Differential variability analysis of single-cell gene expression data.

The advent of single-cell RNA sequencing (scRNA-seq) technologies has enabled gene expression profiling at the single-cell resolution, thereby enabling the quantification and comparison of transcriptional variability among individual cells. Although alterations in transcriptional variability have been observed in various biological states, statistical methods for quantifying and testing differential variability between groups of cells are still lacking. To identify the best practices in differential variability analysis of single-cell gene expression data, we propose and compare 12 statistical pipelines using different combinations of methods for normalization, feature selection, dimensionality reduction and variability calculation. Using high-quality synthetic scRNA-seq datasets, we benchmarked the proposed pipelines and found that the most powerful and accurate pipeline performs simple library size normalization, retains all genes in analysis and uses denSNE-based distances to cluster medoids as the variability measure. By applying this pipeline to scRNA-seq datasets of COVID-19 and autism patients, we have identified cellular variability changes between patients with different severity status or between patients and healthy controls.

Identification of Anticancer Enzymes and Biomarkers for Hepatocellular Carcinoma through Constraint-Based Modeling.

Hepatocellular carcinoma (HCC) results in the abnormal regulation of cellular metabolic pathways. Constraint-based modeling approaches can be utilized to dissect metabolic reprogramming, enabling the identification of biomarkers and anticancer targets for diagnosis and treatment. In this study, two genome-scale metabolic models (GSMMs) were reconstructed by employing RNA sequencing expression patterns of hepatocellular carcinoma (HCC) and their healthy counterparts. An anticancer target discovery (ACTD) framework was integrated with the two models to identify HCC targets for anticancer treatment. The ACTD framework encompassed four fuzzy objectives to assess both the suppression of cancer cell growth and the minimization of side effects during treatment. The composition of a nutrient may significantly affect target identification. Within the ACTD framework, ten distinct nutrient media were utilized to assess nutrient uptake for identifying potential anticancer enzymes. The findings revealed the successful identification of target enzymes within the cholesterol biosynthetic pathway using a cholesterol-free cell culture medium. Conversely, target enzymes in the cholesterol biosynthetic pathway were not identified when the nutrient uptake included a cholesterol component. Moreover, the enzymes PGS1 and CRL1 were detected in all ten nutrient media. Additionally, the ACTD framework comprises dual-group representations of target combinations, pairing a single-target enzyme with an additional nutrient uptake reaction. Additionally, the enzymes PGS1 and CRL1 were identified across the ten-nutrient media. Furthermore, the ACTD framework encompasses two-group representations of target combinations involving the pairing of a single-target enzyme with an additional nutrient uptake reaction. Computational analysis unveiled that cell viability for all dual-target combinations exceeded that of their respective single-target enzymes. Consequently, integrating a target enzyme while adjusting an additional exchange reaction could efficiently mitigate cell proliferation rates and ATP production in the treated cancer cells. Nevertheless, most dual-target combinations led to lower side effects in contrast to their single-target counterparts. Additionally, differential expression of metabolites between cancer cells and their healthy counterparts were assessed via parsimonious flux variability analysis employing the GSMMs to pinpoint potential biomarkers. The variabilities of the fluxes and metabolite flow rates in cancer and healthy cells were classified into seven categories. Accordingly, two secretions and thirteen uptakes (including eight essential amino acids and two conditionally essential amino acids) were identified as potential biomarkers. The findings of this study indicated that cancer cells exhibit a higher uptake of amino acids compared with their healthy counterparts.

Heart Rate Variability Analysis on Electrocardiograms, Seismocardiograms and Gyrocardiograms of Healthy Volunteers and Patients with Valvular Heart Diseases.

