Macromolecular protein complexes carry out most cellular functions. Unfortunately, we lack the subunit composition for many human protein complexes. To address this gap we integrated >25,000 mass spectrometry experiments using a machine learning approach to identify >15,000 human protein complexes. We show our map of protein complexes is highly accurate and more comprehensive than previous maps, placing nearly 70% of human proteins into their physical contexts. We globally characterize our complexes using mass spectrometry based protein covariation data (ProteomeHD.2) and identify covarying complexes suggesting common functional associations. hu.MAP3.0 generates testable functional hypotheses for 472 uncharacterized proteins which we support using AlphaFold modeling. Additionally, we use AlphaFold modeling to identify 5871 mutually exclusive proteins in hu.MAP3.0 complexes suggesting complexes serve different functional roles depending on their subunit composition. We identify expression as the primary way cells and organisms relieve the conflict of mutually exclusive subunits. Finally, we import our complexes to EMBL-EBI's Complex Portal ( https://www.ebi.ac.uk/complexportal/home ) and provide complexes through our hu.MAP3.0 web interface ( https://humap3.proteincomplexes.org/ ). We expect our resource to be highly impactful to the broader research community.

The high heterogeneity between patients can complicate the diagnosis and treatment of pancreatic ductal adenocarcinoma (PDAC). Here, we explored the association of universally differentially expressed genes (UDEGs) with resistance to chemotherapy and immunotherapy in the context of pancreatic cancer. In this work, sixteen up-regulated and three down-regulated genes that were dysregulated in more than 85% of 102 paired and 5% of 521 unpaired PDAC samples were identified and defined as UDEGs. A single-cell level analysis further validated the high expression levels of the up-UDEGs and the low levels of the down-UDEGs in cancer-related ductal cells, which could represent the malignant changes seen in pancreatic cancer. Based on a drug sensitivity analysis, we found that ANLN, GPRC5A and SERPINB5 are closely related to the resistance mechanism of PDAC, and their high expression predicted worse survival for PDAC patients. This suggests that targeting these genes could be a potential way to reduce drug resistance and improve survival. Based on the immune infiltration analysis, the abnormal expression of the UDEGs was found to be related to the formation of an immunosuppressive tumor microenvironment. In conclusion, these UDEGs are common features of PDAC and could be involved in the resistance of pancreatic cancer and might serve as novel drug targets to guide research into drug repurposing.

Thyroid cancer, a prevalent endocrine malignancy, has an age-standardized incidence rate of 9.1 per 100,000 people and a mortality rate of 0.44 per 100,000 as of 2024. Despite significant advances in precision oncology driven by large-scale international consortia, gaps persist in understanding the genomic landscape of thyroid cancer and its impact on therapeutic efficacy across diverse populations.

To address this gap, we performed comprehensive data mining and in silico analyses to identify pathogenic variants in thyroid cancer driver genes, calculate allele frequencies, and assess deleteriousness scores across global populations, including African, Amish, Ashkenazi Jewish, East and South Asian, Finnish and non-Finnish European, Latino, and Middle Eastern groups. Additionally, pharmacogenomic profiling, in silico drug prescription, and clinical trial data were analyzed to prioritize targeted therapeutic strategies.

Our analysis examined 56,622 variants in 40 thyroid cancer-driver genes across 76,156 human genomes, identifying 5,001 known and predicted oncogenic variants. Enrichment analysis revealed critical pathways such as MAPK, PI3K-AKT-mTOR, and p53 signaling, underscoring their roles in thyroid cancer pathogenesis. High-throughput validation strategies confirmed actionable genomic alterations in RET, BRAF, NRAS, KRAS, and EPHA7. Ligandability assessments identified these proteins as promising therapeutic targets. Furthermore, our findings highlight the clinical potential of targeted drug inhibitors, including vandetanib, dabrafenib, and selumetinib, for improving treatment outcomes.

This study underscores the significance of integrating genomic insights with pharmacogenomic strategies to address disparities in thyroid cancer treatment. The identification of population-specific oncogenic variants and actionable therapeutic targets provides a foundation for advancing precision oncology. Future efforts should focus on including underrepresented populations, developing population-specific prevention strategies, and fostering global collaboration to ensure equitable access to pharmacogenomic testing and innovative therapies. These initiatives have the potential to transform thyroid cancer care and align with the broader goals of personalized medicine.

Fluorinated chalcones are molecules reported to possess potent anticancer properties against triple-negative breast cancer (TNBC) cells. However, their molecular mechanisms have not yet been fully explored. Using bioinformatics tools, we analyzed the transcriptomes of MDA-MB-231 cells treated with either a novel fluorinated chalcone (compound 3) or a control in order to identify differentially expressed (DE) genes associated with its anticancer activity and determine the biological processes in which these genes are involved. A fluorinated chalcone was synthesized using the Claisen-Schmidt method. The transcriptome of MDA-MB-231 cells was then analyzed on an Illumina NextSeq500, and DE genes with significant changes in expression were identified using the DESeq2 v1.38.0 bioinformatics tool under the strict detection criteria of |log2FC| ≥  2 and adjusted p < 0.05. We identified 504 DE genes, which were enriched in terms related to "regulation of cell death", "cation transport", "response to topologically incorrect proteins", and "response to unfolded proteins". Surprisingly, these genes were involved in "the HSF1-dependent transactivation pathway" and "the attenuation phase pathway". This bioinformatics-based study suggests that the tested fluorinated chalcone could influence HSF-1 silencing in addition to promoting the up-regulation of several genes involved in stress-induced apoptosis. Therefore, the tested compound could have enormous potential as a novel approach for TNBC treatment.

