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).

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.

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.

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.

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).

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.

Pregnancy facilitates maternal liver regeneration after partial hepatectomy.

Liver resection induces robust liver regrowth or regeneration to compensate for the lost tissue mass. In a clinical setting, pregnant women may need liver resection without terminating pregnancy in some cases. However, how pregnancy affects maternal liver regeneration remains elusive. We performed 70% partial hepatectomy (PH) in nonpregnant mice and gestation day 14 mice, and histologically and molecularly compared their liver regrowth during the next 4 days. We found that compared with the nonpregnant state, pregnancy altered the molecular programs driving hepatocyte replication, indicated by enhanced activities of epidermal growth factor receptor and STAT5A, reduced activities of cMet and p70S6K, decreased production of IL-6, TNFα, and hepatocyte growth factor, suppressed cyclin D1 expression, increased cyclin A1 expression, and early activated cyclin A2 expression. As a result, pregnancy allowed the remnant hepatocytes to enter the cell cycle at least 12 h earlier, increased hepatic fat accumulation, and enhanced hepatocyte mitosis. Consequently, pregnancy ameliorated maternal liver regeneration following PH. In addition, a report showed that maternal liver regrowth after PH is driven mainly by hepatocyte hypertrophy rather than hyperplasia during the second half of gestation in young adult mice. In contrast, we demonstrate that maternal liver relies mainly on hepatocyte hyperplasia instead of hypertrophy to restore the lost mass after PH. Overall, we demonstrate that pregnancy facilitates maternal liver regeneration likely via triggering an early onset of hepatocyte replication, accumulating excessive liver fat, and promoting hepatocyte mitosis. The results from our current studies enable us to gain more insights into how maternal liver regeneration progresses during gestation.NEW & NOTEWORTHY We demonstrate that pregnancy may generate positive effects on maternal liver regeneration following partial hepatectomy, which are manifested by early entry of the cell cycle of remnant hepatocytes, increased hepatic fat accumulation, enhanced hepatocyte mitosis, and overall accelerated liver regrowth.

Blazing Signature Filter: a library for fast pairwise similarity comparisons.

BACKGROUND: Identifying similarities between datasets is a fundamental task in data mining and has become an integral part of modern scientific investigation. Whether the task is to identify co-expressed genes in large-scale expression surveys or to predict combinations of gene knockouts which would elicit a similar phenotype, the underlying computational task is often a multi-dimensional similarity test. As datasets continue to grow, improvements to the efficiency, sensitivity or specificity of such computation will have broad impacts as it allows scientists to more completely explore the wealth of scientific data. RESULTS: The Blazing Signature Filter (BSF) is a highly efficient pairwise similarity algorithm which enables extensive data mining within a reasonable amount of time. The algorithm transforms datasets into binary metrics, allowing it to utilize the computationally efficient bit operators and provide a coarse measure of similarity. We demonstrate the utility of our algorithm using two common bioinformatics tasks: identifying data sets with similar gene expression profiles, and comparing annotated genomes. CONCLUSIONS: The BSF is a highly efficient pairwise similarity algorithm that can scale to billions of comparisons without the need for specialized hardware.

Individual Variability of Protein Expression in Human Tissues.

Human tissues are known to exhibit interindividual variability, but a deeper understanding of the different factors affecting protein expression is necessary to further apply this knowledge. Our goal was to explore the proteomic variability between individuals as well as between healthy and diseased samples, and to test the efficacy of machine learning classifiers. In order to investigate whether disparate proteomics data sets may be combined, we performed a retrospective analysis of proteomics data from 9 different human tissues. These data sets represent several different sample prep methods, mass spectrometry instruments, and tissue health. Using these data, we examined interindividual and intertissue variability in peptide expression, and analyzed the methods required to build accurate tissue classifiers. We also evaluated the limits of tissue classification by downsampling the peptide data to simulate situations where less data is available, such as clinical biopsies, laser capture microdissection or potentially single-cell proteomics. Our findings reveal the strong potential for utilizing proteomics data to build robust tissue classifiers, which has many prospective clinical applications for evaluating the applicability of model clinical systems.

Microbial Community Metabolic Modeling: A Community Data-Driven Network Reconstruction.

Metabolic network modeling of microbial communities provides an in-depth understanding of community-wide metabolic and regulatory processes. Compared to single organism analyses, community metabolic network modeling is more complex because it needs to account for interspecies interactions. To date, most approaches focus on reconstruction of high-quality individual networks so that, when combined, they can predict community behaviors as a result of interspecies interactions. However, this conventional method becomes ineffective for communities whose members are not well characterized and cannot be experimentally interrogated in isolation. Here, we tested a new approach that uses community-level data as a critical input for the network reconstruction process. This method focuses on directly predicting interspecies metabolic interactions in a community, when axenic information is insufficient. We validated our method through the case study of a bacterial photoautotroph-heterotroph consortium that was used to provide data needed for a community-level metabolic network reconstruction. Resulting simulations provided experimentally validated predictions of how a photoautotrophic cyanobacterium supports the growth of an obligate heterotrophic species by providing organic carbon and nitrogen sources. J. Cell. Physiol. 231: 2339-2345, 2016. © 2016 Wiley Periodicals, Inc.

Nuclear Factor Erythroid 2-Related Factor 2 Deficiency Results in Amplification of the Liver Fat-Lowering Effect of Estrogen.

