Columbia, we found that networks were generally more robust to the loss of pollinators than plants. Robustness declined with the loss of late‐flowering plants, and with the loss of early‐flying or long‐duration pollinators.

In view of ever-changing conditions both in the external world and in intrinsic brain states, maintaining the robustness of computations poses a challenge, adequate solutions to which we are only beginning to understand. At the level of cell-intrinsic properties, biophysical models of neurons permit one to identify relevant physiological substrates that can serve as regulators of neuronal excitability and to test how feedback loops can stabilize crucial variables such as long-term calcium levels and firing rates. Mathematical theory has also revealed a rich set of complementary computational properties arising from distinct cellular dynamics and even shaping processing at the network level. Here, we provide an overview over recently explored homeostatic mechanisms derived from biophysical models and hypothesize how multiple dynamical characteristics of cells, including their intrinsic neuronal excitability classes, can be stably controlled.

A meiotic driver is a selfish genetic element that interferes with the process of meiosis to promote its own transmission. The most common mechanism of interference is gamete killing, where the meiotic driver kills gametes that do not contain it. A killer meiotic driver is predicted to spread rapidly through a population at the expense of other genes in the rest of the genome. The rapid spread of a killer meiotic driver is expected to be chased by the rapid spread of a suppressor that returns fair meiosis. Paradoxically, while this might imply that meiotic drivers should be evolutionarily transient, numerous ancient killer meiotic drivers have been discovered that have persisted for millions of years. To understand the rationale that could potentially explain such evolutionary robustness, we explore different possible mechanisms of killer meiotic drive and the different possible associated mechanisms of suppression. We use a framework that considers how the different stages of meiosis result in different structured interactions among cells with different genotypes in various combinations. Across possible interactions, we show that there are three genotypically distinct drive mechanisms that create alternative selective conditions for the spread of different types of suppressors. We show that killer meiotic drivers are more evolutionarily robust if they operate among sister cells (after meiosis I and before meiosis II) than at any other point during meiosis. The different drive mechanisms we identify make testable predictions that could explain why some killer meiotic drivers are transient while others are ancient.

Background Microbiome-metabolome studies of the human gut have been gaining popularity in recent years, mostly due to accumulating evidence of the interplay between gut microbes, metabolites, and host health. Statistical and machine learning-based methods have been widely applied to analyze such paired microbiome-metabolome data, in the hope of identifying metabolites that are governed by the composition of the microbiome. Such metabolites can be likely modulated by microbiome-based interventions, offering a route for promoting gut metabolic health. Yet, to date, it remains unclear whether findings of microbially associated metabolites in any single study carry over to other studies or cohorts, and how robust and universal are microbiome-metabolites links. Results In this study, we addressed this challenge by performing a comprehensive meta-analysis to identify human gut metabolites that can be predicted based on the composition of the gut microbiome across multiple studies. We term such metabolites “robustly well-predicted”. To this end, we processed data from 1733 samples from 10 independent human gut microbiome-metabolome studies, focusing initially on healthy subjects, and implemented a machine learning pipeline to predict metabolite levels in each dataset based on the composition of the microbiome. Comparing the predictability of each metabolite across datasets, we found 97 robustly well-predicted metabolites. These include metabolites involved in important microbial pathways such as bile acid transformations and polyamines metabolism. Importantly, however, other metabolites exhibited large variation in predictability across datasets, suggesting a cohort- or study-specific relationship between the microbiome and the metabolite. Comparing taxonomic contributors to different models, we found that some robustly well-predicted metabolites were predicted by markedly different sets of taxa across datasets, suggesting that some microbially associated metabolites may be governed by different members of the microbiome in different cohorts. We finally examined whether models trained on a control group of a given study successfully predicted the metabolite’s level in the disease group of the same study, identifying several metabolites where the model was not transferable, indicating a shift in microbial metabolism in disease-associated dysbiosis. Conclusions Combined, our findings provide a better understanding of the link between the microbiome and metabolites and allow researchers to put identified microbially associated metabolites within the context of other studies.

Sortase A, a transpeptidase enzyme is present in many Gram-positive bacteria and helps in the recruitment of the cell surface proteins. Over the last two decades, Sortase A has become an attractive tool for performing in vivo and in vitro ligations. Sortase A-mediated ligation has continuously been used for its specificity, robustness, and highly efficient nature. These properties make it a popular choice among protein engineers as well as researchers from different fields. In this review, we give an overview of Sortase A-mediated ligation of various molecules on the cell surfaces, which can have diverse applications in interdisciplinary fields.

