The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions

MCSdb, a database of proteins residing in membrane contact sites

Organelles do not act as autonomous discrete units but rather as interconnected hubs that engage in extensive communication by forming close contacts called "membrane contact sites (MCSs)". And many proteins have been identified as residing in MCS and playing important roles in maintaining and fulfilling specific functions within these microdomains. However, a comprehensive compilation of these MCS proteins is still lacking. Therefore, we developed MCSdb, a manually curated resource of MCS proteins and complexes from publications. MCSdb documents 7010 MCS protein entries and 263 complexes, involving 24 organelles and 44 MCSs across 11 species. Additionally, MCSdb orchestrates all data into different categories with multitudinous information for presenting MCS proteins. In summary, MCSdb provides a valuable resource for accelerating MCS functional interpretation and interorganelle communication deciphering.

Neuromodulator regulation and emotions: insights from the crosstalk of cell signaling

The unraveling of the regulatory mechanisms that govern neuronal excitability is a major challenge for neuroscientists worldwide. Neurotransmitters play a critical role in maintaining the balance between excitatory and inhibitory activity in the brain. The balance controls cognitive functions and emotional responses. Glutamate and γ-aminobutyric acid (GABA) are the primary excitatory and inhibitory neurotransmitters of the brain, respectively. Disruptions in the balance between excitatory and inhibitory transmission are implicated in several psychiatric disorders, including anxiety disorders, depression, and schizophrenia. Neuromodulators such as dopamine and acetylcholine control cognition and emotion by regulating the excitatory/inhibitory balance initiated by glutamate and GABA. Dopamine is closely associated with reward-related behaviors, while acetylcholine plays a role in aversive and attentional behaviors. Although the physiological roles of neuromodulators have been extensively studied neuroanatomically and electrophysiologically, few researchers have explored the interplay between neuronal excitability and cell signaling and the resulting impact on emotion regulation. This review provides an in-depth understanding of "cell signaling crosstalk" in the context of neuronal excitability and emotion regulation. It also anticipates that the next generation of neurochemical analyses, facilitated by integrated phosphorylation studies, will shed more light on this topic.

Flexible gold standards for transcription factor regulatory interactions in Escherichia coli K-12: architecture of evidence types

Post-genomic implementations have expanded the experimental strategies to identify elements involved in the regulation of transcription initiation. Here, we present for the first time a detailed analysis of the sources of knowledge supporting the collection of transcriptional regulatory interactions (RIs) of Escherichia coli K-12. An RI groups the transcription factor, its effect (positive or negative) and the regulated target, a promoter, a gene or transcription unit. We improved the evidence codes so that specific methods are incorporated and classified into independent groups. On this basis we updated the computation of confidence levels, weak, strong, or confirmed, for the collection of RIs. These updates enabled us to map the RI set to the current collection of HT TF-binding datasets from ChIP-seq, ChIP-exo, gSELEX and DAP-seq in RegulonDB, enriching in this way the evidence of close to one-quarter (1329) of RIs from the current total 5446 RIs. Based on the new computational capabilities of our improved annotation of evidence sources, we can now analyze the internal architecture of evidence, their categories (experimental, classical, HT, computational), and confidence levels. This is how we know that the joint contribution of HT and computational methods increase the overall fraction of reliable RIs (the sum of confirmed and strong evidence) from 49% to 71%. Thus, the current collection has 3912 reliable RIs, with 2718 or 70% of them with classical evidence which can be used to benchmark novel HT methods. Users can selectively exclude the method they want to benchmark, or keep for instance only the confirmed interactions. The recovery of regulatory sites in RegulonDB by the different HT methods ranges between 33% by ChIP-exo to 76% by ChIP-seq although as discussed, many potential confounding factors limit their interpretation. The collection of improvements reported here provides a solid foundation to incorporate new methods and data, and to further integrate the diverse sources of knowledge of the different components of the transcriptional regulatory network. There is no other genomic database that offers this comprehensive high-quality architecture of knowledge supporting a corpus of transcriptional regulatory interactions.

Pan-cancer molecular signatures connecting aspartate transaminase (AST) to cancer prognosis, metabolic and immune signatures

Background

Serum aspartate transaminase (sAST) level is used routinely in conjunction with other clinical assays to assess liver health and disease. Increasing evidence suggests that sAST is associated with all-cause mortality and has prognostic value in several cancers, including gastrointestinal and urothelial cancers. Here, we undertake a systems approach to unravel molecular connections between AST and cancer prognosis, metabolism, and immune signatures at the transcriptomic and proteomic levels.

Methods

We mined public gene expression data across multiple normal and cancerous tissues using the Genotype Tissue Expression (GTEX) resource and The Cancer Genome Atlas (TCGA) to assess the expression of genes encoding AST isoenzymes (GOT1 and GOT2) and their association with disease prognosis and immune infiltration signatures across multiple tumors. We examined the associations between AST and previously reported pan-cancer molecular subtypes characterized by distinct metabolic and immune signatures. We analyzed human protein-protein interaction networks for interactions between GOT1 and GOT2 with cancer-associated proteins. Using public databases and protein-protein interaction networks, we determined whether the subset of proteins that interact with AST (GOT1 and GOT2 interactomes) are enriched with proteins associated with specific diseases, miRNAs and transcription factors.

Results

We show that AST transcript isoforms (GOT1 and GOT2) are expressed across a wide range of normal tissues. AST isoforms are upregulated in tumors of the breast, lung, uterus, and thymus relative to normal tissues but downregulated in tumors of the liver, colon, brain, kidney and skeletal sarcomas. At the proteomic level, we find that the expression of AST is associated with distinct pan-cancer molecular subtypes with an enrichment of specific metabolic and immune signatures. Based on human protein-protein interaction data, AST physically interacts with multiple proteins involved in tumor initiation, suppression, progression, and treatment. We find enrichments in the AST interactomes for proteins associated with liver and lung cancer and dermatologic diseases. At the regulatory level, the GOT1 interactome is enriched with the targets of cancer-associated miRNAs, specifically mir34a - a promising cancer therapeutic, while the GOT2 interactome is enriched with proteins that interact with cancer-associated transcription factors.

Conclusions

Our findings suggest that perturbations in the levels of AST within specific tissues reflect pathophysiological changes beyond tissue damage and have implications for cancer metabolism, immune infiltration, prognosis, and treatment personalization.

Proximity Mapping of Desmosomes Reveals a Striking Shift in Their Molecular Neighborhood Associated With Maturation

Desmosomes are multiprotein adhesion complexes that link intermediate filaments to the plasma membrane, ensuring the mechanical integrity of cells across tissues, but how they participate in the wider signaling network to exert their full function is unclear. To investigate this, we carried out protein proximity mapping using biotinylation (BioID). The combined interactomes of the essential desmosomal proteins desmocollin 2a, plakoglobin, and plakophilin 2a (Pkp2a) in Madin-Darby canine kidney epithelial cells were mapped and their differences and commonalities characterized as desmosome matured from Ca2+ dependence to the mature, Ca2+-independent, hyper-adhesive state, which predominates in tissues. Results suggest that individual desmosomal proteins have distinct roles in connecting to cellular signaling pathways and that these roles alter substantially when cells change their adhesion state. The data provide further support for a dualistic concept of desmosomes in which the properties of Pkp2a differ from those of the other, more stable proteins. This body of data provides an invaluable resource for the analysis of desmosome function.

SPRTN is involved in hepatocellular carcinoma development through the ER stress response

Endoplasmic reticulum (ER) stress, prompted by the accumulation of misfolded or unfolded proteins, triggers the activation of the unfolded protein response (UPR) pathway to restore ER homeostasis. This stress response is implicated in the development of hepatocellular carcinoma (HCC). A biallelic mutation in SPRTN is currently the only known single-gene mutation implicated in the early onset of HCC. However, the exact mechanism linking SPRTN mutations to HCC remains unclear. In our study, we analyzed SPRTN and UPR in 21 human HCC tissue samples using RT-qPCR, immunoblot, and immunohistochemistry. We found alterations in the expression levels of SPRTN and UPR-related genes and proteins in HCC samples. The impact of SPRTN on the ER stress response was assessed in SPRTN-depleted HepG2 cells through RNA sequencing, pull-down assay, comet assay, and mitotic index calculation. We demonstrated that SPRTN interacts with the UPR sensor GRP78. Furthermore, we observed a decrease in SPRTN levels during ER stress, and increased sensitivity to ER stress in SPRTN-depleted cells. These findings suggest an essential role for SPRTN in the ER stress response and provide new insights into HCC pathogenesis. This newly discovered function of SPRTN could significantly enhance our understanding and treatment of HCC.

