Predicting nonsuicidal self-injury and suicidal risk: A comparison between the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition Section II personality disorder and alternative model of personality disorders dimensions.

The clinical relevance of nonsuicidal self-injury (NSSI) has received growing recognition, and NSSI represents a relevant risk factor for suicide. The present study aimed at running a head-to-head comparison between interview scores of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) Section II personality disorders (PDs) criteria, and DSM-5 Alternative Model of Personality Disorder (AMPD) Criterion A and Criterion B measures in providing significant and relevant information for understanding NSSI and suicidal ideation and behavior among psychotherapy participants. To this aim, a clinical sample of 103 adult participants was administered the Clinician-Administered Nonsuicidal Self-Injury Disorder Index (CANDI), the Columbia Suicide Severity Rating Scale (C-SSRS), as well as the Structured Clinical Interview for DSM-5 Personality Disorders, the Structured Clinical Interview for the DSM-5 Alternative Model for Personality Disorders Module I, and a self-report measure of dysfunctional personality traits (i.e., the Personality Inventory for DSM-5 [PID-5]). Logistic ordinal regression dominance analysis results showed that, when compared to the 10 DSM-5 Section II PD symptom counts, the DSM-5 Section III PD measure scores provided the same amount of information in the CANDI Global Severity Index scores (Nagelkerke pseudo-R² value = .41), and a markedly larger information quantity in the case of the C-SSRS Suicidal Ideation (+35.1%), and Suicidal Behavior Index (+35.9%) levels. As a whole, our data suggested the clinical usefulness of the DSM-5 AMPD in understanding NSSI and suicidal ideation and behavior. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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.

Longevity interventions modulate mechanotransduction and extracellular matrix homeostasis in C. elegans.

Dysfunctional extracellular matrices (ECM) contribute to aging and disease. Repairing dysfunctional ECM could potentially prevent age-related pathologies. Interventions promoting longevity also impact ECM gene expression. However, the role of ECM composition changes in healthy aging remains unclear. Here we perform proteomics and in-vivo monitoring to systematically investigate ECM composition (matreotype) during aging in C. elegans revealing three distinct collagen dynamics. Longevity interventions slow age-related collagen stiffening and prolong the expression of collagens that are turned over. These prolonged collagen dynamics are mediated by a mechanical feedback loop of hemidesmosome-containing structures that span from the exoskeletal ECM through the hypodermis, basement membrane ECM, to the muscles, coupling mechanical forces to adjust ECM gene expression and longevity via the transcriptional co-activator YAP-1 across tissues. Our results provide in-vivo evidence that coordinated ECM remodeling through mechanotransduction is required and sufficient to promote longevity, offering potential avenues for interventions targeting ECM dynamics.

In vivo protein turnover rates in varying oxygen tensions nominate MYBBP1A as a mediator of the hyperoxia response.

Oxygen deprivation and excess are both toxic. Thus, the body's ability to adapt to varying oxygen tensions is critical for survival. While the hypoxia transcriptional response has been well studied, the post-translational effects of oxygen have been underexplored. In this study, we systematically investigate protein turnover rates in mouse heart, lung, and brain under different inhaled oxygen tensions. We find that the lung proteome is the most responsive to varying oxygen tensions. In particular, several extracellular matrix (ECM) proteins are stabilized in the lung under both hypoxia and hyperoxia. Furthermore, we show that complex 1 of the electron transport chain is destabilized in hyperoxia, in accordance with the exacerbation of associated disease models by hyperoxia and rescue by hypoxia. Moreover, we nominate MYBBP1A as a hyperoxia transcriptional regulator, particularly in the context of rRNA homeostasis. Overall, our study highlights the importance of varying oxygen tensions on protein turnover rates and identifies tissue-specific mediators of oxygen-dependent responses.

Multi-scale photocatalytic proximity labeling reveals cell surface neighbors on and between cells.