Sleep disturbances are prevalent among elderly populations and are linked to various health complications. Understanding the underlying biological mechanisms contributing to sleep disorders is crucial for developing targeted interventions. In this study, we measured 355 plasma proteins in an elderly Japanese cohort (n=77) using a high-throughput proteomic platform. Additionally, we collected over 25,000 person-days of physical activity and sleep behavior data from wrist-worn wearable devices, focusing on total sleep time (TST) across 24 h and daytime sleep. Fragmented sleep was observed as one of the most prevalent sleep disturbances in this population. In protein expression analysis, we identified 9 protein biomarkers associated with increased secondary sleep TST, defined as additional sleep episodes outside of the main sleep episode within 24 h. These findings may suggest disruptions in circadian rhythms or underlying health conditions. Functional analysis revealed that biological processes related to inflammation play a significant role in regulating sleep behavior. Further analysis showed an association of 12 proteins with daytime sleep and 5 proteins with afternoon sleep. Overall, this study identified inflammatory biomarkers and biological processes associated with sleep behavior in the elderly, presenting promising opportunities for developing diagnostic tools and targeted clinical interventions.

Exudates of nonhealing wounds contain drivers of pathogenicity. We utilized >800 exudates from nonhealing and healing wounds of diverse etiologies, collected by 3 different methods, to develop a wound-specific, cell-based functional biomarker assay. Human dermal fibroblast proliferation served as readout to (i) differentiate between healing and nonhealing wounds, (ii) follow the healing process of individual patients, and (iii) assess the effects of therapeutics for chronic wounds ex vivo. We observed a strong correlation between wound chronicity and inhibitory effects of individual exudates on fibroblast proliferation, with good diagnostic sensitivity (76-90%, depending on the sample collection method). Transition of a clinically nonhealing to a healing phenotype restored fibroblast proliferation and extracellular matrix formation while reducing inflammatory cytokine production. Transcriptional analysis of fibroblasts exposed to ex vivo nonhealing wound exudates revealed an induction of inflammatory cytokine and chemokine pathways and the unfolded protein response, indicating that these changes may contribute to the pathology of nonhealing wounds. Testing the wound therapeutics, PDGF and silver sulfadiazine, yielded responses in line with clinical experience and indicates the usefulness of the assay to search for and profile new therapeutics.

Group 3 innate lymphoid cells (ILC3s) have clear roles in regulating mucosal immunity and tissue homeostasis in the intestine, though the immunological functions in lungs remain unclear. This study aimed to demonstrate the dynamic responses of ILC3s to acute inflammation upon LPS challenge. Microarray data and single-cell RNA sequencing (scRNA-seq) data obtained from the GEO database were combined to analyze the function of ILC3 subset, confirmed by flow cytometry assay and qRT-PCR. The gene enrichment analysis of intersected genes identified between microarray data in bacterial pneumonia and single-cell RNA sequencing of intestinal ILC3s were closely related to TNF-alpha effects on cytokine activity, cell motility and apoptosis pathway, indicating the possibility of intestinal ILC3s migration to the lung. Furthermore, the cellular landscapes of ILC3s in lung and intestine at different times after pulmonary infection exhibited varied ILC3 statuses. ILC3s in lung expanded a lot at 48 h while intestinal ILC3s decreased at 72 h response to LPS challenge, with higher expression of marked genes related to TNF-alpha effects on cytokine activity, cell motility and apoptosis pathway. The main findings in our study may serve as valuable resources for understanding the roles that ILC3s play upon LPS challenge, which may offer opportunities for translating ILC3s as therapeutic targets to regulate LPS-induced pulmonary inflammation.

Interstitial cystitis/bladder pain syndrome (IC/BPS) is a poorly understood and underdiagnosed syndrome of chronic bladder/pelvic pain with urinary frequency and urgency. Though IC/BPS can be hereditary, little is known of its genetic etiology. Using the eMERGE data, we confirmed known phenotypic associations such as gastroesophageal reflux disease and irritable bowel syndrome and detected new associations, including osteoarthrosis/osteoarthritis and Barrett's esophagus. An exome wide ultra-rare variants analysis in 348 IC/BPS and 11,981 controls extended the previously reported association with ATP2C1 and ATP2A2, implicated in Mendelian desquamating skin disorders, but did not provide evidence for other previously proposed pathogenic pathways such as bladder development, nociception or inflammation. Pathway analysis detected new associations with "anaphase-promoting complex-dependent catabolic process", the "regulation of MAPK cascade" and "integrin binding". These findings suggest perturbations in biological networks for epithelial integrity and cell cycle progression in IC/BPS pathogenesis, and provide a roadmap for its future investigation.

Erythrodermic atopic dermatitis (EAD) and erythrodermic psoriasis (EP) are rare yet debilitating inflammatory skin disorders that propose challenges in diagnosis and discovering effective therapeutic targets. Despite their clinical and histological similarities, the underlying molecular mechanisms and systemic biomarkers of these diseases are substantially unclear. In this study, we sought to investigate the differential serum proteome of EP and EAD patients and identify biomarkers for these two subtypes of erythroderma. We recruited 14 EAD patients, 14 EP patients and 14 healthy controls. Serum samples were collected and analyzed using the Olink high-throughput platform to assess the levels of 269 inflammation-/immune response-/cardiovascular-related biomarkers. Both EAD and EP patients exhibited enhanced immune activation and dysregulated cardiovascular profiles compared to healthy controls. EAD demonstrated a more pronounced inflammation tone, characterized by Th1/Th2/Th22/IL-1-dominant patterns, as well as increased TNF superfamily, Th17, and apoptosis markers. Conversely, EP displayed inflammation with Th1/Th17/TNF-skewing and mild Th2 upregulation, along with notable increases in epidermal-development markers. Disease severity in EAD was strongly correlated with apoptosis/Th2 markers, while correlated with Th17 markers in EP. Furthermore, a panel of eight markers (IL-17A/IL-17C/PI3/CCL20/SH2D1A/SIRT2/DFFA/IL-13) was identified that effectively discriminated between EP and EAD, with an Area Under the Curve greater than 0.8. Our study comprehensively characterizes the circulating molecular profiles in EAD and EP patients, providing insights into the similarities and complexities of their inflammation phenotypes. The identified serum biomarkers have the potential to differentiate between EP and EAD, which could aid in the diagnosis and guiding tailored therapeutics.

