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Alveolar macrophages (AM) perform a primary defense mechanism in the lung through phagocytosis of inhaled particles and microorganisms. AM are known to be relatively immunosuppressive consistent with the aim to limit alveolar inflammation and maintain effective gas exchange in the face of these constant challenges. How AM respond to T cell derived cytokine signals, which are critical to the defense against inhaled pathogens, is less well understood. For example, successful containment ofMycobacterium tuberculosis(Mtb) in lung macrophages is highly dependent on IFN-γ secreted by Th-1 lymphocytes, however, the proteomic IFN-γ response profile in AM remains mostly unknown. In this study, we measured IFN-γ induced protein abundance changes in human AM and autologous blood monocytes (MN). AM cells were activated by IFN-γ stimulation resulting in STAT1 phosphorylation and production of MIG/CXCL9 chemokine. However, the global proteomic response to IFN-γ in AM was dramatically limited in comparison to that of MN (9 AM vs 89 MN differentially abundant proteins). AM hypo-responsiveness was not explained by reduced JAK-STAT1 signaling nor increased SOCS1 expression. These findings suggest that AM have a tightly regulated response to IFN-γ which may prevent excessive pulmonary inflammation but may also provide a niche for the initial survival and growth of Mtb and other intracellular pathogens in the lung.

Synchrotron X-ray footprinting (XF) is a growing structural biology technique that leverages radiation-induced chemical modifications via X-ray radiolysis of water to produce hydroxyl radicals that probe changes in macromolecular structure and dynamics in solution states of interest. The X-ray Footprinting of Biological Materials (XFP) beamline at the National Synchrotron Light Source II provides the structural biology community with access to instrumentation and expert support in the XF method, and is also a platform for development of new technological capabilities in this field. The design and implementation of a new high-throughput endstation device based around use of a 96-well PCR plate form factor and supporting diagnostic instrumentation for synchrotron XF is described. This development enables a pipeline for rapid comprehensive screening of the influence of sample chemistry on hydroxyl radical dose using a convenient fluorescent assay, illustrated here with a study of 26 organic compounds. The new high-throughput endstation device and sample evaluation pipeline now available at the XFP beamline provide the worldwide structural biology community with a robust resource for carrying out well optimized synchrotron XF studies of challenging biological systems with complex sample compositions.

Protein footprinting with mass spectrometry is an established structural biology technique for mapping solvent accessibility and assessing molecular-level interactions of proteins. In hydroxyl radical protein footprinting (HRPF), hydroxyl (OH) radicals generated by water radiolysis or other methods covalently label protein side chains. Due to the wide dynamic range of OH reactivity, not all side chains are easily detected in a single experiment. Novel reagent development and the use of radical chain reactions for labeling, including trifluoromethyl radicals, is a potential approach to normalize the labeling across a diverse set of residues. HRPF in the presence of a trifluoromethylation reagent under the right conditions could provide a “one-pot” reaction for multiplex labeling of protein side chains. Towards this goal, we have systematically evaluated amino acid labeling with the recently investigated Langlois’ reagent activated by X-ray mediated water radiolysis, followed by three different mass spectrometry methods. We compared the reactivity of CF(3) and OH radical labeling for all 20 protein side chains in a competition free environment. We found all 20 amino acids exhibited CF(3) or OH labeling in Langlois’ reagent. Our investigations provide the evidence and knowledge set to perfect hydroxyl radical activated trifluoromethyl chemistry as “one-pot” reaction for multiplex labeling of protein side chains to achieve higher resolution in HRPF.

