Arginine-NO metabolites are associated with morbidity in single ventricle infants undergoing stage 2 palliation.

BACKGROUND: Infants with single ventricle heart disease (SVHD) suffer morbidity from insufficient pulmonary blood flow, which may be related to impaired arginine metabolism. No prior study has reported quantitative mapping of arginine metabolites to evaluate the relationship between circulating metabolite levels and outcomes. METHODS: Prospective cohort study of 75 SVHD cases peri-Stage 2 and 50 healthy controls. We targeted pre- and post-op absolute serum quantification of 9 key members of the arginine metabolism pathway by tandem mass spectrometry. Primary outcomes were length of stay (LOS) and post-Stage 2 hypoxemia. RESULTS: Pre-op cases showed alteration in 6 metabolites including decreased arginine and increased asymmetric dimethyl arginine (ADMA) levels compared to controls. Post-op cases demonstrated decreased arginine and citrulline levels persisting through 48 h. Adjusting for clinical variables, lower pre-op and 2 h post-op concentrations of multiple metabolites, including arginine and citrulline, were associated with longer post-op LOS (p < 0.01). Increased ADMA at 24 h was associated with greater post-op hypoxemia burden (p < 0.05). CONCLUSION: Arginine metabolism is impaired in interstage SVHD infants and is further deranged following Stage 2 palliation. Patients with greater metabolite alterations experience greater post-op morbidity. Decreased arginine metabolism may be an important driver of pathology in SVHD. IMPACT: Interstage infants with SVHD have significantly altered arginine-nitric oxide metabolism compared to healthy children with deficiency of multiple pathway intermediates persisting through 48 h post-Stage 2 palliation. After controlling for clinical covariates and classic catheterization-derived predictors of Stage 2 readiness, both lower pre-operation and lower post-operation circulating metabolite levels were associated with longer post-Stage 2 LOS while increased post-Stage 2 ADMA concentration was associated with greater post-op hypoxemia. Arginine metabolism mapping offers potential for development using personalized medicine strategies as a biomarker of Stage 2 readiness and therapeutic target to improve pulmonary vascular health in infants with SVHD.

Joint representation of molecular networks from multiple species improves gene classification.

Network-based machine learning (ML) has the potential for predicting novel genes associated with nearly any health and disease context. However, this approach often uses network information from only the single species under consideration even though networks for most species are noisy and incomplete. While some recent methods have begun addressing this shortcoming by using networks from more than one species, they lack one or more key desirable properties: handling networks from more than two species simultaneously, incorporating many-to-many orthology information, or generating a network representation that is reusable across different types of and newly-defined prediction tasks. Here, we present GenePlexusZoo, a framework that casts molecular networks from multiple species into a single reusable feature space for network-based ML. We demonstrate that this multi-species network representation improves both gene classification within a single species and knowledge-transfer across species, even in cases where the inter-species correspondence is undetectable based on shared orthologous genes. Thus, GenePlexusZoo enables effectively leveraging the high evolutionary molecular, functional, and phenotypic conservation across species to discover novel genes associated with diverse biological contexts.

The proteomic fingerprint in infants with single ventricle heart disease in the interstage period: evidence of chronic inflammation and widespread activation of biological networks.