HNRNPA2B1 and HNRNPR stabilize ASCL1 in an m6A-dependent manner to promote neuroblastoma progression

HNRNPA2B1 and HNRNPR stabilize ASCL1 mRNA in neuroblastoma, but whether their regulatory effects depend on m6A modification and whether their function involves ASCL1 remain unknown. This study investigated the m6A-dependent binding of HNRNPA2B1 and HNRNPR to ASCL1 and subsequent regulation, as well as the expression, clinical significance, and function of HNRNPA2B1 and HNRNPR in neuroblastoma. We revealed that METTL14 mediated ASCL1 m6A modification to stabilize ASCL1. HNRNPA2B1 and HNRNPR significantly enriched ASCL1 mRNA by binding to the 5' and 3' untranslated regions, respectively, and METTL14 knockdown reduced this enrichment. Mutations in m6A sites in the untranslated regions of ASCL1 mRNA considerably decreased probe capacity to engage HNRNPA2B1 and HNRNPR. HNRNPR interacts with IGF2BP1, and knocking down either impaired binding to ASCL1 mRNA. HNRNPA2B1 and HNRNPR knockdown suppressed neuroblastoma cell growth and invasion, while ASCL1 overexpression restored these effects. The high HNRNPA2B1 and HNRNPR expression in neuroblastoma correlated with ASCL1 expression. Thus, HNRNPA2B1 and HNRNPR bind and stabilize ASCL1 mRNA in an m6A-dependent manner to promote neuroblastoma progression. This study not only discovered a new mechanism underlying the high ASCL1 expression in neuroblastoma but also identified the HNRNPA2B1/HNRNPR/ASCL1 axis as a promising target for inhibiting neuroblastoma progression.

Tyr352 as a Predominant Phosphosite in the Understudied Kinase and Molecular Target, HIPK1: Implications for Cancer Therapy

Homeodomain-interacting protein kinase 1 (HIPK1) is majorly found in the nucleoplasm. HIPK1 is associated with cell proliferation, tumor necrosis factor-mediated cellular apoptosis, transcription regulation, and DNA damage response, and thought to play significant roles in health and common diseases such as cancer. Despite this, HIPK1 remains an understudied molecular target. In the present study, based on a systematic screening and mapping approach, we assembled 424 qualitative and 44 quantitative phosphoproteome datasets with 15 phosphosites in HIPK1 reported across multiple studies. These HIPK1 phosphosites were not currently attributed to any functions. Among them, Tyr352 within the kinase domain was identified as the predominant phosphosite modulated in 22 differential datasets. To analyze the functional association of HIPK1 Tyr352, we first employed a stringent criterion to derive its positively and negatively correlated protein phosphosites. Subsequently, we categorized the correlated phosphosites in known interactors, known/predicted kinases, and substrates of HIPK1, for their prioritized validation. Bioinformatics analysis identified their significant association with biological processes such as the regulation of RNA splicing, DNA-templated transcription, and cellular metabolic processes. HIPK1 Tyr352 was also identified to be upregulated in Her2+ cell lines and a subset of pancreatic and cholangiocarcinoma tissues. These data and the systems biology approach undertaken in the present study serve as a platform to explore the functional role of other phosphosites in HIPK1, and by extension, inform cancer drug discovery and oncotherapy innovation. In all, this study highlights the comprehensive phosphosite map of HIPK1 kinase and the first of its kind phosphosite-centric analysis of HIPK1 kinase based on global-level phosphoproteomics datasets derived from human cellular differential experiments across distinct experimental conditions.

Genetic determinants of micronucleus formation in vivo

Genomic instability arising from defective responses to DNA damage1 or mitotic chromosomal imbalances2 can lead to the sequestration of DNA in aberrant extranuclear structures called micronuclei (MN). Although MN are a hallmark of ageing and diseases associated with genomic instability, the catalogue of genetic players that regulate the generation of MN remains to be determined. Here we analyse 997 mouse mutant lines, revealing 145 genes whose loss significantly increases (n = 71) or decreases (n = 74) MN formation, including many genes whose orthologues are linked to human disease. We found that mice null for Dscc1, which showed the most significant increase in MN, also displayed a range of phenotypes characteristic of patients with cohesinopathy disorders. After validating the DSCC1-associated MN instability phenotype in human cells, we used genome-wide CRISPR-Cas9 screening to define synthetic lethal and synthetic rescue interactors. We found that the loss of SIRT1 can rescue phenotypes associated with DSCC1 loss in a manner paralleling restoration of protein acetylation of SMC3. Our study reveals factors involved in maintaining genomic stability and shows how this information can be used to identify mechanisms that are relevant to human disease biology1.

Illuminating function of the understudied druggable kinome

The human kinome, with more than 500 proteins, is crucial for cell signaling and disease. Yet, about one-third of kinases lack in-depth study. The Data and Resource Generating Center for Understudied Kinases has developed multiple resources to address this challenge including creation of a heavy amino acid peptide library for parallel reaction monitoring and quantitation of protein kinase expression, use of understudied kinases tagged with a miniTurbo-biotin ligase to determine interaction networks by proximity-dependent protein biotinylation, NanoBRET probe development for screening chemical tool target specificity in live cells, characterization of small molecule chemical tools inhibiting understudied kinases, and computational tools for defining kinome architecture. These resources are available through the Dark Kinase Knowledgebase, supporting further research into these understudied protein kinases.

Deep analysis of skin molecular heterogeneities and their significance on the precise treatment of patients with psoriasis

Background

Psoriasis is a highly heterogeneous autoinflammatory disease. At present, heterogeneity in disease has not been adequately translated into concrete treatment options. Our aim was to develop and verify a new stratification scheme that identifies the heterogeneity of psoriasis by the integration of large-scale transcriptomic profiles, thereby identifying patient subtypes and providing personalized treatment options whenever possible.

Methods

We performed functional enrichment and network analysis of upregulated differentially expressed genes using microarray datasets of lesional and non-lesional skin samples from 250 psoriatic patients. Unsupervised clustering methods were used to identify the skin subtypes. Finally, an Xgboost classifier was utilized to predict the effects of methotrexate and commonly prescribed biologics on skin subtypes.

Results

Based on the 163 upregulated differentially expressed genes, psoriasis patients were categorized into three subtypes (subtypes A-C). Immune cells and proinflammatory-related pathways were markedly activated in subtype A, named immune activation. Contrastingly, subtype C, named stroma proliferation, was enriched in integrated stroma cells and tissue proliferation-related signaling pathways. Subtype B was modestly activated in all the signaling pathways. Notably, subtypes A and B presented good responses to methotrexate and interleukin-12/23 inhibitors (ustekinumab) but inadequate responses to tumor necrosis factor-α inhibitors and interleukin-17A receptor inhibitors. Contrastly, subtype C exhibited excellent responses to tumor necrosis factor-α inhibitors (etanercept) and interleukin-17A receptor inhibitors (brodalumab) but not methotrexate and interleukin-12/23 inhibitors.

Conclusions

Psoriasis patients can be assorted into three subtypes with different molecular and cellular characteristics based on the heterogeneity of the skin's immune cells and the stroma, determining the clinical responses of conventional therapies.

Taking Me away: the function of phosphorylation on histone lysine demethylases

Histone lysine demethylases (KDMs) regulate eukaryotic gene transcription by catalysing the removal of methyl groups from histone proteins. These enzymes are intricately regulated by the kinase signalling system in response to internal and external stimuli. Here, we review the mechanisms by which kinase-mediated phosphorylation influence human histone KDM function. These include the changing of histone KDM subcellular localisation or chromatin binding, the altering of protein half-life, changes to histone KDM complex formation that result in histone demethylation, non-histone demethylation or demethylase-independent effects, and effects on histone KDM complex dissociation. We also explore the structural context of phospho-sites on histone KDMs and evaluate how this relates to function.

Current updates in sarcoma biomarker discovery: emphasis on next-generation sequencing-based methods

Soft tissue sarcomas comprise a heterogeneous group of neoplasms. Although soft tissue malignancies make up only 2% of adult cancers, classification based on histomorphology presents a diagnostic challenge. Characterisation of soft tissue sarcomas by molecular analysis is rapidly evolving to improve diagnostic accuracy and develop targeted therapies. This review highlights the advances in molecular techniques, including current next-generation sequencing-based assays (fusion detection by RNA sequencing, targeted/whole exome sequencing, microRNA profiling), as well as emerging methods (liquid biopsies, DNA methylation profiling, single-cell molecular profiling and next-generation immunohistochemistry) for future clinical applications.

Selective Modulation of the Human Glucocorticoid Receptor Compromises GR Chromatin Occupancy and Recruitment of p300/CBP and the Mediator Complex

Exogenous glucocorticoids are frequently used to treat inflammatory disorders and as adjuncts for the treatment of solid cancers. However, their use is associated with severe side effects and therapy resistance. Novel glucocorticoid receptor (GR) ligands with a patient-validated reduced side effect profile have not yet reached the clinic. GR is a member of the nuclear receptor family of transcription factors and heavily relies on interactions with coregulator proteins for its transcriptional activity. To elucidate the role of the GR interactome in the differential transcriptional activity of GR following treatment with the selective GR agonist and modulator dagrocorat compared to classic (ant)agonists, we generated comprehensive interactome maps by high-confidence proximity proteomics in lung epithelial carcinoma cells. We found that dagrocorat and the antagonist RU486 both reduced GR interaction with CREB-binding protein/p300 and the mediator complex compared to the full GR agonist dexamethasone. Chromatin immunoprecipitation assays revealed that these changes in GR interactome were accompanied by reduced GR chromatin occupancy with dagrocorat and RU486. Our data offer new insights into the role of differential coregulator recruitment in shaping ligand-specific GR-mediated transcriptional responses.