Objective: To determine the inflammatory analytes that predict clinical progression and evaluate their performance against biomarkers of neurodegeneration. Methods: A longitudinal study of MCI-AD patients in a Discovery cohort over 15 months, with replication in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) MCI cohort over 36 months. Fifty-three inflammatory analytes were measured in the CSF and plasma with a RBM multiplex analyte platform. Inflammatory analytes that predict clinical progression on Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB) and Mini Mental State Exam scores were assessed in multivariate regression models. To provide context, key analyte results in ADNI were compared against biomarkers of neurodegeneration, hippocampal volume, and CSF neurofilament light (NfL), in receiver operating characteristic (ROC) analyses evaluating highest quartile of CDR-SB change over two years (≥3 points). Results: Cerebrospinal fluid inflammatory analytes in relation to cognitive decline were best described by gene ontology terms, natural killer cell chemotaxis, and endothelial cell apoptotic process and in plasma, extracellular matrix organization, blood coagulation, and fibrin clot formation described the analytes. CSF CCL2 was most robust in predicting rate of cognitive change and analytes that correlated to CCL2 suggest IL-10 pathway dysregulation. The ROC curves for ≥3 points change in CDR-SB over 2 years when comparing baseline hippocampal volume, CSF NfL, and CCL2 were not significantly different. Interpretation: Baseline levels of immune cell chemotactic cytokine CCL2 in the CSF and IL-10 pathway dysregulation impact longitudinal cognitive and functional decline in MCI-AD. CCL2’s utility appears comparable to biomarkers of neurodegeneration in predicting rapid decline.

The redox-based modifications of cysteine residues in proteins regulate their function in many biological processes. The gas molecule H(2)S has been shown to persulfidate redox sensitive cysteine residues resulting in an H(2)S-modified proteome known as the sulfhydrome. Tandem Mass Tags (TMT) multiplexing strategies for large-scale proteomic analyses have become increasingly prevalent in detecting cysteine modifications. Here we developed a TMT-based proteomics approach for selectively trapping and tagging cysteine persulfides in the cellular proteomes. We revealed the natural protein sulfhydrome of two human cell lines, and identified insulin as a novel substrate in pancreatic beta cells. Moreover, we showed that under oxidative stress conditions, increased H(2)S can target enzymes involved in energy metabolism by switching specific cysteine modifications to persulfides. Specifically, we discovered a Redox Thiol Switch, from protein S-glutathioinylation to S-persulfidation (RTS(GS)). We propose that the RTS(GS) from S-glutathioinylation to S-persulfidation is a potential mechanism to fine tune cellular energy metabolism in response to different levels of oxidative stress.

Synchrotron radiolysis generates hydroxyl radicals (•OH) that are successful footprinting reagents. Here, we describe a new reagent for the synchrotron platform, the trifluoromethyl radical (•CF(3)). The radical is produced by •OH displacement of •CF(3) from sodium triflinate (Langlois reagent). Upon X-ray beam exposure, the reagent labels proteins extensively without any additional chemicals on the millisecond or shorter timescale. The •CF(3) is comparably reactive to •OH and produces footprinting information that complements that of •OH alone. This reagent in combination with •OH should enable novel chemistry for protein footprinting on the synchrotron platform.

MotivationProtein phosphorylation is a key post-translational modification that plays a central role in many cellular processes. With recent advances in biotechnology, thousands of phosphorylated sites can be identified and quantified in a given sample, enabling proteome-wide screening of cellular signaling. However, the kinase(s) that phosphorylate most (> 90%) of the identified phosphorylation sites are unknown. Knowledge of kinase-substrate associations is also mostly limited to a small number of well-studied kinases, with 20% of known kinases accounting for the phosphorylation of 87% of currently annotated sites. The scarcity of available annotations calls for the development of computational algorithms for more comprehensive and reliable prediction of kinase-substrate associations.ResultsTo broadly utilize available structural, functional, evolutionary, and contextual information in predicting kinase-substrate associations, we develop a network-based machine learning framework. Our framework integrates a multitude of data sources to characterize the landscape of functional relationships and associations among phosphosites and kinases. To construct a phosphosite-phosphosite association network, we use sequence similarity, shared biological pathways, co-evolution, co-occurrence, and co-phosphorylation of phosphosites across different biological states. To construct a kinase-kinase association network, we integrate protein-protein interactions, shared biological pathways, and membership in common kinase families. We use node embeddings computed from these heterogeneous networks to train machine learning models for predicting kinase-substrate associations. Our systematic computational experiments using the PhosphositePLUS database shows that the resulting algorithm, NetKSA, outperforms state-of-the-art algorithms and resources, including KinomeXplorer and LinkPhinder, in reliably predicting KSAs. By stratifying the ranking of kinases, NetKSA also enables annotation of phosphosites that are targeted by relatively less-studied kinases. Finally, we observe that the performance of NetKSA is robust to the choice of network embedding algorithms, while each type of network contributes valuable information that is complementary to the information provided by other networks.ConclusionRepresentation of available functional information on kinases and phosphorylation sites, along with integrative machine learning algorithms, has the potential to significantly enhance our knowledge on kinase-substrate associations.AvailabilityThe code and data are available atcompbio.case.edu/NetKSA.

