timsTOF HT Improves Protein Identification and Quantitative Reproducibility for Deep Unbiased Plasma Protein Biomarker Discovery.

Mass spectrometry (MS) is a valuable tool for plasma proteome profiling and disease biomarker discovery. However, wide-ranging plasma protein concentrations, along with technical and biological variabilities, present significant challenges for deep and reproducible protein quantitation. Here, we evaluated the qualitative and quantitative performance of timsTOF HT and timsTOF Pro 2 mass spectrometers for analysis of neat plasma samples (unfractionated) and plasma samples processed using the Proteograph Product Suite (Proteograph) that enables robust deep proteomics sampling prior to mass spectrometry. Samples were evaluated across a wide range of peptide loading masses and liquid chromatography (LC) gradients. We observed up to a 76% increase in total plasma peptide precursors identified and a >2-fold boost in quantifiable plasma peptide precursors (CV < 20%) with timsTOF HT compared to Pro 2. Additionally, approximately 4.5 fold more plasma peptide precursors were detected by both timsTOF HT and timsTOF Pro 2 in the Proteograph analyzed plasma vs neat plasma. In an exploratory analysis of 20 late-stage lung cancer and 20 control plasma samples with the Proteograph, which were expected to exhibit distinct proteomes, an approximate 50% increase in total and statistically significant plasma peptide precursors (q < 0.05) was observed with timsTOF HT compared to Pro 2. Our data demonstrate the superior performance of timsTOF HT for identifying and quantifying differences between biologically diverse samples, allowing for improved disease biomarker discovery in large cohort studies. Moreover, researchers can leverage data sets from this study to optimize their liquid chromatography-mass spectrometry (LC-MS) workflows for plasma protein profiling and biomarker discovery. (ProteomeXchange identifier: PXD047854 and PXD047839).

Metabolomic, photoprotective, and photosynthetic acclimatory responses to post-flowering drought in sorghum.

Climate change is globally affecting rainfall patterns, necessitating the improvement of drought tolerance in crops. Sorghum bicolor is a relatively drought-tolerant cereal. Functional stay-green sorghum genotypes can maintain green leaf area and efficient grain filling during terminal post-flowering water deprivation, a period of ~10 weeks. To obtain molecular insights into these characteristics, two drought-tolerant genotypes, BTx642 and RTx430, were grown in replicated control and terminal post-flowering drought field plots in California's Central Valley. Photosynthetic, photoprotective, and water dynamics traits were quantified and correlated with metabolomic data collected from leaves, stems, and roots at multiple timepoints during control and drought conditions. Physiological and metabolomic data were then compared to longitudinal RNA sequencing data collected from these two genotypes. The unique metabolic and transcriptomic response to post-flowering drought in sorghum supports a role for the metabolite galactinol in controlling photosynthetic activity through regulating stomatal closure in post-flowering drought. Additionally, in the functional stay-green genotype BTx642, photoprotective responses were specifically induced in post-flowering drought, supporting a role for photoprotection in the molecular response associated with the functional stay-green trait. From these insights, new pathways are identified that can be targeted to maximize yields under growth conditions with limited water.

mzapy: An Open-Source Python Library Enabling Efficient Extraction and Processing of Ion Mobility Spectrometry-Mass Spectrometry Data in the MZA File Format.

Analysis of ion mobility spectrometry (IMS) data has been challenging and limited the full utility of these measurements. Unlike liquid chromatography-mass spectrometry, where a plethora of tools with well-established algorithms exist, the incorporation of the additional IMS dimension requires upgrading existing computational pipelines and developing new algorithms to fully exploit the advantages of the technology. We have recently reported MZA, a new and simple mass spectrometry data structure based on the broadly supported HDF5 format and created to facilitate software development. While this format is inherently supportive of application development, the availability of core libraries in popular programming languages with standard mass spectrometry utilities will facilitate fast software development and broader adoption of the format. To this end, we present a Python package, mzapy, for efficient extraction and processing of mass spectrometry data in the MZA format, especially for complex data containing ion mobility spectrometry dimension. In addition to raw data extraction, mzapy contains supporting utilities enabling tasks including calibration, signal processing, peak finding, and generating plots. Being implemented in pure Python and having minimal and largely standardized dependencies makes mzapy uniquely suited to application development in the multiomics domain. The mzapy package is free and open-source, includes comprehensive documentation, and is structured to support future extension to meet the evolving needs of the MS community. The software source code is freely available at https://github.com/PNNL-m-q/mzapy.