DPCD is a regulator of R2TP in ciliogenesis initiation through Akt signaling

R2TP is a chaperone complex consisting of the AAA+ ATPases RUVBL1 and RUVBL2, as well as RPAP3 and PIH1D1 proteins. R2TP is responsible for the assembly of macromolecular complexes mainly acting through different adaptors. Using proximity-labeling mass spectrometry, we identified deleted in primary ciliary dyskinesia (DPCD) as an adaptor of R2TP. Here, we demonstrate that R2TP-DPCD influences ciliogenesis initiation through a unique mechanism by interaction with Akt kinase to regulate its phosphorylation levels rather than its stability. We further show that DPCD is a heart-shaped monomeric protein with two domains. A highly conserved region in the cysteine- and histidine-rich domains-containing proteins and SGT1 (CS) domain of DPCD interacts with the RUVBL2 DII domain with high affinity to form a stable R2TP-DPCD complex both in cellulo and in vitro. Considering that DPCD is one among several CS-domain-containing proteins found to associate with RUVBL1/2, we propose that RUVBL1/2 are CS-domain-binding proteins that regulate complex assembly and downstream signaling.

Toward Unified AI Drug Discovery with Multimodal Knowledge

Background: In real-world drug discovery, human experts typically grasp molecular knowledge of drugs and proteins from multimodal sources including molecular structures, structured knowledge from knowledge bases, and unstructured knowledge from biomedical literature. Existing multimodal approaches in AI drug discovery integrate either structured or unstructured knowledge independently, which compromises the holistic understanding of biomolecules. Besides, they fail to address the missing modality problem, where multimodal information is missing for novel drugs and proteins. Methods: In this work, we present KEDD, a unified, end-to-end deep learning framework that jointly incorporates both structured and unstructured knowledge for vast AI drug discovery tasks. The framework first incorporates independent representation learning models to extract the underlying characteristics from each modality. Then, it applies a feature fusion technique to calculate the prediction results. To mitigate the missing modality problem, we leverage sparse attention and a modality masking technique to reconstruct the missing features based on top relevant molecules. Results: Benefiting from structured and unstructured knowledge, our framework achieves a deeper understanding of biomolecules. KEDD outperforms state-of-the-art models by an average of 5.2% on drug-target interaction prediction, 2.6% on drug property prediction, 1.2% on drug-drug interaction prediction, and 4.1% on protein-protein interaction prediction. Through qualitative analysis, we reveal KEDD's promising potential in assisting real-world applications. Conclusions: By incorporating biomolecular expertise from multimodal knowledge, KEDD bears promise in accelerating drug discovery.

Illuminating the functions of the understudied Fructosamine-3-kinase (FN3K) using a multi-omics approach reveals new links to lipid, carbon, and co-factor metabolic pathways

Fructosamine-3-kinases (FN3Ks) are a conserved family of repair enzymes that phosphorylate reactive sugars attached to lysine residues in peptides and proteins. Although FN3Ks are present across the tree of life and share detectable sequence similarity to eukaryotic protein kinases, the biological processes regulated by these kinases are largely unknown. To address this knowledge gap, we leveraged the FN3K CRISPR Knock-Out (KO) cell line alongside an integrative multi-omics study combining transcriptomics, metabolomics, and interactomics to place these enzymes in a pathway context. The integrative analyses revealed the enrichment of pathways related to oxidative stress response, lipid biosynthesis (cholesterol and fatty acids), carbon and co-factor metabolism. Moreover, enrichment of nicotinamide adenine dinucleotide (NAD) binding proteins and localization of human FN3K (HsFN3K) to mitochondria suggests potential links between FN3Ks and NAD-mediated energy metabolism and redox balance. We report specific binding of HsFN3K to NAD compounds in a metal and concentration-dependent manner and provide insight into their binding mode using modeling and experimental site-directed mutagenesis. By identifying a potential link between FN3Ks, redox regulation, and NAD-dependent metabolic processes, our studies provide a framework for targeting these understudied kinases in diabetic complications and metabolic disorders where redox balance is altered.

Generic model to unravel the deeper insights of viral infections: an empirical application of evolutionary graph coloring in computational network biology

PURPOSE

Graph coloring approach has emerged as a valuable problem-solving tool for both theoretical and practical aspects across various scientific disciplines, including biology. In this study, we demonstrate the graph coloring's effectiveness in computational network biology, more precisely in analyzing protein-protein interaction (PPI) networks to gain insights about the viral infections and its consequences on human health. Accordingly, we propose a generic model that can highlight important hub proteins of virus-associated disease manifestations, changes in disease-associated biological pathways, potential drug targets and respective drugs. We test our model on SARS-CoV-2 infection, a highly transmissible virus responsible for the COVID-19 pandemic. The pandemic took significant human lives, causing severe respiratory illnesses and exhibiting various symptoms ranging from fever and cough to gastrointestinal, cardiac, renal, neurological, and other manifestations.

METHODS

To investigate the underlying mechanisms of SARS-CoV-2 infection-induced dysregulation of human pathobiology, we construct a two-level PPI network and employed a differential evolution-based graph coloring (DEGCP) algorithm to identify critical hub proteins that might serve as potential targets for resolving the associated issues. Initially, we concentrate on the direct human interactors of SARS-CoV-2 proteins to construct the first-level PPI network and subsequently applied the DEGCP algorithm to identify essential hub proteins within this network. We then build a second-level PPI network by incorporating the next-level human interactors of the first-level hub proteins and use the DEGCP algorithm to predict the second level of hub proteins.

RESULTS

We first identify the potential crucial hub proteins associated with SARS-CoV-2 infection at different levels. Through comprehensive analysis, we then investigate the cellular localization, interactions with other viral families, involvement in biological pathways and processes, functional attributes, gene regulation capabilities as transcription factors, and their associations with disease-associated symptoms of these identified hub proteins. Our findings highlight the significance of these hub proteins and their intricate connections with disease pathophysiology. Furthermore, we predict potential drug targets among the hub proteins and identify specific drugs that hold promise in preventing or treating SARS-CoV-2 infection and its consequences.

CONCLUSION

Our generic model demonstrates the effectiveness of DEGCP algorithm in analyzing biological PPI networks, provides valuable insights into disease biology, and offers a basis for developing novel therapeutic strategies for other viral infections that may cause future pandemic.

SATB2 organizes the 3D genome architecture of cognition in cortical neurons

The DNA-binding protein SATB2 is genetically linked to human intelligence. We studied its influence on the three-dimensional (3D) epigenome by mapping chromatin interactions and accessibility in control versus SATB2-deficient cortical neurons. We find that SATB2 affects the chromatin looping between enhancers and promoters of neuronal-activity-regulated genes, thus influencing their expression. It also alters A/B compartments, topologically associating domains, and frequently interacting regions. Genes linked to SATB2-dependent 3D genome changes are implicated in highly specialized neuronal functions and contribute to cognitive ability and risk for neuropsychiatric and neurodevelopmental disorders. Non-coding DNA regions with a SATB2-dependent structure are enriched for common variants associated with educational attainment, intelligence, and schizophrenia. Our data establish SATB2 as a cell-type-specific 3D genome modulator, which operates both independently and in cooperation with CCCTC-binding factor (CTCF) to set up the chromatin landscape of pyramidal neurons for cognitive processes.

The GATOR2 complex maintains lysosomal-autophagic function by inhibiting the protein degradation of MiT/TFEs

Lysosomes are central to metabolic homeostasis. The microphthalmia bHLH-LZ transcription factors (MiT/TFEs) family members MITF, TFEB, and TFE3 promote the transcription of lysosomal and autophagic genes and are often deregulated in cancer. Here, we show that the GATOR2 complex, an activator of the metabolic regulator TORC1, maintains lysosomal function by protecting MiT/TFEs from proteasomal degradation independent of TORC1, GATOR1, and the RAG GTPase. We determine that in GATOR2 knockout HeLa cells, members of the MiT/TFEs family are ubiquitylated by a trio of E3 ligases and are degraded, resulting in lysosome dysfunction. Additionally, we demonstrate that GATOR2 protects MiT/TFE proteins in pancreatic ductal adenocarcinoma and Xp11 translocation renal cell carcinoma, two cancers that are driven by MiT/TFE hyperactivation. In summary, we find that the GATOR2 complex has independent roles in TORC1 regulation and MiT/TFE protein protection and thus is central to coordinating cellular metabolism with control of the lysosomal-autophagic system.

