BraInMap Elucidates the Macromolecular Connectivity Landscape of Mammalian Brain

An integrative systems-biology approach defines mechanisms of Alzheimer's disease neurodegeneration

Despite years of intense investigation, the mechanisms underlying neuronal death in Alzheimer's disease, remain incompletely understood. To define relevant pathways, we conducted an unbiased, genome-scale forward genetic screen for age-associated neurodegeneration in Drosophila. We also measured proteomics, phosphoproteomics, and metabolomics in Drosophila models of Alzheimer's disease and identified Alzheimer's genetic variants that modify gene expression in disease-vulnerable neurons in humans. We then used a network model to integrate these data with previously published Alzheimer's disease proteomics, lipidomics and genomics. Here, we computationally predict and experimentally confirm how HNRNPA2B1 and MEPCE enhance toxicity of the tau protein, a pathological feature of Alzheimer's disease. Furthermore, we demonstrated that the screen hits CSNK2A1 and NOTCH1 regulate DNA damage in Drosophila and human stem cell-derived neural progenitor cells. Our study identifies candidate pathways that could be targeted to ameliorate neurodegeneration in Alzheimer's disease.

Nutrigenomic insights and cardiovascular benefits of blackberry (Rubus ulmifolius Schott.) and mugwort (Artemisia campestris L.)

Blackberry (Rubus ulmifolius Schott) and mugwort (Artemisia campestris L.) are plants traditionally used to treat various pathologies, including hypertension. The vasodilatory and hypotensive effects of blackberry were investigated through experiments in rat models (n = 5 rats per group) and compared with those of mugwort, which had been demonstrated previously. A nutrigenomic experiment in mouse models (n = 3 mice per group) was also performed for both plants to associate biomarker genes with these effects. Additionally, a phytochemical analysis was carried out to identify the bioactive molecules responsible for the cardiovascular effects. A dose-dependent hypotensive effect and a carbachol-like vasodilatory effect were observed for blackberry and compared with those of mugwort. These effects were associated with the deregulation of gene expression related to vessel lumen expansion (Amotl2, Cdh1 and Tfcp2l1) and circulatory system morphology and activity (Dsp, Ahnak, Prcp and Smtnl2) for both plants. Their functional potential also includes antiproliferative, antimicrobial, anti-inflammatory and appetite-regulating properties. Chlorogenic acids, quercetin and kaempferol derivatives were identified in blackberry as the main bioactive molecules likely to be responsible for its cardiovascular effect. The blackberry extract exhibited a vasorelaxant effect 20 times greater than mugwort, attributed to the exclusive presence of the hypotensive galloyl-bis-HHDP glucose derivative and a more pronounced upregulation of Tfcp2l1, which is involved in epithelial cell maturation. This study validates the traditional use of blackberry and mugwort in treatment of hypertension, identifies marker genes and bioactive molecules for vasodilatory and hypotensive effects and expands their potential applications to cancer prevention, inflammation reduction and appetite regulation.

CellFIE: Integrating Pathway Discovery With Pooled Profiling of Perturbations Uncovers Pathways of Huntington's Disease, Including Genetic Modifiers of Neuronal Development and Morphology

Genomic screens and GWAS are powerful tools for identifying disease-modifying genes, but it is often challenging to understand the pathways by which these genes function. Here, we take an integrated approach that combines network analysis and an imaging-based pooled genetic perturbation study to examine modifiers of Huntington's disease (HD). The computational analysis highlighted several genes in a subnetwork enriched for modifiers of neuronal development and morphology. To test the functional roles of these genes, we developed an experimental pipeline that allows pooled CRISPRi KD of 21 genes in human iPSC-derived neurons followed by optical analysis of genotypes, neuronal arborization, multiplexed pathway activity and morphological fingerprint readout. This approach recovered known genes involved in morphology and confirmed unexpected links from the network between several genetic modifiers of HD and morphology. Our approach overcomes challenges in pooled measurement of neuronal function and health and could be adapted for other phenotypes in HD and other neurological diseases.

Progress toward a comprehensive brain protein interactome

Protein-protein interactions (PPIs) in the brain play critical roles across all aspects of the central nervous system, from synaptic transmission, glial development, myelination, to cell-to-cell communication, and more. Understanding these interactions is crucial for deciphering neurological mechanisms and the underlying biochemical machinery affected in neurological disorders. Recently, advances in proteomics techniques have significantly enhanced our ability to study interactions among the proteins expressed in the brain. Here, we review some of the high-throughput studies characterizing brain PPIs, using affinity purification, proximity labeling, co-fractionation, and chemical cross-linking mass spectrometry methods, as well as yeast two-hybrid assays. We present the current state of the field, discuss challenges, and highlight promising future directions.

SEC-MX: an approach to systematically study the interplay between protein assembly states and phosphorylation

A protein's molecular interactions and post-translational modifications (PTMs), such as phosphorylation, can be co-dependent and reciprocally co-regulate each other. Although this interplay is central for many biological processes, a systematic method to simultaneously study assembly states and PTMs from the same sample is critically missing. Here, we introduce SEC-MX (Size Exclusion Chromatography fractions MultipleXed), a global quantitative method combining Size Exclusion Chromatography and PTM-enrichment for simultaneous characterization of PTMs and assembly states. SEC-MX enhances throughput, allows phosphopeptide enrichment, and facilitates quantitative differential comparisons between biological conditions. Conducting SEC-MX on HEK293 and HCT116 cells, we generate a proof-of-concept dataset, mapping thousands of phosphopeptides and their assembly states. Our analysis reveals intricate relationships between phosphorylation events and assembly states and generates testable hypotheses for follow-up studies. Overall, we establish SEC-MX as a valuable tool for exploring protein functions and regulation beyond abundance changes.

Discovery and significance of protein-protein interactions in health and disease

The identification of individual protein-protein interactions (PPIs) began more than 40 years ago, using protein affinity chromatography and antibody co-immunoprecipitation. As new technologies emerged, analysis of PPIs increased to a genome-wide scale with the introduction of intracellular tagging methods, affinity purification (AP) followed by mass spectrometry (MS), and co-fractionation MS (CF-MS). Now, combining the resulting catalogs of interactions with complementary methods, including crosslinking MS (XL-MS) and cryogenic electron microscopy (cryo-EM), helps distinguish direct interactions from indirect ones within the same or between different protein complexes. These powerful approaches and the promise of artificial intelligence applications like AlphaFold herald a future where PPIs and protein complexes, including energy-driven protein machines, will be understood in exquisite detail, unlocking new insights in the contexts of both basic biology and disease.

An integrative systems-biology approach defines mechanisms of Alzheimer's disease neurodegeneration

Despite years of intense investigation, the mechanisms underlying neuronal death in Alzheimer's disease, the most common neurodegenerative disorder, remain incompletely understood. To define relevant pathways, we integrated the results of an unbiased, genome-scale forward genetic screen for age-associated neurodegeneration in Drosophila with human and Drosophila Alzheimer's disease-associated multi-omics. We measured proteomics, phosphoproteomics, and metabolomics in Drosophila models of Alzheimer's disease and identified Alzheimer's disease human genetic variants that modify expression in disease-vulnerable neurons. We used a network optimization approach to integrate these data with previously published Alzheimer's disease multi-omic data. We computationally predicted and experimentally demonstrated how HNRNPA2B1 and MEPCE enhance tau-mediated neurotoxicity. Furthermore, we demonstrated that the screen hits CSNK2A1 and NOTCH1 regulate DNA damage in Drosophila and human iPSC-derived neural progenitor cells. Our work identifies candidate pathways that could be targeted to ameliorate neurodegeneration in Alzheimer's disease.