Academic discovery in biomedicine is a growing enterprise with tens of billions of dollars in research funding available to universities and hospitals. Protecting and optimizing the resultant intellectual property is required in order for the discoveries to have an impact on society. To achieve that, institutions must create a multidisciplinary, collaborative system of review and support, and utilize connections to industry partners. In this study, we outline the efforts of Case Western Reserve University, coordinated through its Clinical and Translational Science Collaborative (CTSC), to promote entrepreneurial culture, and achieve goals of product development and startup formation for biomedical and population health discoveries arising from the academic ecosystem in Cleveland. The CTSC Office of Translation and Innovation, with the university’s Technology Transfer Office (TTO), helps identify and derisk promising IP while building interdisciplinary project teams to optimize the assets through key preclinical derisking steps. The benefits of coordinating funding across multiple programs, assuring dedicated project management to oversee optimizing the IP, and ensuring training to help improve proposals and encourage an entrepreneurial culture, are discussed in the context of a case study of therapeutic assets, the Council to Advance Human Health. This case study highlights best practices in academic innovation.

Refined bio-crude production from hydrothermal liquefaction of algae holds the potential to replace fossil-based conventional liquid fuels. The microalgae act as natural carbon sequestrators by consuming CO2. However, this absorbed CO2 is released to the atmosphere during the combustion of the bio-crude. Thus, the life-cycle greenhouse gas (GHG) emissions of refined bio-crude are linked to the production and supply of the materials involved and the process energy demands. One prominent raw material is CO2, which is the main source of carbon for algae and the subsequent products. The emissions associated with the supply of CO2 can have a considerable impact on the sustainability of the algae-based refined bio-crude production process. Furthermore, the diurnal algae growth cycle complicates the CO2 supply scenarios. Traditionally, studies have relied on CO2 supplied from existing power plants. However, there is potential for building natural gas or biomass-based power plants with the primary aim of supplying CO2 to the biorefinery. Alternately, a direct air capture (DAC) process can extract CO2 directly from the air. The life-cycle GHG emissions associated with the production of refined bio-crude through hydrothermal liquefaction of algae are presented in this study. Different CO2 supply scenarios, including existing fossil fuel power plants and purpose-built CO2 sources, are compared. The integration of the CO2 sources with the algal biorefinery is also presented. The CO2 supply from biomass-based power plants has the highest potential for GHG reduction, with a GHG footprint of −57 g CO2 eq./MJ refined bio-crude. The CO2 supply from the DAC process has a GHG footprint of 49 CO2 eq./MJ refined bio-crude, which is very similar to the scenario that considers the supply of CO2 from an existing conventional natural gas-based plant and takes credit for the carbon utilization.