A Specialized Centrosome-Proteasome Axis Mediates Proteostasis and Influences Cardiac Stress through Txlnb

Background

Centrosomes localize to perinuclear foci where they serve multifunctional roles, arranging the microtubule organizing center (MTOC) and anchoring ubiquitin-proteasome system (UPS) machinery. In mature cardiomyocytes, centrosomal proteins redistribute into a specialized perinuclear cage-like structure, and a potential centrosome-UPS interface has not been studied. Taxilin-beta (Txlnb), a cardiomyocyte-enriched protein, belongs to a family of centrosome adapter proteins implicated in protein quality control. We hypothesize that Txlnb plays a key role in centrosomal-proteasomal crosstalk in cardiomyocytes.

Methods

Integrative bioinformatics assessed centrosomal gene dysregulation in failing hearts. Txlnb gain/loss-of-function studies were conducted in cultured cardiomyocytes and mice. Txlnb's role in cardiac proteotoxicity and hypertrophy was examined using CryAB-R120G mice and transverse aortic constriction (TAC), respectively. Molecular modeling investigated Txlnb structure/function.

Results

Human failing hearts show consistent dysregulation of many centrosome-associated genes, alongside UPS-related genes. Txlnb emerged as a candidate regulator of cardiomyocyte proteostasis that localizes to the perinuclear centrosomal compartment. Txlnb's interactome strongly supports its involvement in cytoskeletal, microtubule, and UPS processes, particularly centrosome-related functions. Overexpressing Txlnb in cardiomyocytes reduced ubiquitinated protein accumulation and enhanced proteasome activity during hypertrophy. Txlnb-knockout (KO) mouse hearts exhibit proteasomal insufficiency and altered cardiac growth, evidenced by ubiquitinated protein accumulation, decreased 26Sβ5 proteasome activity, and lower mass with age. In Cryab-R120G mice, Txlnb loss worsened heart failure, causing lower ejection fractions. After TAC, Txlnb-KO mice also showed reduced ejection fraction, increased heart mass, and elevated ubiquitinated protein accumulation. Investigations into the molecular mechanisms revealed that Txlnb-KO did not affect proteasomal subunit expression but led to the upregulation of Txlna and several centrosomal proteins (Cep63, Ofd1, and Tubg) suggesting altered centrosomal dynamics. Structural predictions support Txlnb's role as a specialized centrosomal-adapter protein bridging centrosomes with proteasomes, confirmed by microtubule-dependent perinuclear localization.

Conclusions

Together, these data provide initial evidence connecting Txlnb to cardiac proteostasis, hinting at the potential importance of functional bridging between specialized centrosomes and UPS in cardiomyocytes.

Harmonin homology domain-mediated interaction of RTEL1 helicase with RPA and DNA provides insights into its recruitment to DNA repair sites

The regulator of telomere elongation helicase 1 (RTEL1) plays roles in telomere DNA maintenance, DNA repair, and genome stability by dismantling D-loops and unwinding G-quadruplex structures. RTEL1 comprises a helicase domain, two tandem harmonin homology domains 1&2 (HHD1 and HHD2), and a Zn2+-binding RING domain. In vitro D-loop disassembly by RTEL1 is enhanced in the presence of replication protein A (RPA). However, the mechanism of RTEL1 recruitment at non-telomeric D-loops remains unknown. In this study, we have unravelled a direct physical interaction between RTEL1 and RPA. Under DNA damage conditions, we showed that RTEL1 and RPA colocalise in the cell. Coimmunoprecipitation showed that RTEL1 and RPA interact, and the deletion of HHDs of RTEL1 significantly reduced this interaction. NMR chemical shift perturbations (CSPs) showed that RPA uses its 32C domain to interact with the HHD2 of RTEL1. Interestingly, HHD2 also interacted with DNA in the in vitro experiments. HHD2 structure was determined using X-ray crystallography, and NMR CSPs mapping revealed that both RPA 32C and DNA competitively bind to HHD2 on an overlapping surface. These results establish novel roles of accessory HHDs in RTEL1's functions and provide mechanistic insights into the RPA-mediated recruitment of RTEL1 to DNA repair sites.

A foundational atlas of autism protein interactions reveals molecular convergence

Translating high-confidence (hc) autism spectrum disorder (ASD) genes into viable treatment targets remains elusive. We constructed a foundational protein-protein interaction (PPI) network in HEK293T cells involving 100 hcASD risk genes, revealing over 1,800 PPIs (87% novel). Interactors, expressed in the human brain and enriched for ASD but not schizophrenia genetic risk, converged on protein complexes involved in neurogenesis, tubulin biology, transcriptional regulation, and chromatin modification. A PPI map of 54 patient-derived missense variants identified differential physical interactions, and we leveraged AlphaFold-Multimer predictions to prioritize direct PPIs and specific variants for interrogation in Xenopus tropicalis and human forebrain organoids. A mutation in the transcription factor FOXP1 led to reconfiguration of DNA binding sites and altered development of deep cortical layer neurons in forebrain organoids. This work offers new insights into molecular mechanisms underlying ASD and describes a powerful platform to develop and test therapeutic strategies for many genetically-defined conditions.

Protein interaction map of APOBEC3 enzyme family reveals deamination-independent role in cellular function

Human APOBEC3 enzymes are a family of single-stranded (ss)DNA and RNA cytidine deaminases that act as part of the intrinsic immunity against viruses and retroelements. These enzymes deaminate cytosine to form uracil which can functionally inactivate or cause degradation of viral or retroelement genomes. In addition, APOBEC3s have deamination independent antiviral activity through protein and nucleic acid interactions. If expression levels are misregulated, some APOBEC3 enzymes can access the human genome leading to deamination and mutagenesis, contributing to cancer initiation and evolution. While APOBEC3 enzymes are known to interact with large ribonucleoprotein complexes, the function and RNA dependence is not entirely understood. To further understand their cellular roles, we determined by affinity purification mass spectrometry (AP-MS) the protein interaction network for the human APOBEC3 enzymes and map a diverse set of protein-protein and protein-RNA mediated interactions. Our analysis identified novel RNA-mediated interactions between APOBEC3C, APOBEC3H Haplotype I and II, and APOBEC3G with spliceosome proteins, and APOBEC3G and APOBEC3H Haplotype I with proteins involved in tRNA methylation and ncRNA export from the nucleus. In addition, we identified RNA-independent protein-protein interactions with APOBEC3B, APOBEC3D, and APOBEC3F and the prefoldin family of protein folding chaperones. Interaction between prefoldin 5 (PFD5) and APOBEC3B disrupted the ability of PFD5 to induce degradation of the oncogene cMyc, implicating the APOBEC3B protein interaction network in cancer. Altogether, the results uncover novel functions and interactions of the APOBEC3 family and suggest they may have fundamental roles in cellular RNA biology, their protein-protein interactions are not redundant, and there are protein-protein interactions with tumor suppressors, suggesting a role in cancer biology.

Meta-Research: understudied genes are lost in a leaky pipeline between genome-wide assays and reporting of results

Present-day publications on human genes primarily feature genes that already appeared in many publications prior to completion of the Human Genome Project in 2003. These patterns persist despite the subsequent adoption of high-throughput technologies, which routinely identify novel genes associated with biological processes and disease. Although several hypotheses for bias in the selection of genes as research targets have been proposed, their explanatory powers have not yet been compared. Our analysis suggests that understudied genes are systematically abandoned in favor of better-studied genes between the completion of -omics experiments and the reporting of results. Understudied genes remain abandoned by studies that cite these -omics experiments. Conversely, we find that publications on understudied genes may even accrue a greater number of citations. Among 45 biological and experimental factors previously proposed to affect which genes are being studied, we find that 33 are significantly associated with the choice of hit genes presented in titles and abstracts of - omics studies. To promote the investigation of understudied genes we condense our insights into a tool, find my understudied genes (FMUG), that allows scientists to engage with potential bias during the selection of hits. We demonstrate the utility of FMUG through the identification of genes that remain understudied in vertebrate aging. FMUG is developed in Flutter and is available for download at fmug.amaral.northwestern.edu as a MacOS/Windows app.

Shared Proteins and Pathways of Cardiovascular and Cognitive Diseases: Relation to Vascular Cognitive Impairment

One of the primary goals of systems medicine is the detection of putative proteins and pathways involved in disease progression and pathological phenotypes. Vascular cognitive impairment (VCI) is a heterogeneous condition manifesting as cognitive impairment resulting from vascular factors. The precise mechanisms underlying this relationship remain unclear, which poses challenges for experimental research. Here, we applied computational approaches like systems biology to unveil and select relevant proteins and pathways related to VCI by studying the crosstalk between cardiovascular and cognitive diseases. In addition, we specifically included signals related to oxidative stress, a common etiologic factor tightly linked to aging, a major determinant of VCI. Our results show that pathways associated with oxidative stress are quite relevant, as most of the prioritized vascular cognitive genes and proteins were enriched in these pathways. Our analysis provided a short list of proteins that could be contributing to VCI: DOLK, TSC1, ATP1A1, MAPK14, YWHAZ, CREB3, HSPB1, PRDX6, and LMNA. Moreover, our experimental results suggest a high implication of glycative stress, generating oxidative processes and post-translational protein modifications through advanced glycation end-products (AGEs). We propose that these products interact with their specific receptors (RAGE) and Notch signaling to contribute to the etiology of VCI.