LipidOz enables automated elucidation of lipid carbon-carbon double bond positions from ozone-induced dissociation mass spectrometry data.

Lipids play essential roles in many biological processes and disease pathology, but unambiguous identification of lipids is complicated by the presence of multiple isomeric species differing by fatty acyl chain length, stereospecifically numbered (sn) position, and position/stereochemistry of double bonds. Conventional liquid chromatography-mass spectrometry (LC-MS/MS) analyses enable the determination of fatty acyl chain lengths (and in some cases sn position) and number of double bonds, but not carbon-carbon double bond positions. Ozone-induced dissociation (OzID) is a gas-phase oxidation reaction that produces characteristic fragments from lipids containing double bonds. OzID can be incorporated into ion mobility spectrometry (IMS)-MS instruments for the structural characterization of lipids, including additional isomer separation and confident assignment of double bond positions. The complexity and repetitive nature of OzID data analysis and lack of software tool support have limited the application of OzID for routine lipidomics studies. Here, we present an open-source Python tool, LipidOz, for the automated determination of lipid double bond positions from OzID-IMS-MS data, which employs a combination of traditional automation and deep learning approaches. Our results demonstrate the ability of LipidOz to robustly assign double bond positions for lipid standard mixtures and complex lipid extracts, enabling practical application of OzID for future lipidomics.

MZA: A Data Conversion Tool to Facilitate Software Development and Artificial Intelligence Research in Multidimensional Mass Spectrometry.

Modern mass spectrometry-based workflows employing hybrid instrumentation and orthogonal separations collect multidimensional data, potentially allowing deeper understanding in omics studies through adoption of artificial intelligence methods. However, the large volume of these rich spectra challenges existing data storage and access technologies, therefore precluding informatics advancements. We present MZA (pronounced m-za), the mass-to-charge (m/z) generic data storage and access tool designed to facilitate software development and artificial intelligence research in multidimensional mass spectrometry measurements. Composed of a data conversion tool and a simple file structure based on the HDF5 format, MZA provides easy, cross-platform and cross-programming language access to raw MS-data, enabling fast development of new tools in data science programming languages such as Python and R. The software executable, example MS-data and example Python and R scripts are freely available at https://github.com/PNNL-m-q/mza.

Maternal hepatocytes heterogeneously and dynamically exhibit developmental phenotypes partially via yes-associated protein 1 during pregnancy.

Pregnancy induces reprogramming of maternal physiology to support fetal development and growth. Maternal hepatocytes undergo hypertrophy and hyperplasia to drive maternal liver growth and alter their gene expression profiles simultaneously. This study aimed to further understand maternal hepatocyte adaptation to pregnancy. Timed pregnancies were generated in mice. In a nonpregnant state, most hepatocytes expressed Cd133, α-fetal protein (Afp) and epithelial cell adhesion molecule (Epcam) mRNAs, whereas overall, at the protein level, they exhibited a CD133-/AFP- phenotype; however, pericentral hepatocytes were EpCAM+. As pregnancy advanced, although most maternal hepatocytes retained Cd133, Afp, and Epcam mRNA expression, they generally displayed a phenotype of CD133+/AFP+, and EpCAM protein expression was switched from pericentral to periportal maternal hepatocytes. In addition, we found that the Hippo/yes-associated protein (YAP) pathway does not respond to pregnancy. Yap1 gene deletion specifically in maternal hepatocytes did not affect maternal liver growth or metabolic zonation. However, the absence of Yap1 gene eliminated CD133 protein expression without interfering with Cd133 transcript expression in maternal livers. We demonstrated that maternal hepatocytes acquire heterogeneous and dynamic developmental phenotypes, resembling fetal hepatocytes, partially via YAP1 through a posttranscriptional mechanism. Moreover, maternal liver is a new source of AFP. In addition, maternal liver grows and maintains its metabolic zonation independent of the Hippo/YAP1 pathway. Our findings revealed a novel and gestation-dependent phenotypic plasticity in adult hepatocytes.NEW & NOTEWORTHY We found that maternal hepatocytes exhibit developmental phenotypes in a temporal and spatial manner, similarly to fetal hepatocytes. They acquire this new property partially via yes-associated protein 1.