The cell membrane proteome is the primary biohub for cell communication, yet we are only beginning to understand the dynamic protein neighborhoods that form on the cell surface and between cells. Proximity labeling proteomics (PLP) strategies using chemically reactive probes are powerful approaches to yield snapshots of protein neighborhoods but are currently limited to one single resolution based on the probe labeling radius. Here, we describe a multi-scale PLP method with tunable resolution using a commercially available histological dye, Eosin Y, which upon visible light illumination, activates three different photo-probes with labeling radii ranging from ∼100 to 3000 Å. We applied this platform to profile neighborhoods of the oncogenic epidermal growth factor receptor (EGFR) and orthogonally validated >20 neighbors using immuno-assays and AlphaFold-Multimer prediction that generated plausible binary interaction models. We further profiled the protein neighborhoods of cell-cell synapses induced by bi-specific T-cell engagers (BiTEs) and chimeric antigen receptor (CAR)T cells at longer length scales. This integrated multi-scale PLP platform maps local and distal protein networks on cell surfaces and between cells. We believe this information will aid in the systematic construction of the cell surface interactome and reveal new opportunities for immunotherapeutics.

Proteomic and genetic analyses of influenza A viruses identify pan-viral host targets.

Influenza A Virus (IAV) is a recurring respiratory virus with limited availability of antiviral therapies. Understanding host proteins essential for IAV infection can identify targets for alternative host-directed therapies (HDTs). Using affinity purification-mass spectrometry and global phosphoproteomic and protein abundance analyses using three IAV strains (pH1N1, H3N2, H5N1) in three human cell types (A549, NHBE, THP-1), we map 332 IAV-human protein-protein interactions and identify 13 IAV-modulated kinases. Whole exome sequencing of patients who experienced severe influenza reveals several genes, including scaffold protein AHNAK, with predicted loss-of-function variants that are also identified in our proteomic analyses. Of our identified host factors, 54 significantly alter IAV infection upon siRNA knockdown, and two factors, AHNAK and coatomer subunit COPB1, are also essential for productive infection by SARS-CoV-2. Finally, 16 compounds targeting our identified host factors suppress IAV replication, with two targeting CDK2 and FLT3 showing pan-antiviral activity across influenza and coronavirus families. This study provides a comprehensive network model of IAV infection in human cells, identifying functional host targets for pan-viral HDT.

SARS-CoV-2 variants evolve convergent strategies to remodel the host response.

SARS-CoV-2 variants of concern (VOCs) emerged during the COVID-19 pandemic. Here, we used unbiased systems approaches to study the host-selective forces driving VOC evolution. We discovered that VOCs evolved convergent strategies to remodel the host by modulating viral RNA and protein levels, altering viral and host protein phosphorylation, and rewiring virus-host protein-protein interactions. Integrative computational analyses revealed that although Alpha, Beta, Gamma, and Delta ultimately converged to suppress interferon-stimulated genes (ISGs), Omicron BA.1 did not. ISG suppression correlated with the expression of viral innate immune antagonist proteins, including Orf6, N, and Orf9b, which we mapped to specific mutations. Later Omicron subvariants BA.4 and BA.5 more potently suppressed innate immunity than early subvariant BA.1, which correlated with Orf6 levels, although muted in BA.4 by a mutation that disrupts the Orf6-nuclear pore interaction. Our findings suggest that SARS-CoV-2 convergent evolution overcame human adaptive and innate immune barriers, laying the groundwork to tackle future pandemics.

Next-generation proteomics for quantitative Jumbophage-bacteria interaction mapping.

Host-pathogen interactions are pivotal in regulating establishment, progression, and outcome of an infection. While affinity-purification mass spectrometry has become instrumental in characterizing such interactions, it suffers from limitations in scalability and biological authenticity. Here we present the use of co-fractionation mass spectrometry for high throughput analysis of host-pathogen interactions from native viral infections of two jumbophages (ϕKZ and ϕPA3) in Pseudomonas aeruginosa. This approach enabled the detection of > 6000 unique host-pathogen interactions for each phage, encompassing > 50% of their respective proteomes. This deep coverage provided evidence for interactions between KZ-like phage proteins and the host ribosome, and revealed protein complexes for previously undescribed phage ORFs, including a ϕPA3 complex showing strong structural and sequence similarity to ϕKZ non-virion RNA polymerase. Interactome-wide comparison across phages showed similar perturbed protein interactions suggesting fundamentally conserved mechanisms of phage predation within the KZ-like phage family. To enable accessibility to this data, we developed PhageMAP, an online resource for network query, visualization, and interaction prediction ( https://phagemap.ucsf.edu/ ). We anticipate this study will lay the foundation for the application of co-fractionation mass spectrometry for the scalable profiling of host-pathogen interactomes and protein complex dynamics upon infection.

Rapid Profiling of Protein Complex Reorganization in Perturbed Systems.

