Computation,wiring, and plasticity in synaptic clusters

Synaptic clusters on dendrites are extraordinarily compact computational building blocks. They contribute to key local computations through biophysical and biochemical signaling that utilizes convergence in space and time as an organizing principle. However, these computations can only arise in very special contexts. Dendritic cluster computations, their highly organized input connectivity, and the mechanisms for their formation are closely linked, yet these have not been analyzed as parts of a single process. Here, we examine these linkages. The sheer density of axonal and dendritic arborizations means that there are far more potential connections (close enough for a spine to reach an axon) than actual ones. We see how dendritic clusters draw upon electrical, chemical, and mechano–chemical signaling to implement the rules for formation of connections and subsequent computations. Crucially, the same mechanisms that underlie their functions also underlie their formation.     

Deep generative models for 3D molecular structure

Deep generative models have gained recent popularity for chemical design. Many of these models have historically operated in 2D space; however, more recently explicit 3D molecular generative models have become of interest, which are the topic of this article. Dozens of published models have been developed in the last few years to generate molecules directly in 3D, outputting both the atom types and coordinates, either in one-shot or adding atoms or fragments step-by-step. These 3D generative models can also be guided by structural information such as a binding pocket representation to successfully generate molecules with docking score ranges similar to known actives, but still showing lower computational efficiency and generation throughput than 1D/2D generative models and sometimes producing unrealistic conformations. We advocate for a unified benchmark of metrics to evaluate generation and propose perspectives to be addressed in next implementations.     

Principles of Alternating Access in Multidrug and Toxin Extrusion (MATE) Transporters

The multidrug and toxin extrusion (MATE) transporters catalyze active efflux of a broad range of chemically- and structurally-diverse compounds including antimicrobials and chemotherapeutics, thus contributing to multidrug resistance in pathogenic bacteria and cancers. Multiple methodological approaches have been taken to investigate the structural basis of energy transduction and substrate translocation in MATE transporters. Crystal structures representing members from all three MATE subfamilies have been interpreted within the context of an alternating access mechanism that postulates occupation of distinct structural intermediates in a conformational cycle powered by electrochemical ion gradients. Here we review the structural biology of MATE transporters, integrating the crystallographic models with biophysical and computational studies to define the molecular determinants that shape the transport energy landscape. This holistic analysis highlights both shared and disparate structural and functional features within the MATE family, which underpin an emerging theme of mechanistic diversity within the framework of a conserved structural scaffold.     

The ugly,bad, and good stories of large-scale biomolecular simulations

Molecular modeling of large biomolecular assemblies exemplifies a disruptive area holding both promises and contentions. Propelled by peta and exascale computing, several simulation methodologies have now matured into user-friendly tools that are successfully employed for modeling viruses, membranous nano-constructs, and key pieces of the genetic machinery. We present three unifying biophysical themes that emanate from some of the most recent multi-million atom simulation endeavors. Despite connecting molecular changes with phenotypic outcomes, the quality measures of these simulations remain questionable. We discuss the existing and upcoming strategies for constructing representative ensembles of large systems, how new computing technologies will boost this area, and make a point that integrative modeling guided by experimental data is the future of biomolecular computations.     

Determination of tissue contributions to the circulating lipid pool in cold exposure via systematic assessment of lipid profiles

Plasma lipid levels are altered in chronic conditions such as type 2 diabetes and cardiovascular disease as well as during acute stresses such as fasting and cold exposure. Advances in MS-based lipidomics have uncovered a complex plasma lipidome of more than 500 lipids that serve functional roles, including as energy substrates and signaling molecules. This plasma lipid pool is maintained through regulation of tissue production, secretion, and uptake. A major challenge in understanding the lipidome complexity is establishing the tissues of origin and uptake for various plasma lipids, which is valuable for determining lipid functions. Using cold exposure as an acute stress, we performed global lipidomics on plasma and in nine tissues that may contribute to the circulating lipid pool. We found that numerous species of plasma acylcarnitines (ACars) and ceramides (Cers) were significantly altered upon cold exposure. Through computational assessment, we identified the liver and brown adipose tissue as major contributors and consumers of circulating ACars, in agreement with our previous work. We further identified the kidney and intestine as novel contributors to the circulating ACar pool and validated these findings with gene expression analysis. Regression analysis also identified that the brown adipose tissue and kidney are interactors with the plasma Cer pool. Taken together, these studies provide an adaptable computational tool to assess tissue contribution to the plasma lipid pool. Our findings have further implications in understanding the function of plasma ACars and Cers, which are elevated in metabolic diseases.     