Mouse models of Alzheimer's disease (AD) show progression through stages reflective of human pathology. Proteomics identification of temporal and sex-linked factors driving AD-related pathways can be used to dissect initiating and propagating events of AD stages to develop biomarkers or design interventions. In the present study, we conducted label-free proteome measurements of mouse hippocampus tissue with variables of time (3, 6, and 9 months), genetic background (5XFAD versus WT), and sex (equal males and females). These time points are associated with well-defined phenotypes with respect to the following: Aβ42 plaque deposition, memory deficits, and neuronal loss, allowing correlation of proteome-based molecular signatures with the mouse model stages. Our data show 5XFAD mice exhibit increases in known human AD biomarkers as amyloid-beta peptide, APOE, GFAP, and ITM2B are upregulated across all time points/stages. At the same time, 23 proteins are here newly associated with Alzheimer's pathology as they are also dysregulated in 5XFAD mice. At a pathways level, the 5XFAD-specific upregulated proteins are significantly enriched for DNA damage and stress-induced senescence at 3-month only, while at 6-month, the AD-specific proteome signature is altered and significantly enriched for membrane trafficking and vesicle-mediated transport protein annotations. By 9-month, AD-specific dysregulation is also characterized by significant neuroinflammation with innate immune system, platelet activation, and hyper-reactive astrocyte-related enrichments. Aside from these temporal changes, analysis of sex-linked differences in proteome signatures uncovered novel sex and AD-associated proteins. Pathway analysis revealed sex-linked differences in the 5XFAD model to be involved in the regulation of well-known human AD-related processes of amyloid fibril formation, wound healing, lysosome biogenesis, and DNA damage. Verification of the discovery results by Western blot and parallel reaction monitoring confirm the fundamental conclusions of the study and poise the 5XFAD model for further use as a molecular tool for understanding AD.

Background: An important part of biomedical research is the translation of discoveries into clinical or community applications that impact patient health. For a vast majority of clinical applications and sustainable community interventions, a time-tested way to get innovations to patients is through licensing of the technology and commercial development, often through startups. While biomedical scientists and trainees are schooled in discovery research, the processes of commercialization are foreign or intimidating. Further, many trainees will not aspire to a faculty position, and other avenues of advancement are desirable. Methods: At Case Western Reserve University, we developed and launched a Translational Fellows Program to provide such training for the community, focusing specifically on graduate students and postdoctoral fellows. The goals of this program include familiarizing our trainees with the principles of entrepreneurship, product development, and startups. This is accomplished through study of their laboratory’s technology to identify points of translational focus and to increase awareness to potentially move ideas and products toward societal impact. This program leverages much of our existing infrastructure and provides a mechanism for the prioritization of the translation of the technology as well as “release-time” to promote effort. Results: Launched in summer 2020, our first cohort had 3 of the 12 fellows launching startups based on their technology and submitting an National Institutes of Health Small Business Innovation Research (SBIR) proposal. At least 80% reported increased knowledge and confidence in five of six key translational competencies. Conclusion: We are now continuing and improving the program and searching for sustainable support to stabilize the program for a long-term productive future.

Adaptive changes in lysosomal capacity are driven by the transcription factors TFEB and TFE3 in response to increased autophagic flux and endolysosomal stress, yet the molecular details of their activation are unclear. LC3 and GABARAP members of the ATG8 protein family are required for selective autophagy and sensing perturbation within the endolysosomal system. Here, we show that during the conjugation of ATG8 to single membranes (CASM), Parkin-dependent mitophagy, and Salmonella-induced xenophagy, the membrane conjugation of GABARAP, but not LC3, is required for activation of TFEB/TFE3 to control lysosomal capacity. GABARAP directly binds to a previously unidentified LC3-interacting motif (LIR) in the FLCN/FNIP tumor suppressor complex and mediates sequestration to GABARAP-conjugated membrane compartments. This disrupts FLCN/FNIP GAP function toward RagC/D, resulting in impaired substrate-specific mTOR-dependent phosphorylation of TFEB. Thus, the GABARAP-FLCN/FNIP-TFEB axis serves as a molecular sensor that coordinates lysosomal homeostasis with perturbations and cargo flux within the autophagy-lysosomal network.

Protein phosphorylation is a ubiquitous regulatory mechanism that plays a central role in cellular signaling. According to recent estimates, up to 70% of human proteins can be phosphorylated. Therefore, characterization of phosphorylation dynamics is critical for understanding a broad range of biological and biochemical processes. Technologies based on mass spectrometry are rapidly advancing to meet the needs for high-throughput screening of phosphorylation. These technologies enable untargeted quantification of thousands of phosphorylation sites in a given sample. Many labs are already utilizing these technologies to comprehensively characterize signaling landscapes by examining perturbations with drugs and knockdown approaches, or by assessing diverse phenotypes in cancers, neuro-degerenational diseases, infectious diseases, and normal development. Here, we comprehensively investigate the concept of “co-phosphorylation”, defined as the correlated phosphorylation of a pair of phosphosites across various biological states. We integrate nine publicly available phospho-proteomics datasets for various diseases (including breast cancer, ovarian cancer and Alzhemier’s disease) and utilize functional data related to sequence, evolutionary histories, kinase annotations, and pathway annotations to investigate the functional relevance of co-phosphorylation. Our results across a broad range of studies consistently show that functionally associated sites tend to exhibit significant positive or negative co-phosphorylation. Specifically, we show that co-phosphorylation can be used to predict with high precision the sites that are on the same pathway or that are targeted by the same kinase. Overall, these results establish co-phosphorylation as a useful resource for analyzing phospho-proteins in a network context, which can help extend our knowledge on cellular signaling and its dysregulation.