Analysis of Complexome Profiles with the Gaussian Interaction Profiler (GIP) Reveals Novel Protein Complexes in Plasmodium falciparum

Complexome profiling is an experimental approach to identify interactions by integrating native separation of protein complexes and quantitative mass spectrometry. In a typical complexome profile, thousands of proteins are detected across typically ≤100 fractions. This relatively low resolution leads to similar abundance profiles between proteins that are not necessarily interaction partners. To address this challenge, we introduce the Gaussian Interaction Profiler (GIP), a Gaussian mixture modeling-based clustering workflow that assigns protein clusters by modeling the migration profile of each cluster. Uniquely, the GIP offers a way to prioritize actual interactors over spuriously comigrating proteins. Using previously analyzed human fibroblast complexome profiles, we show good performance of the GIP compared to other state-of-the-art tools. We further demonstrate GIP utility by applying it to complexome profiles from the transmissible lifecycle stage of malaria parasites. We unveil promising novel associations for future experimental verification, including an interaction between the vaccine target Pfs47 and the hypothetical protein PF3D7_0417000. Taken together, the GIP provides methodological advances that facilitate more accurate and automated detection of protein complexes, setting the stage for more varied and nuanced analyses in the field of complexome profiling. The complexome profiling data have been deposited to the ProteomeXchange Consortium with the dataset identifier PXD050751.

Trends in co-fractionation mass spectrometry: A new gold-standard in global protein interaction network discovery

Co-fractionation mass spectrometry (CF-MS) uses biochemical fractionation to isolate and characterize macromolecular complexes from cellular lysates without the need for affinity tagging or capture. In recent years, this has emerged as a powerful technique for elucidating global protein-protein interaction networks in a wide variety of biospecimens. This review highlights the latest advancements in CF-MS experimental workflows including machine learning-guided analyses, for uncovering dynamic and high-resolution protein interaction landscapes with enhanced sensitivity, accuracy and throughput, enabling better biophysical characterization of endogenous protein complexes. By addressing challenges and emergent opportunities in the field, this review underscores the transformative potential of CF-MS in advancing our understanding of functional protein interaction networks in health and disease.

Alternative proteoforms and proteoform-dependent assemblies in humans and plants

The variability of proteins at the sequence level creates an enormous potential for proteome complexity. Exploring the depths and limits of this complexity is an ongoing goal in biology. Here, we systematically survey human and plant high-throughput bottom-up native proteomics data for protein truncation variants, where substantial regions of the full-length protein are missing from an observed protein product. In humans, Arabidopsis, and the green alga Chlamydomonas, approximately one percent of observed proteins show a short form, which we can assign by comparison to RNA isoforms as either likely deriving from transcript-directed processes or limited proteolysis. While some detected protein fragments align with known splice forms and protein cleavage events, multiple examples are previously undescribed, such as our observation of fibrocystin proteolysis and nuclear translocation in a green alga. We find that truncations occur almost entirely between structured protein domains, even when short forms are derived from transcript variants. Intriguingly, multiple endogenous protein truncations of phase-separating translational proteins resemble cleaved proteoforms produced by enteroviruses during infection. Some truncated proteins are also observed in both humans and plants, suggesting that they date to the last eukaryotic common ancestor. Finally, we describe novel proteoform-specific protein complexes, where the loss of a domain may accompany complex formation.

A Multiplexed SEC-MS Approach to Systematically Study the Interplay Between Protein Assembly-States and Phosphorylation Events

Protein molecular interactions and post-translational modifications (PTMs), such as phosphorylation, can be co-dependent and reciprocally co-regulate each other. Although this interplay is central for many biological processes, a systematic method to simultaneously study assembly-states and PTMs from the same sample is critically missing. Here, we introduce SEC-MX (Size Exclusion Chromatography fractions MultipleXed), a global quantitative method combining Size Exclusion Chromatography and PTM-enrichment for simultaneous characterization of PTMs and assembly-states. SEC-MX enhances throughput, allows phosphopeptide enrichment, and facilitates quantitative differential comparisons between biological conditions. Applying SEC-MX to HEK293 and HCT116 cells, we generated a proof-of-concept dataset mapping thousands of phosphopeptides and their assembly-states. Our analysis revealed intricate relationships between phosphorylation events and assembly-states and generated testable hypotheses for follow-up studies. Overall, we establish SEC-MX as a valuable tool for exploring protein functions and regulation beyond abundance changes.

Single-cell transcriptome analysis of cavernous tissues reveals the key roles of pericytes in diabetic erectile dysfunction

Erectile dysfunction (ED) affects a significant proportion of men aged 40-70 and is caused by cavernous tissue dysfunction. Presently, the most common treatment for ED is phosphodiesterase 5 inhibitors; however, this is less effective in patients with severe vascular disease such as diabetic ED. Therefore, there is a need for development of new treatment, which requires a better understanding of the cavernous microenvironment and cell-cell communications under diabetic condition. Pericytes are vital in penile erection; however, their dysfunction due to diabetes remains unclear. In this study, we performed single-cell RNA sequencing to understand the cellular landscape of cavernous tissues and cell type-specific transcriptional changes in diabetic ED. We found a decreased expression of genes associated with collagen or extracellular matrix organization and angiogenesis in diabetic fibroblasts, chondrocytes, myofibroblasts, valve-related lymphatic endothelial cells, and pericytes. Moreover, the newly identified pericyte-specific marker, Limb Bud-Heart (Lbh), in mouse and human cavernous tissues, clearly distinguishing pericytes from smooth muscle cells. Cell-cell interaction analysis revealed that pericytes are involved in angiogenesis, adhesion, and migration by communicating with other cell types in the corpus cavernosum; however, these interactions were highly reduced under diabetic conditions. Lbh expression is low in diabetic pericytes, and overexpression of LBH prevents erectile function by regulating neurovascular regeneration. Furthermore, the LBH-interacting proteins (Crystallin Alpha B and Vimentin) were identified in mouse cavernous pericytes through LC-MS/MS analysis, indicating that their interactions were critical for maintaining pericyte function. Thus, our study reveals novel targets and insights into the pathogenesis of ED in patients with diabetes.

A genomic hotspot of diversifying selection and structural change in the hoary bat (Lasiurus cinereus)

Background

Previous work found that numerous genes positively selected within the hoary bat (Lasiurus cinereus) lineage are physically clustered in regions of conserved synteny. Here I further validate and expand on those finding utilizing an updated L. cinereus genome assembly and additional bat species as well as other tetrapod outgroups.

Methods

A chromosome-level assembly was generated by chromatin-contact mapping and made available by DNAZoo (www.dnazoo.org). The genomic organization of orthologous genes was extracted from annotation data for multiple additional bat species as well as other tetrapod clades for which chromosome-level assemblies were available from the National Center for Biotechnology Information (NCBI). Tests of branch-specific positive selection were performed for L. cinereus using PAML as well as with the HyPhy package for comparison.