Building Structural Models of a Whole Mycoplasma Cell

Building structural models of entire cells has been a long-standing cross-discipline challenge for the research community, as it requires an unprecedented level of integration between multiple sources of biological data and enhanced methods for computational modeling and visualization. Here, we present the first 3D structural models of an entire Mycoplasma genitalium (MG) cell, built using the CellPACK suite of computational modeling tools. Our model recapitulates the data described in recent whole-cell system biology simulations and provides a structural representation for all MG proteins, DNA and RNA molecules, obtained by combining experimental and homology-modeled structures and lattice-based models of the genome. We establish a framework for gathering, curating and evaluating these structures, exposing current weaknesses of modeling methods and the boundaries of MG structural knowledge, and visualization methods to explore functional characteristics of the genome and proteome. We compare two approaches for data gathering, a manually-curated workflow and an automated workflow that uses homologous structures, both of which are appropriate for the analysis of mesoscale properties such as crowding and volume occupancy. Analysis of model quality provides estimates of the regularization that will be required when these models are used as starting points for atomic molecular dynamics simulations.     

Accelerated brain aging with opioid misuse and HIV: New insights on the role of glially derived pro-inflammation mediators and neuronal chloride homeostasis

Opioid use disorder (OUD) has become a national crisis and contributes to the spread of human immunodeficiency virus (HIV) infection. Emerging evidence and advances in experimental models, methodology, and our understanding of disease processes at the molecular and cellular levels reveal that opioids per se can directly exacerbate the pathophysiology of neuroHIV. Despite substantial inroads, the impact of OUD on the severity, development, and prognosis of neuroHIV and HIV-associated neurocognitive disorders is not fully understood. In this review, we explore current evidence that OUD and neuroHIV interact to accelerate cognitive deficits and enhance the neurodegenerative changes typically seen with aging, through their effects on neuroinflammation. We suggest new thoughts on the processes that may underlie accelerated brain aging, including dysregulation of neuronal inhibition, and highlight findings suggesting that opioids, through actions at the μ-opioid receptor, interact with HIV in the central nervous system to promote unique structural and functional comorbid deficits not seen in either OUD or neuroHIV alone.     

Structure-based analyses of gut microbiome-related proteins by neural networks and molecular dynamics simulations

An imbalance in the gut microbiome is linked to immune disorders, such as autoimmune, allergic, and chronic inflammatory disorders. Elucidation of disease mechanisms is a matter of urgency. It requires precise elucidation of the structure-based mechanisms of protein interactions involved in disease onset. In addition, an understanding of the protein dynamics is vital because these fluctuations affect the function and interaction of disease-associated proteins. Experimental evaluation of not only protein interactions, functions, and structures but also the dynamics are time-consuming; therefore, computational predictions are necessary to elucidate disease mechanisms. Here, we introduce recent studies on structure-based analyses of proteins using computational approaches, particularly artificial intelligence (AI) and molecular dynamics (MD) simulations.     

Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.     

Hormone-sensitive lipase is localized at synapses and is necessary for normal memory functioning in mice

