PeakBot: machine-learning-based chromatographic peak picking.

MOTIVATION: Chromatographic peak picking is among the first steps in data processing workflows of raw LC-HRMS datasets in untargeted metabolomics applications. Its performance is crucial for the holistic detection of all metabolic features as well as their relative quantification for statistical analysis and metabolite identification. Random noise, non-baseline separated compounds and unspecific background signals complicate this task. RESULTS: A machine-learning-based approach entitled PeakBot was developed for detecting chromatographic peaks in LC-HRMS profile-mode data. It first detects all local signal maxima in a chromatogram, which are then extracted as super-sampled standardized areas (retention-time versus m/z). These are subsequently inspected by a custom-trained convolutional neural network that forms the basis of PeakBot's architecture. The model reports if the respective local maximum is the apex of a chromatographic peak or not as well as its peak center and bounding box. In training and independent validation datasets used for development, PeakBot achieved a high performance with respect to discriminating between chromatographic peaks and background signals (accuracy of 0.99). For training the machine-learning model a minimum of 100 reference features are needed to learn their characteristics to achieve high-quality peak-picking results for detecting such chromatographic peaks in an untargeted fashion. PeakBot is implemented in python (3.8) and uses the TensorFlow (2.5.0) package for machine-learning related tasks. It has been tested on Linux and Windows OSs. AVAILABILITY AND IMPLEMENTATION: The package is available free of charge for non-commercial use (CC BY-NC-SA). It is available at https://github.com/christophuv/PeakBot. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The response of the maize nitrate transport system to nitrogen demand and supply across the lifecycle.

An understanding of nitrate (NO3-) uptake throughout the lifecycle of plants, and how this process responds to nitrogen (N) availability, is an important step towards the development of plants with improved nitrogen use efficiency (NUE). NO3- uptake capacity and transcript levels of putative high- and low-affinity NO3- transporters (NRTs) were profiled across the lifecycle of dwarf maize (Zea mays) plants grown at reduced and adequate NO3-. Plants showed major changes in high-affinity NO3- uptake capacity across the lifecycle, which varied with changing relative growth rates of roots and shoots. Transcript abundances of putative high-affinity NRTs (predominantly ZmNRT2.1 and ZmNRT2.2) were correlated with two distinct peaks in high-affinity root NO3- uptake capacity and also N availability. The reduction in NO3- supply during the lifecycle led to a dramatic increase in NO3- uptake capacity, which preceded changes in transcript levels of NRTs, suggesting a model with short-term post-translational regulation and longer term transcriptional regulation of NO3- uptake capacity. These observations offer new insight into the control of NO3- uptake by both plant developmental processes and N availability, and identify key control points that may be targeted by future plant improvement programmes to enhance N uptake relative to availability and/or demand.

Nutritional requirements of the BY series of Saccharomyces cerevisiae strains for optimum growth.

Among the vast variety of Saccharomyces cerevisiae strains, the BY family is particularly important because the widely used deletion collections are based on this background. Here we demonstrate that some standard growth media recipes require substantial modifications to provide optimum growth conditions for auxotrophic BY strains and to avoid growth arrest before glucose is depleted. In addition to the essential supplements that are required to satisfy auxotrophic requirements, we found the four amino acids phenylalanine, glutamic acid, serine, and threonine to be indispensable for optimum growth, despite the fact that BY is 'prototrophic' for these amino acids. Interestingly, other widely used S. cerevisiae strains, such as strains of the CEN.PK family, are less sensitive to lack of the described supplements. Furthermore, we found that the concentration of inositol in yeast nitrogen base is too low to support fast proliferation of yeast cultures until glucose is exhausted. Depletion of inositol during exponential growth induces characteristic changes, namely a decrease in glucose uptake and maximum specific growth rate, increased cell size, reduced viability, and accumulation of lipid storage pools. Thus, several of the existing growth media recipes need to be revised to achieve optimum growth conditions for BY-derived strains.

Recon 2.2: from reconstruction to model of human metabolism.