Results

Twelve genes exhibiting significant diversifying selection in the L. cinereus lineage were clustered within a 12-Mb genomic window; one of these (Trpc4) also exhibited diversifying selection in bats generally. Ten of the 12 genes are landmarks of two distinct blocks of ancient synteny that are not linked in other tetrapod clades. Bats are further distinguished by frequent structural rearrangements within these synteny blocks, which are rarely observed in other Tetrapoda. Patterns of gene order and orientation among bat taxa are incompatible with phylogeny as presently understood, implying parallel evolution or subsequent reversals. Inferences of positive selection were found to be robust to alternative phylogenetic topologies as well as a strong shift in background nucleotide composition in some taxa.

Discussion

This study confirms and further localizes a genomic hotspot of protein-coding divergence in the hoary bat, one that also exhibits an increased tempo of structural change in bats compared with other mammals. Most genes in the two synteny blocks have elevated expression in brain tissue in humans and model organisms, and genetic studies implicate the selected genes in cranial and neurological development, among other functions.

Genetic architecture of trait variance in craniofacial morphology

The genetic architecture of trait variance has long been of interest in genetics and evolution. One of the earliest attempts to understand this architecture was presented in Lerner's Genetic Homeostasis (1954). Lerner proposed that heterozygotes should be better able to tolerate environmental perturbations because of functional differences between the alleles at a given locus, with each allele optimal for slightly different environments. This greater robustness to environmental variance, he argued, would result in smaller trait variance for heterozygotes. The evidence for Lerner's hypothesis has been inconclusive. To address this question using modern genomic methods, we mapped loci associated with differences in trait variance (vQTL) on 1,101 individuals from the F34 of an advanced intercross between LG/J and SM/J mice. We also mapped epistatic interactions for these vQTL in order to understand the influence of epistasis for the architecture of trait variance. We did not find evidence supporting Lerner's hypothesis, that heterozygotes tend to have smaller trait variances than homozygotes. We further show that the effects of most mapped loci on trait variance are produced by epistasis affecting trait means and that those epistatic effects account for about a half of the differences in genotypic-specific trait variances. Finally, we propose a model where the different interactions between the additive and dominance effects of the vQTL and their epistatic partners can explain Lerner's original observations but can also be extended to include other conditions where heterozygotes are not the least variable genotype.

DIP-MS: ultra-deep interaction proteomics for the deconvolution of protein complexes

Most proteins are organized in macromolecular assemblies, which represent key functional units regulating and catalyzing most cellular processes. Affinity purification of the protein of interest combined with liquid chromatography coupled to tandem mass spectrometry (AP-MS) represents the method of choice to identify interacting proteins. The composition of complex isoforms concurrently present in the AP sample can, however, not be resolved from a single AP-MS experiment but requires computational inference from multiple time- and resource-intensive reciprocal AP-MS experiments. Here we introduce deep interactome profiling by mass spectrometry (DIP-MS), which combines AP with blue-native-PAGE separation, data-independent acquisition with mass spectrometry and deep-learning-based signal processing to resolve complex isoforms sharing the same bait protein in a single experiment. We applied DIP-MS to probe the organization of the human prefoldin family of complexes, resolving distinct prefoldin holo- and subcomplex variants, complex-complex interactions and complex isoforms with new subunits that were experimentally validated. Our results demonstrate that DIP-MS can reveal proteome modularity at unprecedented depth and resolution.

A transient protein folding response targets aggregation in the early phase of TDP-43-mediated neurodegeneration

Understanding the mechanisms that drive TDP-43 pathology is integral to combating amyotrophic lateral sclerosis (ALS), frontotemporal lobar degeneration (FTLD) and other neurodegenerative diseases. Here we generated a longitudinal quantitative proteomic map of the cortex from the cytoplasmic TDP-43 rNLS8 mouse model of ALS and FTLD, and developed a complementary open-access webtool, TDP-map ( https://shiny.rcc.uq.edu.au/TDP-map/ ). We identified distinct protein subsets enriched for diverse biological pathways with temporal alterations in protein abundance, including increases in protein folding factors prior to disease onset. This included increased levels of DnaJ homolog subfamily B member 5, DNAJB5, which also co-localized with TDP-43 pathology in diseased human motor cortex. DNAJB5 over-expression decreased TDP-43 aggregation in cell and cortical neuron cultures, and knockout of Dnajb5 exacerbated motor impairments caused by AAV-mediated cytoplasmic TDP-43 expression in mice. Together, these findings reveal molecular mechanisms at distinct stages of ALS and FTLD progression and suggest that protein folding factors could be protective in neurodegenerative diseases.

Co-fractionation-mass spectrometry to characterize native mitochondrial protein assemblies in mammalian neurons and brain

Human mitochondrial (mt) protein assemblies are vital for neuronal and brain function, and their alteration contributes to many human disorders, e.g., neurodegenerative diseases resulting from abnormal protein-protein interactions (PPIs). Knowledge of the composition of mt protein complexes is, however, still limited. Affinity purification mass spectrometry (MS) and proximity-dependent biotinylation MS have defined protein partners of some mt proteins, but are too technically challenging and laborious to be practical for analyzing large numbers of samples at the proteome level, e.g., for the study of neuronal or brain-specific mt assemblies, as well as altered mtPPIs on a proteome-wide scale for a disease of interest in brain regions, disease tissues or neurons derived from patients. To address this challenge, we adapted a co-fractionation-MS platform to survey native mt assemblies in adult mouse brain and in human NTERA-2 embryonal carcinoma stem cells or differentiated neuronal-like cells. The workflow consists of orthogonal separations of mt extracts isolated from chemically cross-linked samples to stabilize PPIs, data-dependent acquisition MS to identify co-eluted mt protein profiles from collected fractions and a computational scoring pipeline to predict mtPPIs, followed by network partitioning to define complexes linked to mt functions as well as those essential for neuronal and brain physiological homeostasis. We developed an R/CRAN software package, Macromolecular Assemblies from Co-elution Profiles for automated scoring of co-fractionation-MS data to define complexes from mtPPI networks. Presently, the co-fractionation-MS procedure takes 1.5-3.5 d of proteomic sample preparation, 31 d of MS data acquisition and 8.5 d of data analyses to produce meaningful biological insights.

Proteomics elucidating physiological and pathological functions of TDP-43

Trans-activation response DNA binding protein of 43 kDa (TDP-43) regulates a great variety of cellular processes in the nucleus and cytosol. In addition, a defined subset of neurodegenerative diseases is characterized by nuclear depletion of TDP-43 as well as cytosolic mislocalization and aggregation. To perform its diverse functions TDP-43 can associate with different ribonucleoprotein complexes. Combined with transcriptomics, MS interactome studies have unveiled associations between TDP-43 and the spliceosome machinery, polysomes and RNA granules. Moreover, the highly dynamic, low-valency interactions regulated by its low-complexity domain calls for innovative proximity labeling methodologies. In addition to protein partners, the analysis of post-translational modifications showed that they may play a role in the nucleocytoplasmic shuttling, RNA binding, liquid-liquid phase separation and protein aggregation of TDP-43. Here we review the various TDP-43 ribonucleoprotein complexes characterized so far, how they contribute to the diverse functions of TDP-43, and roles of post-translational modifications. Further understanding of the fluid dynamic properties of TDP-43 in ribonucleoprotein complexes, RNA granules, and self-assemblies will advance the understanding of RNA processing in cells and perhaps help to develop novel therapeutic approaches for TDPopathies.