Hormone-sensitive lipase (HSL) is mainly present in adipose tissue where it hydrolyzes diacylglycerol. Although expression of HSL has also been reported in the brain, its presence in different cellular compartments is uncertain, and its role in regulating brain lipid metabolism remains hitherto unexplored. We hypothesized that HSL might play a role in regulating the availability of bioactive lipids necessary for neuronal function and therefore investigated whether dampening HSL activity could lead to brain dysfunction. In mice, we found HSL protein and enzymatic activity throughout the brain, localized within neurons and enriched in synapses. HSL-null mice were then analyzed using a battery of behavioral tests. Relative to wild-type littermates, HSL-null mice showed impaired short-term and long-term memory, yet preserved exploratory behaviors. Molecular analysis of the cortex and hippocampus showed increased expression of genes involved in glucose utilization in the hippocampus, but not cortex, of HSL-null mice compared with controls. Furthermore, lipidomics analyses indicated an impact of HSL deletion on the profile of bioactive lipids, including a decrease in endocannabinoids and eicosanoids that are known to modulate neuronal activity, cerebral blood flow, and inflammation processes. Accordingly, mild increases in the expression of proinflammatory cytokines in HSL mice compared with littermates were suggestive of low-grade inflammation. We conclude that HSL has a homeostatic role in maintaining pools of lipids required for normal brain function. It remains to be tested, however, whether the recruitment of HSL for the synthesis of these lipids occurs during increased neuronal activity or whether HSL participates in neuroinflammatory responses.     

What does engram encode?: Heterogeneous memory engrams for different aspects of experience

Long-lasting synaptic changes within the neuronal network mediate memory. Neurons bearing such physical traces of memory (memory engram cells) are often equated with neurons expressing immediate early genes (IEGs) during a specific experience. However, past studies observed the expression of different IEGs in non-overlapping neurons or synaptic plasticity in neurons that do not express a particular IEG. Importantly, recent studies revealed that distinct subsets of neurons expressing different IEGs or even IEG negative-(yet active) neurons support different aspects of memory or computation, suggesting a more complex nature of memory engram cells than previously thought. In this short review, we introduce studies revealing such heterogeneous composition of the memory engram and discuss how the memory system benefits from it.     

First 3D-Structural Data of Full-Length Guanylyl Cyclase 1 in Rod-Outer-Segment Preparations of Bovine Retina by Cross-Linking/Mass Spectrometry

The rod-outer-segment guanylyl cyclase 1 (ROS-GC1) is a key transmembrane protein for retinal phototransduction. Mutations of ROS-GC1 correlate with different retinal diseases that often lead to blindness. No structural data are available for ROS-GC1 so far. We performed a 3D-structural analysis of native ROS-GC1 from bovine retina by cross-linking/mass spectrometry (XL-MS) and computational modeling. Absolute quantification and activity measurements of native ROS-GC1 were performed by MS-based assays directly in bovine retina samples. Our data present the first 3D-structural analysis of active, full-length ROS-GC1 derived from bovine retina. We propose a novel domain organization for the intracellular domain ROS-GC1. Our XL-MS data of native ROS-GC1 from rod-outer-segment preparations of bovine retina agree with a dimeric architecture. Our integrated approach can serve as a blueprint for conducting 3D-structural studies of membrane proteins in their native environment.     

SP3-Enabled Rapid and High Coverage Chemoproteomic Identification of Cell-State–Dependent Redox-Sensitive Cysteines

Proteinaceous cysteine residues act as privileged sensors of oxidative stress. As reactive oxygen and nitrogen species have been implicated in numerous pathophysiological processes, deciphering which cysteines are sensitive to oxidative modification and the specific nature of these modifications is essential to understanding protein and cellular function in health and disease. While established mass spectrometry-based proteomic platforms have improved our understanding of the redox proteome, the widespread adoption of these methods is often hindered by complex sample preparation workflows, prohibitive cost of isotopic labeling reagents, and requirements for custom data analysis workflows. Here, we present the SP3-Rox redox proteomics method that combines tailored low cost isotopically labeled capture reagents with SP3 sample cleanup to achieve high throughput and high coverage proteome-wide identification of redox-sensitive cysteines. By implementing a customized workflow in the free FragPipe computational pipeline, we achieve accurate MS1-based quantitation, including for peptides containing multiple cysteine residues. Application of the SP3-Rox method to cellular proteomes identified cysteines sensitive to the oxidative stressor GSNO and cysteine oxidation state changes that occur during T cell activation.     

Are We Restoring Thyroid Hormone Signaling in Levothyroxine-Treated Patients With Residual Symptoms of Hypothyroidism?