Background: The quantitative structure-activity relationship is an analysis method that can be applied for designing new molecules. In 1997, Hopfinger and coworkers developed the 4DQSAR methodology aiming to eliminate the question of which conformation to use in a QSAR study. In this work, the 4D-QSAR methodology was used to quantitatively determine the influence of structural descriptors on the activity of aryl pyrimidine derivatives as inhibitors of the TGF-β1 receptor. The members of the TGF-β subfamily are interesting molecular targets, since they play an important function in the growth and development of cell cellular including proliferation, apoptosis, differentiation, Epithelial-Mesenchymal Transition (EMT), and migration. In late stages, TGF-β exerts tumor-promoting effects, increasing tumor invasiveness, and metastasis. Therefore, TGF-β is an attractive target for cancer therapy. Objective: The major goal of the current research is to develop 4D-QSAR models aiming to propose new structures of aryl pyrimidine derivatives. Materials and Methods: Molecular dynamics simulation was carried out to generate the conformational ensemble profile of a data set with aryl pyrimidine derivatives. The conformations were overlaid into a three-dimensional cubic box, according to the three-ordered atom alignment. The occupation of the grid cells by the interaction of pharmacophore elements provides the Grid Cell Occupancy Descriptors (GCOD), the dependent variables used to build the 4D-QSAR models. The best models were validated (internal and external validation) using several statistical parameters. Docking molecular studies were performed to better understand the binding mode of pyrimidine derivatives inside the TGF-β active site. Results : The 4D-QSAR model presented seven descriptors and acceptable statistical parameters (R2 = 0.89, q2 = 0.68, R2 pred = 0.65, r2 m = 0.55, R2 P = 0.68 and R2 rand = 0.21) besides pharmacophores groups important for the activity of these compounds. The molecular docking studies helped to understand the pharmacophoric groups and proposed substituents that increase the potency of aryl pyrimidine derivatives. Conclusion: The best QSAR model showed adequate statistical parameters that ensure their fitness, robustness, and predictivity. Structural modifications were assessed, and five new structures were proposed as candidates for a drug for cancer treatment.

Solution-phase hydrogen/deuterium exchange (HDX) coupled to mass spectrometry (MS) is a widespread tool for structural analysis across academia and the biopharmaceutical industry. By monitoring the exchangeability of backbone amide protons, HDX-MS can reveal information about higher-order structure and dynamics throughout a protein, can track protein folding pathways, map interaction sites, and assess conformational states of protein samples. The combination of the versatility of the hydrogen/deuterium exchange reaction with the sensitivity of mass spectrometry has enabled the study of extremely challenging protein systems, some of which cannot be suitably studied using other techniques. Improvements over the past three decades have continually increased throughput, robustness, and expanded the limits of what is feasible for HDX-MS investigations. To provide an overview for researchers seeking to utilize and derive the most from HDX-MS for protein structural analysis, we summarize the fundamental principles, basic methodology, strengths and weaknesses, and the established applications of HDX-MS while highlighting new developments and applications.

Summary Micro-electrode arrays (MEAs) are increasingly used to characterize neuronal network activity of human induced pluripotent stem cell (hiPSC)-derived neurons. Despite their gain in popularity, MEA recordings from hiPSC-derived neuronal networks are not always used to their full potential in respect to experimental design, execution, and data analysis. Therefore, we benchmarked the robustness of MEA-derived neuronal activity patterns from ten healthy individual control lines, and uncover comparable network phenotypes. To achieve standardization, we provide recommendations on experimental design and analysis. With such standardization, MEAs can be used as a reliable platform to distinguish (disease-specific) network phenotypes. In conclusion, we show that MEAs are a powerful and robust tool to uncover functional neuronal network phenotypes from hiPSC-derived neuronal networks, and provide an important resource to advance the hiPSC field toward the use of MEAs for disease phenotyping and drug discovery., Highlights • MEAs are a robust tool to model neuronal network functioning • Neuronal networks from different healthy donors show comparable network activity • MEAs are able to distinguish disease-specific neuronal network phenotypes • We provide recommendations to standardize neuronal network recordings on MEA, In this article, Mossink and colleagues demonstrate that micro-electrode arrays (MEAs) are a highly robust tool to uncover genotype/phenotype interactions in hiPSC-derived excitatory neuronal networks, and provide an important resource for the design, execution, and analysis of hiPSC-derived neuronal networks studies on MEA.

Organ growth is tightly regulated across environmental conditions to generate appropriate final size. While the size of some organs is free to vary, others need to maintain constant size to function properly. This poses a unique problem: how is robust final size achieved when environmental conditions can alter some major growth processes? While we know that brain growth is “spared” from the effects of the environment from humans to fruit flies, we do not understand how this process alters growth dynamics across brain compartments. Here, we explore how this robustness in brain size is achieved by examining differences in growth patterns between the larval body, the brain, and a brain compartment – the mushroom bodies – in Drosophila melanogaster across both thermal and nutritional conditions. We identify key differences in patterns of growth between the whole brain and mushroom bodies that are likely to underlie robustness of final organ shape. Further, we show that these differences produce distinct brain shapes across environments.Significance of StudyA long-standing question in Biology has been how fully functional multicellular organisms with highly specialized organs are generated, given that organs initiate growth at different times across development. Although the genetic mechanisms that underlie growth has been studied extensively, we are yet to understand how growth pattern of organs produces distinct final shapes across changing environmental conditions. We use the Drosophila brain, to reveal that key differences in growth dynamics are likely to underlie robustness of final organ shape and are tuned by nutrition and temperature. Further deepening our knowledge of how final organ shape is maintained across environmental conditions.