Heart rate variability (HRV) is the physiological variation in the intervals between consecutive heartbeats that reflects the activity of the autonomic nervous system. This parameter is traditionally evaluated based on electrocardiograms (ECG signals). Seismocardiography (SCG) and/or gyrocardiography (GCG) are used to monitor cardiac mechanical activity; therefore, they may be used in HRV analysis and the evaluation of valvular heart diseases (VHDs) simultaneously. The purpose of this study was to compare the time domain, frequency domain and nonlinear HRV indices obtained from electrocardiograms, seismocardiograms (SCG signals) and gyrocardiograms (GCG signals) in healthy volunteers and patients with valvular heart diseases. An analysis of the time domain, frequency domain and nonlinear heart rate variability was conducted on electrocardiograms and gyrocardiograms registered from 29 healthy male volunteers and 30 patients with valvular heart diseases admitted to the Columbia University Medical Center (New York City, NY, USA). The results of the HRV analysis show a strong linear correlation with the HRV indices calculated from the ECG, SCG and GCG signals and prove the feasibility and reliability of HRV analysis despite the influence of VHDs on the SCG and GCG waveforms.

Optimized number of suction cups required to predict annual nitrate leaching under varying conditions in Denmark.

Nitrate concentrations in soil water leaving the root zone measured by suction cups combined with water transport modeling is a commonly used practice in Denmark for calculating nitrate leaching. Two suction cups installed in one plot giving one water sample and replicated four times, (eight total suction cups) to reduce variability between samples. For practical reasons, it would be beneficial to minimize the number of suction cups used yet maintain reliable predictions. To assess the variability in reducing suction replicates, this study analyzed data from five research sites across Denmark representing annual field nitrate leaching predictions for different combinations of soil, weather conditions, crops, N-fertilizer rates, and winter soil cover, covering a total of 173 annual nitrate leaching experiments. The analysis was conducted having different nitrate leaching predictions using different numbers of replicates of suction cup measurements. Linear regression was used to identify the different influences of leaching year (hydrological year), N rate applied, soil characteristics, and crop sequence on nitrate leaching. The analyses were set up on three 2-yr and two 3-yr field experiments in five different sites. Crop effects showed that cereals and winter cover sown in autumn 2017 had significantly more nitrate leaching than in 2015 and 2016 leaching years due to high precipitation rates in the autumn. Furthermore, decreasing the number of suction cup replicates from four (eight total) to three replicates (six total) did not have a significant effect on nitrate leaching prediction. In contrast, decreasing replicates from four to two (four total) and one (two total) replicates did show a significant difference. Therefore, using three replicates is a viable solution for future sampling strategies and a good trade-off between costs and accuracy.

An improved algorithm for flux variability analysis.

Flux balance analysis (FBA) is an optimization based approach to find the optimal steady state of a metabolic network, commonly of microorganisms such as yeast strains and Escherichia coli. However, the resulting solution from an FBA is typically not unique, as the optimization problem is, more often than not, degenerate. Flux variability analysis (FVA) is a method to determine the range of possible reaction fluxes that still satisfy, within some optimality factor, the original FBA problem. The resulting range of reaction fluxes can be utilized to determine metabolic reactions of high importance, amongst other analyses. In the literature, this has been done by solving [Formula: see text] linear programs (LPs), with n being the number of reactions in the metabolic network. However, FVA can be solved with less than [Formula: see text] LPs by utilizing the basic feasible solution property of bounded LPs to reduce the number of LPs that are needed to be solved. In this work, a new algorithm is proposed to solve FVA that requires less than [Formula: see text] LPs. The proposed algorithm is benchmarked on a problem set of 112 metabolic network models ranging from single cell organisms (iMM904, ect) to a human metabolic system (Recon3D). Showing a reduction in the number of LPs required to solve the FVA problem and thus the time to solve an FVA problem.

Monitoring the Evolution of Asynchrony between Mean Arterial Pressure and Mean Cerebral Blood Flow via Cross-Entropy Methods.

