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The central component AcrB of the Escherichia coli drug efflux complex AcrA–AcrB–TolC has been extensively investigated by X‐ray crystallography of detergent–protein 3‐D crystals. In these crystals, AcrB packs as trimers – the functional unit. We visualized the AcrB–AcrB interaction in its native environment by examining E. coli lipid reconstituted 2‐D crystals, which were overwhelmingly formed by asymmetric trimers stabilized by strongly‐interacting monomers from adjacent trimers. Most interestingly, we observed lattices formed by an arrangement of AcrB monomers distinct from that in traditional trimers. This hitherto unobserved packing, might play a role in the biogenesis of trimeric AcrB.E. coli lipid reconstituted AcrB 2D crystals show predominantly asymmetric trimers. Inter monomer–monomer interactions stabilize adjacent AcrB trimers in 2D crystal. Crystals of AcrB containing non‐trimeric arrangement of monomers were also observed. [ABSTRACT FROM AUTHOR]

Serum total immunoglobulin E levels (total IgE) capture the state of the immune system in relation to allergic sensitization. High levels are associated with airway obstruction and poor clinical outcomes in pediatric asthma. Inconsistent patient response to anti-IgE therapies motivates discovery of molecular mechanisms underlying serum IgE level differences in children with asthma. To uncover these mechanisms using complementary metabolomic and transcriptomic data, abundance levels of 529 named metabolites and expression levels of 22,772 genes were measured among children with asthma in the Childhood Asthma Management Program (CAMP, N=564) and the Genetic Epidemiology of Asthma in Costa Rica Study (GACRS, N=309) via the TOPMed initiative. Gene-metabolite associations dependent on IgE were identified within each cohort using multivariate linear models and were interpreted in a biochemical context using network topology, pathway and chemical enrichment, and representation within reactions. A total of 1,617 total IgE-dependent gene-metabolite associations from GACRS and 29,885 from CAMP met significance cutoffs. Of these, glycine and guanidinoacetic acid (GAA) were associated with the most genes in both cohorts, and the associations represented reactions central to glycine, serine, and threonine metabolism and arginine and proline metabolism. Pathway and chemical enrichment analysis further highlighted additional related pathways of interest. The results of this study suggest that GAA may modulate total IgE levels in two independent pediatric asthma cohorts with different characteristics, supporting the use of L-Arginine as a potential therapeutic for asthma exacerbation. Other potentially new targetable pathways are also uncovered., Competing Interests: Conflict of Interest Statement STW receives royalties from UpToDate Inc. and is on the Board of Histolix Inc. JLS is a scientific advisor to TruDiagnostic Inc, Precion Inc, and Ahara Corp and is on the Metabolomics Society Board. RSK received payment from AAAAI for an invited lecture pertaining to the subject matter of the manuscript.

Motivation: IntLIM uncovers phenotype-dependent linear associations between two types of analytes (e.g. genes and metabolites) in a multi-omic dataset, which may reflect chemically or biologically relevant relationships., Results: The new IntLIM R package includes newly added support for generalized data types, covariate correction, continuous phenotypic measurements, model validation and unit testing. IntLIM analysis uncovered biologically relevant gene-metabolite associations in two separate datasets, and the run time is improved over baseline R functions by multiple orders of magnitude., Availability and Implementation: IntLIM is available as an R package with a detailed vignette (https://github.com/ncats/IntLIM) and as an R Shiny app (see Supplementary Figs S1-S6) (https://intlim.ncats.io/)., Supplementary Information: Supplementary data are available at Bioinformatics Advances online., (Published by Oxford University Press 2023.)

Motivation: Functional interpretation of high-throughput metabolomic and transcriptomic results is a crucial step in generating insight from experimental data. However, pathway and functional information for genes and metabolites are distributed among many siloed resources, limiting the scope of analyses that rely on a single knowledge source., Results: RaMP-DB 2.0 is a web interface, relational database, API and R package designed for straightforward and comprehensive functional interpretation of metabolomic and multi-omic data. RaMP-DB 2.0 has been upgraded with an expanded breadth and depth of functional and chemical annotations (ClassyFire, LIPID MAPS, SMILES, InChIs, etc.), with new data types related to metabolites and lipids incorporated. To streamline entity resolution across multiple source databases, we have implemented a new semi-automated process, thereby lessening the burden of harmonization and supporting more frequent updates. The associated RaMP-DB 2.0 R package now supports queries on pathways, common reactions (e.g. metabolite-enzyme relationship), chemical functional ontologies, chemical classes and chemical structures, as well as enrichment analyses on pathways (multi-omic) and chemical classes. Lastly, the RaMP-DB web interface has been completely redesigned using the Angular framework., Availability and Implementation: The code used to build all components of RaMP-DB 2.0 are freely available on GitHub at https://github.com/ncats/ramp-db, https://github.com/ncats/RaMP-Client/ and https://github.com/ncats/RaMP-Backend. The RaMP-DB web application can be accessed at https://rampdb.nih.gov/., Supplementary Information: Supplementary data are available at Bioinformatics online., (Published by Oxford University Press 2022.)