Interaction Networks Are Driven by Community-Responsive Phenotypes in a Chitin-Degrading Consortium of Soil Microbes.

Soil microorganisms provide key ecological functions that often rely on metabolic interactions between individual populations of the soil microbiome. To better understand these interactions and community processes, we used chitin, a major carbon and nitrogen source in soil, as a test substrate to investigate microbial interactions during its decomposition. Chitin was applied to a model soil consortium that we developed, "model soil consortium-2" (MSC-2), consisting of eight members of diverse phyla and including both chitin degraders and nondegraders. A multiomics approach revealed how MSC-2 community-level processes during chitin decomposition differ from monocultures of the constituent species. Emergent properties of both species and the community were found, including changes in the chitin degradation potential of Streptomyces species and organization of all species into distinct roles in the chitin degradation process. The members of MSC-2 were further evaluated via metatranscriptomics and community metabolomics. Intriguingly, the most abundant members of MSC-2 were not those that were able to metabolize chitin itself, but rather those that were able to take full advantage of interspecies interactions to grow on chitin decomposition products. Using a model soil consortium greatly increased our knowledge of how carbon is decomposed and metabolized in a community setting, showing that niche size, rather than species metabolic capacity, can drive success and that certain species become active carbon degraders only in the context of their surrounding community. These conclusions fill important knowledge gaps that are key to our understanding of community interactions that support carbon and nitrogen cycling in soil. IMPORTANCE The soil microbiome performs many functions that are key to ecology, agriculture, and nutrient cycling. However, because of the complexity of this ecosystem we do not know the molecular details of the interactions between microbial species that lead to these important functions. Here, we use a representative but simplified model community of bacteria to understand the details of these interactions. We show that certain species act as primary degraders of carbon sources and that the most successful species are likely those that can take the most advantage of breakdown products, not necessarily the primary degraders. We also show that a species phenotype, including whether it is a primary degrader or not, is driven in large part by the membership of the community it resides in. These conclusions are critical to a better understanding of the soil microbial interaction network and how these interactions drive central soil microbiome functions.

Uncovering Hidden Members and Functions of the Soil Microbiome Using De Novo Metaproteomics.

Metaproteomics has been increasingly utilized for high-throughput characterization of proteins in complex environments and has been demonstrated to provide insights into microbial composition and functional roles. However, significant challenges remain in metaproteomic data analysis, including creation of a sample-specific protein sequence database. A well-matched database is a requirement for successful metaproteomics analysis, and the accuracy and sensitivity of PSM identification algorithms suffer when the database is incomplete or contains extraneous sequences. When matched DNA sequencing data of the sample is unavailable or incomplete, creating the proteome database that accurately represents the organisms in the sample is a challenge. Here, we leverage a de novo peptide sequencing approach to identify the sample composition directly from metaproteomic data. First, we created a deep learning model, Kaiko, to predict the peptide sequences from mass spectrometry data and trained it on 5 million peptide-spectrum matches from 55 phylogenetically diverse bacteria. After training, Kaiko successfully identified organisms from soil isolates and synthetic communities directly from proteomics data. Finally, we created a pipeline for metaproteome database generation using Kaiko. We tested the pipeline on native soils collected in Kansas, showing that the de novo sequencing model can be employed as an alternative and complementary method to construct the sample-specific protein database instead of relying on (un)matched metagenomes. Our pipeline identified all highly abundant taxa from 16S rRNA sequencing of the soil samples and uncovered several additional species which were strongly represented only in proteomic data.

Activin B promotes the initiation and progression of liver fibrosis.