Introduction: Children with single ventricle heart disease (SVHD) experience significant morbidity across systems and time, with 70% of patients experiencing acute kidney injury, 33% neurodevelopmental impairment, 14% growth failure, and 5.5% of patients suffering necrotizing enterocolitis. Proteomics is a method to identify new biomarkers and mechanisms of injury in complex physiologic states. Methods: Infants with SVHD in the interstage period were compared to similar-age healthy controls. Serum samples were collected, stored at -80°C, and run on a panel of 1,500 proteins in single batch analysis (Somalogic Inc., CO). Partial Least Squares-Discriminant Analysis (PLS-DA) was used to compare the proteomic profile of cases and controls and t-tests to detect differences in individual proteins (FDR <0.05). Protein network analysis with functional enrichment was performed in STRING and Cytoscape. Results: PLS-DA readily discriminated between SVHD cases (n = 33) and controls (n = 24) based on their proteomic pattern alone (Accuracy = 0.96, R2 = 0.97, Q2 = 0.80). 568 proteins differed between groups (FDR <0.05). We identified 25 up-regulated functional clusters and 13 down-regulated. Active biological systems fell into six key groups: angiogenesis and cell proliferation/turnover, immune system activation and inflammation, altered metabolism, neural development, gastrointestinal system, and cardiac physiology and development. Conclusions: We report a clear differentiation in the circulating proteome of patients with SVHD and healthy controls with >500 circulating proteins distinguishing the groups. These proteomic data identify widespread protein dysregulation across multiple biologic systems with promising biological plausibility as drivers of SVHD morbidity.

PyGenePlexus: a Python package for gene discovery using network-based machine learning.

SUMMARY: PyGenePlexus is a Python package that enables a user to gain insight into any gene set of interest through a molecular interaction network informed supervised machine learning model. PyGenePlexus provides predictions of how associated every gene in the network is to the input gene set, offers interpretability by comparing the model trained on the input gene set to models trained on thousands of known gene sets, and returns the network connectivity of the top predicted genes. AVAILABILITY AND IMPLEMENTATION: https://pypi.org/project/geneplexus/ and https://github.com/krishnanlab/PyGenePlexus. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A network-based approach for isolating the chronic inflammation gene signatures underlying complex diseases towards finding new treatment opportunities.

Complex diseases are associated with a wide range of cellular, physiological, and clinical phenotypes. To advance our understanding of disease mechanisms and our ability to treat these diseases, it is critical to delineate the molecular basis and therapeutic avenues of specific disease phenotypes, especially those that are associated with multiple diseases. Inflammatory processes constitute one such prominent phenotype, being involved in a wide range of health problems including ischemic heart disease, stroke, cancer, diabetes mellitus, chronic kidney disease, non-alcoholic fatty liver disease, and autoimmune and neurodegenerative conditions. While hundreds of genes might play a role in the etiology of each of these diseases, isolating the genes involved in the specific phenotype (e.g., inflammation "component") could help us understand the genes and pathways underlying this phenotype across diseases and predict potential drugs to target the phenotype. Here, we present a computational approach that integrates gene interaction networks, disease-/trait-gene associations, and drug-target information to accomplish this goal. We apply this approach to isolate gene signatures of complex diseases that correspond to chronic inflammation and use SAveRUNNER to prioritize drugs to reveal new therapeutic opportunities.

GenePlexus: a web-server for gene discovery using network-based machine learning.

Biomedical researchers take advantage of high-throughput, high-coverage technologies to routinely generate sets of genes of interest across a wide range of biological conditions. Although these technologies have directly shed light on the molecular underpinnings of various biological processes and diseases, the list of genes from any individual experiment is often noisy and incomplete. Additionally, interpreting these lists of genes can be challenging in terms of how they are related to each other and to other genes in the genome. In this work, we present GenePlexus (https://www.geneplexus.net/), a web-server that allows a researcher to utilize a powerful, network-based machine learning method to gain insights into their gene set of interest and additional functionally similar genes. Once a user uploads their own set of human genes and chooses between a number of different human network representations, GenePlexus provides predictions of how associated every gene in the network is to the input set. The web-server also provides interpretability through network visualization and comparison to other machine learning models trained on thousands of known process/pathway and disease gene sets. GenePlexus is free and open to all users without the need for registration.

Real-time reconstruction of high energy, ultrafast laser pulses using deep learning.