The abnormal expression of microRNA (miRNA) can affect the RNA transcription and protein translation, leading to tumor progression and metastasis. Currently, the accurate detection of aberrant expression of miRNA, particularly using a portable detection system, remains a great challenge. Herein, a novel dual-mode biosensor with high sensitivity and robustness for miR-21 detection was developed based on the cis-cleavage and trans-cleavage activities of Cas12a. miRNA can be combined with hairpin DNA-horseradish peroxidase anchored on a CdS/g-C3N4/B-TiO2 photoelectrode, thus the nonenzymatic amplification was triggered to form numerous HRP-modified double-stranded DNA (HRP-dsDNA). Then, HRP-dsDNA can be specifically recognized and efficiently cis-cleaved by Cas12a nucleases to detach HRP from the substrate, while the remaining HRP on HRP-dsDNA can catalyze 4-chloro-1-naphthol (4-CN) to form biocatalytic precipitation (BCP) on the surface of the photoelectrode, and thus the photocurrent can be changed. Meanwhile, the trans-cleavage ability of Cas12a was activated, and nonspecifically degrade the FQ-reporter and a significant fluorescence signal can be generated. Such two different kinds of signals with independent transmission paths can mutually support to improve the performance of the detection platform. Besides, a portable device was constructed for the point-of-care (POC) detection of miR-21. Moreover, the dual-mode detection platform can be easily expanded for the specific detection of other types of biomarkers by changing the sequence of hairpin DNA, thereby promoting the establishment of POC detection for early cancer diagnosis.

Typical traditional probiotics lactobacilli and bifidobacteria are gaining great interest to be developed as living diagnostics and therapeutics for improving human health. However, the mechanistic basis underlying their inherent health beneficial property remain incompletely understood which can slow down the translational pipeline in the functional food and pharmaceutical field. Efficient genome editing will advance the understanding of the molecular mechanism of the probiotics' physiological properties and their interaction with the host and the host microbiota, thereby further promote the development of next-generation designer probiotics with improved robustness and tailored functionalities. With the expansion of genome editing strategies such as CRISPR-Cas-based tools and IPSD assisted genome engineering as well as other synthetic biology technologies, the research and application of these health-promoting bacteria for the food and pharmaceutical industry will be further enhanced.

Microbial cell factories provide a sustainable and economical way to produce chemicals from renewable feedstocks. However, the accumulation of targeted chemicals can reduce the robustness of the industrial strains and affect the production performance. Here, the physiological functions of Mediator tail subunit CgMed16 at l-malate stress were investigated. Deletion of CgMed16 decreased the survival, biomass, and half-maximal inhibitory concentration (IC50 ) by 40.4%, 34.0%, and 30.6%, respectively, at 25 g/L l-malate stress. Transcriptome analysis showed that this growth defect was attributable to changes in the expression of genes involved in lipid metabolism. In addition, tolerance transcription factors CgUSV1 and CgYAP3 were found to interact with CgMed16 to regulate sterol biosynthesis and glycerophospholipid metabolism, respectively, ultimately endowing strains with excellent membrane integrity to resist l-malate stress. Furthermore, a dynamic tolerance system (DTS) was constructed based on CgUSV1, CgYAP3, and an l-malate-driven promoter Pcgr-10 to improve the robustness and productive capacity of C. glabrata. As a result, the biomass, survival, and membrane integrity of C. glabrata 012 (with DTS) increased by 22.6%, 31.3%, and 53.8%, respectively, compared with those of strain 011 (without DTS). Therefore, at shake-flask scale, strain 012 accumulated 35.5 g/L l-malate, and the titer and productivity of l-malate increased by 32.5% and 32.1%, respectively, compared with those of strain 011. This study provides a novel strategy for the rational design and construction of DTS for dynamically enhancing the robustness of industrial strains. This article is protected by copyright. All rights reserved.

Summary Understanding metabolism is fundamental to access and harness bacterial physiology. In most bacteria, nutrient utilization is hierarchically optimized according to their energetic potential and their availability in the environment to maximise growth rates. Low‐throughput methods have been largely used to characterize bacterial metabolic profiles. However, in‐depth analysis of large collections of strains across several conditions is challenging since high‐throughput approaches are still limited – especially for non‐traditional hosts. Here, we developed a high‐throughput dilution‐resolved cultivation method for metabolic footprinting of Pseudomonas putida and Pseudomonas aeruginosa. This method was benchmarked against a conventional low‐throughput time‐resolved cultivation approach using either a synthetic culture medium (where a single carbon source is present) for P. putida or a complex nutrient mixture for P. aeruginosa. Dynamic metabolic footprinting, either by sugar quantification or by targeted exo‐metabolomic analyses, revealed overlaps between the bacterial metabolic profiles irrespective of the cultivation strategy, suggesting a certain level of robustness and flexibility of the high‐throughput dilution‐resolved method. Cultivation of P. putida in microtiter plates imposed a metabolic constraint, dependent on oxygen availability, which altered the pattern of secreted metabolites at the level of sugar oxidation. Deep‐well plates, however, constituted an optimal cultivation set‐up yielding consistent and comparable metabolic profiles across conditions and strains. Altogether, the results illustrate the usefulness of this technological advance for high‐throughput analyses of bacterial metabolism for both biotechnological applications and automation purposes.