The role of activin B, a transforming growth factor ß (TGFß) superfamily cytokine, in liver health and disease is largely unknown. We aimed to investigate whether activin B modulates liver fibrogenesis. Liver and serum activin B, along with its analog activin A, were analyzed in patients with liver fibrosis from different etiologies and in mouse acute and chronic liver injury models. Activin B, activin A, or both was immunologically neutralized in mice with progressive or established carbon tetrachloride (CCl4 )-induced liver fibrosis. Hepatic and circulating activin B was increased in human patients with liver fibrosis caused by several liver diseases. In mice, hepatic and circulating activin B exhibited persistent elevation following the onset of several types of liver injury, whereas activin A displayed transient increases. The results revealed a close correlation of activin B with liver injury regardless of etiology and species. Injured hepatocytes produced excessive activin B. Neutralizing activin B largely prevented, as well as improved, CCl4 -induced liver fibrosis, which was augmented by co-neutralizing activin A. Mechanistically, activin B mediated the activation of c-Jun-N-terminal kinase (JNK), the induction of inducible nitric oxide synthase (iNOS) expression, and the maintenance of poly (ADP-ribose) polymerase 1 (PARP1) expression in injured livers. Moreover, activin B directly induced a profibrotic expression profile in hepatic stellate cells (HSCs) and stimulated these cells to form a septa structure. Conclusions: We demonstrate that activin B, cooperating with activin A, mediates the activation or expression of JNK, iNOS, and PARP1 and the activation of HSCs, driving the initiation and progression of liver fibrosis.

SimELIT: A Novel GUI-Based Comprehensive Ion Trajectory Simulation Software for Mass Spectrometry.

Ion trajectory simulation in mass spectrometry systems from injection to detection is technically challenging but very important for better understanding the ion dynamics in instrument development. Here, we present SimELIT (Simulator of Eulerian and Lagrangian Ion Trajectories), a novel ion trajectory simulation platform. SimELIT is built upon a suite of multiphysics solvers compiled into OpenFOAM (an open-source numerical solver library particularly used for computational mechanics), with a simple web-based graphical user interface (GUI) allowing users to define the details of OpenFOAM cases and run simulations. SimELIT is a modular program and can provide extensions of physics (e.g., gas flows, electrodynamic fields) and thus enable ion trajectory simulations from the ion source to detector. The current version (SimELIT) provides two numerical solvers for ion trajectory simulations─(1) a Lagrangian particle tracker in vacuum and (2) a Eulerian ion density solver in background gas in the presence of electric fields. Here, we describe the architecture of SimELIT, including its use of Docker and the React Framework, and demonstrate the computation of ion trajectories of multiple m/z values in a static/linear voltage drop in vacuum (across a 1 m long flight tube). Further, the drift motion of ions under 1 Torr pressure conditions in a static background (N2) gas through a 20 V/cm static electric field is shown. The results produced from SimELIT were compared with SIMION and theoretical estimates. In addition, we report the computation of ion trajectories in electrodynamic fields within a planar FAIMS device operating at atmospheric pressure.

Nrf2 deficiency causes hepatocyte dedifferentiation and reduced albumin production in an experimental extrahepatic cholestasis model.

The transcription factor Nrf2 modulates the initiation and progression of a number of diseases including liver disorders. We evaluated whether Nrf2 mediates hepatic adaptive responses to cholestasis. Wild-type and Nrf2-null mice were subjected to bile duct ligation (BDL) or a sham operation. As cholestasis progressed to day 15 post-BDL, hepatocytes in the wild-type mice exhibited a tendency to dedifferentiate, indicated by the very weak expression of hepatic progenitor markers: CD133 and tumor necrosis factor-like weak induced apoptosis receptor (Fn14). During the same period, Nrf2 deficiency augmented this tendency, manifested by higher CD133 expression, earlier, stronger, and continuous induction of Fn14 expression, and markedly reduced albumin production. Remarkably, as cholestasis advanced to the late stage (40 days after BDL), hepatocytes in the wild-type mice exhibited a Fn14+ phenotype and strikingly upregulated the expression of deleted in malignant brain tumor 1 (DMBT1), a protein essential for epithelial differentiation during development. In contrast, at this stage, hepatocytes in the Nrf2-null mice entirely inhibited the upregulation of DMBT1 expression, displayed a strong CD133+/Fn14+ phenotype indicative of severe dedifferentiation, and persistently reduced albumin production. We revealed that Nrf2 maintains hepatocytes in the differentiated state potentially via the increased activity of the Nrf2/DMBT1 pathway during cholestasis.

A global lipid map reveals host dependency factors conserved across SARS-CoV-2 variants.