We report a method for the phase reconstruction of an ultrashort laser pulse based on the deep learning of the nonlinear spectral changes induce by self-phase modulation. The neural networks were trained on simulated pulses with random initial phases and spectra, with pulse durations between 8.5 and 65 fs. The reconstruction is valid with moderate spectral resolution, and is robust to noise. The method was validated on experimental data produced from an ultrafast laser system, where near real-time phase reconstructions were performed. This method can be used in systems with known linear and nonlinear responses, even when the fluence is not known, making this method ideal for difficult to measure beams such as the high energy, large aperture beams produced in petawatt systems.

A flexible, interpretable, and accurate approach for imputing the expression of unmeasured genes.

While there are >2 million publicly-available human microarray gene-expression profiles, these profiles were measured using a variety of platforms that each cover a pre-defined, limited set of genes. Therefore, key to reanalyzing and integrating this massive data collection are methods that can computationally reconstitute the complete transcriptome in partially-measured microarray samples by imputing the expression of unmeasured genes. Current state-of-the-art imputation methods are tailored to samples from a specific platform and rely on gene-gene relationships regardless of the biological context of the target sample. We show that sparse regression models that capture sample-sample relationships (termed SampleLASSO), built on-the-fly for each new target sample to be imputed, outperform models based on fixed gene relationships. Extensive evaluation involving three machine learning algorithms (LASSO, k-nearest-neighbors, and deep-neural-networks), two gene subsets (GPL96-570 and LINCS), and multiple imputation tasks (within and across microarray/RNA-seq datasets) establishes that SampleLASSO is the most accurate model. Additionally, we demonstrate the biological interpretability of this method by showing that, for imputing a target sample from a certain tissue, SampleLASSO automatically leverages training samples from the same tissue. Thus, SampleLASSO is a simple, yet powerful and flexible approach for harmonizing large-scale gene-expression data.

Supervised learning is an accurate method for network-based gene classification.

BACKGROUND: Assigning every human gene to specific functions, diseases and traits is a grand challenge in modern genetics. Key to addressing this challenge are computational methods, such as supervised learning and label propagation, that can leverage molecular interaction networks to predict gene attributes. In spite of being a popular machine-learning technique across fields, supervised learning has been applied only in a few network-based studies for predicting pathway-, phenotype- or disease-associated genes. It is unknown how supervised learning broadly performs across different networks and diverse gene classification tasks, and how it compares to label propagation, the widely benchmarked canonical approach for this problem. RESULTS: In this study, we present a comprehensive benchmarking of supervised learning for network-based gene classification, evaluating this approach and a classic label propagation technique on hundreds of diverse prediction tasks and multiple networks using stringent evaluation schemes. We demonstrate that supervised learning on a gene's full network connectivity outperforms label propagaton and achieves high prediction accuracy by efficiently capturing local network properties, rivaling label propagation's appeal for naturally using network topology. We further show that supervised learning on the full network is also superior to learning on node embeddings (derived using node2vec), an increasingly popular approach for concisely representing network connectivity. These results show that supervised learning is an accurate approach for prioritizing genes associated with diverse functions, diseases and traits and should be considered a staple of network-based gene classification workflows. AVAILABILITY AND IMPLEMENTATION: The datasets and the code used to reproduce the results and add new gene classification methods have been made freely available. CONTACT: arjun@msu.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Near- and Extended-Edge X-Ray-Absorption Fine-Structure Spectroscopy Using Ultrafast Coherent High-Order Harmonic Supercontinua.

Recent advances in high-order harmonic generation have made it possible to use a tabletop-scale setup to produce spatially and temporally coherent beams of light with bandwidth spanning 12 octaves, from the ultraviolet up to x-ray photon energies >1.6 keV. Here we demonstrate the use of this light for x-ray-absorption spectroscopy at the K- and L-absorption edges of solids at photon energies near 1 keV. We also report x-ray-absorption spectroscopy in the water window spectral region (284-543 eV) using a high flux high-order harmonic generation x-ray supercontinuum with 10^{9} photons/s in 1% bandwidth, 3 orders of magnitude larger than has previously been possible using tabletop sources. Since this x-ray radiation emerges as a single attosecond-to-femtosecond pulse with peak brightness exceeding 10^{26} photons/s/mrad^{2}/mm^{2}/1% bandwidth, these novel coherent x-ray sources are ideal for probing the fastest molecular and materials processes on femtosecond-to-attosecond time scales and picometer length scales.