Reliable mapping of system-level individual differences is a critical first step toward precision medicine for complex disorders such as schizophrenia. Disrupted structural covariance indicates a system-level brain maturational disruption in schizophrenia. However, most studies examine structural covariance at the group level. This prevents subject-level inferences. Here, we introduce a Network Template Perturbation approach to construct individual differential structural covariance network (IDSCN) using regional gray-matter volume. IDSCN quantifies how structural covariance between two nodes in a patient deviates from the normative covariance in healthy subjects. We analyzed T1 images from 1287 subjects, including 107 first-episode (drug-naive) patients and 71 controls in the discovery datasets and established robustness in 213 first-episode (drug-naive), 294 chronic, 99 clinical high-risk patients, and 494 controls from the replication datasets. Patients with schizophrenia were highly variable in their altered structural covariance edges; the number of altered edges was related to severity of hallucinations. Despite this variability, a subset of covariance edges, including the left hippocampus–bilateral putamen/globus pallidus edges, clustered patients into two distinct subgroups with opposing changes in covariance compared to controls, and significant differences in their anxiety and depression scores. These subgroup differences were stable across all seven datasets with meaningful genetic associations and functional annotation for the affected edges. We conclude that the underlying physiology of affective symptoms in schizophrenia involves the hippocampus and putamen/pallidum, predates disease onset, and is sufficiently consistent to resolve morphological heterogeneity throughout the illness course. The two schizophrenia subgroups identified thus have implications for the nosology and clinical treatment.

International audience; The large number of ion channels found in all nervous systems poses fundamental questions concerning how the characteristic intrinsic properties of single neurons are determined by the specific subsets of channels they express. All neurons display many different ion channels with overlapping voltage- and time-dependent properties. We speculate that these overlapping properties promote resilience in neuronal function. Individual neurons of the same cell type show variability in ion channel conductance densities even though they can generate reliable and similar behavior. This complicates a simple assignment of function to any conductance and is associated with variable responses of neurons of the same cell type to perturbations, deletions, and pharmacological manipulation. Ion channel genes often show strong positively correlated expression, which may result from the molecular and developmental rules that determine which ion channels are expressed in a given cell type.

The cell membrane plays a central role in the fitness and performance of microbial cell factories and therefore it is an attractive engineering target. The goal of this work is to develop a systematic framework for identifying membrane features for use as engineering targets. The metrics that describe the composition of the membrane can be visualized as “knobs” that modulate various “outcomes”, such as physical properties of the membrane and metabolic activity in the form of growth and productivity, with these relationships varying depending on the condition. We generated a set of strains with altered membrane lipid composition via expression of des, fabA and fabB and performed a rigorous characterization of these knobs and outcomes across several individual inhibitory conditions. Here, the knobs are the relative abundance of unsaturated lipids and lipids containing cyclic rings; the average lipid length, and the ratio of linear and non-linear lipids (L/nL ratio). The outcomes are membrane permeability, hydrophobicity, fluidity, and specific growth rate. This characterization identified significant correlations between knobs and outcomes that were specific to individual inhibitors, but also were significant across all tested conditions. For example, across all conditions, the L/nL ratio is positively correlated with the cell surface hydrophobicity, and the average lipid length is positively correlated with specific growth rate. A subsequent analysis of the data with the individual inhibitors identified pairs of lipid metrics and membrane properties that were predicted to impact cell growth in seven modeled scenarios with two or more inhibitors. The L/nL ratio and the membrane hydrophobicity were predicted to impact cell growth with the highest frequency. We experimentally validated this prediction in the combined condition of 42 °C, 2.5 mM furfural and 2% v/v ethanol in minimal media. Membrane hydrophobicity was confirmed to be a significant predictor of ethanol production. This work demonstrates that membrane physical properties can be used to predict the performance of biocatalysts in single and multiple inhibitory conditions, and possibly as an engineering target. In this manner, membrane properties can possibly be used as screening or selection metrics for library- or evolution-based strain engineering.

We have shown that water-soluble variants of the human mu opioid receptor (wsMOR) containing a reduced number of hydrophobic residues at the lipid-facing residues of the transmembrane (TM) helices can be expressed in E. coli. In this study, we tested the consequences of increasing the number of mutations on the surface of the transmembrane domain on the receptor's aqueous solubility and ligand binding properties, along with mutation of 11 cysteine residues regardless of their solvent exposure value and location in the protein. We computationally engineered 10 different variants of MOR, and tested four of them for expression in E. coli. We found that all four variants were successfully expressed and could be purified in high quantities. The variants have alpha helical structural content similar to that of the native MOR, and they also display binding affinities for the MOR antagonist (naltrexone) similar to the wsMOR variants we engineered previously that contained many fewer mutations. Furthermore, for these full-length variants, the helical content remains unchanged over a wide range of pH values (pH 6 ~ 9). This study demonstrates the flexibility and robustness of the water-soluble MOR variants with respect to additional designed mutations in the TM domain and changes in pH, whereupon the protein's structural integrity and its ligand binding affinity are maintained. These variants of the full-length MOR with less hydrophobic surface residues and less cysteines can be obtained in large amounts from expression in E. coli and can serve as novel tools to investigate structure-function relationships of the receptor.