Cerebrovascular control is carried out by multiple nonlinear mechanisms imposing a certain degree of coupling between mean arterial pressure (MAP) and mean cerebral blood flow (MCBF). We explored the ability of two nonlinear tools in the information domain, namely cross-approximate entropy (CApEn) and cross-sample entropy (CSampEn), to assess the degree of asynchrony between the spontaneous fluctuations of MAP and MCBF. CApEn and CSampEn were computed as a function of the translation time. The analysis was carried out in 23 subjects undergoing recordings at rest in supine position (REST) and during active standing (STAND), before and after surgical aortic valve replacement (SAVR). We found that at REST the degree of asynchrony raised, and the rate of increase in asynchrony with the translation time decreased after SAVR. These results are likely the consequence of the limited variability of MAP observed after surgery at REST, more than the consequence of a modified cerebrovascular control, given that the observed differences disappeared during STAND. CApEn and CSampEn can be utilized fruitfully in the context of the evaluation of cerebrovascular control via the noninvasive acquisition of the spontaneous MAP and MCBF variability.

A set of common movements within GPCR-G-protein complexes from variability analysis of cryo-EM datasets.

G-protein coupled receptors (GPCRs) are among the most versatile signal transducers in the cell. Once activated, GPCRs sample a large conformational space and couple to G-proteins to initiate distinct signaling pathways. The dynamical behavior of GPCR-G-protein complexes is difficult characterize structurally, and it might hinder obtaining routine high-resolution density maps in single-particle reconstructions. Here, we used variability analysis on the rhodopsin-Gi-Fab16 complex cryo-EM dataset, and the results provide insights into the dynamic nature of the receptor-complex interaction. We compare the outcome of this analysis with recent results obtained on the cannabinoid-Gi- and secretin-Gs-receptor complexes. Despite differences related to the biochemical compositions of the three samples, a set of consensus movements emerges. We anticipate that systematic variability analysis on GPCR-G-protein complexes may provide useful information not only at the biological level, but also for improving the preparation of more stable samples for cryo-EM single-particle analysis.

Autoencoder-Based Extrasystole Detection and Modification of RRI Data for Precise Heart Rate Variability Analysis.

Heart rate variability, which is the fluctuation of the R-R interval (RRI) in electrocardiograms (ECG), has been widely adopted for autonomous evaluation. Since the HRV features that are extracted from RRI data easily fluctuate when arrhythmia occurs, RRI data with arrhythmia need to be modified appropriately before HRV analysis. In this study, we consider two types of extrasystoles-premature ventricular contraction (PVC) and premature atrial contraction (PAC)-which are types of extrasystoles that occur every day, even in healthy persons who have no cardiovascular diseases. A unified framework for ectopic RRI detection and a modification algorithm that utilizes an autoencoder (AE) type of neural network is proposed. The proposed framework consists of extrasystole occurrence detection from the RRI data and modification, whose targets are PVC and PAC. The RRI data are monitored by means of the AE in real time in the detection phase, and a denoising autoencoder (DAE) modifies the ectopic RRI caused by the detected extrasystole. These are referred to as AE-based extrasystole detection (AED) and DAE-based extrasystole modification (DAEM), respectively. The proposed framework was applied to real RRI data with PVC and PAC. The result showed that AED achieved a sensitivity of 93% and a false positive rate of 0.08 times per hour. The root mean squared error of the modified RRI decreased to 31% in PVC and 73% in PAC from the original RRI data by DAEM. In addition, the proposed framework was validated through application to a clinical epileptic seizure problem, which showed that it correctly suppressed the false positives caused by PVC. Thus, the proposed framework can contribute to realizing accurate HRV-based health monitoring and medical sensing systems.

Spatial distribution of salinity and heavy metals in surface soils on the Mugan Plain, the Republic of Azerbaijan.