Dynamic remodeling of Escherichia coli interactome in response to environmental perturbations

Proteins play an essential role in the vital biological processes governing cellular functions. Most proteins function as members of macromolecular machines, with the network of interacting proteins revealing the molecular mechanisms driving the formation of these complexes. Profiling the physiology-driven remodeling of these interactions within different contexts constitutes a crucial component to achieving a comprehensive systems-level understanding of interactome dynamics. Here, we apply co-fractionation mass spectrometry and computational modeling to quantify and profile the interactions of ∼2000 proteins in the bacterium Escherichia coli cultured under 10 distinct culture conditions. The resulting quantitative co-elution patterns revealed large-scale condition-dependent interaction remodeling among protein complexes involved in diverse biochemical pathways in response to the unique environmental challenges. The network-level analysis highlighted interactome-wide biophysical properties and structural patterns governing interaction remodeling. Our results provide evidence of the local and global plasticity of the E. coli interactome along with a rigorous generalizable framework to define protein interaction specificity. We provide an accompanying interactive web application to facilitate the exploration of these rewired networks.

PaxDb 5.0: Curated Protein Quantification Data Suggests Adaptive Proteome Changes in Yeasts

The "Protein Abundances Across Organisms" database (PaxDb) is an integrative metaresource dedicated to protein abundance levels, in tissue-specific or whole-organism proteomes. PaxDb focuses on computing best-estimate abundances for proteins in normal/healthy contexts and expresses abundance values for each protein in "parts per million" in relation to all other protein molecules in the cell. The uniform data reprocessing, quality scoring, and integrated orthology relations have made PaxDb one of the preferred tools for comparisons between individual datasets, tissues, or organisms. In describing the latest version 5.0 of PaxDb, we particularly emphasize the data integration from various types of raw data and how we expanded the number of organisms and tissue groups as well as the proteome coverage. The current collection of PaxDb includes 831 original datasets from 170 species, including 22 Archaea, 81 Bacteria, and 67 Eukaryota. Apart from detailing the data update, we also present a comparative analysis of the human proteome subset of PaxDb against the two most widely used human proteome data resources: Human Protein Atlas and Genotype-Tissue Expression. Lastly, through our protein abundance data, we reveal an evolutionary trend in the usage of sulfur-containing amino acids in the proteomes of Fungi.

Proteomic applications in identifying protein-protein interactions

There are many things that can be used to characterize a protein. Size, isoelectric point, hydrophobicity, structure (primary to quaternary), and subcellular location are just a few parameters that are used. The most important feature of a protein, however, is its function. While there are many experiments that can indicate a protein's role, identifying the molecules it interacts with is probably the most definitive way of determining its function. Owing to technology limitations, protein interactions have historically been identified on a one molecule per experiment basis. The advent of high throughput multiplexed proteomic technologies in the 1990s, however, made identifying hundreds and thousands of proteins interactions within single experiments feasible. These proteomic technologies have dramatically increased the rate at which protein-protein interactions (PPIs) are discovered. While the improvement in mass spectrometry technology was an early driving force in the rapid pace of identifying PPIs, advances in sample preparation and chromatography have recently been propelling the field. In this chapter, we will discuss the importance of identifying PPIs and describe current state-of-the-art technologies that demonstrate what is currently possible in this important area of biological research.

A Structural Network Analysis of Neuronal ArhGAP21/23 Interactors by Computational Modeling

RhoGTPase-activating proteins (RhoGAPs) play multiple roles in neuronal development; however, details of their substrate recognition system remain elusive. ArhGAP21 and ArhGAP23 are RhoGAPs that contain N-terminal PDZ and pleckstrin homology domains. In the present study, the RhoGAP domain of these ArhGAPs was computationally modeled by template-based methods and the AlphaFold2 software program, and their intrinsic RhoGTPase recognition mechanism was analyzed from the domain structures using the protein docking programs HADDOCK and HDOCK. ArhGAP21 was predicted to preferentially catalyze Cdc42, RhoA, RhoB, RhoC, and RhoG and to downregulate RhoD and Tc10 activities. Regarding ArhGAP23, RhoA and Cdc42 were deduced to be its substrates, whereas RhoD downregulation was predicted to be less efficient. The PDZ domains of ArhGAP21/23 possess the FTLRXXXVY sequence, and similar globular folding consists of antiparalleled β-sheets and two α-helices that are conserved with PDZ domains of MAST-family proteins. A peptide docking analysis revealed the specific interaction of the ArhGAP23 PDZ domain with the PTEN C-terminus. The pleckstrin homology domain structure of ArhGAP23 was also predicted, and the functional selectivity for the interactors regulated by the folding and disordered domains in ArhGAP21 and ArhGAP23 was examined by an in silico analysis. An interaction analysis of these RhoGAPs revealed the existence of mammalian ArhGAP21/23-specific type I and type III Arf- and RhoGTPase-regulated signaling. Multiple recognition systems of RhoGTPase substrates and selective Arf-dependent localization of ArhGAP21/23 may form the basis of the functional core signaling necessary for synaptic homeostasis and axon/dendritic transport regulated by RhoGAP localization and activities.

A new view on functions of the lysine demalonylase activity of SIRT5

AIMS

The specificity of the lysine demalonylation substrates of the pharmaceutically attractive tumor promoter/suppressor SIRT5 is not comprehensively clarified. The present study re-analyses publicly available data and highlights potentially pharmaceutically interesting outcomes by the use of bioinformatics.

MATERIALS AND METHODS

The interaction networks of SIRT5 malonylome from the wild-type and ob/ob (obese pre-diabetic type) mice were subjected to the pathway enrichment and gene function prediction analysis using GeneMania (3.5.2) application run under Cytoscape (3.9.1) environment.

KEY FINDINGS

The analysis in the wild-type mice revealed the involvement of SIRT5 malonylome in Eukaryotic translation elongation (ETE; the nodes EF1A1, EEF2, EEF1D, and EEF1G), Amino acid and derivative metabolism (AADM), and Selenoamino acid metabolism (SAM). The tumor promoter/suppressor activity of SIRT5 is mediated through the tumor promoter substrates included in AADM (GLUD1, SHMT1, ACAT1), and the tumor suppressor substrates involved in AADM and SAM (ALDH9A1, BHMT, GNMT). Selen stimulates the expression of SIRT5 and other sirtuins. SIRT5 in turn regulates the selenocysteine synthesis, which creates a regulatory loop. The analysis of SIRT5 malonylome in pre-diabetic ob/ob mice identifies the mTORC1 pathway as a mechanism, which facilitates SIRT5 functions. The comparison of the outcomes of SIRT5 malonylome, succinylome, and glutarylome analysis disclosed several differences.

SIGNIFICANCE

The analysis showed additional aspects of SIRT5 malonylome functions besides the control of glucose metabolism. It defined several unique substrates and pathways, and it showed differences compared to other enzymatic activities of SIRT5, which could be used for pharmaceutical benefits.