Dictyostelium development begins with single-cell starvation and ends with multicellular fruiting bodies. Developmental morphogenesis is accompanied by sweeping transcriptional changes, encompassing nearly half of the 13,000 genes in the genome. We performed time-series RNA-sequencing analyses of the wild type and 20 mutants to explore the relationships between transcription and morphogenesis. These strains show developmental arrest at different stages, accelerated development, or atypical morphologies. Considering eight major morphological transitions, we identified 1371 milestone genes whose expression changes sharply between consecutive transitions. We also identified 1099 genes as members of 21 regulons, which are groups of genes that remain coordinately regulated despite the genetic, temporal, and developmental perturbations. The gene annotations in these groups validate known transitions and reveal new developmental events. For example, DNA replication genes are tightly coregulated with cell division genes, so they are expressed in mid-development although chromosomal DNA is not replicated. Our data set includes 486 transcriptional profiles that can help identify new relationships between transcription and development and improve gene annotations. We show its utility by showing that cycles of aggregation and disaggregation in allorecognition-defective mutants involve dedifferentiation. We also show sensitivity to genetic and developmental conditions in two commonly used actin genes, act6 and act15, and robustness of the coaA gene. Finally, we propose that gpdA is a better mRNA quantitation standard because it is less sensitive to external conditions than commonly used standards. The data set is available for democratized exploration through the web application dictyExpress and the data mining environment Orange.

The fitness cost of complex pleiotropic mutations is generally difficult to assess. On the one hand, it is necessary to identify which molecular properties are directly altered by the mutation. On the other, this alteration modifies the activity of many genetic targets with uncertain consequences. Here, we examine the possibility of addressing these challenges by identifying unique predictors of these costs. To this aim, we consider mutations in the RNA polymerase (RNAP) in Escherichia coli as a model of complex mutations. Changes in RNAP modify the global program of transcriptional regulation, with many consequences. Among others is the difficulty to decouple the direct effect of the mutation from the response of the whole system to such mutation. A problem that we solve quantitatively with data of a set of constitutive genes, those on which the global program acts most directly. We provide a statistical framework that incorporates the direct effects and other molecular variables linked to this program as predictors, which leads to the identification that some genes are more suitable to determine costs than others. Therefore, we not only identified which molecular properties best anticipate fitness, but we also present the paradoxical result that, despite pleiotropy, specific genes serve as more solid predictors. These results have connotations for the understanding of the architecture of robustness in biological systems.

Many strains from Bacillus thuringiensis are known for their genomic robustness and antimicrobial potentials. As a result, the quest for their biotechnological applications, especially in the agroindustry (e.g., as biopesticides), has increased over the years. This study documents the genome sequencing and probing of a Fusarium antagonist (B. thuringiensis strain MORWBS1.1) with possible biopesticidal metabolite producing capacity from South Africa. Based on in vitro evaluation and in silico antiSMASH investigation, B. thuringiensis strain MORWBS1.1 exhibited distinctive genomic properties that could be further exploited for in planta and food additive production purposes. [Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Microphysiological organ-on-chip models offer the potential to improve the prediction of drug safety and efficacy through recapitulation of human physiological responses. The importance of including multiple cell types within tissue models has been well documented. However, the study of cell interactions in vitro can be limited by complexity of the tissue model and throughput of current culture systems. Here, we describe the development of a co-culture microvascular model and relevant assays in a high-throughput thermoplastic organ-on-chip platform, PREDICT96. The system consists of 96 arrayed bilayer microfluidic devices containing retinal microvascular endothelial cells and pericytes cultured on opposing sides of a microporous membrane. Compatibility of the PREDICT96 platform with a variety of quantifiable and scalable assays, including macromolecular permeability, image-based screening, Luminex, and qPCR, is demonstrated. In addition, the bilayer design of the devices allows for channel- or cell type-specific readouts, such as cytokine profiles and gene expression. The microvascular model was responsive to perturbations including barrier disruption, inflammatory stimulation, and fluid shear stress, and our results corroborated the improved robustness of co-culture over endothelial mono-cultures. We anticipate the PREDICT96 platform and adapted assays will be suitable for other complex tissues, including applications to disease models and drug discovery.

A comprehensive transcriptomic survey of pigs can provide a mechanistic understanding of tissue specialization processes underlying economically valuable traits and accelerate their use as a biomedical model. Here we characterize four transcript types (lncRNAs, TUCPs, miRNAs, and circRNAs) and protein-coding genes in 31 adult pig tissues and two cell lines. We uncover the transcriptomic variability among 47 skeletal muscles, and six adipose depots linked to their different origins, metabolism, cell composition, physical activity, and mitochondrial pathways. We perform comparative analysis of the transcriptomes of seven tissues from pigs and nine other vertebrates to reveal that evolutionary divergence in transcription potentially contributes to lineage-specific biology. Long-range promoter–enhancer interaction analysis in subcutaneous adipose tissues across species suggests evolutionarily stable transcription patterns likely attributable to redundant enhancers buffering gene expression patterns against perturbations, thereby conferring robustness during speciation. This study can facilitate adoption of the pig as a biomedical model for human biology and disease and uncovers the molecular bases of valuable traits., A comprehensive transcriptomic survey of the pig could enable mechanistic understanding of tissue specialization and accelerate its use as a biomedical model. Here the authors characterize four distinct transcript types in 31 adult pig tissues to dissect their distinct structural and transcriptional features and uncover transcriptomic variability related to tissue physiology.