Gene set enrichment is a mainstay of functional genomics, but it relies on gene function databases that are incomplete. Here we evaluate five large language models (LLMs) for their ability to discover the common functions represented by a gene set, supported by molecular rationale and a self-confidence assessment. For curated gene sets from Gene Ontology, GPT-4 suggests functions similar to the curated name in 73% of cases, with higher self-confidence predicting higher similarity. Conversely, random gene sets correctly yield zero confidence in 87% of cases. Other LLMs (GPT-3.5, Gemini Pro, Mixtral Instruct and Llama2 70b) vary in function recovery but are falsely confident for random sets. In gene clusters from omics data, GPT-4 identifies common functions for 45% of cases, fewer than functional enrichment but with higher specificity and gene coverage. Manual review of supporting rationale and citations finds these functions are largely verifiable. These results position LLMs as valuable omics assistants.

The stiffening of the extracellular matrix (ECM) with age hinders muscle regeneration by causing intrinsic muscle stem cell (MuSC) dysfunction through a poorly understood mechanism. Here, the study aims to study those age-related molecular changes in the differentiation of MuSCs due to age and/or stiffness. Hence, young and aged MuSCs are seeded onto substrates engineered to mimic a soft and stiff ECM microenvironment to study those molecular changes using single-cell RNA sequencing (scRNA). The trajectory of scRNA data of the MuSCs under four different conditions undergoing differentiation is analyzed as well as the active molecular pathways and transcription factors driving those differentiation fates. Data revealed the presence of a branching point within the trajectory leading to the emergence of an age-related fibroblastic population characterized by activation of the TNF-related apoptosis-inducing ligand (TRAIL) pathway, which is significantly activated in aged cells cultured on stiff substrates. Next, using the collagen cross-linking inhibitor β-aminopropionitrile (BAPN) in vivo, the study elucidates stiffness changes on TRAIL downstream apoptotic targets (caspase 8 and caspase 3) using immunostaining. TRAIL activity is significantly inhibited by BAPN in aged animals, indicating a complex mechanism of age-related declines in muscle function through inflammatory and apoptotic mediators.

In HER2-positive breast cancer, clinical outcome and sensitivity to HER2-targeted therapies are influenced by both tumor and microenvironment features. However, we are currently unable to depict the molecular heterogeneity of this disease with sufficient granularity. Here, by performing gene expression profiling in HER2-positive breast cancers from patients receiving adjuvant trastuzumab in the ALTTO clinical trial (NCT00490139), we identify and characterize five molecular subtypes associated with the risk of distant recurrence: immune-enriched, proliferative/metabolic-enriched, mesenchymal/stroma-enriched, luminal, and ERBB2-dependent. Additionally, we validate the biological profiles of the subtypes and explore their prognostic/predictive value in external cohorts, namely the NeoALTTO trial (NCT00553358), SCAN-B (NCT02306096), I-SPY2 (NCT01042379), METABRIC and TCGA. Immune-enriched tumors present better survival outcomes, in contrast to mesenchymal/stroma-enriched and proliferative/metabolic-enriched tumors, while luminal and ERBB2-dependent tumors are characterized by low and high rates of pathological complete response, respectively. Of note, these molecular subtypes provide the rationale for treatment approaches leveraging the heterogeneous biology of HER2-positive breast cancer.

The Ebola virus (EBOV) causes severe disease in humans, and animal models are needed to evaluate the efficacy of vaccines and therapeutics. While non-human primate (NHP) and rodent EBOV infection models have been well characterized, there is a growing need for an intermediate model. Here, we provide the first report of a small-particle aerosol (AE) EBOV ferret model and disease progression compared with the intramuscular (IM) EBOV ferret model. EBOV infection of ferrets by either route resulted in uniform lethality in 5-6.5 days post infection (dpi) in a dose-dependent manner, with IM-infected ferrets succumbing significantly earlier than AE-infected ferrets. EBOV disease progression differed between AE and IM routes, with significant viremia and presence of virus in target organs occurring earlier in the AE model. In contrast, significant fever, clinical signs of disease, liver pathology, and systemic inflammation occurred earlier in the IM EBOV model. Hepatocellular damage and splenic pathology were noted in both models, while pronounced lung pathology and renal impairment were exclusive to the AE and IM models, respectively. These results demonstrate that small-particle AE and IM ferret EBOV models share numerous common features with NHP and human EBOV infection by these routes and will therefore be useful for the development of vaccine and therapeutic countermeasures.

Aberrant DNA methylation contributes to gene expression deregulation in cancer. However, these alterations' precise regulatory role and clinical implications are still not fully understood. In this study, we performed expression-methylation Quantitative Trait Loci (emQTL) analysis to identify deregulated cancer-driving transcriptional networks linked to CpG demethylation pan-cancer. By analyzing 33 cancer types from The Cancer Genome Atlas, we identified and confirmed significant correlations between CpG methylation and gene expression (emQTL) in cis and trans, both across and within cancer types. Bipartite network analysis of the emQTL revealed groups of CpGs and genes related to important biological processes involved in carcinogenesis including proliferation, metabolism and hormone-signaling. These bipartite communities were characterized by loss of enhancer methylation in specific transcription factor binding regions (TFBRs) and the CpGs were topologically linked to upregulated genes through chromatin loops. Penalized Cox regression analysis showed a significant prognostic impact of the pan-cancer emQTL in many cancer types. Taken together, our integrative pan-cancer analysis reveals a common architecture where hallmark cancer-driving functions are affected by the loss of enhancer methylation and may be epigenetically regulated.