Structure-based prediction of protein-protein interaction network in rice

Comprehensive protein-protein interaction (PPI) maps are critical for understanding the functional organization of the proteome, but challenging to produce experimentally. Here, we developed a computational method for predicting PPIs based on protein docking. Evaluation of performance on benchmark sets demonstrated the ability of the docking-based method to accurately identify PPIs using predicted protein structures. By employing the docking-based method, we constructed a structurally resolved PPI network consisting of 24,653 interactions between 2,131 proteins, which greatly extends the current knowledge on the rice protein-protein interactome. Moreover, we mapped the trait-associated single nucleotide polymorphisms (SNPs) to the structural interactome, and computationally identified 14 SNPs that had significant consequences on PPI network. The protein structural interactome map provided a resource to facilitate functional investigation of PPI-perturbing alleles associated with agronomically important traits in rice.

METTL3 regulates TFRC ubiquitination and ferroptosis through stabilizing NEDD4L mRNA to impact stroke

BACKGROUND

Stroke is a major medical problem, and novel therapeutic targets are urgently needed. This study investigates the protective role and potential mechanisms of the N6-methyladenosine (m6A) RNA methyltransferase METTL3 against cerebral injury resulting from insufficient cerebral blood flow.

METHODS

In this study, we constructed mouse MCAO models and HT-22 cell OGD/R models to mimic ischemic stroke-induced brain injury and neuronal damage. We generated NEDD4L knockout and METTL3 overexpression models and validated therapeutic effects using infarct volume, brain edema, and neurologic scoring. We performed qRT-PCR, western blotting, and co-immunoprecipitation to assess the influence of NEDD4L on ferroptosis markers and TFRC expression. We verified the effect of NEDD4L on TFRC ubiquitination by detecting half-life and ubiquitination. Finally, we validated the impact of METTL3 on NEDD4L mRNA stability and MCAO outcomes in both in vitro and in vivo experimental models.

RESULT

We find NEDD4L expression is downregulated in MCAO models. Overexpressing METTL3 inhibits the iron carrier protein TFRC by upregulating the E3 ubiquitin ligase NEDD4L, thereby alleviating oxidative damage and ferroptosis to protect the brain from ischemic injury. Mechanistic studies show METTL3 can methylate and stabilize NEDD4L mRNA, enhancing NEDD4L expression. As a downstream effector, NEDD4L ubiquitinates and degrades TFRC, reducing iron accumulation and neuronal ferroptosis.

CONCLUSION

In summary, we uncover the METTL3-NEDD4L-TFRC axis is critical for inhibiting post-ischemic brain injury. Enhancing this pathway may serve as an effective strategy for stroke therapy. This study lays the theoretical foundation for developing m6A-related therapies against ischemic brain damage.

FAVA: high-quality functional association networks inferred from scRNA-seq and proteomics data

MOTIVATION

Protein networks are commonly used for understanding how proteins interact. However, they are typically biased by data availability, favoring well-studied proteins with more interactions. To uncover functions of understudied proteins, we must use data that are not affected by this literature bias, such as single-cell RNA-seq and proteomics. Due to data sparseness and redundancy, functional association analysis becomes complex.

RESULTS

To address this, we have developed FAVA (Functional Associations using Variational Autoencoders), which compresses high-dimensional data into a low-dimensional space. FAVA infers networks from high-dimensional omics data with much higher accuracy than existing methods, across a diverse collection of real as well as simulated datasets. FAVA can process large datasets with over 0.5 million conditions and has predicted 4210 interactions between 1039 understudied proteins. Our findings showcase FAVA's capability to offer novel perspectives on protein interactions. FAVA functions within the scverse ecosystem, employing AnnData as its input source.

AVAILABILITY AND IMPLEMENTATION

Source code, documentation, and tutorials for FAVA are accessible on GitHub at https://github.com/mikelkou/fava. FAVA can also be installed and used via pip/PyPI as well as via the scverse ecosystem https://github.com/scverse/ecosystem-packages/tree/main/packages/favapy.

Interpreting protein abundance in Saccharomyces cerevisiae through relational learning

MOTIVATION

Proteomic profiles reflect the functional readout of the physiological state of an organism. An increased understanding of what controls and defines protein abundances is of high scientific interest. Saccharomyces cerevisiae is a well-studied model organism, and there is a large amount of structured knowledge on yeast systems biology in databases such as the Saccharomyces Genome Database, and highly curated genome-scale metabolic models like Yeast8. These datasets, the result of decades of experiments, are abundant in information, and adhere to semantically meaningful ontologies.

RESULTS

By representing this knowledge in an expressive Datalog database we generated data descriptors using relational learning that, when combined with supervised machine learning, enables us to predict protein abundances in an explainable manner. We learnt predictive relationships between protein abundances, function and phenotype; such as α-amino acid accumulations and deviations in chronological lifespan. We further demonstrate the power of this methodology on the proteins His4 and Ilv2, connecting qualitative biological concepts to quantified abundances.

AVAILABILITY AND IMPLEMENTATION

All data and processing scripts are available at the following Github repository: https://github.com/DanielBrunnsaker/ProtPredict.

Nuclear Phosphoproteome Reveals Prolyl Isomerase PIN1 as a Modulator of Oncogene-Induced Senescence

Mammalian cells possess intrinsic mechanisms to prevent tumorigenesis upon deleterious mutations, including oncogene-induced senescence (OIS). The molecular mechanisms underlying OIS are, however, complex and remain to be fully characterized. In this study, we analyzed the changes in the nuclear proteome and phosphoproteome of human lung fibroblast IMR90 cells during the progression of OIS induced by oncogenic RASG12V activation. We found that most of the differentially regulated phosphosites during OIS contained prolyl isomerase PIN1 target motifs, suggesting PIN1 is a key regulator of several promyelocytic leukemia nuclear body proteins, specifically regulating several proteins upon oncogenic Ras activation. We showed that PIN1 knockdown promotes cell proliferation, while diminishing the senescence phenotype and hallmarks of senescence, including p21, p16, and p53 with concomitant accumulation of the protein PML and the dysregulation of promyelocytic leukemia nuclear body formation. Collectively, our data demonstrate that PIN1 plays an important role as a tumor suppressor in response to oncogenic ER:RasG12V activation.

Ups and downs of lysosomal pH: conflicting roles of LAMP proteins?

The acidic pH of lysosomes is critical for catabolism in eukaryotic cells and is altered in neurodegenerative disease including Alzheimer and Parkinson. Recent reports using Drosophila and mammalian cell culture systems have identified novel and, at first sight, conflicting roles for the lysosomal associated membrane proteins (LAMPs) in the regulation of the endolysosomal system.Abbreviation: AD: Alzheimer disease; LAMP: lysosomal associated membrane protein; LTR: LysoTracker; PD: Parkinson disease; TMEM175: transmembrane protein 175; V-ATPase: vacuolar-type H+-translocating ATPase.

Discovery of common molecular signatures and drug repurposing for COVID-19/Asthma comorbidity: ACE2 and multi-partite networks

Angiotensin-converting enzyme 2 (ACE2) is identified as the functional receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the ongoing global coronavirus disease-2019 (COVID-19) pandemic. This study aimed to elucidate potential therapeutic avenues by scrutinizing approved drugs through the identification of the genetic signature associated with SARS-CoV-2 infection in individuals with asthma. This exploration was conducted through an integrated analysis, encompassing interaction networks between the ACE2 receptor and common host (co-host) factors implicated in COVID-19/asthma comorbidity. The comprehensive analysis involved the identification of common differentially expressed genes (cDEGs) and hub-cDEGs, functional annotations, interaction networks, gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), and module construction. Interaction networks were used to identify overlapping disease modules and potential drug targets. Computational biology and molecular docking analyzes were utilized to discern functional drug modules. Subsequently, the impact of the identified drugs on the expression of hub-cDEGs was experimentally validated using a mouse model. A total of 153 cDEGs or co-host factors associated with ACE2 were identified in the COVID-19 and asthma comorbidity. Among these, seven significant cDEGs and proteins - namely, HRAS, IFNG, JUN, CDH1, TLR4, ICAM1, and SCD-were recognized as pivotal host factors linked to ACE2. Regulatory network analysis of hub-cDEGs revealed eight top-ranked transcription factors (TFs) proteins and nine microRNAs as key regulatory factors operating at the transcriptional and post-transcriptional levels, respectively. Molecular docking simulations led to the proposal of 10 top-ranked repurposable drug molecules (Rapamycin, Ivermectin, Everolimus, Quercetin, Estradiol, Entrectinib, Nilotinib, Conivaptan, Radotinib, and Venetoclax) as potential treatment options for COVID-19 in individuals with comorbid asthma. Validation analysis demonstrated that Rapamycin effectively inhibited ICAM1 expression in the HDM-stimulated mice group (p < 0.01). This study unveils the common pathogenesis and genetic signature underlying asthma and SARS-CoV-2 infection, delineated by the interaction networks of ACE2-related host factors. These findings provide valuable insights for the design and discovery of drugs aimed at more effective therapeutics within the context of lung disease comorbidities.