The Republic of Azerbaijan suffers from low agricultural productivity caused by soil salinization and erosion, and limited and insufficient soil data are available for economic and political reasons. In this study, soil salinity and heavy metal levels were assessed. Environmental risk assessment was conducted to evaluate the potential risk posed by soils to human health. Soil guideline values were proposed to monitor soil pollution in the Republic of Azerbaijan. Water extraction and spatial variability analysis were conducted to understand soil salinization and heavy metal pollution. Among the 20 studied elements, the elements Ca, Cl, and S and the heavy metals Cr, Ni, and Pb were classified as problematic on the basis of the geoaccumulation index, and As was also identified as posing a possible risk on the basis of the potential ecological risk index. Based on the developed soil guideline values for agricultural soil, the As, Cr, and Ni in the soil samples exceeded their respective guidelines by 31.3, 41.8, and 61.6%, respectively. Water extraction results confirmed that 99% of the leached ions were cationic salts, and the most problematic ion was Na, followed by Ca, Cl, and S. The extractability values of Cr and Ni were significantly lower than those of other heavy metals, which implies that their actual leaching potential may be overestimated. The linear regression and spatial variability analysis confirmed that leachable salts have accumulated in lowland areas due to the capillary rise of water and evaporation, but the distribution of heavy metals confirmed that As, Cr, and Ni were abundant in agricultural soils. Our results clearly showed that heavy metal soil contamination and high salinity levels are major problems that should be considered when assessing food safety and health hazards in the Mugan Plain of Azerbaijan. Therefore, future studies should be performed for additional environmental risk assessment, detailed hazard identification, and health risk assessment.

VFFVA: dynamic load balancing enables large-scale flux variability analysis.

BACKGROUND: Genome-scale metabolic models are increasingly employed to predict the phenotype of various biological systems pertaining to healthcare and bioengineering. To characterize the full metabolic spectrum of such systems, Fast Flux Variability Analysis (FFVA) is commonly used in parallel with static load balancing. This approach assigns to each core an equal number of biochemical reactions without consideration of their solution complexity. RESULTS: Here, we present Very Fast Flux Variability Analysis (VFFVA) as a parallel implementation that dynamically balances the computation load between the cores in runtime which guarantees equal convergence time between them. VFFVA allowed to gain a threefold speedup factor with coupled models and up to 100 with ill-conditioned models along with a 14-fold decrease in memory usage. CONCLUSIONS: VFFVA exploits the parallel capabilities of modern machines to enable biological insights through optimizing systems biology modeling. VFFVA is available in C, MATLAB, and Python at https://github.com/marouenbg/VFFVA .

Smoothed Spherical Truncation based on Fuzzy Membership Functions: Application to the Molecular Encoding.

A novel spherical truncation method, based on fuzzy membership functions, is introduced to truncate interatomic (or interaminoacid) relations according to smoothing values computed from fuzzy membership degrees. In this method, the molecules are circumscribed into a sphere, so that the geometric centers of the molecules are the centers of the spheres. The fuzzy membership degree of each atom (or aminoacid) is computed from its distance with respect to the geometric center of the molecule, by using a fuzzy membership function. So, the smoothing value to be applied in the truncation of a relation (or interaction) is computed by averaging the fuzzy membership degrees of the atoms (or aminoacids) involved in the relation. This truncation method is rather different from the existing ones, at considering the geometric center for the whole molecule and not only for atom-groups, as well as for using fuzzy membership functions to compute the smoothing values. A variability study on a set comprised of 20,469 compounds (15,050 drug-like compounds, 2994 drugs approved, 880 natural products from African sources, and 1545 plant-derived natural compounds exhibiting anti-cancerous activity) demonstrated that the truncation method proposed allows to determine molecular encodings with better ability for discriminating among structurally different molecules than the encodings obtained without applying truncation or applying non-fuzzy truncation functions. Moreover, a principal component analysis revealed that orthogonal chemical information of the molecules is encoded by using the method proposed. Lastly, a modeling study proved that the truncation method improves the modeling ability of existing geometric molecular descriptors, at allowing to develop more robust models than the ones built only using non-truncated descriptors. In this sense, a comparison and statistical assessment were performed on eight chemical datasets. As a result, the models based on the truncated molecular encodings yielded statistically better results than 12 procedures considered from the literature. It can thus be stated that the proposed truncation method is a relevant strategy for obtaining better molecular encodings, which will be ultimately useful in enhancing the modeling ability of existing encodings both on small-to-medium size molecules and biomacromolecules. © 2019 Wiley Periodicals, Inc.