Analysis two types of K complexes on the human EEG based on classical continuous wavelet transform

In our work, we compare EEG time-frequency features for two types of K-complexes detected in volunteers performing the monotonous psychomotor test with their eyes closed. Type I K-complexes preceded spontaneous awakenings, while after type II K-complexes, subjects continued to sleep at least for 10 s after. The total number of K-complexes in the group of 18 volunteers was 646, of which of which type I K-complexes was 150 and type II K-complexes was 496. Time-frequency analysis was performed using continuous wavelet transform. EEG wavelet spectral power was averaged upon several brain zones for each of the classical frequency ranges (slow wave, δ, θ, α, β₁, β₂, γ bands). The low-frequency oscillatory activity ( δ-band) preceding type I K-complexes was asymmetrical and most prominent in the left hemisphere. Statistically significant differences were obtained by averaging over the left and right hemispheres, as well as projections of the motor area of the brain, p<0.05. The maximal differences between the types I and II of K-complexes were demonstrated in δ-, θ-bands in the occipital and posterior temporal regions. The high amplitude of the motor cortex projection response in β₂-band, [20;30] Hz, related to the sensory-motor modality of task in monotonous psychomotor test. The δ-oscillatory activity preceding type I K-complexes was asymmetrical and most prominent in the left hemisphere may be due to the important role of the left hemisphere in spontaneous awakening from sleep during monotonous work, which is an interesting issue for future research.

A genome-wide association study of tinnitus reveals shared genetic links to neuropsychiatric disorders

Tinnitus, a phantom perception of sound in the absence of any external sound source, is a prevalent health condition often accompanied by psychiatric comorbidities. Recent genome-wide association studies (GWAS) highlighted a polygenic nature of tinnitus susceptibility. A shared genetic component between tinnitus and psychiatric conditions remains elusive. Here we present a GWAS using the UK Biobank to investigate the genetic processes linked to tinnitus and tinnitus-related distress, followed by gene-set enrichment analyses. The UK Biobank sample comprised 132,438 individuals with tinnitus and genotype data. Among the study sample, 38,525 individuals reported tinnitus, and 26,889 participants mentioned they experienced tinnitus-related distress in daily living. The genome-wide association analyses were conducted on tinnitus and tinnitus-related distress. We conducted enrichment analyses using FUMA to further understand the genetic processes linked to tinnitus and tinnitus-related distress. A genome-wide significant locus (lead SNP: rs71595470) for tinnitus was obtained in the vicinity of GPM6A. Nineteen independent loci reached suggestive association with tinnitus. Fifteen independent loci reached suggestive association with tinnitus-related distress. The enrichment analysis revealed a shared genetic component between tinnitus and psychiatric traits, such as bipolar disorder, feeling worried, cognitive ability, fast beta electroencephalogram, and sensation seeking. Metabolic, cardiovascular, hematological, and pharmacological gene sets revealed a significant association with tinnitus. Anxiety and stress-related gene sets revealed a significant association with tinnitus-related distress. The GWAS signals for tinnitus were enriched in the hippocampus and cortex, and for tinnitus-related distress were enriched in the brain and spinal cord. This study provides novel insights into genetic processes associated with tinnitus and tinnitus-related distress and demonstrates a shared genetic component underlying tinnitus and psychiatric conditions. Further collaborative attempts are necessary to identify genetic components underlying the phenotypic heterogeneity in tinnitus and provide biological insight into the etiology.

Depression-Associated Negr1 Gene-Deficiency Induces Alterations in the Monoaminergic Neurotransmission Enhancing Time-Dependent Sensitization to Amphetamine in Male Mice

In GWAS studies, the neural adhesion molecule encoding the neuronal growth regulator 1 (NEGR1) gene has been consistently linked with both depression and obesity. Although the linkage between NEGR1 and depression is the strongest, evidence also suggests the involvement of NEGR1 in a wide spectrum of psychiatric conditions. Here we show the expression of NEGR1 both in tyrosine- and tryptophan hydroxylase-positive cells. Negr1-/- mice show a time-dependent increase in behavioral sensitization to amphetamine associated with increased dopamine release in both the dorsal and ventral striatum. Upregulation of transcripts encoding dopamine and serotonin transporters and higher levels of several monoamines and their metabolites was evident in distinct brain areas of Negr1-/- mice. Chronic (23 days) escitalopram-induced reduction of serotonin and dopamine turnover is enhanced in Negr1-/- mice, and escitalopram rescued reduced weight of hippocampi in Negr1-/- mice. The current study is the first to show alterations in the brain monoaminergic systems in Negr1-deficient mice, suggesting that monoaminergic neural circuits contribute to both depressive and obesity-related phenotypes linked to the human NEGR1 gene.

Where protein structure and cell diversity meet

Protein-protein interaction networks - interactomes - are charted with the hope to understand how phenotypes emerge and how they are altered in disease states. Early efforts to map interactomes have focused on the assembly of context agnostic, reference networks. However, recent studies have mapped interactomes across different cell lines and tissues, finding highly variable interactomes due to the rewiring of protein-protein interactions in different contexts. Increasing evidence points to significant links between protein structure and interactome diversity seen across cell types and tissues. We discuss how recent findings support the key role of alternative splicing and phosphorylation, two well-established regulators of protein structural and functional diversity, in defining cell type- and tissue-specific interactomes. Moreover, we show that intrinsically disordered protein regions are most favorably equipped to support interactome rewiring by acting as hubs of protein structure and function regulation.

ARH Family of ADP-Ribose-Acceptor Hydrolases

The ARH family of ADP-ribose-acceptor hydrolases consists of three 39-kDa members (ARH1-3), with similarities in amino acid sequence. ARH1 was identified based on its ability to cleave ADP-ribosyl-arginine synthesized by cholera toxin. Mammalian ADP-ribosyltransferases (ARTCs) mimicked the toxin reaction, with ARTC1 catalyzing the synthesis of ADP-ribosyl-arginine. ADP-ribosylation of arginine was stereospecific, with β-NAD+ as substrate and, α-anomeric ADP-ribose-arginine the reaction product. ARH1 hydrolyzed α-ADP-ribose-arginine, in addition to α-NAD+ and O-acetyl-ADP-ribose. Thus, ADP-ribose attached to oxygen-containing or nitrogen-containing functional groups was a substrate. Arh1 heterozygous and knockout (KO) mice developed tumors. Arh1-KO mice showed decreased cardiac contractility and developed myocardial fibrosis. In addition to Arh1-KO mice showed increased ADP-ribosylation of tripartite motif-containing protein 72 (TRIM72), a membrane-repair protein. ARH3 cleaved ADP-ribose from ends of the poly(ADP-ribose) (PAR) chain and released the terminal ADP-ribose attached to (serine)protein. ARH3 also hydrolyzed α-NAD+ and O-acetyl-ADP-ribose. Incubation of Arh3-KO cells with H2O2 resulted in activation of poly-ADP-ribose polymerase (PARP)-1, followed by increased nuclear PAR, increased cytoplasmic PAR, leading to release of Apoptosis Inducing Factor (AIF) from mitochondria. AIF, following nuclear translocation, stimulated endonucleases, resulting in cell death by Parthanatos. Human ARH3-deficiency is autosomal recessive, rare, and characterized by neurodegeneration and early death. Arh3-KO mice developed increased brain infarction following ischemia-reperfusion injury, which was reduced by PARP inhibitors. Similarly, PARP inhibitors improved survival of Arh3-KO cells treated with H2O2. ARH2 protein did not show activity in the in vitro assays described above for ARH1 and ARH3. ARH2 has a restricted tissue distribution, with primary involvement of cardiac and skeletal muscle. Overall, the ARH family has unique functions in biological processes and different enzymatic activities.