A comprehensive understanding of host dependency factors for SARS-CoV-2 remains elusive. Here, we map alterations in host lipids following SARS-CoV-2 infection using nontargeted lipidomics. We find that SARS-CoV-2 rewires host lipid metabolism, significantly altering hundreds of lipid species to effectively establish infection. We correlate these changes with viral protein activity by transfecting human cells with each viral protein and performing lipidomics. We find that lipid droplet plasticity is a key feature of infection and that viral propagation can be blocked by small-molecule glycerolipid biosynthesis inhibitors. We find that this inhibition was effective against the main variants of concern (alpha, beta, gamma, and delta), indicating that glycerolipid biosynthesis is a conserved host dependency factor that supports this evolving virus.

A global lipid map reveals host dependency factors conserved across SARS-CoV-2 variants.

A comprehensive understanding of host dependency factors for SARS-CoV-2 remains elusive. We mapped alterations in host lipids following SARS-CoV-2 infection using nontargeted lipidomics. We found that SARS-CoV-2 rewires host lipid metabolism, altering 409 lipid species up to 64-fold relative to controls. We correlated these changes with viral protein activity by transfecting human cells with each viral protein and performing lipidomics. We found that lipid droplet plasticity is a key feature of infection and that viral propagation can be blocked by small-molecule glycerolipid biosynthesis inhibitors. We found that this inhibition was effective against the main variants of concern (alpha, beta, gamma, and delta), indicating that glycerolipid biosynthesis is a conserved host dependency factor that supports this evolving virus.

Vaccination before or after SARS-CoV-2 infection leads to robust humoral response and antibodies that effectively neutralize variants.

Current coronavirus disease 2019 (COVID-19) vaccines effectively reduce overall morbidity and mortality and are vitally important to controlling the pandemic. Individuals who previously recovered from COVID-19 have enhanced immune responses after vaccination (hybrid immunity) compared with their naïve-vaccinated peers; however, the effects of post-vaccination breakthrough infections on humoral immune response remain to be determined. Here, we measure neutralizing antibody responses from 104 vaccinated individuals, including those with breakthrough infections, hybrid immunity, and no infection history. We find that human immune sera after breakthrough infection and vaccination after natural infection broadly neutralize SARS-CoV-2 (severe acute respiratory coronavirus 2) variants to a similar degree. Although age negatively correlates with antibody response after vaccination alone, no correlation with age was found in breakthrough or hybrid immune groups. Together, our data suggest that the additional antigen exposure from natural infection substantially boosts the quantity, quality, and breadth of humoral immune response regardless of whether it occurs before or after vaccination.

Activation of Proneuronal Transcription Factor Ascl1 in Maternal Liver Ensures a Healthy Pregnancy.

BACKGROUND & AIMS: Maternal liver shows robust adaptations to pregnancy to accommodate the metabolic needs of the developing and growing placenta and fetus by largely unknown mechanisms. We found that Ascl1, a gene encoding a basic helix-loop-helix transcription factor essential for neuronal development, is highly activated in maternal hepatocytes during the second half of gestation in mice. METHODS: To investigate whether and how Ascl1 plays a pregnancy-dependent role, we deleted the Ascl1 gene specifically in maternal hepatocytes from midgestation until term. RESULTS: As a result, we identified multiple Ascl1-dependent phenotypes. Maternal livers lacking Ascl1 showed aberrant hepatocyte structure, increased hepatocyte proliferation, enlarged hepatocyte size, reduced albumin production, and increased release of liver enzymes, indicating maternal liver dysfunction. Simultaneously, maternal pancreas and spleen and the placenta showed marked overgrowth; and the maternal ceca microbiome showed alterations in relative abundance of several bacterial subpopulations. Moreover, litters born from maternal hepatic Ascl1-deficient dams experienced abnormal postnatal growth after weaning, implying an adverse pregnancy outcome. Mechanistically, we found that maternal hepatocytes deficient for Ascl1 showed robust activation of insulin-like growth factor 2 expression, which may contribute to the Ascl1-dependent phenotypes widespread in maternal and uteroplacental compartments. CONCLUSIONS: In summary, we show that maternal liver, via activating Ascl1 expression, modulates the adaptations of maternal organs and the growth of the placenta to maintain a healthy pregnancy. Our studies show that Ascl1 is a novel and critical regulator of the physiology of pregnancy.

Improving Signal to Noise Ratios in Ion Mobility Spectrometry and Structures for Lossless Ion Manipulations (SLIM) using a High Dynamic Range Analog-to-Digital Converter.