SARS-CoV-2 infection can result in long-term chronic cardiovascular (CV) damage after the acute phase of the illness. COVID-19 frequently causes active myocarditis, SARS-CoV-2 can directly infect and kill cardiac cells, causing severe pathology and dysfunction across the organs and cells. Till now, the pathogenesis of COVID-19-associated cardiac injuries has not been understood, but there are several factors that contribute to the progression of cardiac injuries, such as genetic, dietary, and environmental. Among them ranges of host genetic factor including metabolizing, inflammation, and coagulation related genes have a role to contribute the cardiac injuries induced by COVID-19. Hereditary DNA sequence variations contribute to the risk of illness in almost all of these diseases. Hence, we comprehended the occurrence of genetic variations of metabolizing, inflammation and coagulation-related genes in the general population, their expression in various diseases, and their impact on cardiac injuries induced by COVID-19.

We utilized multiple databases, including PubMed (Medline), EMBASE, and Google Scholar, for literature searches.

The genes involved in metabolism (APOE, MTHFR), coagulation (PAI-1, ACE2), and immune factors (CRP, ESR, and troponin I) may have a role in the progression of COVID-19-associated cardiac injuries. The risk factors for CVD are significantly varied between and within different regions. In healthy individuals, the ACE I allele is responsible for the predisposition to CAD, but the ACE D haplotype is responsible for susceptibility and severity, which ultimately leads to heart failure. Patients who carry the T allele of rs12329760 in the TMPRSS2 gene are at risk for developing the severe form of COVID-19. IL-6 (rs1800796/rs1800795) polymorphism is associated with an increased mortality rate and susceptibility to severe COVID-19 disease. While the putative role of IL-6 associated with chronic, inflammatory diseases like cardiac and cerebrovascular disease is well known.

The occurrence of genetic variations in the ACE-2, AGT, DPP-IV, TMPRSS2, FUIRN, IL-4, IL-6, IFN-γ, and CYP2D6 genes is varied among different populations. Examining the correlation between these variations and their protein levels and cardiac injuries induced by COVID-19 may provide valuable insights into the pathogenesis of cardiac injuries induced by COVID-19.

The large-scale proteomic platform known as the SomaScan® assay is capable of simultaneously measuring thousands of proteins in patient specimens through next-generation aptamer-based multiplexed technology. While previous studies have utilized patient peripheral blood to suggest serum biomarkers of prognostic or diagnostic value in osteosarcoma (OSA), the most common primary pediatric bone cancer, they have ultimately been limited in the robustness of their analyses. We propose utilizing this aptamer-based technology to describe the systemic proteomic milieu in patients diagnosed with this disease.

To determine novel biomarkers associated with overall survival in OSA, we deployed the SomaLogic SomaScan® 7k assay to investigate the plasma proteomic profile of naive primary, recurrent, and metastatic OSA patients. Following identification of differentially expressed proteins (DEPs) between 2-year deceased and survivor cohorts, publicly available databases including Survival Genie, TIGER, and KM Plotter Immunotherapy, among others, were utilized to investigate the significance of our proteomic findings.

Apo-transcobalamin-II (APO-TCN2) was identified as the most DEP between 2-year deceased and survivor cohorts (Log2 fold change = 6.8, P-value = 0.0017). Survival analysis using the Survival Genie web-based platform indicated that increased intratumoral TCN2 expression was associated with better overall survival in both OSA (TARGET-OS) and sarcoma (TCGA-SARC) datasets. Cell-cell communication analysis using the TIGER database suggested that TCN2+ Myeloid cells likely interact with marginal zone and immunoglobin-producing B lymphocytes expressing the TCN2 receptor (CD320) to promote their proliferation and survival in both non-small cell lung cancer and melanoma tumors. Analysis of publicly available OSA scRNA-sequencing datasets identified similar populations in naive primary tumors. Furthermore, circulating APO-TCN2 levels in OSA were then associated with a plasma proteomic profile likely necessary for robust B lymphocyte proliferation, infiltration, and formation of intratumoral tertiary lymphoid structures for improved anti-tumor immunity.

Overall, APO-TCN2, a circulatory protein previously described in various lymphoproliferative disorders, was associated with 2-year survival status in patients diagnosed with OSA. The relevance of this protein and apparent immunological function (anti-tumor B lymphocyte responses) was suggested using publicly available solid tumor RNA-sequencing datasets. Further studies characterizing the biological function of APO-TCN2 and its relevance in these diseases is warranted.

Macromolecular protein complexes carry out most functions in the cell including essential functions required for cell survival. Unfortunately, we lack the subunit composition for all human protein complexes. To address this gap we integrated >25,000 mass spectrometry experiments using a machine learning approach to identify > 15,000 human protein complexes. We show our map of protein complexes is highly accurate and more comprehensive than previous maps, placing ~75% of human proteins into their physical contexts. We globally characterize our complexes using protein co-variation data (ProteomeHD.2) and identify co-varying complexes suggesting common functional associations. Our map also generates testable functional hypotheses for 472 uncharacterized proteins which we support using AlphaFold modeling. Additionally, we use AlphaFold modeling to identify 511 mutually exclusive protein pairs in hu.MAP3.0 complexes suggesting complexes serve different functional roles depending on their subunit composition. We identify expression as the primary way cells and organisms relieve the conflict of mutually exclusive subunits. Finally, we import our complexes to EMBL-EBI's Complex Portal (https://www.ebi.ac.uk/complexportal/home) as well as provide complexes through our hu.MAP3.0 web interface (https://humap3.proteincomplexes.org/). We expect our resource to be highly impactful to the broader research community.

Ongoing viral transcription from the reservoir of HIV-1 infected long-lived memory CD4+ T cells presents a barrier to cure and associates with poorer health outcomes for people living with HIV, including chronic immune activation and inflammation. We previously reported that didehydro-cortistatin A (dCA), an HIV-1 Tat inhibitor, blocks HIV-1 transcription. Here, we examine the impact of dCA on host immune CD4+ T-cell transcriptional and epigenetic states. We performed a comprehensive analysis of genome-wide transcriptomic and DNA methylation profiles upon long-term dCA treatment of primary human memory CD4+ T cells. dCA prompted specific transcriptional and DNA methylation changes in cell cycle, histone, interferon-response, and T-cell lineage transcription factor genes, through inhibition of both HIV-1 and Mediator kinases. These alterations establish a tolerogenic Treg/Th2 phenotype, reducing viral gene expression and mitigating inflammation in primary CD4+ T cells during HIV-1 infection. In addition, dCA suppresses the expression of lineage-defining transcription factors for Th17 and Th1 cells, critical HIV-1 targets, and reservoirs. dCA's benefits thus extend beyond viral transcription inhibition, modulating the immune cell landscape to limit HIV-1 acquisition and inflammatory environment linked to HIV infection.