Background: Assigning chromatin states genome-wide (e.g. promoters, enhancers, etc.) is commonly performed to improve functional interpretation of these states. However, computational methods to assign chromatin state suffer from the following drawbacks: they typically require data from multiple assays, which may not be practically feasible to obtain, and they depend on peak calling algorithms, which require careful parameterization and often exclude the majority of the genome. To address these drawbacks, we propose a novel learning technique built upon the Self-Organizing Map (SOM), Self-Organizing Map with Variable Neighborhoods (SOM-VN), to learn a set of representative shapes from a single, genome-wide, chromatin accessibility dataset to associate with a chromatin state assignment in which a particular RE is prevalent. These shapes can then be used to assign chromatin state using our workflow., Results: We validate the performance of the SOM-VN workflow on 14 different samples of varying quality, namely one assay each of A549 and GM12878 cell lines and two each of H1 and HeLa cell lines, primary B-cells, and brain, heart, and stomach tissue. We show that SOM-VN learns shapes that are (1) non-random, (2) associated with known chromatin states, (3) generalizable across sets of chromosomes, and (4) associated with magnitude and multimodality. We compare the accuracy of SOM-VN chromatin states against the Clustering Aggregation Tool (CAGT), an unsupervised method that learns chromatin accessibility signal shapes but does not associate these shapes with REs, and we show that overall precision and recall is increased when learning shapes using SOM-VN as compared to CAGT. We further compare enhancer state assignments from SOM-VN in signals above a set threshold to enhancer state assignments from Predicting Enhancers from ATAC-seq Data (PEAS), a deep learning method that assigns enhancer chromatin states to peaks. We show that the precision-recall area under the curve for the assignment of enhancer states is comparable to PEAS., Conclusions: Our work shows that the SOM-VN workflow can learn relationships between REs and chromatin accessibility signal shape, which is an important step toward the goal of assigning and comparing enhancer state across multiple experiments and phenotypic states.

Introduction: Current guidelines for choosing antidepressant medications involve a trial-and-error process. Most patients try multiple antidepressants before finding an effective antidepressant. This study uses demographic and clinical information to create models predicting effectiveness of different antidepressants in treating sadness in a nationally representative sample of US adults., Methods: A secondary analysis of the Collaborative Psychiatric Epidemiology Survey (CPES) was performed. Participants with or without a mental health diagnosis who reported sadness as a symptom, and were taking fluoxetine (n=156), sertraline (n=224), citalopram (n=91), paroxetine (n=156), venlafaxine (n=69), bupropion (n=92), or trazadone (n=26) within the past year were included. Two sets of principal component analyses (PCAs) and logistic regressions were performed: one determined associations between symptom clusters and antidepressant effectiveness for sadness, and the other created models to predict effectiveness. Both PCAs controlled for psychiatric and medical diagnoses, substance use, psychiatric medications, alternative treatments, and demographics., Results: Anxiety was associated with ineffectiveness of fluoxetine in treating sadness. Low mood scores were associated with ineffectiveness of paroxetine and venlafaxine, and fatigue was associated with ineffectiveness of sertraline. The models for predicting drug effectiveness had a mean accuracy of 83% and internal validity of 72%., Limitations: CPES data were collected from 2001-2003, so newer drugs were not included. Effectiveness was for sadness, so results are not directly comparable to studies using overall depressive symptom reductions as outcomes., Conclusion: Since fewer than 50% of patients currently respond to their first antidepressant, this model could provide modest improvement to choosing starting antidepressants in treating sadness., Competing Interests: Declaration of Competing Interest None., (Copyright © 2020 Elsevier B.V. All rights reserved.)

As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study., Competing Interests: The authors declare no conflict of interest.

Background: Proteomic measurements, which closely reflect phenotypes, provide insights into gene expression regulations and mechanisms underlying altered phenotypes. Further, integration of data on proteome and transcriptome levels can validate gene signatures associated with a phenotype. However, proteomic data is not as abundant as genomic data, and it is thus beneficial to use genomic features to predict protein abundances when matching proteomic samples or measurements within samples are lacking., Results: We evaluate and compare four data-driven models for prediction of proteomic data from mRNA measured in breast and ovarian cancers using the 2017 DREAM Proteogenomics Challenge data. Our results show that Bayesian network, random forests, LASSO, and fuzzy logic approaches can predict protein abundance levels with median ground truth-predicted correlation values between 0.2 and 0.5. However, the most accurately predicted proteins differ considerably between approaches., Conclusions: In addition to benchmarking aforementioned machine learning approaches for predicting protein levels from transcript levels, we discuss challenges and potential solutions in state-of-the-art proteogenomic analyses.