Signal digitization is a commonly overlooked part of ion mobility-mass spectrometry (IMS-MS) workflows, yet it greatly affects signal-to-noise ratio and MS resolution measurements. Here, we report on the integration of a 2 GS/s, 14-bit ADC with structures for lossless ion manipulations (SLIM-IMS-MS) and compare the performance to a commonly used 8-bit ADC. The 14-bit ADC provided a reduction in the digitized noise by a factor of ∼6, owing largely to the use of smaller bit sizes. The low baseline allowed threshold voltage levels to be set very close to the MCP baseline voltage, allowing for as much signal to be acquired as possible without overloading or excessive digitization of MCP baseline noise. Analyses of Agilent tuning mixture ions and a mixture of heavy labeled phosphopeptides showed that the 14-bit ADC provided a ∼1.5-2× signal-to-noise (S/N) increase for high intensity ions, such as the Agilent tuning mixture ions and the 2+ and 3+ charge states of many phosphopeptide constituents. However, signal enhancements were as much as 10-fold for low intensity ions, and the 14-bit ADC enabled discernible signal intensities otherwise lost using an 8-bit digitizer. Additionally, the 14-bit ADC required ∼14-fold fewer mass spectra to be averaged to produce a mass spectrum with a similar S/N as the 8-bit ADC, demonstrating ∼10× higher measurement throughput. The high resolution, low baseline, and fast speed of the new 14-bit ADC enables high performance digitization of MS, IMS-MS, and SLIM-IMS-MS spectra and provides a much better picture of analyte profiles in complex mixtures.

Circadian clock core component Bmal1 dictates cell cycle rhythm of proliferating hepatocytes during liver regeneration.

After partial hepatectomy (PH), the majority of remnant hepatocytes synchronously enter and rhythmically progress through the cell cycle for three major rounds to regain lost liver mass. Whether and how the circadian clock core component Bmal1 modulates this process remains elusive. We performed PH on Bmal1+/+ and hepatocyte-specific Bmal1 knockout (Bmal1hep-/-) mice and compared the initiation and progression of the hepatocyte cell cycle. After PH, Bmal1+/+ hepatocytes exhibited three major waves of nuclear DNA synthesis. In contrast, in Bmal1hep-/- hepatocytes, the first wave of nuclear DNA synthesis was delayed by 12 h, and the third such wave was lost. Following PH, Bmal1+/+ hepatocytes underwent three major waves of mitosis, whereas Bmal1hep-/- hepatocytes fully abolished mitotic oscillation. These Bmal1-dependent disruptions in the rhythmicity of hepatocyte cell cycle after PH were accompanied by suppressed expression peaks of a group of cell cycle components and regulators and dysregulated activation patterns of mitogenic signaling molecules c-Met and epidermal growth factor receptor. Moreover, Bmal1+/+ hepatocytes rhythmically accumulated fat as they expanded following PH, whereas this phenomenon was largely inhibited in Bmal1hep-/- hepatocytes. In addition, during late stages of liver regrowth, Bmal1 absence in hepatocytes caused the activation of redox sensor Nrf2, suggesting an oxidative stress state in regenerated liver tissue. Collectively, we demonstrated that during liver regeneration, Bmal1 partially modulates the oscillation of S-phase progression, fully controls the rhythmicity of M-phase advancement, and largely governs fluctuations in fat metabolism in replicating hepatocytes, as well as eventually determines the redox state of regenerated livers.NEW & NOTEWORTHY We demonstrated that Bmal1 centrally controls the synchronicity and rhythmicity of the cell cycle and lipid accumulation in replicating hepatocytes during liver regeneration. Bmal1 plays these roles, at least in part, by ensuring formation of the expression peaks of cell cycle components and regulators, as well as the timing and levels of activation of mitogenic signaling molecules.

AutoCCS: automated collision cross-section calculation software for ion mobility spectrometry-mass spectrometry.