Monocytes are plastic cells that assume different polarization states that can either promote inflammation or tissue repair and inflammation resolution. Polarized monocytes are partially defined by their transcriptional profiles that are influenced by environmental stimuli. The airway monocyte response in pediatric acute respiratory distress syndrome (PARDS) is undefined. To identify differentially expressed genes and networks using a novel transcriptomic reporter assay with donor monocytes exposed to the airway fluid of intubated children with and at-risk for PARDS. To determine differences in gene expression at two time points using the donor monocyte assay exposed to airway fluid from intubated children with PARDS obtained 48-96 hours following initial tracheal aspirate sampling.

In vitro pilot study carried out using airway fluid supernatant.

Academic 40-bed PICU.

Fifty-seven children: 44 children with PARDS and 13 children at-risk for PARDS.

None.

We performed bulk RNA sequencing using a transcriptomic reporter assay of monocytes exposed to airway fluid from intubated children to discover gene networks differentiating PARDS from at-risk for PARDS and those differentiating mild/moderate from severe PARDS. We also report differences in gene expression in children with PARDS 48-96 hours following initial tracheal aspirate sampling. We found that interleukin (IL)-10, IL-4, and IL-13, cytokine/chemokine signaling, and the senescence-associated secretory phenotype are upregulated in monocytes exposed to airway fluid from intubated children with PARDS compared with those at-risk for PARDS. Signaling by NOTCH, histone deacetylation/acetylation, DNA methylation, chromatin modifications (B-WICH complex), and RNA polymerase I transcription and its associated regulatory apparatus were upregulated in children with PARDS 48-96 hours following initial tracheal aspirate sampling.

We identified gene networks important to the PARDS airway immune response using bulk RNA sequencing from a monocyte reporter assay that exposed monocytes to airway fluid from intubated children with and at-risk for PARDS. Mechanistic investigations are needed to validate our findings.

Protein biomarkers may contribute to the identification of vulnerable subgroups for premature mortality. This study aimed to investigate the association of plasma proteins with all-cause and cause-specific mortality among individuals with and without baseline type 2 diabetes (T2D) and evaluate their impact on the prediction of all-cause mortality in two prospective Cooperative Health Research in the Region of Augsburg (KORA) studies.

The discovery cohort comprised 1545 participants (median follow-up 15.6 years; 244 with T2D: 116 total, 62 cardiovascular, 31 cancer-related and 23 other-cause deaths; 1301 without T2D: 321 total, 114 cardiovascular, 120 cancer-related and 87 other-cause deaths). The validation cohort comprised 1031 participants (median follow-up 6.9 years; 203 with T2D: 76 total, 45 cardiovascular, 19 cancer-related and 12 other-cause deaths; 828 without T2D: 169 total, 74 cardiovascular, 39 cancer-related and 56 other-cause deaths). We used Cox regression to examine associations of 233 plasma proteins with all-cause and cause-specific mortality and Lasso regression to construct prediction models for all-cause mortality stratifying by baseline T2D. C-index, category-free net reclassification index (cfNRI), and integrated discrimination improvement (IDI) were conducted to evaluate the predictive performance of built prediction models.

Thirty-five and 62 proteins, with 29 overlapping, were positively associated with all-cause mortality in the group with and without T2D, respectively. Out of these, in the group with T2D, 35, eight, and 26 were positively associated with cardiovascular, cancer-related, and other-cause mortality, while in the group without T2D, 55, 41, and 47 were positively associated with respective cause-specific outcomes in the pooled analysis of both cohorts. Regulation of insulin-like growth factor (IGF) transport and uptake by IGF-binding proteins emerged as a unique pathway enriched for all-cause and cardiovascular mortality in individuals with T2D. The combined model containing the selected proteins (five and 12 proteins, with four overlapping, in the group with and without T2D, respectively) and clinical risk factors improved the prediction of all-cause mortality by C-index, cfNRI, and IDI.

This study uncovered shared and unique mortality-related proteins in persons with and without T2D and emphasized the role of proteins in improving the prediction of mortality in different T2D subgroups.

People with a spinal cord injury are at an increased risk of metabolic dysfunction due to skeletal muscle atrophy and the transition of paralyzed muscle to a glycolytic, insulin-resistant phenotype. Providing doses of exercise through electrical muscle stimulation may provide a therapeutic intervention to help restore metabolic function for people with a spinal cord injury, but high-frequency and high-force electrically induced muscle contractions increase fracture risk for the underlying osteoporotic skeletal system. Therefore, we investigated the acute molecular responses after a session of either a 3 Hz or 1 Hz electrically induced exercise program. Ten people with a complete spinal cord injury completed a 1 h (3 Hz) or 3 h (1 Hz) unilateral electrically induced exercise session prior to a skeletal muscle biopsy of the vastus lateralis. The number of pulses was held constant. Tissue samples were analyzed for genomic and epigenomic expression profiles. There was a strong acute response after the 3 Hz exercise leading to the upregulation of early response genes (NR4A3, PGC-1α, ABRA, IRS2, EGR1, ANKRD1, and MYC), which have prominent roles in regulating molecular pathways that control mitochondrial biogenesis, contractile protein synthesis, and metabolism. Additionally, these genes, and others, contributed to the enrichment of pathways associated with signal transduction, cellular response to stimuli, gene expression, and metabolism. While there were similar trends observed after the 1 Hz exercise, the magnitude of gene expression changes did not reach our significance thresholds, despite a constant number of stimuli delivered. There were also no robust acute changes in muscle methylation after either form of exercise. Taken together, this study supports that a dose of low-force electrically induced exercise for 1 h using a 3 Hz stimulation frequency is suitable to trigger an acute genomic response in people with chronic paralysis, consistent with an expression signature thought to improve the metabolic and contractile phenotype of paralyzed muscle, if performed on a regular basis.