In Vivo Prenylomic Profiling in the Brain of a Transgenic Mouse Model of Alzheimer's Disease Reveals Increased Prenylation of a Key Set of Proteins

Dysregulation of protein prenylation has been implicated in many diseases, including Alzheimer's disease (AD). Prenylomic analysis, the combination of metabolic incorporation of an isoprenoid analogue (C15AlkOPP) into prenylated proteins with a bottom-up proteomic analysis, has allowed the identification of prenylated proteins in various cellular models. Here, transgenic AD mice were administered with C15AlkOPP through intracerebroventricular (ICV) infusion over 13 days. Using prenylomic analysis, 36 prenylated proteins were enriched in the brains of AD mice. Importantly, the prenylated forms of 15 proteins were consistently upregulated in AD mice compared to nontransgenic wild-type controls. These results highlight the power of this in vivo metabolic labeling approach to identify multiple post-translationally modified proteins that may serve as potential therapeutic targets for a disease that has proved refractory to treatment thus far. Moreover, this method should be applicable to many other types of protein modifications, significantly broadening its scope.

Adipocyte-Specific Laminin Alpha 4 Deletion Preserves Adipose Tissue Health despite Increasing Adiposity

Laminins are heterotrimeric glycoproteins with structural and functional roles in basement membranes. The predominant laminin alpha chain found in adipocyte basement membranes is laminin α4 (LAMA4). Global LAMA4 deletion in mice leads to reduced adiposity and increased energy expenditure, but also results in vascular defects that complicate the interpretation of metabolic data. Here, we describe the generation and initial phenotypic analysis of an adipocyte-specific LAMA4 knockout mouse (Lama4^AKO). We first performed an in-silico analysis to determine the degree to which laminin α4 was expressed in human and murine adipocytes. Next, male Lama4^AKO and control mice were fed chow or high-fat diets and glucose tolerance was assessed along with serum insulin and leptin levels. Adipocyte area was measured in both epididymal and inguinal white adipose tissue (eWAT and iWAT, respectively), and eWAT was used for RNA-sequencing. We found that laminin α4 was highly expressed in human and murine adipocytes. Further, chow-fed Lama4^AKO mice are like control mice in terms of body weight, body composition, and glucose tolerance, although they have larger eWAT adipocytes and lower insulin levels. High-fat-fed Lama4^AKO mice are fatter and more glucose tolerant when compared to control mice. Transcriptionally, the eWAT of high-fat fed Lama4^AKO mice resembles that of chow-fed control mice. We conclude from these findings that adipocyte-specific LAMA4 deletion is protective in an obesogenic environment, even though overall adiposity is increased.

Scalable multiplex co-fractionation/mass spectrometry platform for accelerated protein interactome discovery

Co-fractionation/mass spectrometry (CF/MS) enables the mapping of endogenous macromolecular networks on a proteome scale, but current methods are experimentally laborious, resource intensive and afford lesser quantitative accuracy. Here, we present a technically efficient, cost-effective and reproducible multiplex CF/MS (mCF/MS) platform for measuring and comparing, simultaneously, multi-protein assemblies across different experimental samples at a rate that is up to an order of magnitude faster than previous approaches. We apply mCF/MS to map the protein interaction landscape of non-transformed mammary epithelia versus breast cancer cells in parallel, revealing large-scale differences in protein-protein interactions and the relative abundance of associated macromolecules connected with cancer-related pathways and altered cellular processes. The integration of multiplexing capability within an optimized workflow renders mCF/MS as a powerful tool for systematically exploring physical interaction networks in a comparative manner.

Co-fractionation/mass spectrometry (CF/MS) allows mapping protein interactomes but efficiency and quantitative accuracy are limited. Here, the authors develop a reproducible multiplexed CF/MS method and apply it to characterize interactome rewiring in breast cancer cells.

Identification of ABCA5 among ATP-Binding Cassette Transporter Family as a New Biomarker for Colorectal Cancer

Background

The increasing incidence and mortality of colorectal cancer (CRC) urgently requires updated biomarkers. The ABC transporter family is a widespread family of membrane-bound proteins involved in the transportation of substrates associated with ATP hydrolysis, including metabolites, amino acids, peptides and proteins, sterols and lipids, organic and inorganic ions, sugars, metals, and drugs. They play an important role in the maintenance of homeostasis in the body.

Purpose

This study aims to search for new markers in the ABC transporter gene family for diagnostic and prognostic purposes through data mining of The Cancer Genome Atlas (TCGA) and GEO (Gene Expression Omnibus) datasets.

Methods

A total of 980 samples, including 684 CRC patients and 296 controls from five different datasets, were included for analysis. The construction of the PPI (protein-protein interaction) network and pathway analysis were performed in STRING database and DAVID (database for annotation, visualization, and integrated discovery), respectively. In addition, GSEA (gene set enrichment analysis) and WGCNA (weighted gene co-expression network analysis) were also used for functional analysis.

Results

After several rounds of screening and validation, only the ABCB5 gene was retained among the 49 genes.

Conclusions

The results demonstrated that ABCA5 expression is reduced in CRC and patients with high ABCA5 expression have better OS, which can provide guidance for better management and treatment of CRC in the future.

Protein interaction networks in neurodegenerative diseases: from physiological function to aggregation

The accumulation of protein inclusions is linked to many neurodegenerative diseases that typically develop in older individuals, due to a combination of genetic and environmental factors. In rare familial neurodegenerative disorders, genes encoding for aggregation-prone proteins are often mutated. While the underlying mechanism leading to these diseases still remains to be fully elucidated, efforts in the past 20 years revealed a vast network of protein–protein interactions that play a major role in regulating the aggregation of key proteins associated with neurodegeneration. Misfolded proteins that can oligomerize and form insoluble aggregates associate with molecular chaperones and other elements of the proteolytic machineries that are the frontline workers attempting to protect the cells by promoting clearance and preventing aggregation. Proteins that are normally bound to aggregation-prone proteins can become sequestered and mislocalized in protein inclusions, leading to their loss of function. In contrast, mutations, posttranslational modifications, or misfolding of aggregation-prone proteins can lead to gain of function by inducing novel or altered protein interactions, which in turn can impact numerous essential cellular processes and organelles, such as vesicle trafficking and the mitochondria. This review examines our current knowledge of protein–protein interactions involving several key aggregation-prone proteins that are associated with Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, or amyotrophic lateral sclerosis. We aim to provide an overview of the protein interaction networks that play a central role in driving or mitigating inclusion formation, while highlighting some of the key proteomic studies that helped to uncover the extent of these networks.