Multimodal nonlinear optical imaging of unstained retinas in the epi-direction with a sub-40 fs Yb-fiber laser.

Ultrafast lasers have potential use in ophthalmology for diagnoses through non-invasive imaging as well as for surgical therapies or for evaluating pharmacological therapies. New ultrafast laser sources, operating at 1.07 µm and sub-40 fs pulse durations, offer exciting possibilities in multiphoton imagining of the retina as the bulk of the eye is relatively transparent to this wavelength, three-photon excitation is not absorbed by DNA, and this wavelength has a greater penetration depth compared to the commonly used 800 nm Ti:Sapphire laser. In this work, we present the first epi-direction detected cross-section and depth-resolved images of unstained isolated retinas obtained using multiphoton microscopy with an ultrafast fiber laser centered at 1.07 µm and a ~38 fs pulse duration. Spectral and temporal characterization of the autofluorescence signals show two distinct regions; the first one from the nerve fiber layer to the inner receptor layer, and the second being the retinal pigmented epithelium and choroid.

Epi-direction detected multimodal imaging of an unstained mouse retina with a Yb-fiber laser.

In this work, we present all epi-direction detected images of an unstained mouse retina using multiphoton microscopy with a sub-50 fs Yb-fiber laser centered at 1.07 µm. This wavelength is particularly interesting as the fundamental wavelength is transparent to the anterior segment of the eye and the higher harmonics are above DNA-damaging UV wavelengths. We present a characterization of the multimodal signals emitted from the different retinal layers, as well as from the choroid and the sclera. By characterizing native multiphoton signals from the retina, we move closer to having Yb-fiber considered for in vivo diagnosis of retinal disease through multiphoton microscopy as well as for corrective therapies.

Helicity-Selective Enhancement and Polarization Control of Attosecond High Harmonic Waveforms Driven by Bichromatic Circularly Polarized Laser Fields.

High harmonics driven by two-color counterrotating circularly polarized laser fields are a unique source of bright, circularly polarized, extreme ultraviolet, and soft x-ray beams, where the individual harmonics themselves are completely circularly polarized. Here, we demonstrate the ability to preferentially select either the right or left circularly polarized harmonics simply by adjusting the relative intensity ratio of the bichromatic circularly polarized driving laser field. In the frequency domain, this significantly enhances the harmonic orders that rotate in the same direction as the higher-intensity driving laser. In the time domain, this helicity-dependent enhancement corresponds to control over the polarization of the resulting attosecond waveforms. This helicity control enables the generation of circularly polarized high harmonics with a user-defined polarization of the underlying attosecond bursts. In the future, this technique should allow for the production of bright highly elliptical harmonic supercontinua as well as the generation of isolated elliptically polarized attosecond pulses.

Mechanisms and time-resolved dynamics for trihydrogen cation (H3+) formation from organic molecules in strong laser fields.

Strong-field laser-matter interactions often lead to exotic chemical reactions. Trihydrogen cation formation from organic molecules is one such case that requires multiple bonds to break and form. We present evidence for the existence of two different reaction pathways for H3+ formation from organic molecules irradiated by a strong-field laser. Assignment of the two pathways was accomplished through analysis of femtosecond time-resolved strong-field ionization and photoion-photoion coincidence measurements carried out on methanol isotopomers, ethylene glycol, and acetone. Ab initio molecular dynamics simulations suggest the formation occurs via two steps: the initial formation of a neutral hydrogen molecule, followed by the abstraction of a proton from the remaining CHOH2+ fragment by the roaming H2 molecule. This reaction has similarities to the H2 + H2+ mechanism leading to formation of H3+ in the universe. These exotic chemical reaction mechanisms, involving roaming H2 molecules, are found to occur in the ~100 fs timescale. Roaming molecule reactions may help to explain unlikely chemical processes, involving dissociation and formation of multiple chemical bonds, occurring under strong laser fields.