Viral infection causes a wide variety of human diseases including cancer and COVID-19. Viruses invade host cells and associate with host molecules, potentially disrupting the normal function of hosts that leads to fatal diseases. Novel viral genome prediction is crucial for understanding the complex viral diseases like AIDS and Ebola. While most existing computational techniques classify viral genomes, the efficiency of the classification depends solely on the structural features extracted. The state-of-the-art DNN models achieved excellent performance by automatic extraction of classification features, but the degree of model explainability is relatively poor. During model training for viral prediction, proposed CNN, CNN-LSTM based methods (EdeepVPP, EdeepVPP-hybrid) automatically extracts features. EdeepVPP also performs model interpretability in order to extract the most important patterns that cause viral genomes through learned filters. It is an interpretable CNN model that extracts vital biologically relevant patterns (features) from feature maps of viral sequences. The EdeepVPP-hybrid predictor outperforms all the existing methods by achieving 0.992 mean AUC-ROC and 0.990 AUC-PR on 19 human metagenomic contig experiment datasets using 10-fold cross-validation. We evaluate the ability of CNN filters to detect patterns across high average activation values. To further asses the robustness of EdeepVPP model, we perform leave-one-experiment-out cross-validation. It can work as a recommendation system to further analyze the raw sequences labeled as 'unknown' by alignment-based methods. We show that our interpretable model can extract patterns that are considered to be the most important features for predicting virus sequences through learned filters.

Isogenic cells growing in identical environments show cell-to-cell variations because of the stochasticity in gene expression. High levels of variation or noise can disrupt robust gene expression and result in tremendous consequences for cell behaviors. In this work, we showed evidence from single-cell RNA-sequencing data analysis that microRNAs (miRNAs) can reduce gene expression noise at the mRNA level in mouse cells. We identified that the miRNA expression level, number of targets, target pool abundance, and strength of miRNA interactions with its targets are the key features contributing to noise repression. miRNAs tend to work together in cooperative subnetworks to repress target noise synergistically in a cell-type-specific manner. By building a physical model of posttranscriptional regulation and observing in synthetic gene circuits, we demonstrated that accelerated degradation with elevated transcriptional activation of the miRNA target provides resistance to extrinsic fluctuations. Together, through the integrated analysis of single-cell RNA and miRNA expression profiles, we demonstrated that miRNAs are important posttranscriptional regulators for reducing gene expression noise and conferring robustness to biological processes.

The use of genomic and phenotypic data to scan for outliers is a mainstay for studies of hybridization and speciation. Geographic cline analysis of natural hybrid zones is widely used to identify putative signatures of selection by detecting deviations from baseline patterns of introgression. As with other outlier-based approaches, demographic histories can make neutral regions appear to be under selection and vice versa. In this study, we use a forward-time individual-based simulation approach to evaluate the robustness of geographic cline analysis under different evolutionary scenarios. We modelled multiple stepping-stone hybrid zones with distinct age, deme sizes, and migration rates, and evolving under different types of selection. We found that drift distorts cline shapes and increases false positive rates for signatures of selection. This effect increases with hybrid zone age, particularly if migration between demes is low. Drift can also distort the signature of deleterious effects of hybridization, with genetic incompatibilities and particularly underdominance prone to spurious typing as adaptive introgression. Our results suggest that geographic clines are most useful for outlier analysis in young hybrid zones with large populations of hybrid individuals. Current approaches may overestimate adaptive introgression and underestimate selection against maladaptive genotypes.

Phylogenomic subsampling is a procedure by which small sets of loci are selected from large genome-scale data sets and used for phylogenetic inference. This step is often motivated by either computational limitations associated with the use of complex inference methods or as a means of testing the robustness of phylogenetic results by discarding loci that are deemed potentially misleading. Although many alternative methods of phylogenomic subsampling have been proposed, little effort has gone into comparing their behavior across different data sets. Here, I calculate multiple gene properties for a range of phylogenomic data sets spanning animal, fungal, and plant clades, uncovering a remarkable predictability in their patterns of covariance. I also show how these patterns provide a means for ordering loci by both their rate of evolution and their relative phylogenetic usefulness. This method of retrieving phylogenetically useful loci is found to be among the top performing when compared with alternative subsampling protocols. Relatively common approaches such as minimizing potential sources of systematic bias or increasing the clock-likeness of the data are found to fare worse than selecting loci at random. Likewise, the general utility of rate-based subsampling is found to be limited: loci evolving at both low and high rates are among the least effective, and even those evolving at optimal rates can still widely differ in usefulness. This study shows that many common subsampling approaches introduce unintended effects in off-target gene properties and proposes an alternative multivariate method that simultaneously optimizes phylogenetic signal while controlling for known sources of bias.