Mitochondrial antiviral-signalling protein is a client of the BAG6 protein quality control complex

The heterotrimeric BAG6 complex coordinates the direct handover of newly synthesised tail-anchored (TA) membrane proteins from an SGTA-bound preloading complex to the endoplasmic reticulum (ER) delivery component TRC40. In contrast, defective precursors, including aberrant TA proteins, form a stable complex with this cytosolic protein quality control factor, enabling such clients to be either productively re-routed or selectively degraded. We identify the mitochondrial antiviral-signalling protein (MAVS) as an endogenous TA client of both SGTA and the BAG6 complex. Our data suggest that the BAG6 complex binds to a cytosolic pool of MAVS before its misinsertion into the ER membrane, from where it can subsequently be removed via ATP13A1-mediated dislocation. This BAG6-associated fraction of MAVS is dynamic and responds to the activation of an innate immune response, suggesting that BAG6 may modulate the pool of MAVS that is available for coordinating the cellular response to viral infection.

Summary: Mitochondrial antiviral-signalling protein (MAVS) is a favoured client of the cytosolic BAG6 complex. We discuss how this dynamic interaction may modulate MAVS biogenesis at signalling membranes.

The genomic basis of high-elevation adaptation in wild house mice (Mus musculus domesticus) from South America

Understanding the genetic basis of environmental adaptation in natural populations is a central goal in evolutionary biology. The conditions at high elevation, particularly the low oxygen available in the ambient air, impose a significant and chronic environmental challenge to metabolically active animals with lowland ancestry. To understand the process of adaptation to these novel conditions and to assess the repeatability of evolution over short timescales, we examined the signature of selection from complete exome sequences of house mice (Mus musculus domesticus) sampled across two elevational transects in the Andes of South America. Using phylogenetic analysis, we show that house mice colonized high elevations independently in Ecuador and Bolivia. Overall, we found distinct responses to selection in each transect and largely nonoverlapping sets of candidate genes, consistent with the complex nature of traits that underlie adaptation to low oxygen availability (hypoxia) in other species. Nonetheless, we also identified a small subset of the genome that appears to be under parallel selection at the gene and SNP levels. In particular, three genes (Col22a1, Fgf14, and srGAP1) bore strong signatures of selection in both transects. Finally, we observed several patterns that were common to both transects, including an excess of derived alleles at high elevation, and a number of hypoxia-associated genes exhibiting a threshold effect, with a large allele frequency change only at the highest elevations. This threshold effect suggests that selection pressures may increase disproportionately at high elevations in mammals, consistent with observations of some high-elevation diseases in humans.

Proteomics-Based Identification of Interaction Partners of the Xenobiotic Detoxification Enzyme FMO3 Reveals Involvement in Urea Cycle

The hepatic xenobiotic metabolizing enzyme flavin-containing monooxygenase 3 (FMO3) has been implicated in the development of cardiometabolic disease primarily due to its enzymatic product trimethylamine-N oxide (TMAO), which has recently been shown to be associated with multiple chronic diseases, including kidney and coronary artery diseases. Although TMAO may have causative roles as a pro-inflammatory mediator, the possibility for roles in metabolic disease for FMO3, irrespective of TMAO formation, does exist. We hypothesized that FMO3 may interact with other proteins known to be involved in cardiometabolic diseases and that modulating the expression of FMO3 may impact on these interaction partners. Here, we combine a co-immunoprecipitation strategy coupled to unbiased proteomic workflow to report a novel protein:protein interaction network for FMO3. We identified 51 FMO3 protein interaction partners, and through gene ontology analysis, have identified urea cycle as an enriched pathway. Using mice deficient in FMO3 on two separate backgrounds, we validated and further investigated expressional and functional associations between FMO3 and the identified urea cycle genes. FMO3-deficient mice showed hepatic overexpression of carbamoylphosphate synthetase (CPS1), the rate-limiting gene of urea cycle, and increased hepatic urea levels, especially in mice of FVB (Friend leukemia virus B strain) background. Finally, overexpression of FMO3 in murine AML12 hepatocytes led to downregulation of CPS1. Although there is past literature linking TMAO to urea cycle, this is the first published work showing that FMO3 and CPS1 may directly interact, implicating a role for FMO3 in chronic kidney disease irrespective of TMAO formation.

Meta-analysis defines principles for the design and analysis of co-fractionation mass spectrometry experiments

Co-fractionation mass spectrometry (CF-MS) has emerged as a powerful technique for interactome mapping. However, there is little consensus on optimal strategies for the design of CF-MS experiments or their computational analysis. Here, we reanalyzed a total of 206 CF-MS experiments to generate a uniformly processed resource containing over 11 million measurements of protein abundance. We used this resource to benchmark experimental designs for CF-MS studies and systematically optimize computational approaches to network inference. We then applied this optimized methodology to reconstruct a draft-quality human interactome by CF-MS and predict over 700,000 protein-protein interactions across 27 eukaryotic species or clades. Our work defines new resources to illuminate proteome organization over evolutionary timescales and establishes best practices for the design and analysis of CF-MS studies.

PCprophet: a framework for protein complex prediction and differential analysis using proteomic data

Despite the availability of methods for analyzing protein complexes, systematic analysis of complexes under multiple conditions remains challenging. Approaches based on biochemical fractionation of intact, native complexes and correlation of protein profiles have shown promise. However, most approaches for interpreting cofractionation datasets to yield complex composition and rearrangements between samples depend considerably on protein-protein interaction inference. We introduce PCprophet, a toolkit built on size exclusion chromatography-sequential window acquisition of all theoretical mass spectrometry (SEC-SWATH-MS) data to predict protein complexes and characterize their changes across experimental conditions. We demonstrate improved performance of PCprophet over state-of-the-art approaches and introduce a Bayesian approach to analyze altered protein-protein interactions across conditions. We provide both command-line and graphical interfaces to support the application of PCprophet to any cofractionation MS dataset, independent of separation or quantitative liquid chromatography-MS workflow, for the detection and quantitative tracking of protein complexes and their physiological dynamics.

ComplexFinder: A software package for the analysis of native protein complex fractionation experiments

Identification of protein complexes and quantitative distribution of a single protein across different complexes are fundamental to unravel cellular mechanisms and of biological and clinical relevance. A recently introduced method, complexome profiling, combines fractionation techniques to separate native protein complexes with high-resolution mass spectrometry and allows to identify protein complexes in an unbiased manner. Due to recent advances in mass spectrometry instrumentation, the analysis time can be reduced dramatically while the coverage of thousands of proteins remains constant, which leads to an increased data acquisition rate and reduces the burden to initiate such complex experiments. Therefore, the development of novel computational pipelines for the analysis of such comprehensive complexome profiles is required. Usually, potential complex formations are assembled by correlation analysis. However, a major challenge in such an analysis is, that a protein can occur in multiple complexes of varying composition. Hence, signal profiles of proteins of the same complex might show high local similarities but do correlate poorly over all acquired fractions. Here, we describe ComplexFinder; a python-based computational pipeline that enables machine-learning based prediction of novel protein-protein interactions incorporating numerous measures of distance between signal profiles. Importantly, each signal profile is represented by an ensemble of peak-like models. These models allow the calculation of local similarities, enabling peak-centric comparison between biological conditions and the estimation of the composition of specific complexes. From the predicted protein-protein interactions, a protein connectivity network is constructed, which is used to assemble proteins into macromolecular complexes incorporating peak-centric information. ComplexFinder enables the peak-centric analysis of complexome profiling data utilizing various LC-MS/MS quantification strategies including label-free, SILAC, TMT as well as pulseSILAC. The source code is freely available at https://github.com/hnolcol/ComplexFinder.