Classification of Sleep Apnea Severity by Electrocardiogram Monitoring Using a Novel Wearable Device.

Sleep apnea (SA) is a prevalent disorder diagnosed by polysomnography (PSG) based on the number of apnea-hypopnea events per hour of sleep (apnea-hypopnea index, AHI). PSG is expensive and technically complex; therefore, its use is rather limited to the initial diagnostic phase and simpler devices are required for long-term follow-up. The validity of single-parameter wearable devices for the assessment of sleep apnea severity is still debated. In this context, a wearable electrocardiogram (ECG) acquisition system (ECG belt) was developed and its suitability for the classification of sleep apnea severity was investigated using heart rate variability analysis with or without data pre-filtering. Several classification algorithms were compared and support vector machine was preferred due to its simplicity and overall performance. Whole-night ECG signals from 241 patients with a suspicion of sleep apnea were recorded using both the ECG belt and patched ECG during PSG recordings. 65% of patients had an obstructive sleep apnea and the median AHI was 21 [IQR: 7-40] h - 1 . The classification accuracy obtained from the ECG belt (accuracy: 72%, sensitivity: 70%, specificity: 74%) was comparable to the patched ECG (accuracy: 74%, sensitivity: 88%, specificity: 61%). The highest classification accuracy was obtained for the discrimination between individuals with no or mild SA vs. moderate to severe SA. In conclusion, the ECG belt provided signals comparable to patched ECG and could be used for the assessment of sleep apnea severity, especially during follow-up.

Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living.

Air pollution has been a looming issue of the 21st century that has also significantly impacted the surrounding environment and societal health. Recently, previous studies have conducted extensive research on air pollution and air quality monitoring. Despite this, the fields of air pollution and air quality monitoring remain plagued with unsolved problems. In this study, the Pollution Weather Prediction System (PWP) is proposed to perform air pollution prediction for outdoor sites for various pollution parameters. In the presented research work, we introduced a PWP system configured with pollution-sensing units, such as SDS021, MQ07-CO, NO2-B43F, and Aeroqual Ozone (O3). These sensing units were utilized to collect and measure various pollutant levels, such as PM2.5, PM10, CO, NO2, and O3, for 90 days at Symbiosis International University, Pune, Maharashtra, India. The data collection was carried out between the duration of December 2019 to February 2020 during the winter. The investigation results validate the success of the presented PWP system. In the conducted experiments, linear regression and artificial neural network (ANN)-based AQI (air quality index) predictions were performed. Furthermore, the presented study also found that the customized linear regression methodology outperformed other machine-learning methods, such as linear, ridge, Lasso, Bayes, Huber, Lars, Lasso-lars, stochastic gradient descent (SGD), and ElasticNet regression methodologies, and the customized ANN regression methodology used in the conducted experiments. The overall AQI values of the air pollutants were calculated based on the summation of the AQI values of all the presented air pollutants. In the end, the web and mobile interfaces were developed to display air pollution prediction values of a variety of air pollutants.

Peak Detection Algorithm for Vital Sign Detection Using Doppler Radar Sensors.

An accurate method for detecting vital signs obtained from a Doppler radar sensor is proposed. A Doppler radar sensor can remotely obtain vital signs such as heartbeat and respiration rate, but the vital signs obtained by using the sensor do not show clear peaks like in electrocardiography (ECG) because of the operating characteristics of the radar. The proposed peak detection algorithm extracts the vital signs from the raw data. The algorithm shows the mean accuracy of 96.78% compared to the peak count from the reference ECG sensor and a processing time approximately two times faster than the gradient-based algorithm. To verify whether heart rate variability (HRV) analysis similar to that with an ECG sensor is possible for a radar sensor when applying the proposed method, the continuous parameter variations of the HRV in the time domain are analyzed using data processed with the proposed peak detection algorithm. Experimental results with six subjects show that the proposed method can obtain the heart rate with high accuracy but cannot obtain the information for an HRV analysis because the proposed method cannot overcome the characteristics of the radar sensor itself.