Conventional cell-free protein synthesis systems had been the major platform to study the mechanism behind translating genetic information into proteins, as proven in the central dogma of molecular biology. Albeit being powerful research tools, most of the in vitro methods at the time failed to produce enough protein for practical use. Tremendous efforts were being made to overcome the limitations of in vitro translation systems, though mostly with limited success. While great knowledge was accumulated on the translation mechanism and ribosome structure, researchers rationalized that it may be impossible to fully reconstitute such a complex molecular process in a test tube. This review will examine how we have solved the difficulties holding back progress. Our newly developed cell-free protein synthesis system is based on wheat embryos and has many excellent characteristics, in addition to its high translation activity and robustness. Combined with other novel elementary technologies, we have established cell-free protein synthesis systems for practical use in research and applied sciences.

Oscillation is ubiquitous and important in both bio-engineering and biosystems. We here analyze the features of oscillation in a well-studied biochemical model of glycolysis with autocatalytic kinetic step. This Selkov model is followed deterministically using mass action kinetics. We explore the sensitivity analysis technique to assess degree of susceptibility of the system with respect to different input parameters for a thorough understanding of the merit of individual parameters controlling the core dynamics. Emphasis is concentrated on the system’s biological response via oscillation, bracketing the range of perturbation allowed to the kinetic parameters. An interesting observation is the switchover of the dynamics from non-oscillating to damping followed by stable sustained undulation with one or two controlling kinetic parameters which are quite appealing. The result may be illuminating in understanding the dynamics behind oscillations in biochemical systems in general. A key outcome of the study is the prioritization of most sensitive parameter for the network model controlling the dynamical features via scatterplot analysis.

With the current COVID-19 pandemic, we all realized how important interactions are. Interactions are everywhere. At the cellular level, protein interactions play a key role and their ensemble, also called interactome, is often referred as the basic building blocks of life. Given its importance, the maintenance of the integrity of the interactome is a real challenge in the cell. Many events during evolution can disrupt interactomes and potentially result in different characteristics for the organisms. However, the molecular underpinnings of changes in interactions at the cellular level are still largely unexplored. Among the perturbations, hybridization puts in contact two different interactomes, which may lead to many changes in the protein interaction network of the hybrid, including gains and losses of interactions. We recently investigated the fate of the interactomes after hybridization between yeast species using a comparative proteomics approach. A large-scale conservation of the interactions was observed in hybrids, but we also noticed the presence of proteostasis-related changes. This suggests that, despite a general robustness, small differences may accumulate in hybrids and perturb their protein physiology. Here, we summarize our work with a broader perspective on the importance of interactions.

The differentiation of neural stem cells (NSCs) into neurons is proposed to be critical in devising potential cell-based therapeutic strategies for central nervous system (CNS) diseases, however, the determination and prediction of differentiation is complex and not yet clearly established, especially at the early stage. We hypothesize that deep learning could extract minutiae from large-scale datasets, and present a deep neural network model for predictable reliable identification of NSCs fate. Remarkably, using only bright field images without artificial labelling, our model is surprisingly effective at identifying the differentiated cell types, even as early as 1 day of culture. Moreover, our approach showcases superior precision and robustness in designed independent test scenarios involving various inducers, including neurotrophins, hormones, small molecule compounds and even nanoparticles, suggesting excellent generalizability and applicability. We anticipate that our accurate and robust deep learning-based platform for NSCs differentiation identification will accelerate the progress of NSCs applications.

Gene doping is banned in human sports, horseracing, and equestrian sports. One possible form of gene doping is to administer exogenous genes, called transgenes. Several transgene detection methods based on quantitative PCR have been developed. In this study, we investigated the robustness of digital PCR and real-time PCR in transgene detection using primers and probes that matched (P-true) or incompletely matched (P-false) the template DNA. Fluorescence intensity was significantly reduced when substituted probes were used compared to that using the matched probe in both digital and real-time PCR assays. Digital PCR yielded a similar copy number regardless of the probe (P-true: 1230.7, P-false: 1229.7), whereas real-time PCR revealed a decrease in sensitivity based on Cq values (P-true: 23.5, P-false: 29.7). When substituted primers were used, the detected copy number decreased in the digital PCR assay, and the Cq value in real-time PCR was much higher. Interestingly, digital PCR copy numbers improved by performing PCR at a low annealing temperature, even if a substituted probe was used. Thus, when primer and probe sequences did not completely match the template transgene, digital PCR was relatively robust, but real-time PCR was less sensitive. Although PCR specificity may be reduced, PCR sensitivity can be improved by lowering the annealing temperature. If the target sequence is substituted to escape doping detection, it may be desirable to set the annealing temperature lower and use a more robust method, such as digital PCR, to increase the detection of positive cases, which will also result in fewer false-negative results.