Protein complexes constitute the primary functional modules of cellular activity. To respond to perturbations, complexes undergo changes in their abundance, subunit composition, or state of modification. Understanding the function of biological systems requires global strategies to capture this contextual state information. Methods based on cofractionation paired with mass spectrometry have demonstrated the capability for deep biological insight, but the scope of studies using this approach has been limited by the large measurement time per biological sample and challenges with data analysis. There has been little uptake of this strategy into the broader life science community despite its rich biological information content. We present a rapid integrated experimental and computational workflow to assess the reorganization of protein complexes across multiple cellular states. The workflow combines short gradient chromatography and DIA/SWATH mass spectrometry with a data analysis toolset to quantify changes in a complex organization. We applied the workflow to study the global protein complex rearrangements of THP-1 cells undergoing monocyte to macrophage differentiation and subsequent stimulation of macrophage cells with lipopolysaccharide. We observed substantial proteome reorganization on differentiation and less pronounced changes in macrophage stimulation. We establish our integrated differential pipeline for rapid and state-specific profiling of protein complex organization.

Phosphorylation-linked complex profiling identifies assemblies required for Hippo signal integration.

While several computational methods have been developed to predict the functional relevance of phosphorylation sites, experimental analysis of the interdependency between protein phosphorylation and Protein-Protein Interactions (PPIs) remains challenging. Here, we describe an experimental strategy to establish interdependencies between protein phosphorylation and complex formation. This strategy is based on three main steps: (i) systematically charting the phosphorylation landscape of a target protein; (ii) assigning distinct proteoforms of the target protein to different protein complexes by native complex separation (AP-BNPAGE) and protein correlation profiling; and (iii) analyzing proteoforms and complexes in cells lacking regulators of the target protein. We applied this strategy to YAP1, a transcriptional co-activator for the control of organ size and tissue homeostasis that is highly phosphorylated and among the most connected proteins in human cells. We identified multiple YAP1 phosphosites associated with distinct complexes and inferred how both are controlled by Hippo pathway members. We detected a PTPN14/LATS1/YAP1 complex and suggest a model how PTPN14 inhibits YAP1 via augmenting WW domain-dependent complex formation and phosphorylation by LATS1/2.

Are problem buying and problem gambling addictive, impulsive, or compulsive in nature? A network analysis and latent dimension analysis study in Italian community-dwelling adults.

Prominent scholars suggested that the impulsive-obsessive compulsive continuum may represent a framework to understand both substance and behavioral addictions. However, the characterization of pathological buying (PB) and problem gambling (PG) within the compulsive-impulsive spectrum has not been extensively investigated. To explore the relationships among PB, PG, alcohol and substance abuse, DSM-5 obsessive-compulsive and related disorders, and impulsive dimensions, a sample of 1,005 Italian community-dwelling adult participants (55.5% female), was administered self-reported measures of PB, PG, and other theoretically-relevant constructs. We expected to observe a multidimensional structure in our data; moreover, DSM-5 obsessive compulsive and related disorders were hypothesized to be accounted for by a common dimension. Three dimensions were identified and replicated across two different, non-redundant methods (i.e., exploratory graph analysis and exploratory factor analysis), namely, substance use and gambling, obsessive and compulsive phenomena, and impulsivity dimensions. Specifically, PG seemed to represent a behavioral variant of addiction vulnerability, PB seemed more akin to obsessive-compulsive spectrum disorders, and disinhibition dimension represented the common core of negative urgency, lack of premeditation, lack of perseverance, sensation seeking (SS), and positive urgency. Our findings may be helpful in improving our knowledge on the similarities and differences between PB and PG.

Next-generation interaction proteomics for quantitative Jumbophage-bacteria interaction mapping.

