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In this report, we present multiparameter deformability cytometry (m-DC), in which we explore a large set of parameters describing the physical phenotypes of pluripotent cells and their derivatives. m-DC utilizes microfluidic inertial focusing and hydrodynamic stretching of single cells in conjunction with high-speed video recording to realize high-throughput characterization of over 20 different cell motion and morphology-derived parameters. Parameters extracted from videos include size, deformability, deformation kinetics, and morphology. We train support vector machines that provide evidence that these additional physical measurements improve classification of induced pluripotent stem cells, mesenchymal stem cells, neural stem cells, and their derivatives compared to size and deformability alone. In addition, we utilize visual interactive stochastic neighbor embedding to visually map the high-dimensional physical phenotypic spaces occupied by these stem cells and their progeny and the pathways traversed during differentiation. This report demonstrates the potential of m-DC for improving understanding of physical differences that arise as cells differentiate and identifying cell subpopulations in a label-free manner. Ultimately, such approaches could broaden our understanding of subtle changes in cell phenotypes and their roles in human biology.

Rapid growth in size and complexity of biological data sets has led to the 'Big Data to Knowledge' challenge. We develop advanced data integration methods for multi-level analysis of genomic, transcriptomic, ribosomal profiling, proteomic and fluxomic data. First, we show that pairwise integration of primary omics data reveals regularities that tie cellular processes together in Escherichia coli: the number of protein molecules made per mRNA transcript and the number of ribosomes required per translated protein molecule. Second, we show that genome-scale models, based on genomic and bibliomic data, enable quantitative synchronization of disparate data types. Integrating omics data with models enabled the discovery of two novel regularities: condition invariant in vivo turnover rates of enzymes and the correlation of protein structural motifs and translational pausing. These regularities can be formally represented in a computable format allowing for coherent interpretation and prediction of fitness and selection that underlies cellular physiology.

Finding the minimal set of gene functions needed to sustain life is of both fundamental and practical importance. Minimal gene lists have been proposed by using comparative genomics-based core proteome definitions. A definition of a core proteome that is supported by empirical data, is understood at the systems-level, and provides a basis for computing essential cell functions is lacking. Here, we use a systems biology-based genome-scale model of metabolism and expression to define a functional core proteome consisting of 356 gene products, accounting for 44% of the Escherichia coli proteome by mass based on proteomics data. This systems biology core proteome includes 212 genes not found in previous comparative genomics-based core proteome definitions, accounts for 65% of known essential genes in E. coli, and has 78% gene function overlap with minimal genomes (Buchnera aphidicola and Mycoplasma genitalium). Based on transcriptomics data across environmental and genetic backgrounds, the systems biology core proteome is significantly enriched in nondifferentially expressed genes and depleted in differentially expressed genes. Compared with the noncore, core gene expression levels are also similar across genetic backgrounds (two times higher Spearman rank correlation) and exhibit significantly more complex transcriptional and posttranscriptional regulatory features (40% more transcription start sites per gene, 22% longer 5UTR). Thus, genome-scale systems biology approaches rigorously identify a functional core proteome needed to support growth. This framework, validated by using high-throughput datasets, facilitates a mechanistic understanding of systems-level core proteome function through in silico models; it de facto defines a paleome.

The regulators GadE, GadW and GadX (which we refer to as GadEWX) play a critical role in the transcriptional regulation of the glutamate-dependent acid resistance (GDAR) system in Escherichia coli K-12 MG1655. However, the genome-wide regulatory role of GadEWX is still unknown. Here we comprehensively reconstruct the genome-wide GadEWX transcriptional regulatory network and RpoS involvement in E. coli K-12 MG1655 under acidic stress. Integrative data analysis reveals that GadEWX regulons consist of 45 genes in 31 transcription units and 28 of these genes were associated with RpoS-binding sites. We demonstrate that GadEWX directly and coherently regulate several proton-generating/consuming enzymes with pairs of negative-feedback loops for pH homeostasis. In addition, GadEWX regulate genes with assorted functions, including molecular chaperones, acid resistance, stress response and other regulatory activities. These results show how GadEWX simultaneously coordinate many cellular processes to produce the overall response of E. coli to acid stress.