Objective To determine the key inflammatory pathways that are activated in the peripheral and CNS compartments at the mild cognitive impairment (MCI) stage of Alzheimer’s disease (AD). Methods A cross‐sectional study of patients with clinical and biomarker characteristics consistent with MCI‐AD in a discovery cohort, with replication in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Inflammatory analytes were measured in the CSF and plasma with the same validated multiplex analyte platform in both cohorts and correlated with AD biomarkers (CSF Aβ42, total tau (t‐tau), phosphorylated tau (p‐tau) to identify key inflammatory pathway activations. The pathways were additionally validated by evaluating genes related to all analytes in coexpression networks of brain tissue transcriptome from an autopsy confirmed AD cohort to interrogate if the same pathway activations were conserved in the brain tissue gene modules. Results Analytes of the tumor necrosis factor (TNF) signaling pathway (KEGG ID:4668) in the CSF and plasma best correlated with CSF t‐tau and p‐tau levels, and analytes of the complement and coagulation pathway (KEGG ID:4610) best correlated with CSF Aβ42 levels. The top inflammatory signaling pathways of significance were conserved in the peripheral and the CNS compartments. They were also confirmed to be enriched in AD brain transcriptome gene clusters. Interpretation A cell‐protective rather than a proinflammatory analyte profile predominates in the CSF in relation to neurodegeneration markers among MCI‐AD patients. Analytes from the TNF signaling and the complement and coagulation pathways are relevant in evaluating disease severity at the MCI stage of AD.

The development of a high-flux beamline for X-ray footprinting at NSLS-II is presented., Hydroxyl-radical mediated synchrotron X-ray footprinting (XF) is a powerful solution-state technique in structural biology for the study of macromolecular structure and dynamics of proteins and nucleic acids, with several synchrotron resources available to serve the XF community worldwide. The XFP (Biological X-ray Footprinting) beamline at the NSLS-II was constructed on a three-pole wiggler source at 17-BM to serve as the premier beamline for performing this technique, providing an unparalleled combination of high flux density broadband beam, flexibility in beam morphology, and sample handling capabilities specifically designed for XF experiments. The details of beamline design, beam measurements, and science commissioning results for a standard protein using the two distinct XFP endstations are presented here. XFP took first light in 2016 and is now available for general user operations through peer-reviewed proposals. Currently, beam sizes from 450 µm × 120 µm to 2.7 mm × 2.7 mm (FWHM) are available, with a flux of 1.6 × 1016 photons s−1 (measured at 325 mA ring current) in a broadband (∼5–16 keV) beam. This flux is expected to rise to 2.5 × 1016 photons s−1 at the full NSLS-II design current of 500 mA, providing an incident power density of >500 W mm−2 at full focus.