A comprehensive review on DDX3X liquid phase condensation in health and neurodevelopmental disorders

DEAD-box helicases are global regulators of liquid-liquid phase separation (LLPS), a process that assembles membraneless organelles inside cells. An outstanding member of the DEAD-box family is DDX3X, a multi-functional protein that plays critical roles in RNA metabolism, including RNA transcription, splicing, nucleocytoplasmic export, and translation. The diverse functions of DDX3X result from its ability to bind and remodel RNA in an ATP-dependent manner. This capacity enables the protein to act as an RNA chaperone and an RNA helicase, regulating ribonucleoprotein complex assembly. DDX3X and its orthologs from mouse, yeast (Ded1), and C. elegans (LAF-1) can undergo LLPS, driving the formation of neuronal granules, stress granules, processing bodies or P-granules. DDX3X has been related to several human conditions, including neurodevelopmental disorders, such as intellectual disability and autism spectrum disorder. Although the research into the pathogenesis of aberrant biomolecular condensation in neurodegenerative diseases is increasing rapidly, the role of LLPS in neurodevelopmental disorders is underexplored. This review summarizes current findings relevant for DDX3X phase separation in neurodevelopment and examines how disturbances in the LLPS process can be related to neurodevelopmental disorders.

Significance of Sequence Features in Classification of Protein-Protein Interactions Using Machine Learning

Protein-protein interactions are crucial for the entry of viruses into the cell. Understanding the mechanism of interactions is essential in studying human-virus association, developing new biologics and drug candidates, as well as viral infections and antiviral responses. Experimental methods to analyze human-virus protein-protein interactions based on protein sequence data are time-consuming and labor-intensive, so machine learning models are being developed to predict interactions and determine large-scale interactomes between species. The present work highlights the importance of sequence features in classifying interacting and non-interacting proteins from the protein sequence data. Higher dimensional amino acid sequence features such as Amino Acid Composition (AAC), Dipeptide Composition (DPC), Grouped Amino Acid Composition (GAAC), Pseudo-Amino Acid Composition (PAAC) etc., are extracted. Following feature extraction, three datasets were created: Dataset 1 contains all of the extracted features. While Datasets 2 and 3 contain the most relevant features obtained through dimensionality reduction. To analyze the importance of high-dimensional features and their participation in protein-protein interactions, a random forest classifier is trained on three datasets. With dimensionality reduction, the model exhibited exceptional accuracy, indicating that dimensionality reduction fails to capture the complexity of interactions and the underlying relationships between human and viral proteins. As a result of retaining high-dimensional features, it is possible to capture all the characteristics of protein-protein interactions that resemble host-pathogen associations, leading to the development of biologically meaningful models. Our proposed approach is a more realistic and comprehensive classification model, leading to deeper insights and better applications in virology and drug development.

CAMSAP2 enhances lung cancer cell metastasis by mediating RASAL2 degradation

AIMS

Cancer metastasis significantly contributes to mortality in lung cancer patients. Calmodulin-regulated spectrin-associated protein family member 2 (CAMSAP2) plays a significant role in cancer cell migration; however, its role in lung cancer metastasis and the underlying mechanism remain largely unknown. The present study aimed to investigate the impact of CAMSAP2 on lung cancer.

MAIN METHODS

The clinical relevance of CAMSAP2 in lung cancer patients was assessed using public database. RNA interference experiments were conducted to investigate role of CAMSAP2 in cell migration through transwell and wound healing assays. Molecular mechanisms were explored by identifying the possible interacting partners and pathways using the BioGRID and KEGG pathway analyses. The impact of CAMSAP2 on Ras protein activator-like 2 (RASAL2)-mediated lung cancer metastasis was investigated through biochemical assays. Additionally, in vivo experimentation using a murine tail vein metastasis model was performed to comprehend CAMSAP2's influence on metastasis.

KEY FINDINGS

A high expression level of CAMSAP2 was associated with poor overall survival in lung cancer patients and it positively correlated with cell migration in non-small cell lung cancer (NSCLC) cell lines. Knockdown of CAMSAP2 inhibited lung cancer cell motility in vitro and metastasis in vivo. Proteomic and biochemical analyses revealed the interaction between CAMSAP2 and RASAL2, which facilitates the degradation of RASAL2 through the ubiquitin-proteasome system. These degradation processes resulted in the activation of the extracellular signal-regulated kinase (ERK) signaling pathway, thereby promoting lung cancer metastasis. Collectively, the results of this study suggest that CAMSAP2 is a crucial regulator of cancer cell migration and metastasis and a promising therapeutic target for lung cancer.

The declining insulinogenic index correlates with inflammation and metabolic dysregulation in non-obese individuals assessed by blood gene expression

AIMS

Diabetes onset is difficult to predict. Since decreased insulinogenic index (IGI) is observed in prediabetes, and blood gene expression correlates with insulin secretion, candidate biomarkers can be identified.

METHODS

We collected blood from 96 participants (54 males, 42 females) in 2008 (age: 52.5 years) and 2016 for clinical and gene expression analyses. IGI was derived from values of insulin and glucose at fasting and at 30 min post-OGTT. Two subgroups were identified based on IGI variation: "Minor change in IGI" group with absolute value variation between -0.05 and +0.05, and "Decrease in IGI" group with a variation between -20 and -0.05.

RESULTS

Following the comparison of "Minor change in IGI" and "Decrease in IGI" groups at time 0 (2008), we identified 77 genes correlating with declining IGI, related to response to lipid, carbohydrate, and hormone metabolism, response to stress and DNA metabolic processes. Over the eight years, genes correlating to declining IGI were related to inflammation, metabolic and hormonal dysregulation. Individuals with minor change in IGI, instead, featured homeostatic and regenerative responses.

CONCLUSIONS

By blood gene expression analysis of non-obese individuals, we identified potential gene biomarkers correlating to declining IGI, associated to a pathophysiology of inflammation and metabolic dysregulation.

ECDEP: identifying essential proteins based on evolutionary community discovery and subcellular localization

BACKGROUND

In cellular activities, essential proteins play a vital role and are instrumental in comprehending fundamental biological necessities and identifying pathogenic genes. Current deep learning approaches for predicting essential proteins underutilize the potential of gene expression data and are inadequate for the exploration of dynamic networks with limited evaluation across diverse species.

RESULTS

We introduce ECDEP, an essential protein identification model based on evolutionary community discovery. ECDEP integrates temporal gene expression data with a protein-protein interaction (PPI) network and employs the 3-Sigma rule to eliminate outliers at each time point, constructing a dynamic network. Next, we utilize edge birth and death information to establish an interaction streaming source to feed into the evolutionary community discovery algorithm and then identify overlapping communities during the evolution of the dynamic network. SVM recursive feature elimination (RFE) is applied to extract the most informative communities, which are combined with subcellular localization data for classification predictions. We assess the performance of ECDEP by comparing it against ten centrality methods, four shallow machine learning methods with RFE, and two deep learning methods that incorporate multiple biological data sources on Saccharomyces. Cerevisiae (S. cerevisiae), Homo sapiens (H. sapiens), Mus musculus, and Caenorhabditis elegans. ECDEP achieves an AP value of 0.86 on the H. sapiens dataset and the contribution ratio of community features in classification reaches 0.54 on the S. cerevisiae (Krogan) dataset.

CONCLUSIONS

Our proposed method adeptly integrates network dynamics and yields outstanding results across various datasets. Furthermore, the incorporation of evolutionary community discovery algorithms amplifies the capacity of gene expression data in classification.

An ERK5-PFKFB3 axis regulates glycolysis and represents a therapeutic vulnerability in pediatric diffuse midline glioma

Metabolic reprogramming in pediatric diffuse midline glioma is driven by gene expression changes induced by the hallmark histone mutation H3K27M, which results in aberrantly permissive activation of oncogenic signaling pathways. Previous studies of diffuse midline glioma with altered H3K27 (DMG-H3K27a) have shown that the RAS pathway, specifically through its downstream kinase, extracellular-signal-related kinase 5 (ERK5), is critical for tumor growth. Further downstream effectors of ERK5 and their role in DMG-H3K27a metabolic reprogramming have not been explored. We establish that ERK5 is a critical regulator of cell proliferation and glycolysis in DMG-H3K27a. We demonstrate that ERK5 mediates glycolysis through activation of transcription factor MEF2A, which subsequently modulates expression of glycolytic enzyme PFKFB3. We show that in vitro and mouse models of DMG-H3K27a are sensitive to the loss of PFKFB3. Multi-targeted drug therapy against the ERK5-PFKFB3 axis, such as with small-molecule inhibitors, may represent a promising therapeutic approach in patients with pediatric diffuse midline glioma.