MOTIVATION: Ion mobility spectrometry (IMS) separations are increasingly used in conjunction with mass spectrometry (MS) for separation and characterization of ionized molecular species. Information obtained from IMS measurements includes the ion's collision cross section (CCS), which reflects its size and structure and constitutes a descriptor for distinguishing similar species in mixtures that cannot be separated using conventional approaches. Incorporating CCS into MS-based workflows can improve the specificity and confidence of molecular identification. At present, there is no automated, open-source pipeline for determining CCS of analyte ions in both targeted and untargeted fashion, and intensive user-assisted processing with vendor software and manual evaluation is often required. RESULTS: We present AutoCCS, an open-source software to rapidly determine CCS values from IMS-MS measurements. We conducted various IMS experiments in different formats to demonstrate the flexibility of AutoCCS for automated CCS calculation: (i) stepped-field methods for drift tube-based IMS (DTIMS), (ii) single-field methods for DTIMS (supporting two calibration methods: a standard and a new enhanced method) and (iii) linear calibration for Bruker timsTOF and non-linear calibration methods for traveling wave based-IMS in Waters Synapt and Structures for Lossless Ion Manipulations. We demonstrated that AutoCCS offers an accurate and reproducible determination of CCS for both standard and unknown analyte ions in various IMS-MS platforms, IMS-field methods, ionization modes and collision gases, without requiring manual processing. AVAILABILITY AND IMPLEMENTATION: https://github.com/PNNL-Comp-Mass-Spec/AutoCCS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Demo datasets are publicly available at MassIVE (Dataset ID: MSV000085979).

Representing Organic Matter Thermodynamics in Biogeochemical Reactions via Substrate-Explicit Modeling.

Predictive biogeochemical modeling requires data-model integration that enables explicit representation of the sophisticated roles of microbial processes that transform substrates. Data from high-resolution organic matter (OM) characterization are increasingly available and can serve as a critical resource for this purpose, but their incorporation into biogeochemical models is often prohibited due to an over-simplified description of reaction networks. To fill this gap, we proposed a new concept of biogeochemical modeling-termed substrate-explicit modeling-that enables parameterizing OM-specific oxidative degradation pathways and reaction rates based on the thermodynamic properties of OM pools. Based on previous developments in the literature, we characterized the resulting kinetic models by only two parameters regardless of the complexity of OM profiles, which can greatly facilitate the integration with reactive transport models for ecosystem simulations by alleviating the difficulty in parameter identification. The two parameters include maximal growth rate (µmax) and harvest volume (Vh) (i.e., the volume that a microbe can access for harvesting energy). For every detected organic molecule in a given sample, our approach provides a systematic way to formulate reaction kinetics from chemical formula, which enables the evaluation of the impact of OM character on biogeochemical processes across conditions. In a case study of two sites with distinct OM thermodynamics using ultra high-resolution metabolomics datasets derived from Fourier transform ion cyclotron resonance mass spectrometry analyses, our method predicted how oxidative degradation is primarily driven by thermodynamic efficiency of OM consistent with experimental rate measurements (as shown by correlation coefficients of up to 0.61), and how biogeochemical reactions can vary in response to carbon and/or oxygen limitations. Lastly, we showed that incorporation of enzymatic regulations into substrate-explicit models is critical for more reasonable predictions. This result led us to present integrative biogeochemical modeling as a unifying framework that can ideally describe the dynamic interplay among microbes, enzymes, and substrates to address advanced questions and hypotheses in future studies. Altogether, the new modeling concept we propose in this work provides a foundational platform for unprecedented predictions of biogeochemical and ecosystem dynamics through enhanced integration with diverse experimental data and extant modeling approaches.

A global lipid map defines a network essential for Zika virus replication.

Zika virus (ZIKV), an arbovirus of global concern, remodels intracellular membranes to form replication sites. How ZIKV dysregulates lipid networks to allow this, and consequences for disease, is poorly understood. Here, we perform comprehensive lipidomics to create a lipid network map during ZIKV infection. We find that ZIKV significantly alters host lipid composition, with the most striking changes seen within subclasses of sphingolipids. Ectopic expression of ZIKV NS4B protein results in similar changes, demonstrating a role for NS4B in modulating sphingolipid pathways. Disruption of sphingolipid biosynthesis in various cell types, including human neural progenitor cells, blocks ZIKV infection. Additionally, the sphingolipid ceramide redistributes to ZIKV replication sites, and increasing ceramide levels by multiple pathways sensitizes cells to ZIKV infection. Thus, we identify a sphingolipid metabolic network with a critical role in ZIKV replication and show that ceramide flux is a key mediator of ZIKV infection.