IntroductionLevothyroxine (LT4) at doses that maintain the serum thyroid-stimulating hormone levels within the normal range constitutes the standard of care for the treatment of hypothyroidism. After a few months, this eliminates the signs and symptoms of overt hypothyroidism in the majority of patients, owing to the endogenous activation of thyroxine to triiodothyronine, the biologically active thyroid hormone. Still, a small percentage of the patients (10%-20%) exhibit residual symptoms, despite having normal serum thyroid-stimulating hormone levels. These symptoms include cognitive, mood, and metabolic deficits, with a significant impairment in psychological well-being and quality of life.ObjectiveTo provide a summary of progress in the approach of patients with hypothyroidism that exhibit residual symptoms despite treatment.MethodsWe reviewed the current literature and here we focused on the mechanisms leading to a deficiency of T3 in some LT4-treated patients, the role of residual thyroid tissue and the rationale for combination therapy with LT4 + liothyronine (LT3).ResultsA score of clinical trials comparing therapy with LT4 versus LT4 + LT3 concluded that both are safe and equally effective (neither is superior); however, these trials failed to recruit a sufficiently large number of patients with residual symptoms. New clinical trials that considered LT4-treated symptomatic patients revealed that such patients benefit from and prefer therapy containing LT4 + LT3; desiccated thyroid extract has also been used with similar results. A practical approach to patients with residual symptoms and on initiation of combination therapy with LT4 + LT3 is provided.ConclusionA recent joint statement of the American, British, and European Thyroid Associations recommends that a trial with combination therapy be offered to patients with hypothyroidism that do not fully benefit from therapy with LT4.     

Gut microbiome-derived glycine lipids are diet-dependent modulators of hepatic injury and atherosclerosis

Oral and gut Bacteroidetes produce unique classes of serine-glycine lipodipeptides and glycine aminolipids that signal through host Toll-like receptor 2. These glycine lipids have also been detected in human arteries, but their effects on atherosclerosis are unknown. Here, we sought to investigate the bioactivity of bacterial glycine lipids in mouse models of atherosclerosis. Lipid 654 (L654), a serine-glycine lipodipeptide species, was first tested in a high-fat diet (HFD)-fed Ldlr^?/? model of atherosclerosis. Intraperitoneal administration of L654 over 7 weeks to HFD-fed Ldlr^?/? mice resulted in hypocholesterolemic effects and significantly attenuated the progression of atherosclerosis. We found that L654 also reduced liver inflammatory and extracellular matrix gene expression, which may be related to inhibition of macrophage activation as demonstrated in vivo by lower major histocompatibility complex class II gene expression and confirmed in cell experiments. In addition, L654 and other bacterial glycine lipids in feces, liver, and serum were markedly reduced alongside changes in Bacteroidetes relative abundance in HFD-fed mice. Finally, we tested the bioactivities of L654 and related lipid 567 in chow-fed Apoe^?/? mice, which displayed much higher fecal glycine lipids relative to HFD-fed Ldlr^?/? mice. Administration of L654 or lipid 567 for 7 weeks to these mice reduced the liver injury marker alanine aminotransferase, but other effects seen in Ldlr^?/? were not observed. Therefore, we conclude that conditions in which gut microbiome-derived glycine lipids are lost, such as HFD, may exacerbate the development of atherosclerosis and liver injury, whereas correction of such depletion may protect from these disorders.     

Mass Spectrometry–Based Proteomics Analysis of Human Substantia Nigra From Parkinson's Disease Patients Identifies Multiple Pathways Potentially Involved in the Disease