Veterinary systems biology for bridging the phenotype-genotype gap via computational modeling for disease epidemiology and animal welfare

Veterinary systems biology is an innovative approach that integrates biological data at the molecular and cellular levels, allowing for a more extensive understanding of the interactions and functions of complex biological systems in livestock and veterinary science. It has tremendous potential to integrate multi-omics data with the support of vetinformatics resources for bridging the phenotype-genotype gap via computational modeling. To understand the dynamic behaviors of complex systems, computational models are frequently used. It facilitates a comprehensive understanding of how a host system defends itself against a pathogen attack or operates when the pathogen compromises the host's immune system. In this context, various approaches, such as systems immunology, network pharmacology, vaccinology and immunoinformatics, can be employed to effectively investigate vaccines and drugs. By utilizing this approach, we can ensure the health of livestock. This is beneficial not only for animal welfare but also for human health and environmental well-being. Therefore, the current review offers a detailed summary of systems biology advancements utilized in veterinary sciences, demonstrating the potential of the holistic approach in disease epidemiology, animal welfare and productivity.

Multi-modal features-based human-herpesvirus protein-protein interaction prediction by using LightGBM

The identification of human-herpesvirus protein-protein interactions (PPIs) is an essential and important entry point to understand the mechanisms of viral infection, especially in malignant tumor patients with common herpesvirus infection. While natural language processing (NLP)-based embedding techniques have emerged as powerful approaches, the application of multi-modal embedding feature fusion to predict human-herpesvirus PPIs is still limited. Here, we established a multi-modal embedding feature fusion-based LightGBM method to predict human-herpesvirus PPIs. In particular, we applied document and graph embedding approaches to represent sequence, network and function modal features of human and herpesviral proteins. Training our LightGBM models through our compiled non-rigorous and rigorous benchmarking datasets, we obtained significantly better performance compared to individual-modal features. Furthermore, our model outperformed traditional feature encodings-based machine learning methods and state-of-the-art deep learning-based methods using various benchmarking datasets. In a transfer learning step, we show that our model that was trained on human-herpesvirus PPI dataset without cytomegalovirus data can reliably predict human-cytomegalovirus PPIs, indicating that our method can comprehensively capture multi-modal fusion features of protein interactions across various herpesvirus subtypes. The implementation of our method is available at https://github.com/XiaodiYangpku/MultimodalPPI/.

Proteomic analysis of SARS-CoV-2 particles unveils a key role of G3BP proteins in viral assembly

Considerable progress has been made in understanding the molecular host-virus battlefield during SARS-CoV-2 infection. Nevertheless, the assembly and egress of newly formed virions are less understood. To identify host proteins involved in viral morphogenesis, we characterize the proteome of SARS-CoV-2 virions produced from A549-ACE2 and Calu-3 cells, isolated via ultracentrifugation on sucrose cushion or by ACE-2 affinity capture. Bioinformatic analysis unveils 92 SARS-CoV-2 virion-associated host factors, providing a valuable resource to better understand the molecular environment of virion production. We reveal that G3BP1 and G3BP2 (G3BP1/2), two major stress granule nucleators, are embedded within virions and unexpectedly favor virion production. Furthermore, we show that G3BP1/2 participate in the formation of cytoplasmic membrane vesicles, that are likely virion assembly sites, consistent with a proviral role of G3BP1/2 in SARS-CoV-2 dissemination. Altogether, these findings provide new insights into host factors required for SARS-CoV-2 assembly with potential implications for future therapeutic targeting.

Extracellular Proximity Labeling Reveals an Expanded Interactome for the Matrisome Protein TIMP2

Classical methods of investigating protein-protein interactions (PPIs) are generally performed in non-living systems, yet in recent years new technologies utilizing proximity labeling (PL) have given researchers the tools to explore proximal PPIs in living systems. PL has distinct advantages over traditional protein interactome studies, such as the ability to identify weak and transient interactions in vitro and in vivo. Most PL studies are performed on targets within the cell or on the cell membrane. We have adapted the original PL method to investigate PPIs within the extracellular compartment, using both BioID2 and TurboID, that we term extracellular PL (ePL). To demonstrate the utility of this modified technique, we investigate the interactome of the widely expressed matrisome protein tissue inhibitor of metalloproteinases 2 (TIMP2). Tissue inhibitors of metalloproteinases (TIMPs) are a family of multi-functional proteins that were initially defined by their ability to inhibit the enzymatic activity of metalloproteinases (MPs), the major mediators of extracellular matrix (ECM) breakdown and turnover. TIMP2 exhibits a broad expression profile and is often abundant in both normal and diseased tissues. Understanding the functional transformation of matrisome regulators, like TIMP2, during the evolution of tissue microenvironments associated with disease progression is essential for the development of ECM-targeted therapeutics. Using carboxyl- and amino-terminal fusion proteins of TIMP2 with BioID2 and TurboID, we describe the TIMP2 proximal interactome. We also illustrate how the TIMP2 interactome changes in the presence of different stimuli, in different cell types, in unique culture conditions (2D vs 3D), and with different reaction kinetics (BioID2 vs. TurboID); demonstrating the power of this technique versus classical PPI methods. We propose that the screening of matrisome targets in disease models using ePL will reveal new therapeutic targets for further comprehensive studies.

Understanding YTHDF2-mediated mRNA Degradation By m6A-BERT-Deg

N6-methyladenosine (m6A) is the most abundant mRNA modification within mammalian cells, holding pivotal significance in the regulation of mRNA stability, translation, and splicing. Furthermore, it plays a critical role in the regulation of RNA degradation by primarily recruiting the YTHDF2 reader protein. However, the selective regulation of mRNA decay of the m6A-methylated mRNA through YTHDF2 binding is poorly understood. To improve our understanding, we developed m6A-BERT-Deg, a BERT model adapted for predicting YTHDF2-mediated degradation of m6A-methylated mRNAs. We meticulously assembled a high-quality training dataset by integrating multiple data sources for the HeLa cell line. To overcome the limitation of small training samples, we employed a pre-training-fine-tuning strategy by first performing a self-supervised pre-training of the model on 427,760 unlabeled m6A site sequences. The test results demonstrated the importance of this pre-training strategy in enabling m6A-BERT-Deg to outperform other benchmark models. We further conducted a comprehensive model interpretation and revealed a surprising finding that the presence of co-factors in proximity to m6A sites may disrupt YTHDF2-mediated mRNA degradation, subsequently enhancing mRNA stability. We also extended our analyses to the HEK293 cell line, shedding light on the context-dependent YTHDF2-mediated mRNA degradation.

Experimental and Computational Analysis of Newly Identified Pathogenic Mutations in the Creatine Transporter SLC6A8

Creatine is an essential metabolite for the storage and rapid supply of energy in muscle and nerve cells. In humans, impaired metabolism, transport, and distribution of creatine throughout tissues can cause varying forms of mental disability, also known as creatine deficiency syndrome (CDS). So far, 80 mutations in the creatine transporter (SLC6A8) have been associated to CDS. To better understand the effect of human genetic variants on the physiology of SLC6A8 and their possible impact on CDS, we studied 30 missense variants including 15 variants of unknown significance, two of which are reported here for the first time. We expressed these variants in HEK293 cells and explored their subcellular localization and transport activity. We also applied computational methods to predict variant effect and estimate site-specific changes in thermodynamic stability. To explore variants that might have a differential effect on the transporter's conformers along the transport cycle, we constructed homology models of the inward facing, and outward facing conformations. In addition, we used mass-spectrometry to study proteins that interact with wild type SLC6A8 and five selected variants in HEK293 cells. In silico models of the protein complexes revealed how two variants impact the interaction interface of SLC6A8 with other proteins and how pathogenic variants lead to an enrichment of ER protein partners. Overall, our integrated analysis disambiguates the pathogenicity of 15 variants of unknown significance revealing diverse mechanisms of pathogenicity, including two previously unreported variants obtained from patients suffering from the creatine deficiency syndrome.