High fidelity hypothermic preservation of primary tissues in organ transplant preservative for single cell transcriptome analysis.

BACKGROUND: High-fidelity preservation strategies for primary tissues are in great demand in the single cell RNAseq community. A reliable method would greatly expand the scope of feasible multi-site collaborations and maximize the utilization of technical expertise. When choosing a method, standardizability and fidelity are important factors to consider due to the susceptibility of single-cell RNAseq analysis to technical noise. Existing approaches such as cryopreservation and chemical fixation are less than ideal for failing to satisfy either or both of these standards. RESULTS: Here we propose a new strategy that leverages preservation schemes developed for organ transplantation. We evaluated the strategy by storing intact mouse kidneys in organ transplant preservative solution at hypothermic temperature for up to 4 days (6 h, 1, 2, 3, and 4 days), and comparing the quality of preserved and fresh samples using FACS and single cell RNAseq. We demonstrate that the strategy effectively maintained cell viability, transcriptome integrity, cell population heterogeneity, and transcriptome landscape stability for samples after up to 3 days of preservation. The strategy also facilitated the definition of the diverse spectrum of kidney resident immune cells, to our knowledge the first time at single cell resolution. CONCLUSIONS: Hypothermic storage of intact primary tissues in organ transplant preservative maintains the quality and stability of the transcriptome of cells for single cell RNAseq analysis. The strategy is readily generalizable to primary specimens from other tissue types for single cell RNAseq analysis.

Missing RRI Interpolation Algorithm based on Locally Weighted Partial Least Squares for Precise Heart Rate Variability Analysis.

The R-R interval (RRI) fluctuation in electrocardiogram (ECG) is called heart rate variability (HRV), which reflects activities of the autonomic nervous system (ANS) and has been used for various health monitoring services. Accurate R wave detection is crucial for success in HRV-based health monitoring services; however, ECG artifacts often cause missing R waves and deteriorate the accuracy of HRV analysis. The present work proposes a new missing RRI interpolation technique based on Just-In-Time (JIT) modeling. In the JIT modeling framework, a local regression model is built by weighing samples stored in the database according to the distance from a query and output is estimated only when an estimate is requested. The proposed method builds a local model and estimates missing RRI only when an RRI detection error is detected. Locally weighted partial least squares (LWPLS) is adopted for local model construction. The proposed method is referred to as LWPLS-based RRI interpolation (LWPLS-RI). The performance of the proposed LWPLS-RI was evaluated through its application to RRI data with artificial missing RRIs. We used the MIT-BIH Normal Sinus Rhythm Database for nominal RRI dataset construction. Missing RRIs were artificially introduced and they were interpolated by the proposed LWPLS-RI. In addition, MEAN that replaces the missing RRI by a mean of the past RRI data was compared as a conventional method. The result showed that the proposed LWPLS-RI improved root mean squared error (RMSE) of RRI by about 70% in comparison with MEAN. In addition, the proposed method realized precise HRV analysis. The proposed method will contribute to the realization of precise HRV-based health monitoring services.

Trajectory analysis of discrete goal-directed pointing movements: How many trials are needed for reliable data?

A powerful tool in motor behavior research is trajectory analysis of discrete goal-directed pointing movements. The purpose of the present analysis was to estimate the minimum number of trials per participant required to achieve the conventional level of reliability for trajectory analysis. We analyzed basic measurements of movement and three common methods of trajectory analysis within the framework of generalizability theory. Generalizability studies were used to decompose the total variance of these variables into the percent contributions from person, trial, and the person-by-trial interaction. Decision studies were then used to determine the minimum number of trials required to achieve the conventional level of reliability. The number of trials per participant needed for reliable data of discrete goal-directed pointing movements depended on the dependent variable-for example, reaction times required six or ten trials, movement times required three trials, and constant error required 47 trials. For trajectory analysis, ten or fewer trials were required for reliable dependent variables during the first half of the movement (up to peak velocity or 70% of the displacement). The number of trials required for the second half of the movement rapidly increased to 47 trials at movement termination. This increase in the number of trials required for reliable analysis of the second half of the movement was indicative of online control. Finally, correlation analysis was performed with simulated correlations on subsets of trials, and all 32 trials were required. However, 18 trials might be used without a practically significant change in the correlations.