From fuzziness to precision medicine: on the rapidly evolving proteomics with implications in mitochondrial connectivity to rare human disease

Mitochondrial (mt) dysfunction is linked to rare diseases (RDs) such as respiratory chain complex (RCC) deficiency, MELAS, and ARSACS. Yet, how altered mt protein networks contribute to these ailments remains understudied. In this perspective article, we identified 21 mt proteins from public repositories that associate with RCC deficiency, MELAS, or ARSACS, engaging in a relatively small number of protein-protein interactions (PPIs), underscoring the need for advanced proteomic and interactomic platforms to uncover the complete scope of mt connectivity to RDs. Accordingly, we discuss innovative untargeted label-free proteomics in identifying RD-specific mt or other macromolecular assemblies and mapping of protein networks in complex tissue, organoid, and stem cell-differentiated neurons. Furthermore, tag- and label-based proteomics, genealogical proteomics, and combinatorial affinity purification-mass spectrometry, along with advancements in detecting and integrating transient PPIs with single-cell proteomics and transcriptomics, collectively offer seminal follow-ups to enrich for RD-relevant networks, with implications in RD precision medicine.

Protein Interaction Network Biology in Neuroscience

Mapping the intricate networks of cellular proteins in the human brain has the potential to address unsolved questions in molecular neuroscience, including the molecular basis of cognition, synaptic plasticity, long-term potentiation, learning, and memory. Perturbations to the protein-protein interaction networks (PPIN) present in neurons, glia, and other cell-types have been linked to multifactorial neurological disorders. Yet while knowledge of brain PPINs is steadily improving, the complexity and dynamic nature of the heterogeneous central nervous system in normal and disease contexts poses a formidable experimental challenge. In this review, the recent applications of functional proteomics and systems biology approaches to study PPINs central to normal neuronal function, during neurodevelopment, and in neurodegenerative disorders are summarized. How systematic PPIN analysis offers a unique mechanistic framework to explore intra- and inter-cellular functional modules governing neuronal activity and brain function is also discussed. Finally, future technological advancements needed to address outstanding questions facing neuroscience are outlined.

Mitochondria under the spotlight: On the implications of mitochondrial dysfunction and its connectivity to neuropsychiatric disorders

Neuropsychiatric disorders (NPDs) such as bipolar disorder (BD), schizophrenia (SZ) and mood disorder (MD) are hard to manage due to overlapping symptoms and lack of biomarkers. Risk alleles of BD/SZ/MD are emerging, with evidence suggesting mitochondrial (mt) dysfunction as a critical factor for disease onset and progression. Mood stabilizing treatments for these disorders are scarce, revealing the need for biomarker discovery and artificial intelligence approaches to design synthetically accessible novel therapeutics. Here, we show mt involvement in NPDs by associating 245 mt proteins to BD/SZ/MD, with 7 common players in these disease categories. Analysis of over 650 publications suggests that 245 NPD-linked mt proteins are associated with 800 other mt proteins, with mt impairment likely to rewire these interactions. High dosage of mood stabilizers is known to alleviate manic episodes, but which compounds target mt pathways is another gap in the field that we address through mood stabilizer-gene interaction analysis of 37 prescriptions and over-the-counter psychotropic treatments, which we have refined to 15 mood-stabilizing agents. We show 26 of the 245 NPD-linked mt proteins are uniquely or commonly targeted by one or more of these mood stabilizers. Further, induced pluripotent stem cell-derived patient neurons and three-dimensional human brain organoids as reliable BD/SZ/MD models are outlined, along with multiomics methods and machine learning-based decision making tools for biomarker discovery, which remains a bottleneck for precision psychiatry medicine.

Molecular architecture of postsynaptic Interactomes

The postsynaptic density (PSD) plays an essential role in the organization of the synaptic signaling machinery. It contains a set of core scaffolding proteins that provide the backbone to PSD protein-protein interaction networks (PINs). These core scaffolding proteins can be seen as three principal layers classified by protein family, with DLG proteins being at the top, SHANKs along the bottom, and DLGAPs connecting the two layers. Early studies utilizing yeast two hybrid enabled the identification of direct protein-protein interactions (PPIs) within the multiple layers of scaffolding proteins. More recently, mass-spectrometry has allowed the characterization of whole interactomes within the PSD. This expansion of knowledge has further solidified the centrality of core scaffolding family members within synaptic PINs and provided context for their role in neuronal development and synaptic function. Here, we discuss the scaffolding machinery of the PSD, their essential functions in the organization of synaptic PINs, along with their relationship to neuronal processes found to be impaired in complex brain disorders.

Iron-sulfur cluster biogenesis, trafficking, and signaling: Roles for CGFS glutaredoxins and BolA proteins

The synthesis and trafficking of iron-sulfur (Fe-S) clusters in both prokaryotes and eukaryotes requires coordination within an expanding network of proteins that function in the cytosol, nucleus, mitochondria, and chloroplasts in order to assemble and deliver these ancient and essential cofactors to a wide variety of Fe-S-dependent enzymes and proteins. This review focuses on the evolving roles of two ubiquitous classes of proteins that operate in this network: CGFS glutaredoxins and BolA proteins. Monothiol or CGFS glutaredoxins possess a Cys-Gly-Phe-Ser active site that coordinates an Fe-S cluster in a homodimeric complex. CGFS glutaredoxins also form [2Fe-2S]-bridged heterocomplexes with BolA proteins, which possess an invariant His and an additional His or Cys residue that serve as cluster ligands. Here we focus on recent discoveries in bacteria, fungi, humans, and plants that highlight the shared and distinct roles of CGFS glutaredoxins and BolA proteins in Fe-S cluster biogenesis, Fe-S cluster storage and trafficking, and Fe-S cluster signaling to transcriptional factors that control iron metabolism--.

Depression-Associated Gene Negr1-Fgfr2 Pathway Is Altered by Antidepressant Treatment

The Negr1 gene has been significantly associated with major depression in genetic studies. Negr1 encodes for a cell adhesion molecule cleaved by the protease Adam10, thus activating Fgfr2 and promoting neuronal spine plasticity. We investigated whether antidepressants modulate the expression of genes belonging to Negr1-Fgfr2 pathway in Flinders sensitive line (FSL) rats, in a corticosterone-treated mouse model of depression, and in mouse primary neurons. Negr1 and Adam10 were the genes mostly affected by antidepressant treatment, and in opposite directions. Negr1 was down-regulated by escitalopram in the hypothalamus of FSL rats, by fluoxetine in the hippocampal dentate gyrus of corticosterone-treated mice, and by nortriptyline in hippocampal primary neurons. Adam10 mRNA was increased by nortriptyline administration in the hypothalamus, by escitalopram in the hippocampus of FSL rats, and by fluoxetine in mouse dorsal dentate gyrus. Similarly, nortriptyline increased Adam10 expression in hippocampal cultures. Fgfr2 expression was increased by nortriptyline in the hypothalamus of FSL rats and in hippocampal neurons. Lsamp, another IgLON family protein, increased in mouse dentate gyrus after fluoxetine treatment. These findings suggest that Negr1-Fgfr2 pathway plays a role in the modulation of synaptic plasticity induced by antidepressant treatment to promote therapeutic efficacy by rearranging connectivity in corticolimbic circuits impaired in depression.