Single-cell RNA sequencing analysis identifies one subpopulation of endothelial cells that proliferates and another that undergoes the endothelial-mesenchymal transition in regenerating pig hearts

Background: In our previous work, we demonstrated that when newborn pigs undergo apical resection (AR) on postnatal day 1 (P1), the animals' hearts were completely recover from a myocardial infarction (MI) that occurs on postnatal day 28 (P28); single-nucleus RNA sequencing (snRNAseq) data suggested that this recovery was achieved by regeneration of pig cardiomyocyte subpopulations in response to MI. However, coronary vasculature also has a key role in promoting cardiac repair. Method: Thus, in this report, we used autoencoder algorithms to analyze snRNAseq data from endothelial cells (ECs) in the hearts of the same animals. Main results: Our results identified five EC clusters, three composed of vascular ECs (VEC1-3) and two containing lymphatic ECs (LEC1-2). Cells from VEC1 expressed elevated levels of each of five cell-cyclespecific markers (Aurora Kinase B [AURKB], Marker of Proliferation Ki-67 [MKI67], Inner Centromere Protein [INCENP], Survivin [BIRC5], and Borealin [CDCA8]), as well as a number of transcription factors that promote EC proliferation, while (VEC3 was enriched for genes that regulate intercellular junctions, participate in transforming growth factor β (TGFβ), bone morphogenic protein (BMP) signaling, and promote the endothelial mesenchymal transition (EndMT). The remaining VEC2 did not appear to participate directly in the angiogenic response to MI, but trajectory analyses indicated that it may serve as a reservoir for the generation of VEC1 and VEC3 ECs in response to MI. Notably, only the VEC3 cluster was more populous in regenerating (i.e., ARP1MIP28) than non-regenerating (i.e., MIP28) hearts during the 1-week period after MI induction, which suggests that further investigation of the VEC3 cluster could identify new targets for improving myocardial recovery after MI. Histological analysis of KI67 and EndMT marker PDGFRA demonstrated that while the expression of proliferation of endothelial cells was not significantly different, expression of EndMT markers was significantly higher among endothelial cells of ARP1MIP28 hearts compared to MIP28 hearts, which were consistent with snRNAseq analysis of clusters VEC1 and VEC3. Furthermore, upregulated secrete genes by VEC3 may promote cardiomyocyte proliferation via the Pi3k-Akt and ERBB signaling pathways, which directly contribute to cardiac muscle regeneration. Conclusion: In regenerative heart, endothelial cells may express EndMT markers, and this process could contribute to regeneration via a endothelial-cardiomyocyte crosstalk that supports cardiomyocyte proliferation.

A multimodal graph neural network framework for cancer molecular subtype classification

BACKGROUND

The recent development of high-throughput sequencing has created a large collection of multi-omics data, which enables researchers to better investigate cancer molecular profiles and cancer taxonomy based on molecular subtypes. Integrating multi-omics data has been proven to be effective for building more precise classification models. Most current multi-omics integrative models use either an early fusion in the form of concatenation or late fusion with a separate feature extractor for each omic, which are mainly based on deep neural networks. Due to the nature of biological systems, graphs are a better structural representation of bio-medical data. Although few graph neural network (GNN) based multi-omics integrative methods have been proposed, they suffer from three common disadvantages. One is most of them use only one type of connection, either inter-omics or intra-omic connection; second, they only consider one kind of GNN layer, either graph convolution network (GCN) or graph attention network (GAT); and third, most of these methods have not been tested on a more complex classification task, such as cancer molecular subtypes.

RESULTS

In this study, we propose a novel end-to-end multi-omics GNN framework for accurate and robust cancer subtype classification. The proposed model utilizes multi-omics data in the form of heterogeneous multi-layer graphs, which combine both inter-omics and intra-omic connections from established biological knowledge. The proposed model incorporates learned graph features and global genome features for accurate classification. We tested the proposed model on the Cancer Genome Atlas (TCGA) Pan-cancer dataset and TCGA breast invasive carcinoma (BRCA) dataset for molecular subtype and cancer subtype classification, respectively. The proposed model shows superior performance compared to four current state-of-the-art baseline models in terms of accuracy, F1 score, precision, and recall. The comparative analysis of GAT-based models and GCN-based models reveals that GAT-based models are preferred for smaller graphs with less information and GCN-based models are preferred for larger graphs with extra information.

Non-apoptotic FAS signaling controls mTOR activation and extrafollicular maturation in human B cells

Defective FAS (CD95/Apo-1/TNFRSF6) signaling causes autoimmune lymphoproliferative syndrome (ALPS). Hypergammaglobulinemia is a common feature in ALPS with FAS mutations (ALPS-FAS), but paradoxically, fewer conventional memory cells differentiate from FAS-expressing germinal center (GC) B cells. Resistance to FAS-induced apoptosis does not explain this phenotype. We tested the hypothesis that defective non-apoptotic FAS signaling may contribute to impaired B cell differentiation in ALPS. We analyzed secondary lymphoid organs of patients with ALPS-FAS and found low numbers of memory B cells, fewer GC B cells, and an expanded extrafollicular (EF) B cell response. Enhanced mTOR activity has been shown to favor EF versus GC fate decision, and we found enhanced PI3K/mTOR and BCR signaling in ALPS-FAS splenic B cells. Modeling initial T-dependent B cell activation with CD40L in vitro, we showed that FAS competent cells with transient FAS ligation showed specifically decreased mTOR axis activation without apoptosis. Mechanistically, transient FAS engagement with involvement of caspase-8 induced nuclear exclusion of PTEN, leading to mTOR inhibition. In addition, FASL-dependent PTEN nuclear exclusion and mTOR modulation were defective in patients with ALPS-FAS. In the early phase of activation, FAS stimulation promoted expression of genes related to GC initiation at the expense of processes related to the EF response. Hence, our data suggest that non-apoptotic FAS signaling acts as molecular switch between EF versus GC fate decisions via regulation of the mTOR axis and transcription. The defect of this modulatory circuit may explain the observed hypergammaglobulinemia and low memory B cell numbers in ALPS.

PPGR: a comprehensive perennial plant genomes and regulation database

Perennial woody plants hold vital ecological significance, distinguished by their unique traits. While significant progress has been made in their genomic and functional studies, a major challenge persists: the absence of a comprehensive reference platform for collection, integration and in-depth analysis of the vast amount of data. Here, we present PPGR (Resource for Perennial Plant Genomes and Regulation; https://ngdc.cncb.ac.cn/ppgr/) to address this critical gap, by collecting, integrating, analyzing and visualizing genomic, gene regulation and functional data of perennial plants. PPGR currently includes 60 species, 847 million protein-protein/TF (transcription factor)-target interactions, 9016 transcriptome samples under various environmental conditions and genetic backgrounds. Noteworthy is the focus on genes that regulate wood production, seasonal dormancy, terpene biosynthesis and leaf senescence representing a wealth of information derived from experimental data, literature mining, public databases and genomic predictions. Furthermore, PPGR incorporates a range of multi-omics search and analysis tools to facilitate browsing and application of these extensive datasets. PPGR represents a comprehensive and high-quality resource for perennial plants, substantiated by an illustrative case study that demonstrates its capacity in unraveling gene functions and shedding light on potential regulatory processes.

(Pro)renin receptor mediates tubular epithelial cell pyroptosis in diabetic kidney disease via DPP4-JNK pathway

BACKGROUND

(Pro)renin receptor (PRR) is highly expressed in renal tubules, which is involved in physiological and pathological processes. However, the role of PRR, expressed in renal tubular epithelial cells, in diabetic kidney disease (DKD) remain largely unknown.

METHODS

In this study, kidney biopsies, urine samples, and public RNA-seq data from DKD patients were used to assess PRR expression and cell pyroptosis in tubular epithelial cells. The regulation of tubular epithelial cell pyroptosis by PRR was investigated by in situ renal injection of adeno-associated virus9 (AAV9)-shRNA into db/db mice, and knockdown or overexpression of PRR in HK-2 cells. To reveal the underlined mechanism, the interaction of PRR with potential binding proteins was explored by using BioGrid database. Furthermore, the direct binding of PRR to dipeptidyl peptidase 4 (DPP4), a pleiotropic serine peptidase which increases blood glucose by degrading incretins under diabetic conditions, was confirmed by co-immunoprecipitation assay and immunostaining.

RESULTS

Higher expression of PRR was found in renal tubules and positively correlated with kidney injuries of DKD patients, in parallel with tubular epithelial cells pyroptosis. Knockdown of PRR in kidneys significantly blunted db/db mice to kidney injury by alleviating renal tubular epithelial cells pyroptosis and the resultant interstitial inflammation. Moreover, silencing of PRR blocked high glucose-induced HK-2 pyroptosis, whereas overexpression of PRR enhanced pyroptotic cell death of HK-2 cells. Mechanistically, PRR selectively bound to cysteine-enrich region of C-terminal of DPP4 and augmented the protein abundance of DPP4, leading to the downstream activation of JNK signaling and suppression of SIRT3 signaling and FGFR1 signaling, and then subsequently mediated pyroptotic cell death.

CONCLUSIONS

This study identified the significant role of PRR in the pathogenesis of DKD; specifically, PRR promoted tubular epithelial cell pyroptosis via DPP4 mediated signaling, highlighting that PRR could be a promising therapeutic target in DKD.