The current existing classifiers for distinguishing malignant from benign pulmonary nodules is limited by effectiveness or clinical practicality. In our study, we aimed to develop and validate a gene classifier for lung cancer diagnosis. To identify the genes involved in this process, we used the weighted gene co-expression network analysis to analyze gene expression datasets from Gene Expression Omnibus (GEO). We identified the three most relevant modules associated with malignant nodules and performed functional enrichment analysis on them. The results indicated significant involvement in metabolic, immune-related, cell cycle, and viral-related processes. All three modules showed enrichment in metabolic reprogramming pathways. Based on these genes, we intersected genes from the three modules with metabolic reprogramming-related genes and further intersected with differentially expressed genes to get 78 genes. After machine learning algorithms and manual selection, we finally got a nine-gene classifier consisting of SEC24D, RPSA, PSME3, PSMD8, PSMB7, NCOA1, MED12, LPCAT1, and AKR1C3. Our developed and validated classifier-based model demonstrated good discrimination, with an area under the curve (AUC) of 0.763 in the development cohort, 0.744 in the internal validation cohort, and 0.718 in the external validation cohort, and outperformed previous clinical models. Moreover, the addition of nodule size improved the predictive capability of the classifier. We further verify the expression of the gene in the classifier using TCGA lung cancer samples and found eight of the genes showed significant differential expression in lung adenocarcinoma while all nine genes showed significant differential expression in lung squamous carcinoma. Our findings underscore the significance of metabolic reprogramming pathways in patients with malignant pulmonary nodules, and our gene classifier can assist clinicians in differentiating benign from malignant pulmonary nodules in clinical settings.

Based on data from the Global Cancer Statistics 2022, lung cancer stands as the most lethal cancer worldwide, with age-adjusted incidence and mortality rates of 23.6 and 16.9 per 100,000 people, respectively. Despite significant strides in precision oncology driven by large-scale international research consortia, there remains a critical need to deepen our understanding of the genomic landscape across diverse racial and ethnic groups. To address this challenge, we performed comprehensive in silico analyses and data mining to identify pathogenic variants in genes that drive lung cancer. We subsequently calculated the allele frequencies and assessed the deleteriousness of these oncogenic variants among populations such as African, Amish, Ashkenazi Jewish, East and South Asian, Finnish and non-Finnish European, Latino, and Middle Eastern. Our analysis examined 117,707 variants within 86 lung cancer-associated genes across 75,109 human genomes, uncovering 8042 variants that are known or predicted to be pathogenic. We prioritized variants based on their allele frequencies and deleterious scores, and identified those with potential significance for response to anti-cancer therapies through in silico drug simulations, current clinical pharmacogenomic guidelines, and ongoing late-stage clinical trials targeting lung cancer-driving proteins. In conclusion, it is crucial to unite global efforts to create public health policies that emphasize prevention strategies and ensure access to clinical trials, pharmacogenomic testing, and cancer research for these groups in developed nations.

Following acute enterovirus (EV) infection, outcomes vary based on factors like the immune response, viral cell entry receptor expression levels, tissue tropism, and genetic factors of both the host and virus. While most individuals exhibit mild, self-limited symptoms, others may suffer severe complications or prolonged infections that can lead to autoimmune disorders.

To elucidate host responses to EV infection, we performed whole exome sequencing on blood samples from both infected and uninfected individuals. Our initial focus was on genes encoding EV entry receptors-PSGL-1, SCARB2, and ANAXA2 for EV-A71, and CD155 for poliovirus-and on host genes ACBD3 and PI4KΒ, crucial for EV replication.

Although no specific genetic variants directly associated with EV infection were identified, we discovered 118 variants across 116 genes enriched in East Asian populations through multi-layered variant filtering. These variants were further analyzed for their potential impacts on organs, biological processes, and molecular pathways. Phenome-wide association studies were conducted to refine our understanding of their contributions to EV infection susceptibility.

Our findings aim to develop a predictive panel based on these 118 variants, which could help susceptible individuals during EV outbreaks, guiding targeted clinical interventions and preventative strategies.

The druggable proteome refers to proteins that can bind to small molecules with appropriate chemical affinity, inducing a favorable clinical response. Predicting druggable proteins through screening and in silico modeling is imperative for drug design. To contribute to this field, we developed an accurate predictive classifier for druggable cancer-driving proteins using amino acid composition descriptors of protein sequences and 13 machine learning linear and non-linear classifiers. The optimal classifier was achieved with the support vector machine method, utilizing 200 tri-amino acid composition descriptors. The high performance of the model is evident from an area under the receiver operating characteristics (AUROC) of 0.975 ± 0.003 and an accuracy of 0.929 ± 0.006 (threefold cross-validation). The machine learning prediction model was enhanced with multi-omics approaches, including the target-disease evidence score, the shortest pathways to cancer hallmarks, structure-based ligandability assessment, unfavorable prognostic protein analysis, and the oncogenic variome. Additionally, we performed a drug repurposing analysis to identify drugs with the highest affinity capable of targeting the best predicted proteins. As a result, we identified 79 key druggable cancer-driving proteins with the highest ligandability, and 23 of them demonstrated unfavorable prognostic significance across 16 TCGA PanCancer types: CDKN2A, BCL10, ACVR1, CASP8, JAG1, TSC1, NBN, PREX2, PPP2R1A, DNM2, VAV1, ASXL1, TPR, HRAS, BUB1B, ATG7, MARK3, SETD2, CCNE1, MUTYH, CDKN2C, RB1, and SMARCA4. Moreover, we prioritized 11 clinically relevant drugs targeting these proteins. This strategy effectively predicts and prioritizes biomarkers, therapeutic targets, and drugs for in-depth studies in clinical trials. Scripts are available at https://github.com/muntisa/machine-learning-for-druggable-proteins .