BackgroundAt the beginning of the transcription process, the RNA polymerase (RNAP) core enzyme requires a σ-factor to recognize the genomic location at which the process initiates. Although the crucial role of σ-factors has long been appreciated and characterized for many individual promoters, we do not yet have a genome-scale assessment of their function.ResultsUsing multiple genome-scale measurements, we elucidated the network of σ-factor and promoter interactions in Escherichia coli. The reconstructed network includes 4,724 σ-factor-specific promoters corresponding to transcription units (TUs), representing an increase of more than 300% over what has been previously reported. The reconstructed network was used to investigate competition between alternative σ-factors (the σ70 and σ38 regulons), confirming the competition model of σ substitution and negative regulation by alternative σ-factors. Comparison with σ-factor binding in Klebsiella pneumoniae showed that transcriptional regulation of conserved genes in closely related species is unexpectedly divergent.ConclusionsThe reconstructed network reveals the regulatory complexity of the promoter architecture in prokaryotic genomes, and opens a path to the direct determination of the systems biology of their transcriptional regulatory networks.

The ferric uptake regulator (Fur) plays a critical role in the transcriptional regulation of iron metabolism. However, the full regulatory potential of Fur remains undefined. Here we comprehensively reconstruct the Fur transcriptional regulatory network in Escherichia coli K-12 MG1655 in response to iron availability using genome-wide measurements. Integrative data analysis reveals that a total of 81 genes in 42 transcription units are directly regulated by three different modes of Fur regulation, including apo- and holo-Fur activation and holo-Fur repression. We show that Fur connects iron transport and utilization enzymes with negative-feedback loop pairs for iron homeostasis. In addition, direct involvement of Fur in the regulation of DNA synthesis, energy metabolism and biofilm development is found. These results show how Fur exhibits a comprehensive regulatory role affecting many fundamental cellular processes linked to iron metabolism in order to coordinate the overall response of E. coli to iron availability.

Determining how facultative anaerobic organisms sense and direct cellular responses to electron acceptor availability has been a subject of intense study. However, even in the model organism Escherichia coli, established mechanisms only explain a small fraction of the hundreds of genes that are regulated during electron acceptor shifts. Here we propose a qualitative model that accounts for the full breadth of regulated genes by detailing how two global transcription factors (TFs), ArcA and Fnr of E. coli, sense key metabolic redox ratios and act on a genome-wide basis to regulate anabolic, catabolic, and energy generation pathways. We first fill gaps in our knowledge of this transcriptional regulatory network by carrying out ChIP-chip and gene expression experiments to identify 463 regulatory events. We then interfaced this reconstructed regulatory network with a highly curated genome-scale metabolic model to show that ArcA and Fnr regulate >80% of total metabolic flux and 96% of differential gene expression across fermentative and nitrate respiratory conditions. Based on the data, we propose a feedforward with feedback trim regulatory scheme, given the extensive repression of catabolic genes by ArcA and extensive activation of chemiosmotic genes by Fnr. We further corroborated this regulatory scheme by showing a 0.71 r(2) (p

The tropical climate variabilities, such as Indian Ocean Dipole (IOD) and El Niño Southern Oscillation (ENSO), are accompanied by changes in the tropical deep convection which can influence the atmospheric circulation in the Southern Hemisphere (SH). To investigate each role of IOD and ENSO in the September‐November (SON) circulation, we examine teleconnection patterns associated with IOD and ENSO events using the ERA5 monthly averaged data from 1979 to 2020. Our approach is to calculate the power spectral density (PSD) of the sea level pressure (SLP) and meridional wind and geopotential height at 300 hPa that are decomposed by zonal wave numbers (ZWNs), and to compute their correlations with IOD and ENSO at each latitudinal band. The main results are that IOD (ENSO) is negatively (positively) correlated with PSDs of ZWN2 and ZWN3 (ZWN1) at 300 hPa in the SH middle latitudes. Considering the Rossby wave train, IOD (ENSO) considerably affects the variability of the ZWN3 (ZWN1) pattern, which influences the meridional exchange of momentum. Additionally, the relationship between IOD and ZWN3 has become tighter in recent years, which is not seen in that with ENSO. The IOD and ENSO events also modify the SLP patterns and meridional surface winds, modulating the sea ice extent in the Southern Ocean. During the highly positive 2019 IOD event, the variability of the middle latitudes atmospheric circulation was considerably larger than climatology, suggesting a higher chance of more extreme weather patterns associated with more frequent intense IOD events in the warming climate. Plain Language Summary: The two tropical climate variabilities, Indian Ocean Dipole (IOD) and El Niño Southern Oscillation (ENSO), can change the location and intensity of tropical convection, generate anomalous Rossby wave trains heading to the Southern Hemisphere (SH), and the zonal wave pattern of the middle latitudes' atmospheric circulation and further the Antarctic climate. We analyzed this low‐high latitudes teleconnection in the austral spring when IOD and ENSO strongly affect the wave patterns using atmospheric reanalysis data. The results show that ENSO is positively correlated with zonal wave number 1 (ZWN1) activity, but IOD negatively correlates with ZWN2 and ZWN3 activities in the SH middle latitudes. The relationship between IOD and ZWN3 has become tighter in recent years, which is not seen in that with ENSO. The IOD and ENSO events also modify the sea level pressure patterns and meridional surface winds in the Southern Ocean, leaving the ZWN3 features. The surface wind patterns affect the sea ice concentration, but with a difference between IOD and ENSO. Key Points: Indian Ocean Dipole (IOD)'s Rossby wave train is partially similar to the climatology; a negative IOD enormously increases zonal wavenumber 3IOD (El Niño Southern Oscillation) is negatively (positively) correlated with zonal wavenumber 2 and 3 (1) in austral springThe extremely positive IOD in 2019 exhibited more significant increases and unusual changes in wave patterns [ABSTRACT FROM AUTHOR]