INTRODUCTION: The human genome-scale metabolic reconstruction details all known metabolic reactions occurring in humans, and thereby holds substantial promise for studying complex diseases and phenotypes. Capturing the whole human metabolic reconstruction is an on-going task and since the last community effort generated a consensus reconstruction, several updates have been developed. OBJECTIVES: We report a new consensus version, Recon 2.2, which integrates various alternative versions with significant additional updates. In addition to re-establishing a consensus reconstruction, further key objectives included providing more comprehensive annotation of metabolites and genes, ensuring full mass and charge balance in all reactions, and developing a model that correctly predicts ATP production on a range of carbon sources. METHODS: Recon 2.2 has been developed through a combination of manual curation and automated error checking. Specific and significant manual updates include a respecification of fatty acid metabolism, oxidative phosphorylation and a coupling of the electron transport chain to ATP synthase activity. All metabolites have definitive chemical formulae and charges specified, and these are used to ensure full mass and charge reaction balancing through an automated linear programming approach. Additionally, improved integration with transcriptomics and proteomics data has been facilitated with the updated curation of relationships between genes, proteins and reactions. RESULTS: Recon 2.2 now represents the most predictive model of human metabolism to date as demonstrated here. Extensive manual curation has increased the reconstruction size to 5324 metabolites, 7785 reactions and 1675 associated genes, which now are mapped to a single standard. The focus upon mass and charge balancing of all reactions, along with better representation of energy generation, has produced a flux model that correctly predicts ATP yield on different carbon sources. CONCLUSION: Through these updates we have achieved the most complete and best annotated consensus human metabolic reconstruction available, thereby increasing the ability of this resource to provide novel insights into normal and disease states in human. The model is freely available from the Biomodels database (http://identifiers.org/biomodels.db/MODEL1603150001).

A Consensus Genome-scale Reconstruction of Chinese Hamster Ovary Cell Metabolism.

Chinese hamster ovary (CHO) cells dominate biotherapeutic protein production and are widely used in mammalian cell line engineering research. To elucidate metabolic bottlenecks in protein production and to guide cell engineering and bioprocess optimization, we reconstructed the metabolic pathways in CHO and associated them with >1,700 genes in the Cricetulus griseus genome. The genome-scale metabolic model based on this reconstruction, iCHO1766, and cell-line-specific models for CHO-K1, CHO-S, and CHO-DG44 cells provide the biochemical basis of growth and recombinant protein production. The models accurately predict growth phenotypes and known auxotrophies in CHO cells. With the models, we quantify the protein synthesis capacity of CHO cells and demonstrate that common bioprocess treatments, such as histone deacetylase inhibitors, inefficiently increase product yield. However, our simulations show that the metabolic resources in CHO are more than three times more efficiently utilized for growth or recombinant protein synthesis following targeted efforts to engineer the CHO secretory pathway. This model will further accelerate CHO cell engineering and help optimize bioprocesses.

3D numerical simulation of a lab-on-a-chip--increasing measurement sensitivity of interdigitated capacitors by passivation optimization.

Interdigital electrode structures (IDES) play a major role in many technical and analytical applications. In particular, they are a key technology in modern lab-on-a-chip (LOC) devices. As high sensitivity is a key component of any (bio)analytical method, the presented work is aimed at designing a novel dielectric sensing system, which exhibits maximum sensor sensitivity using passivated dielectric microsensors. Although the implementation of high-ε(r) dielectric passivation materials such as tantalum oxide or titanium oxide showed increased sensor sensitivity by a factor of 5, simulations revealed that sensor sensitivity is ultimately determined by the dielectric properties of the analyte. Ideally, dielectric properties of the passivation material need to be adjusted to the dielectric properties of the material under investigation and any deviations (e.g. higher or lower dielectric constants) will result in significant loss of sensitivity. To address these shortcomings we have developed a novel dielectric sensing concept based on a dual-material passivation geometry. The novel design consists of electric flux barriers that are layered between the finger electrodes, as well as electric flux guides which are located above the electrode structures that direct the entire generated electric flux to the object under investigation. Our 3D numerical results clearly show that the novel design offers two main advantages: firstly, the measurement sensitivity is further increased by more than a factor of two in comparison to a homogeneous passivation material sensing strategy. Secondly, maximum sensitivity for a given set of finger geometries can be achieved using a single sensor design regardless of the frequency-dependent dielectric properties of the measured objects. Hence, the novel approach is capable of reducing design and manufacturing costs of lab-on-a-chip devices.