Observational studies have shown that the correlation between neurodegenerative diseases and colorectal cancer (CRC) remains controversial. Therefore, this study aimed to verify the causal association between these two diseases.

Mendelian randomization (MR) analysis was used to assess the causal relationships between five major neurodegenerative diseases and CRC. Multivariable MR (MVMR) analysis was conducted to assess the direct causal effect of neurodegenerative diseases on CRC. Colocalization and pathway enrichment analyses were conducted to further elucidate our results. Sensitivity analysis was conducted to assess the robustness of the results.

Genetically predicted Alzheimer's disease (AD) nominally increased CRC risk (OR = 1.0620, 95%CI = 1.0127-1.1136, P = 0.013). There was no causal effect of genetically predicted CRC on neurodegenerative diseases. Furthermore, we demonstrated that genetically predicted AD marginally increased colon cancer risk (OR = 1.1621, 95%CI = 1.0267-1.3153, P = 0.017). Genetically predicted Lewy body dementia (LBD) had a significant causal effect on the increasing risk of colon cancer (IVW OR = 1.1779, 95%CI = 1.0694-1.2975, P = 0.001). MVMR indicated that effect of AD on colon cancer was driven by LBD, type 2 diabetes, body mass index, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglyceride, total cholesterol (TC), processed meat consumption, smoking, alcohol consumption, and educational attainment, whereas the effect of LBD on colon cancer was only influenced by TC. Colocalization and pathway enrichment analysis suggested that LBD and colon cancer possibly shared causal variants (nearby gene APOE), and ERBB4 signaling and lipid metabolism may mediate the causal association between LBD and colon cancer. Sensitivity analysis confirmed the reliability of our findings.

Our study demonstrated that genetic vulnerabilities to AD nominally increased the overall risk of CRC and colon cancer. Genetically predicted LBD indicated an elevated risk of colon cancer, potentially linked to ERBB4 signaling and lipid metabolism.

Hepatocellular carcinoma (HCC) is a major cause of cancer mortality worldwide, and metastasis is the main cause of early recurrence and poor prognosis. However, the mechanism of metastasis remains poorly understood.

To determine the possible mechanism affecting HCC metastasis and provide a possible theoretical basis for HCC treatment.

The candidate molecule lecithin-cholesterol acyltransferase (LCAT) was screened by gene microarray and bioinformatics analysis. The expression levels of LCAT in clinical cohort samples was detected by quantitative real-time polymerase chain reaction and western blotting. The proliferation, migration, invasion and tumor-forming ability were measured by Cell Counting Kit-8, Transwell cell migration, invasion, and clonal formation assays, respectively. Tumor formation was detected in nude mice after LCAT gene knockdown or overexpression. The immunohistochemistry for Ki67, E-cadherin, N-cadherin, matrix metalloproteinase 9 and vascular endothelial growth factor were performed in liver tissues to assess the effect of LCAT on HCC. Gene set enrichment analysis (GSEA) on various gene signatures were analyzed with GSEA version 3.0. Three machine-learning algorithms (random forest, support vector machine, and logistic regression) were applied to predict HCC metastasis in The Cancer Genome Atlas and GEO databases.

LCAT was identified as a novel gene relating to HCC metastasis by using gene microarray in HCC tissues. LCAT was significantly downregulated in HCC tissues, which is correlated with recurrence, metastasis and poor outcome of HCC patients. Functional analysis indicated that LCAT inhibited HCC cell proliferation, migration and invasion both in vitro and in vivo. Clinicopathological data showed that LCAT was negatively associated with HCC size and metastasis (HCC size ≤ 3 cm vs 3-9 cm, P < 0.001; 3-9 cm vs > 9 cm, P < 0.01; metastatic-free HCC vs extrahepatic metastatic HCC, P < 0.05). LCAT suppressed the growth, migration and invasion of HCC cell lines via PI3K/AKT/mTOR signaling. Our results indicated that the logistic regression model based on LCAT, TNM stage and the serum level of α-fetoprotein in HCC patients could effectively predict high metastatic risk HCC patients.

LCAT is downregulated at translational and protein levels in HCC and might inhibit tumor metastasis via attenuating PI3K/AKT/mTOR signaling. LCAT is a prognostic marker and potential therapeutic target for HCC.

Regulatory factor X 6 (RFX6) is crucial for pancreatic endocrine development and differentiation. The RFX6 variant p.His293LeufsTer7 is significantly enriched in the Finnish population, with almost 1:250 individuals as a carrier. Importantly, the FinnGen study indicates a high predisposition for heterozygous carriers to develop type 2 and gestational diabetes. However, the precise mechanism of this predisposition remains unknown.

To understand the role of this variant in beta cell development and function, we used CRISPR technology to generate allelic series of pluripotent stem cells. We created two isogenic stem cell models: a human embryonic stem cell model; and a patient-derived stem cell model. Both were differentiated into pancreatic islet lineages (stem-cell-derived islets, SC-islets), followed by implantation in immunocompromised NOD-SCID-Gamma mice.

Stem cell models of the homozygous variant RFX6-/- predictably failed to generate insulin-secreting pancreatic beta cells, mirroring the phenotype observed in Mitchell-Riley syndrome. Notably, at the pancreatic endocrine stage, there was an upregulation of precursor markers NEUROG3 and SOX9, accompanied by increased apoptosis. Intriguingly, heterozygous RFX6+/- SC-islets exhibited RFX6 haploinsufficiency (54.2% reduction in protein expression), associated with reduced beta cell maturation markers, altered calcium signalling and impaired insulin secretion (62% and 54% reduction in basal and high glucose conditions, respectively). However, RFX6 haploinsufficiency did not have an impact on beta cell number or insulin content. The reduced insulin secretion persisted after in vivo implantation in mice, aligning with the increased risk of variant carriers to develop diabetes.