Abstract Background The increasing number of infections caused by strains of Klebsiella pneumoniae that are resistant to multiple antibiotics has developed into a major medical problem worldwide. The development of next-generation sequencing technologies now permits rapid sequencing of many K. pneumoniae isolates, but sequence information alone does not provide important structural and operational information for its genome. Results Here we take a systems biology approach to annotate the K. pneumoniae MGH 78578 genome at the structural and operational levels. Through the acquisition and simultaneous analysis of multiple sample-matched –omics data sets from two growth conditions, we detected 2677, 1227, and 1066 binding sites for RNA polymerase, RpoD, and RpoS, respectively, 3660 RNA polymerase-guided transcript segments, and 3585 transcription start sites throughout the genome. Moreover, analysis of the transcription start site data identified 83 probable leaderless mRNAs, while analysis of unannotated transcripts suggested the presence of 119 putative open reading frames, 15 small RNAs, and 185 antisense transcripts that are not currently annotated. Conclusions These findings highlight the strengths of systems biology approaches to the refinement of sequence-based annotations, and to provide new insight into fundamental genome-level biology for this important human pathogen.

As the electric vehicle market continues to surge, the proper assessment of used batteries has become increasingly important. However, current technologies for assessing used batteries, which involve separately estimating the State-of-Health (SoH) of the pack and its individual modules, require multiple times of cycling tests and lead to time inefficiency and power consumption. The proposed DeepSUGAR, a deep learning-based framework for SoH estimation using a generative algorithm based on graphical representation techniques to reveal individual module health, offers the advantage of estimating the status of internal modules replying on battery pack SoH. The cycling profiles of a simultaneously measured 14S7P pack and its constituent modules were analyzed, and a convolutional neural network (CNN) was trained by spatializing cycling curves to estimate SoH. DeepSUGAR, trained on pack data, showed outstanding performance with an RMSE of 5.31 × 10−3 and its applicability was validated by testing with module data, resulting in an RMSE of 7.38 × 10−3. Furthermore, the generated module cycling profiles from pack SoH using the deep generative model were fed into the trained CNN and showed a remarkable performance with an RMSE of 8.38 × 10−3. DeepSUGAR can significantly reduce power consumption, processing cost, and carbon dioxide emissions by integrating module-level diagnosis within the pack-level assessment process. [ABSTRACT FROM AUTHOR]

Recently, the incidence of skin cancer has been increasing owing to the development of science and technology and the increase in outdoor activities. Research on photothermal therapy as a treatment technique for similar skin cancer is in progress. Photothermal therapy is a treatment technique that removes tumor tissue by increasing the temperature. It has the advantage of rapid recovery and a low risk of secondary infection. In this study, a numerical investigation of photothermal therapy based on heat transfer is conducted on squamous cell carcinoma present inside the skin layer. Analysis is performed by varying the number of injections of gold nanoparticles, volume fraction of gold nanoparticles in the tumor, and laser intensity. In addition, conditions for maximizing expression of apoptosis in the tumor and minimizing amount of thermal damage to surrounding normal tissues are identified through the variable which is apoptosis retention ratio, thermal hazard value and effective apoptosis retention ratio. It was confirmed that the optimal therapeutic effect was shown when the volume fraction of injected GNPs was 10−3, the number of injections was 6 times, and the irradiated laser intensity was 140 mW for the tumor presented in this study. Ultimately, these results are expected to accelerate the commercialization of photothermal therapy. [ABSTRACT FROM AUTHOR]