Modern commercial layer breeds represent the culmination of ~7000 years of natural genetic selection. This selection was driven in former times by a combination of genetic-shift and -drift events, that led to chickens being favoured as domesticated species for meat and egg production. More recently, in the early 20th century, the concept of hybrid vigour was discovered and accelerated the natural breeding progress that delivered new genetic lines and more favourable production traits. In the mid-20th century, the broiler-type and egg layer-type lines diverged and, in the 21st century, genetic analysis has further accelerated the progress made towards extended primary breeding characteristics such as egg quality, production and feed-intake traits, together with secondary breeding characteristics such as behavioural traits that have improved robustness in different housing systems, climates and feed types, which together have significantly improved welfare traits. Most recently, there has been the adoption of higher-powered computational analytics together with quantitative trait loci and single-nucleotide polymorphism assessment, which have further improved the uniformity of production traits within breeds. Most importantly, this has provided the primary breeding companies with improved and broader basis of selection of the modern commercial layer breeds, which also improved the alignment of layer strains with market requirements, and diverse variations in housing, nutritional and environmental conditions. This is also testament to the speed with which the commercial layer geneticists can respond to changing welfare policy on factors such as stocking density and beak treatment. The present paper reviews the modern approaches to genetic selection, including considerations of and benefits to the welfare state of commercial layers.

Ongoing neuronal activity in the brain establishes functional networks that reflect normal and pathological brain function. Most estimates of these functional networks suffer from low spatiotemporal resolution and indirect measures of neuronal population activity, limiting the accuracy and reliability in their reconstruction over time. Here, we studied the stability of neuronal avalanche dynamics and corresponding reconstructed functional networks in the adult brain. Using chronically implanted high-density microelectrode arrays, the local field potential (LFP) of resting-state activity was recorded in prefrontal and premotor cortex of awake nonhuman primates. Avalanche dynamics revealed stable scaling exhibiting an inverted parabolic profile and collapse exponent of 2 in line with a critical branching process over many days and weeks. Functional networks were based on a Bayesian-derived estimator and demonstrated stable integrative properties characterized by nontrivial high neighborhood overlap between strongly connected nodes and robustness to weak-link pruning. Entropy-based mixing analysis revealed significant changes in strong link weights over weeks. The long-term stability in avalanche scaling and integrative network organization in the face of individual link weight changes should support the development of noninvasive biomarkers to characterize normal and abnormal brain states in the adult brain., Author Summary The brain is spontaneously active even in the absence of specific sensations or movements. This ongoing activity arises from the interactions among hundreds of thousands of neurons, which has been captured by a variety of distinct functional networks predictive of healthy and pathological brain functions. The global dynamical states and overarching network principles that underlie such ongoing brain activity are not well understood. Here we identify avalanche dynamics and “friendship” networks as two major features of ongoing activity in the frontal cortex of nonhuman primates. We demonstrate their stability over weeks in the face of local network changes. Deviation from avalanche dynamics and “friendship” organization might provide robust biomarkers to identify normal and pathological brain states.

Nearly neutral theory is an extension of the neutral theory and contends that the borderline mutations, whose effects lie between the selected and the neutral classes, are important at the molecular level. Under the strict neutral theory, the evolutionary rate is equal to the neutral mutation rate. Under the near-neutrality, the situation is not so simple and the most significant difference between the neutral and the nearly neutral theories is that the latter predicts a negative correlation between evolutionary rate and species population size. The nearly neutral theory also predicts abundant rare alleles in the population as compared with strict neutrality. Genome-wide data on protein evolution are mostly in accord with the nearly neutral theory. Genetic regulatory systems are highly complex. The near-neutrality concept may be extended to the evolution of such systems, where epigenetics and robustness are important for gene expression and many mutations are weakly selected. Key Concepts: The emphasis of significance of weak selection in evolution distinguishes the nearly neutral theory from the neutral theory. The nearly neutral theory contends that the interplay of drift and weak selection is important and predicts that evolution is more rapid in small populations than in large populations. Many observed patterns of protein evolution by measuring synonymous and nonsynonymous divergences are in accord with the nearly neutral theory. Observed molecular polymorphisms within a population often show abundance of rare alleles, in accord with the prediction of the nearly neutral theory. Numerous complex systems work together in living cells such as those in chromatin modelling/remodelling and in various signalling pathways. Interplay of drift and weak selection is important for evolution of such complex systems. Keywords: slightly deleterious mutations; drift; effectiveness of selection; interplay of drift and selection for evolution of complex systems

Antioxidant proteins perform significant functions in disease control and delaying aging which can prevent free radicals from damaging organisms. Accurate identification of antioxidant proteins has important implications for the development of new drugs and the treatment of related diseases, as they play a critical role in the control or prevention of cancer and aging-related conditions. Since experimental identification techniques are time-consuming and expensive, many computational methods have been proposed to identify antioxidant proteins. Although the accuracy of these methods is acceptable, there are still some challenges. In this study, we developed a computational model called ANPrAod to identify antioxidant proteins based on a support vector machine. In order to eliminate potential redundant features and improve prediction accuracy, 673 amino acid reduction alphabets were calculated by us to find the optimal feature representation scheme. The final model could produce an overall accuracy of 87.53% with the ROC of 0.7266 in five-fold cross-validation, which was better than the existing methods. The results of the independent dataset also demonstrated the excellent robustness and reliability of ANPrAod, which could be a promising tool for antioxidant protein identification and contribute to hypothesis-driven experimental design.