There is a limited understanding of structural attributes that encode the iatrogenic transmissibility and various phenotypes of prions causing the most common human prion disease, sporadic Creutzfeldt-Jakob disease (sCJD). Here we report the detailed structural differences between major sCJD MM1, MM2, and VV2 prions determined with two complementary synchrotron hydroxyl radical footprinting techniques—mass spectrometry (MS) and conformation dependent immunoassay (CDI) with a panel of Europium-labeled antibodies. Both approaches clearly demonstrate that the phenotypically distant prions differ in a major way with regard to their structural organization, and synchrotron-generated hydroxyl radicals progressively inhibit their seeding potency in a strain and structure-specific manner. Moreover, the seeding rate of sCJD prions is primarily determined by strain-specific structural organization of solvent-exposed external domains of human prion particles that control the seeding activity. Structural characteristics of human prion strains suggest that subtle changes in the organization of surface domains play a critical role as a determinant of human prion infectivity, propagation rate, and targeting of specific brain structures., Author summary Sporadic human prion diseases are conceivably the most heterogenous neurodegenerative disorders and a growing body of research indicates that they are caused by distinct strains of prions. By parallel monitoring their replication potency and progressive hydroxyl radical modification of amino acid side chains during synchrotron irradiation, we identified major differences in the structural organization that correlate with distinct inactivation susceptibility of a given human prion strain. Furthermore, our data demonstrated, for the first time, that seeding activity of different strains of infectious brain-derived human prions is primarily function of distinct solvent-exposed structural domains, and implicate them in the initial binding of cellular isoform of prion protein (PrPC) as a critical step in human prion replication and infectivity.

Adaptive changes in lysosomal capacity are driven by the transcription factors TFEB and TFE3 in response to increased autophagic flux and endolysosomal stress, yet the molecular details of their activation are unclear. LC3 and GABARAP members of the ATG8 protein family are required for selective autophagy and sensing perturbation within the endolysosomal system. Here we show that during single membrane ATG8 conjugation (SMAC), Parkin-dependent mitophagy, and Salmonella-induced xenophagy, the membrane conjugation of GABARAP, but not LC3, is required for activation of TFEB/TFE3 to control lysosomal homeostasis and capacity. GABARAP directly binds to a novel LC3-interacting motif (LIR) in the FLCN/FNIP tumor suppressor complex with picomolar affinity and regulates its relocalization to these GABARAP-conjugated membrane compartments. This disrupts the regulation of RagC/D by the FLCN/FNIP GAP complex, resulting in impaired mTOR-dependent phosphorylation of TFEB without changing mTOR activity towards other substrates. Thus, the GABARAP-FLCN/FNIP-TFEB axis serves as a universal molecular sensor that coordinates lysosomal homeostasis with perturbations and cargo flux within the autophagy-lysosomal network.

Motivation Protein phosphorylation is a ubiquitous mechanism of post-translational modification that plays a central role in cellular signaling. Phosphorylation is particularly important in the context of cancer, as downregulation of tumor suppressors and upregulation of oncogenes by the dysregulation of associated kinase and phosphatase networks are shown to have key roles in tumor growth and progression. Despite recent advances that enable large-scale monitoring of protein phosphorylation, these data are not fully incorporated into such computational tasks as phenotyping and subtyping of cancers. Results We develop a network-based algorithm, CoPPNet, to enable unsupervised subtyping of cancers using phosphorylation data. For this purpose, we integrate prior knowledge on evolutionary, structural and functional association of phosphosites, kinase–substrate associations and protein–protein interactions with the correlation of phosphorylation of phosphosites across different tumor samples (a.k.a co-phosphorylation) to construct a context-specific-weighted network of phosphosites. We then mine these networks to identify subnetworks with correlated phosphorylation patterns. We apply the proposed framework to two mass-spectrometry-based phosphorylation datasets for breast cancer (BC), and observe that (i) the phosphorylation pattern of the identified subnetworks are highly correlated with clinically identified subtypes, and (ii) the identified subnetworks are highly reproducible across datasets that are derived from different studies. Our results show that integration of quantitative phosphorylation data with network frameworks can provide mechanistic insights into the differences between the signaling mechanisms that drive BC subtypes. Furthermore, the reproducibility of the identified subnetworks suggests that phosphorylation can provide robust classification of disease response and markers. Availability and implementation CoPPNet is available at http://compbio.case.edu/coppnet/. Supplementary information Supplementary data are available at Bioinformatics online.