Phase matching of noncollinear sum and difference frequency high harmonic generation above and below the critical ionization level.

We investigate the macroscopic physics of noncollinear high harmonic generation (HHG) at high pressures. We make the first experimental demonstration of phase matching of noncollinear high-order-difference-frequency generation at ionization fractions above the critical ionization level, which normally sets an upper limit on the achievable cutoff photon energies. Additionally, we show that noncollinear high-order-sum-frequency generation requires much higher pressures for phase matching than single-beam HHG does, which mitigates the short interaction region in this geometry. We also dramatically increase the experimentally realized cutoff energy of noncollinear circularly polarized HHG, reaching photon energies of 90 eV. Finally, we achieve complete angular separation of high harmonic orders without the use of a spectrometer.

Controlling Nonsequential Double Ionization in Two-Color Circularly Polarized Femtosecond Laser Fields.

Atoms undergoing strong-field ionization in two-color circularly polarized femtosecond laser fields exhibit unique two-dimensional photoelectron trajectories and can emit bright circularly polarized extreme ultraviolet and soft-x-ray beams. In this Letter, we present the first experimental observation of nonsequential double ionization in these tailored laser fields. Moreover, we can enhance or suppress nonsequential double ionization by changing the intensity ratio and helicity of the two driving laser fields to maximize or minimize high-energy electron-ion rescattering. Our experimental results are explained through classical simulations, which also provide insight into how to optimize the generation of circularly polarized high harmonic beams.

Ptychographic hyperspectral spectromicroscopy with an extreme ultraviolet high harmonic comb.

We report a proof-of-principle demonstration of a new scheme of spectromicroscopy in the extreme ultraviolet (EUV) spectral range, where the spectral response of the sample at different wavelengths is imaged simultaneously. This scheme is enabled by combining ptychographic information multiplexing (PIM) with a tabletop EUV source based on high harmonic generation, where four spectrally narrow harmonics near 30 nm form a spectral comb structure. Extending PIM from previously demonstrated visible wavelengths to the EUV/X-ray wavelengths promises much higher spatial resolution and a more powerful spectral contrast mechanism, making PIM an attractive spectromicroscopy method in both microscopy and spectroscopy aspects. In addition to spectromicroscopy, this method images the multicolor EUV beam in situ, making this a powerful beam characterization technique. In contrast to other methods, the techniques described here use no hardware to separate wavelengths, leading to efficient use of the EUV radiation.

Bright circularly polarized soft X-ray high harmonics for X-ray magnetic circular dichroism.

We demonstrate, to our knowledge, the first bright circularly polarized high-harmonic beams in the soft X-ray region of the electromagnetic spectrum, and use them to implement X-ray magnetic circular dichroism measurements in a tabletop-scale setup. Using counterrotating circularly polarized laser fields at 1.3 and 0.79 µm, we generate circularly polarized harmonics with photon energies exceeding 160 eV. The harmonic spectra emerge as a sequence of closely spaced pairs of left and right circularly polarized peaks, with energies determined by conservation of energy and spin angular momentum. We explain the single-atom and macroscopic physics by identifying the dominant electron quantum trajectories and optimal phase-matching conditions. The first advanced phase-matched propagation simulations for circularly polarized harmonics reveal the influence of the finite phase-matching temporal window on the spectrum, as well as the unique polarization-shaped attosecond pulse train. Finally, we use, to our knowledge, the first tabletop X-ray magnetic circular dichroism measurements at the N4,5 absorption edges of Gd to validate the high degree of circularity, brightness, and stability of this light source. These results demonstrate the feasibility of manipulating the polarization, spectrum, and temporal shape of high harmonics in the soft X-ray region by manipulating the driving laser waveform.