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra (SN) of the brain. Despite decades of studies, the precise pathogenic mechanism of PD is still elusive. An unbiased proteomic analysis of PD patient’s brain allows the identification of critical proteins and molecular pathways that lead to dopamine cell death and α-synuclein deposition and the resulting devastating clinical symptoms. In this study, we conducted an in-depth proteome analysis of human SN tissues from 15 PD patients and 15 healthy control individuals combining Orbitrap mass spectrometry with the isobaric tandem mass tag–based multiplexing technology. We identified 10,040 proteins with 1140 differentially expressed proteins in the SN of PD patients. Pathway analysis showed that the ribosome pathway was the most enriched one, followed by gamma-aminobutyric acidergic synapse, retrograde endocannabinoid signaling, cell adhesion molecules, morphine addiction, Prion disease, and PD pathways. Strikingly, the majority of the proteins enriched in the ribosome pathway were mitochondrial ribosomal proteins (mitoribosomes). The subsequent protein–protein interaction analysis and the weighted gene coexpression network analysis confirmed that the mitoribosome is the most enriched protein cluster. Furthermore, the mitoribosome was also identified in our analysis of a replication set of ten PD and nine healthy control SN tissues. This study provides potential disease pathways involved in PD and paves the way to study further the pathogenic mechanism of PD.     

Low Cell Number Proteomic Analysis Using In-Cell Protease Digests Reveals a Robust Signature for Cell Cycle State Classification

Comprehensive proteome analysis of rare cell phenotypes remains a significant challenge. We report a method for low cell number MS-based proteomics using protease digestion of mildly formaldehyde-fixed cells in cellulo, which we call the “in-cell digest.” We combined this with averaged MS1 precursor library matching to quantitatively characterize proteomes from low cell numbers of human lymphoblasts. About 4500 proteins were detected from 2000 cells, and 2500 proteins were quantitated from 200 lymphoblasts. The ease of sample processing and high sensitivity makes this method exceptionally suited for the proteomic analysis of rare cell states, including immune cell subsets and cell cycle subphases. To demonstrate the method, we characterized the proteome changes across 16 cell cycle states (CCSs) isolated from an asynchronous TK6 cells, avoiding synchronization. States included late mitotic cells present at extremely low frequency. We identified 119 pseudoperiodic proteins that vary across the cell cycle. Clustering of the pseudoperiodic proteins showed abundance patterns consistent with “waves” of protein degradation in late S, at the G2&M border, midmitosis, and at mitotic exit. These clusters were distinguished by significant differences in predicted nuclear localization and interaction with the anaphase-promoting complex/cyclosome. The dataset also identifies putative anaphase-promoting complex/cyclosome substrates in mitosis and the temporal order in which they are targeted for degradation. We demonstrate that a protein signature made of these 119 high-confidence cell cycle–regulated proteins can be used to perform unbiased classification of proteomes into CCSs. We applied this signature to 296 proteomes that encompass a range of quantitation methods, cell types, and experimental conditions. The analysis confidently assigns a CCS for 49 proteomes, including correct classification for proteomes from synchronized cells. We anticipate that this robust cell cycle protein signature will be crucial for classifying cell states in single-cell proteomes.     

Cholesterol sulfate fluidizes the sterol fraction of the stratum corneum lipid phase and increases its permeability

Desulfation of cholesterol sulfate (CholS) to cholesterol (Chol) is an important event in epidermal homeostasis and necessary for stratum corneum (SC) barrier function. The CholS/Chol ratio decreases during SC maturation but remains high in pathological conditions, such as X-linked ichthyosis, characterized by dry and scaly skin. The aim of this study was to characterize the influence of the CholS/Chol molar ratio on the structure, dynamics, and permeability of SC lipid model mixtures. We synthesized deuterated CholS and investigated lipid models with specifically deuterated components using ²H solid-state NMR spectroscopy at temperatures from 25°C to 80°C. Although the rigid acyl chains in ceramides and fatty acids remained essentially rigid upon variation of the CholS/Chol ratio, both sterols were increasingly fluidized in lipid models containing higher CholS concentrations. We also show the X-ray repeat distance of the lipid lamellar phase (105 Å) and the orthorhombic chain packing of the ceramide’s acyl chains and long free fatty acids did not change upon the variation of the CholS content. However, the Chol phase separation visible in models with high Chol concentration disappeared at the 50:50 CholS/Chol ratio. This increased fluidity resulted in higher permeabilities to model markers of these SC models. These results reveal that a high CholS/Chol ratio fluidizes the sterol fraction and increases the permeability of the SC lipid phase while maintaining the lamellar lipid arrangement with an asymmetric sterol distribution.