Mass spectrometry enables high-throughput screening of phosphoproteins across a broad range of biological contexts. When complemented by computational algorithms, phospho-proteomic data allows the inference of kinase activity, facilitating the identification of dysregulated kinases in various diseases including cancer, Alzheimer’s disease and Parkinson’s disease. To enhance the reliability of kinase activity inference, we present a network-based framework, RoKAI, that integrates various sources of functional information to capture coordinated changes in signaling. Through computational experiments, we show that phosphorylation of sites in the functional neighborhood of a kinase are significantly predictive of its activity. The incorporation of this knowledge in RoKAI consistently enhances the accuracy of kinase activity inference methods while making them more robust to missing annotations and quantifications. This enables the identification of understudied kinases and will likely lead to the development of novel kinase inhibitors for targeted therapy of many diseases. RoKAI is available as web-based tool at http://rokai.io., Kinases drive fundamental changes in cell state, but predicting kinase activity based on substrate-level changes can be challenging. Here the authors introduce a computational framework that utilizes similarities between substrates to robustly infer kinase activity.

This study describes the response of Arthrospira platensis to a variety of temperature conditions as reflected in variations of photosynthetic parameters, pigmentation, and biomass productivity in indoor photobioreactor (PBR) cultivations. These experiments are designed to better understand the impact of temperature, seasonal variations, and acclimation effects on outdoor biomass production. The irradiance level and temperature range (20-39°C) are chosen to enable modeling of semi-continuous operation of large-scale outdoor PBR deployments. Overall, the cultivations are quite stable with some pigment-related instabilities after prolonged high-temperature exposure. Changes in productivity with temperature, as reflected in measured photosynthetic parameters, are immediate and mainly attributable to the temperature dependence of the photosaturation parameter, a secondary factor being variation in pigment content on a longer time scale corresponding to turnover of the culture population. Though pigment changes are not accompanied by significant changes in productivity, prolonged exposure at 35°C and above yields a clear degradation in performance. Productivities in a semi-continuous operation are quantitatively reproduced with a productivity model incorporating photosynthetic parameters measured herein. This study confirms the importance of temperature for biomass and pigment production in Arthrospira cultivations and provides a basis for risk assessments related to temperature mitigation for large-scale outdoor cultivations.

Human estrogen receptor alpha (hERα) is a hormone-responsive nuclear receptor (NR) involved in cell growth and survival that contains both a DNA-binding domain (DBD) and a ligand-binding domain (LBD). Functionally relevant inter-domain interactions between the DBD and LBD have been observed in several other NRs, but for hERα, the detailed structural architecture of the complex is unknown. By utilizing integrated complementary techniques of small-angle X-ray scattering, hydroxyl radical protein footprinting and computational modeling, here we report an asymmetric L-shaped “boot” structure of the multidomain hERα and identify the specific sites on each domain at the domain interface involved in DBD–LBD interactions. We demonstrate the functional role of the proposed DBD–LBD domain interface through site-specific mutagenesis altering the hERα interfacial structure and allosteric signaling. The L-shaped structure of hERα is a distinctive DBD–LBD organization of NR complexes and more importantly, reveals a signaling mechanism mediated by inter-domain crosstalk that regulates this receptor’s allosteric function., The human estrogen receptor alpha (hERα) is a hormone-responsive transcription factor. Here the authors combine small-angle X-ray scattering, hydroxyl radical protein footprinting and computational modeling and show that multidomain hERα adopts an L-shaped boot-like architecture revealing a cross-talk between its DNA-binding domain and Ligand-binding domain.

Hydroxyl radical footprinting (HRF) of proteins with mass spectrometry (MS) is a widespread approach for assessing protein structure. Hydroxyl radicals react with a wide variety of protein side chains, and the ease with which radicals can be generated (by radiolysis or photolysis) has made the approach popular with many laboratories. As some side chains are less reactive and thus cannot be probed, additional specific and nonspecific labeling reagents have been introduced to extend the approach. At the same time, advances in liquid chromatography and MS approaches permit an examination of the labeling of individual residues, transforming the approach to high resolution. Lastly, advances in understanding of the chemistry of the approach have led to the determination of absolute protein topologies from HRF data. Overall, the technology can provide precise and accurate measures of side-chain solvent accessibility in a wide range of interesting and useful contexts for the study of protein structure and dynamics in both academia and industry. Expected final online publication date for the Annual Review of Biophysics Volume 47 is May 20, 2018. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.