Host-pathogen interactions (HPIs) are pivotal in regulating establishment, progression, and outcome of an infection. Affinity-purification mass spectrometry has become instrumental for the characterization of HPIs, however the targeted nature of exogenously expressing individual viral proteins has limited its utility to the analysis of relatively small pathogens. Here we present the use of co-fractionation mass spectrometry (SEC-MS) for the high-throughput analysis of HPIs from native viral infections of two jumbophages ( ϕ KZ and ϕ PA3) in Pseudomonas aeruginosa . This enabled the detection > 6000 unique host-pathogen and > 200 pathogen-pathogen interactions for each phage, encompassing > 50% of the phage proteome. Interactome-wide comparison across phages showed similar perturbed protein interactions suggesting fundamentally conserved mechanisms of phage predation within the KZ-like phage family. Prediction of novel ORFs revealed a ϕ PA3 complex showing strong structural and sequence similarity to ϕ KZ nvRNAp, suggesting ϕ PA3 also possesses two RNA polymerases acting at different stages of the infection cycle. We further expanded our understanding on the molecular organization of the virion packaged and injected proteome by identifying 23 novel virion components and 5 novel injected proteins, as well as providing the first evidence for interactions between KZ-like phage proteins and the host ribosome. To enable accessibility to this data, we developed PhageMAP, an online resource for network query, visualization, and interaction prediction ( https://phagemap.ucsf.edu/ ). We anticipate this study will lay the foundation for the application of co-fractionation mass spectrometry for the scalable profiling of hostpathogen interactomes and protein complex dynamics upon infection.

The joint hierarchical structure of psychopathology and dysfunctional personality domain indicators among community-dwelling adults.

To examine the hierarchical structure of psychopathology and dysfunctional personality domains, 2416 Italian community-dwelling adult volunteers were administered a set of psychometrically sound psychopathology measures and the Personality Inventory for DSM-5 Brief Form+ (PID-5-BF+). Parallel analysis, minimum average partial, and very simple structure results suggested that 1-6 principal components (PCs) should be retained. Goldberg's bass-ackwards model of the joint psychopathology measure and PID-5-BF+ ipsatized domain scale correlation matrix evidenced a hierarchical structure that was consistent with the working model proposed by the Hierarchical Taxonomy of Psychopathology (HiTOP) consortium. Hierarchical agglomerative cluster analysis around latent variables of the psychopathology indicators and PID-5-BF+ domain scales recovered four latent dimensions, which were akin to the corresponding bass-ackwards components and nicely reproduced the HiTOP Internalizing, Externalizing, Thought Disorder, and Eating Pathology dimensions.

The neuropsychological correlates of (sluggish) cognitive tempo scales in school-aged children.

Sluggish Cognitive Tempo (SCT) is a neuropsychiatric construct including lethargy, behavioral sluggishness, and confusion. A growing number of studies in the literature suggest that this set of symptoms refers to neuropsychological constructs such as sustained attention. However, studies focusing on SCT and its neuropsychological correlates in developmental age are scarce. The present study aims to fill this gap. The Child and Adolescent Behavior Inventory (CABI - Teacher and Parent versions, also including the school functioning scale, and the Child Concentration Inventory (CCI-2) were administered to a sample of 128 Italian primary-school children (57.6% F, mean age 8.81, SD 1.07); the neuropsychological constructs involved in the study were sustained attention and reaction times to two computerized tasks. Bivariate non-parametric correlation analyses yielded significant negative associations between teacher-referred SCT and measures of sustained attention (e.g., the Attentional Network Test and the Hearts and Flowers task) as well as CABI-T school-functioning scale; a small-to-moderate positive correlation was found between CABI-T SCT scores and mean reaction times, as a measure of the slowness of behavioral responses on the Attentional Network Test: this result would appear to represent a fine operationalization of the SCT-characteristic of behavioral sluggishness. Implications of these results for operationalizing the SCT construct in developmental age are discussed.

The role of clinical and Personological features in predicting high lethality suicide attempts: A study among mood disorder patients.

Suicidal attempts (SA) represent heterogeneous behaviours ranging in their seriousness from fatal and near-fatal (high-lethality) cases to those that do not require medical attention (low lethality). These considerations stress the need to identify high-risk individuals for high lethality SA in order to target suicide preventive interventions. The present study aims at evaluating the role of sociodemographic and clinical variables and examining personality pathological features in predicting high lethality SA. The sample was composed by 94 patients who were consecutively admitted to the Mood Disorders Unit of the San Raffaele Turro Hospital in Milan. The results of binary logistic regression analyses showed that previous SA and current suicide ideation play a role in predicting serious SA. Considering the DSM-5 personality dysfunctional domains assessed by the Personality Inventory for DSM-5, our logistic regression analyses suggested that high lethality SA was associated with Detachment PID-5 domain. Finally, binary hierarchical regression analysis showed that Detachment domain remained a significant predictor of serious SA over and above the effect of previous SA and suicide ideation. As a whole, our results highlight the importance of a multidimensional approach to develop adequate assessment, effective treatments and prevention of high lethality SA risk.