Microinjection is extensively used across fields to deliver material intracellularly. Here we address the fundamental aspects of introducing exogenous organelles into cells to endow them with artificial functions. Nanocarriers encapsulating biologically active cargo or extreme intraluminal pH were injected directly into the cytosol of cells, where they bypassed subcellular processing pathways and remained intact for several days. Nanocarriers' size was found to dictate their intracellular distribution pattern upon injection, with larger vesicles adopting polarized agglomerated distributions and smaller colloids spreading evenly in the cytosol. This in turn determined the symmetry or asymmetry of their dilution following cell division, ultimately affecting the intracellular dose at a cell population level. As an example of microinjection's applicability, a cell type relevant for cell-based therapies (dendritic cells) was injected with vesicles, and its migratory properties were studied in a co-culture system mimicking lymphatic capillaries.

Membrane transporters of the RND superfamily confer multidrug resistance to pathogenic bacteria, and are essential for cholesterol metabolism and embryonic development in humans. We use high-resolution X-ray crystallography and computational methods to delineate the mechanism of the homotrimeric RND-type proton/drug antiporter AcrB, the active component of the major efflux system AcrAB-TolC in Escherichia coli, and one most complex and intriguing membrane transporters known to date. Analysis of wildtype AcrB and four functionally-inactive variants reveals an unprecedented mechanism that involves two remote alternating-access conformational cycles within each protomer, namely one for protons in the transmembrane region and another for drugs in the periplasmic domain, 50 Å apart. Each of these cycles entails two distinct types of collective motions of two structural repeats, coupled by flanking α-helices that project from the membrane. Moreover, we rationalize how the cross-talk among protomers across the trimerization interface might lead to a more kinetically efficient efflux system.

AcrAB-TolC is the major efflux protein complex in Escherichia coli extruding a vast variety of antimicrobial agents from the cell. The inner membrane component AcrB is a homotrimer, and it has been postulated that the monomers cycle consecutively through three conformational stages designated loose (L), tight (T), and open (O) in a concerted fashion. Binding of drugs has been shown at a periplasmic deep binding pocket in the T conformation. The initial drug-binding step and transport toward this drug-binding site has been elusive thus far. Here we report high resolution structures (1.9-2.25 Å) of AcrB/designed ankyrin repeat protein (DARPin) complexes with bound minocycline or doxorubicin. In the AcrB/doxorubicin cocrystal structure, binding of three doxorubicin molecules is apparent, with one doxorubicin molecule bound in the deep binding pocket of the T monomer and two doxorubicin molecules in a stacked sandwich arrangement in an access pocket at the lateral periplasmic cleft of the L monomer. This access pocket is separated from the deep binding pocket apparent in the T monomer by a switch-loop. The localization and conformational flexibility of this loop seems to be important for large substrates, because a G616N AcrB variant deficient in macrolide transport exhibits an altered conformation within this loop region. Transport seems to be a stepwise process of initial drug uptake in the access pocket of the L monomer and subsequent accommodation of the drug in the deep binding pocket during the L to T transition to the internal deep binding pocket of the T monomer.

The AcrA/AcrB/TolC complex is responsible for intrinsic multidrug resistance (MDR) in Escherichia coli. Together with the periplasmic adaptor protein AcrA and the outer membrane channel TolC, the inner membrane component AcrB forms an efflux complex that spans both the inner and outer membrane and bridges the periplasm of the Gram-negative cell. Within the entire tripartite complex, homotrimeric AcrB plays a central role in energy transduction and substrate selection. In vitro selected designed ankyrin repeat proteins (DARPin) that specifically bind to the periplasmic domain of AcrB were shown to ameliorate diffraction resolution of AcrB/DARPin protein co-crystals (G. Sennhauser, P. Amstutz, C. Briand, O. Storchenegger, M.G. Grutter, Drug export pathway of multidrug exporter AcrB revealed by DARPin inhibitors, PLoS Biol 5 (2007) e7). Structural analysis by X-ray crystallography revealed that 2 DARPin molecules were bound to the trimeric AcrB wildtype protein in the crystal, whereas the V612F and G616N AcrB variant crystal structures show 3 DARPin molecules bound to the trimer. These specific stoichiometric differences were analyzed in solution via densitometry after microchannel electrophoresis, analytical ultracentrifugation and via laser-induced liquid bead ion desorption mass spectrometry (LILBID-MS). Using the latter technology, we investigated the gradual disassembly of the AcrB trimer and bound DARPin ligands in dependence on laser intensity in solution. At low laser intensity, the release of the detergent molecule micelle from the AcrB/DARPin complex was observed. By increasing laser intensity, dimeric and monomeric AcrB species with bound DARPin molecules were detected showing the high affinity binding of DARPin to monomeric AcrB species. High laser intensity LILBID MS experiments indicated a spectral shift of the monomeric AcrB peak of 3.1kDa, representing a low molecular weight ligand in all detergent-solubilized AcrB samples and in the AcrB crystal. The identity of this ligand was further investigated using phospholipid analysis of purified AcrB and AcrB variant samples, and indicated the presence of phosphatidylethanolamine and possibly cardiolipin, both constituents of the Escherichia coli membrane., (Copyright © 2011 Elsevier B.V. All rights reserved.)