Effects and safety of mechanical bathing as a complementary therapy for terminal stage cancer patients from the physiological and psychological perspective: a pilot study.

OBJECTIVE: In palliative care hospitals in Japan, mechanical bathing is conducted to maintain cleanliness. However, the physiological and psychological influence of mechanical bathing on patients has not been sufficiently studied. The objective of this study was to assess, using physiological and psychological indices, the effects of mechanical bathing care for patients in the terminal stage of cancer. METHODS: Mechanical bathing was performed using a Marine Court SB7000 in a supine or semi-seated position. The heart rate variability analysis method was used to measure autonomic nervous system function. The patients' state of anxiety was assessed using the State-Trait Anxiety Inventory (STAI), a psychological index, and patients' verbal responses were also collected after mechanical bathing. RESULTS: Twenty-four patients were enrolled in this study. Their sympathetic and parasympathetic nervous activity did not differ before and after bathing. A significant difference was found between pre- and post-bathing anxiety, as evaluated by STAI (P < 0.0001). In the patient's verbal responses that was collected, the most frequently mentioned descriptors were 'comfortable' and 'relaxed'. Patients were more relaxed after mechanical bathing according to STAI evaluation and their verbal responses. CONCLUSIONS: The findings suggest that the method of bathing used in this study is safe and pain-relieving for terminal stage cancer patients. It is thus possible to provide safe and comfortable care for terminal stage cancer patients using mechanical baths.

In Silico Structure and Sequence Analysis of Bacterial Porins and Specific Diffusion Channels for Hydrophilic Molecules: Conservation, Multimericity and Multifunctionality.

Diffusion channels are involved in the selective uptake of nutrients and form the largest outer membrane protein (OMP) family in Gram-negative bacteria. Differences in pore size and amino acid composition contribute to the specificity. Structure-based multiple sequence alignments shed light on the structure-function relations for all eight subclasses. Entropy-variability analysis results are correlated to known structural and functional aspects, such as structural integrity, multimericity, specificity and biological niche adaptation. The high mutation rate in their surface-exposed loops is likely an important mechanism for host immune system evasion. Multiple sequence alignments for each subclass revealed conserved residue positions that are involved in substrate recognition and specificity. An analysis of monomeric protein channels revealed particular sequence patterns of amino acids that were observed in other classes at multimeric interfaces. This adds to the emerging evidence that all members of the family exist in a multimeric state. Our findings are important for understanding the role of members of this family in a wide range of bacterial processes, including bacterial food uptake, survival and adaptation mechanisms.

Improvement of constraint-based flux estimation during L-phenylalanine production with Escherichia coli using targeted knock-out mutants.

Fed-batch production of the aromatic amino acid L-phenylalanine was studied with recombinant Escherichia coli strains on a 15 L-scale using glycerol as carbon source. Flux Variability Analysis (FVA) was applied for intracellular flux estimation to obtain an insight into intracellular flux distribution during L-phenylalanine production. Variability analysis revealed great flux uncertainties in the central carbon metabolism, especially concerning malate consumption. Due to these results two recombinant strains were genetically engineered differing in the ability of malate degradation and anaplerotic reactions (E. coli FUS4.11 ΔmaeA pF81kan and E. coli FUS4.11 ΔmaeA ΔmaeB pF81kan). Applying these malic enzyme knock-out mutants in the standardized L-phenylalanine production process resulted in almost identical process performances (e.g., L-phenylalanine concentration, production rate and byproduct formation). This clearly highlighted great redundancies in central metabolism in E. coli. Uncertainties of intracellular flux estimations by constraint-based analyses during fed-batch production of L-phenylalanine were drastically reduced by application of the malic enzyme knock-out mutants.