PCfun: a hybrid computational framework for systematic characterization of protein complex function.

In molecular biology, it is a general assumption that the ensemble of expressed molecules, their activities and interactions determine biological function, cellular states and phenotypes. Stable protein complexes-or macromolecular machines-are, in turn, the key functional entities mediating and modulating most biological processes. Although identifying protein complexes and their subunit composition can now be done inexpensively and at scale, determining their function remains challenging and labor intensive. This study describes Protein Complex Function predictor (PCfun), the first computational framework for the systematic annotation of protein complex functions using Gene Ontology (GO) terms. PCfun is built upon a word embedding using natural language processing techniques based on 1 million open access PubMed Central articles. Specifically, PCfun leverages two approaches for accurately identifying protein complex function, including: (i) an unsupervised approach that obtains the nearest neighbor (NN) GO term word vectors for a protein complex query vector and (ii) a supervised approach using Random Forest (RF) models trained specifically for recovering the GO terms of protein complex queries described in the CORUM protein complex database. PCfun consolidates both approaches by performing a hypergeometric statistical test to enrich the top NN GO terms within the child terms of the GO terms predicted by the RF models. The documentation and implementation of the PCfun package are available at https://github.com/sharmavaruns/PCfun. We anticipate that PCfun will serve as a useful tool and novel paradigm for the large-scale characterization of protein complex function.

Mutations in SARS-CoV-2 variants of concern link to increased spike cleavage and virus transmission.

SARS-CoV-2 lineages have diverged into highly prevalent variants termed "variants of concern" (VOCs). Here, we characterized emerging SARS-CoV-2 spike polymorphisms in vitro and in vivo to understand their impact on transmissibility and virus pathogenicity and fitness. We demonstrate that the substitution S:655Y, represented in the gamma and omicron VOCs, enhances viral replication and spike protein cleavage. The S:655Y substitution was transmitted more efficiently than its ancestor S:655H in the hamster infection model and was able to outcompete S:655H in the hamster model and in a human primary airway system. Finally, we analyzed a set of emerging SARS-CoV-2 variants to investigate how different sets of mutations may impact spike processing. All VOCs tested exhibited increased spike cleavage and fusogenic capacity. Taken together, our study demonstrates that the spike mutations present in VOCs that become epidemiologically prevalent in humans are linked to an increase in spike processing and virus transmission.

Evolution of enhanced innate immune evasion by SARS-CoV-2.

The emergence of SARS-CoV-2 variants of concern suggests viral adaptation to enhance human-to-human transmission1,2. Although much effort has focused on the characterization of changes in the spike protein in variants of concern, mutations outside of spike are likely to contribute to adaptation. Here, using unbiased abundance proteomics, phosphoproteomics, RNA sequencing and viral replication assays, we show that isolates of the Alpha (B.1.1.7) variant3 suppress innate immune responses in airway epithelial cells more effectively than first-wave isolates. We found that the Alpha variant has markedly increased subgenomic RNA and protein levels of the nucleocapsid protein (N), Orf9b and Orf6-all known innate immune antagonists. Expression of Orf9b alone suppressed the innate immune response through interaction with TOM70, a mitochondrial protein that is required for activation of the RNA-sensing adaptor MAVS. Moreover, the activity of Orf9b and its association with TOM70 was regulated by phosphorylation. We propose that more effective innate immune suppression, through enhanced expression of specific viral antagonist proteins, increases the likelihood of successful transmission of the Alpha variant, and may increase in vivo replication and duration of infection4. The importance of mutations outside the spike coding region in the adaptation of SARS-CoV-2 to humans is underscored by the observation that similar mutations exist in the N and Orf9b regulatory regions of the Delta and Omicron variants.

COVID-19 pandemic preventive behaviors and causal beliefs among Italian community dwelling adults.

In an online sample of 1054 Italian community-dwelling adults, we assessed the relationships between the frequency of implementation of practices intended to prevent COVID-19 infection, degree of agreement with theories about the origin of the infections, and the frequency of use of different sources of information concerning the COVID-19 pandemic. The results showed that participants' COVID-19-related preventive behaviors and causal beliefs were significantly associated with selected demographic variables and frequency of use of specific sources of information (e.g. scientific journals vs social media).