Transcription factor nuclear factor erythroid 2-related factor 2 (Nrf2) regulates multiple biologic processes, including hepatic lipid metabolism. Estrogen exerts actions affecting energy homeostasis, including a liver fat-lowering effect. Increasing evidence indicates the crosstalk between these two molecules. The aim of this study was to evaluate whether Nrf2 modulates estrogen signaling in hepatic lipid metabolism. Nonalcoholic fatty liver disease (NAFLD) was induced in wild-type and Nrf2-null mice fed a high-fat diet and the liver fat-lowering effect of exogenous estrogen was subsequently assessed. We found that exogenous estrogen eliminated 49% and 90% of hepatic triglycerides in wild-type and Nrf2-null mice with NAFLD, respectively. This observation demonstrates that Nrf2 signaling is antagonistic to estrogen signaling in hepatic fat metabolism; thus, Nrf2 absence results in striking amplification of the liver fat-lowering effect of estrogen. In addition, we found the association of trefoil factor 3 and fatty acid binding protein 5 with the liver fat-lowering effect of estrogen. In summary, we identified Nrf2 as a novel and potent inhibitor of estrogen signaling in hepatic lipid metabolism. Our finding may provide a potential strategy to treat NAFLD by dually targeting Nrf2 and estrogen signaling.

Statistical Modeling of Indirect Paths for UWB Sensors in an Indoor Environment.

In this paper, we present a statistical model of an indirect path generated in an ultra-wideband (UWB) human tracking scenario. When performing moving target detection, an indirect path signal can generate ghost targets that may cause a false alarm. For this purpose, we performed radar measurements in an indoor environment and established a statistical model of an indirect path based on the measurement data. The proposed model takes the form of a modified Saleh-Valenzuela model, which is used in a UWB channel model. An application example of the proposed model for mitigating false alarms is also presented.

A systems biology approach toward understanding seed composition in soybean.

BACKGROUND: The molecular, biochemical, and genetic mechanisms that regulate the complex metabolic network of soybean seed development determine the ultimate balance of protein, lipid, and carbohydrate stored in the mature seed. Many of the genes and metabolites that participate in seed metabolism are unknown or poorly defined; even more remains to be understood about the regulation of their metabolic networks. A global omics analysis can provide insights into the regulation of seed metabolism, even without a priori assumptions about the structure of these networks. RESULTS: With the future goal of predictive biology in mind, we have combined metabolomics, transcriptomics, and metabolic flux technologies to reveal the global developmental and metabolic networks that determine the structure and composition of the mature soybean seed. We have coupled this global approach with interactive bioinformatics and statistical analyses to gain insights into the biochemical programs that determine soybean seed composition. For this purpose, we used Plant/Eukaryotic and Microbial Metabolomics Systems Resource (PMR, http://www.metnetdb.org/pmr, a platform that incorporates metabolomics data to develop hypotheses concerning the organization and regulation of metabolic networks, and MetNet systems biology tools http://www.metnetdb.org for plant omics data, a framework to enable interactive visualization of metabolic and regulatory networks. CONCLUSIONS: This combination of high-throughput experimental data and bioinformatics analyses has revealed sets of specific genes, genetic perturbations and mechanisms, and metabolic changes that are associated with the developmental variation in soybean seed composition. Researchers can explore these metabolomics and transcriptomics data interactively at PMR.

Nrf2 is essential for timely M phase entry of replicating hepatocytes during liver regeneration.

The transcription factor nuclear factor erythroid 2-related factor 2 (Nrf2) regulates various cellular activities, including redox balance, detoxification, metabolism, autophagy, proliferation, and apoptosis. Several studies have demonstrated that Nrf2 regulates hepatocyte proliferation during liver regeneration. The aim of this study was to investigate how Nrf2 modulates the cell cycle of replicating hepatocytes in regenerating livers. Wild-type and Nrf2 null mice were subjected to 2/3 partial hepatectomy (PH) and killed at multiple time points for various analyses. Nrf2 null mice exhibited delayed liver regrowth, although the lost liver mass was eventually restored 7 days after PH. Nrf2 deficiency did not affect the number of hepatocytes entering the cell cycle but did delay hepatocyte mitosis. Mechanistically, the lack of Nrf2 resulted in increased mRNA and protein levels of hepatic cyclin A2 when the remaining hepatocytes were replicating in response to PH. Moreover, Nrf2 deficiency in regenerating livers caused dysregulation of Wee1, Cdc2, and cyclin B1 mRNA and protein expression, leading to decreased Cdc2 activity. Thus, Nrf2 is required for timely M phase entry of replicating hepatocytes by ensuring proper regulation of cyclin A2 and the Wee1/Cdc2/cyclin B1 pathway during liver regeneration.

Quasi-retroreflection from corner cubes with refractive free-form surfaces.

Quasi-retroreflection from corner-cube structures with a refractive free-form surface is studied. It is shown that adjustment of the structural parameters of the free-form surface allows control of quasi-retroreflection. Quasi-retroreflection corner-cube array sheets with specified quasi-retroreflection angle are modeled, and their quasi-retroreflection characteristics are analyzed.

Truncated corner cubes with near-perfect retroreflection efficiency.

By isolating a finite effective volume from a conventional triangular pyramid corner cube, we obtained truncated corner cube structures with greatly enhanced retroreflection efficiency. We explore an optimal truncated corner cube with near 100% retroreflection efficiency based on the expectation that the traveling paths of the optical rays can be localized in the finite effective volume of the structure, and, as a result, truncated corner cubes with perfect efficiency can be produced. As a case study, the retroreflection efficiency of a commercialized 3M truncated corner cube sample is evaluated. Furthermore, it is shown with numerical verification that a truncated corner cube array sheet with near-perfect retroreflection efficiency can be produced.

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.

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.

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.