Our allelic series isogenic SC-islet models represent a powerful tool to elucidate specific aetiologies of diabetes in humans, enabling the sensitive detection of aberrations in both beta cell development and function. We highlight the critical role of RFX6 in augmenting and maintaining the pancreatic progenitor pool, with an endocrine roadblock and increased cell death upon its loss. We demonstrate that RFX6 haploinsufficiency does not affect beta cell number or insulin content but does impair function, predisposing heterozygous carriers of loss-of-function variants to diabetes.

Ultra-deep bulk RNA-seq data for pancreatic differentiation stages 3, 5 and 7 of H1 RFX6 genotypes are deposited in the Gene Expression Omnibus database with accession code GSE234289. Original western blot images are deposited at Mendeley ( https://data.mendeley.com/datasets/g75drr3mgw/2 ).

Mycobacterium abscessus (Mab) infection can be deadly in patients with chronic lung diseases like cystic fibrosis (CF). In vitro and in vivo, Mab may adopt a smooth (S) or rough (R) morphotype, the latter linked to more severe disease conditions. In vitro studies revealed differences in pathogenicity and immune response to S and R morphotypes. We propose that in vivo both morphotypes exist and may transiently switch depending on the environment, having important pathogenic and immunologic consequences. This can be modeled by morphotypic S and R variants of Mab selected based on in vitro growth conditions. Here, we report the first analysis of early transcriptional events in mouse bone marrow derived macrophages (BMDMs) upon infection with media-selected interchangeable Mab-S and Mab-R morphotypes. The early transcriptional events after infection with both morphotypes showed considerable overlap of the pro-inflammatory genes that were differentially regulated compared to the uninfected macrophages. We also observed signature genes significantly differentially regulated in macrophages during infection of media-selected morphotypic Mab-S and Mab-R variants. In conclusion, media-selected Mab-S and Mab-R behave in a similar fashion to stable S and R types with respect to pathogenesis and immune response, serving as a useful model for environmentally influenced morphotype selection.

Splicing factor 3b subunit 1 (SF3B1) is the largest subunit and core component of the spliceosome. Inhibition of SF3B1 was associated with an increase in broad intron retention (IR) on most transcripts, suggesting that IR can be used as a marker of spliceosome inhibition in chronic lymphocytic leukemia (CLL) cells. Furthermore, we separately analyzed exonic and intronic mapped reads on annotated RNA-sequencing transcripts obtained from B cells (n = 98 CLL patients) and healthy volunteers (n = 9). We measured intron/exon ratio to use that as a surrogate for alternative RNA splicing (ARS) and found that 66% of CLL-B cell transcripts had significant IR elevation compared with normal B cells (NBCs) and that correlated with mRNA downregulation and low expression levels. Transcripts with the highest IR levels belonged to biological pathways associated with gene expression and RNA splicing. A >2-fold increase of active pSF3B1 was observed in CLL-B cells compared with NBCs. Additionally, when the CLL-B cells were treated with macrolides (pladienolide-B), a significant decrease in pSF3B1, but not total SF3B1 protein, was observed. These findings suggest that IR/ARS is increased in CLL, which is associated with SF3B1 phosphorylation and susceptibility to SF3B1 inhibitors. These data provide additional support to the relevance of ARS in carcinogenesis and evidence of pSF3B1 participation in this process.

Genes strictly regulate the development of teeth and their surrounding oral structures. Alteration of gene regulation leads to tooth disorders and developmental anomalies in tooth, oral, and facial regions. With the advancement of gene sequencing technology, genomic data is rapidly increasing. However, the large sets of genomic and proteomic data related to tooth development and dental disorders are currently dispersed in many primary databases and literature, making it difficult for users to navigate, extract, study, or analyze. We have curated the scattered genetic data on tooth development and created a knowledgebase called 'Bioinformatics for Dentistry' (https://dentalbioinformatics.com/). This database compiles genomic and proteomic data on human tooth development and developmental anomalies and organizes them according to their roles in different stages of tooth development. The database is built by systemically curating relevant data from the National Library of Medicine (NCBI) GenBank, OMIM: Online Mendelian Inheritance in Man, AlphaFold Protein Structure Database, Reactome pathway knowledgebase, Wiki Pathways, and PubMed. The accuracy of the included data was verified from supporting primary literature. Upon data curation and validation, a simple, easy-to-navigate browser interface was created on WordPress version 6.3.2, with PHP version 8.0. The website is hosted in a cloud hosting service to provide fast and reliable data transfer rate. Plugins are used to ensure the browser's compatibility across different devices. Bioinformatics for Dentistry contains four embedded filters for complex and specific searches and free-text search options for quick and simple searching through the datasets. Bioinformatics for Dentistry is made freely available worldwide, with the hope that this knowledgebase will improve our understanding of the complex genetic regulation of tooth development and will open doors to research initiatives and discoveries. This database will be expanded in the future by incorporating resources and built-in sequence analysis tools, and it will be maintained and updated annually.

Aspergillosis is a major causative factor for morbidity in those with impaired immune systems, often caused by Aspergillus fumigatus. The diagnosis and treatment are difficult due to the diversity of individuals and risk factors and still pose a challenge for medical professionals. To understand the pathogenicity of any organism, it is critical to identify the significant metabolic pathways that are involved. Our work focused on developing kinetic models of critical pathways crucial for the survival of A. fumigatus using COPASI. While focusing on the folate biosynthesis, ergosterol biosynthesis and glycolytic pathway; sensitivity, time-course and steady-state analysis were performed to find the proteins/enzymes that are essential in the pathway and can be considered as potential drug targets. For further analysis of the interaction of drug targets identified, a protein-protein interaction (PPI) network was built, and hub nodes were identified using the Cytohubba package from Cytoscape. Based on the findings, dihydropteroate-synthase, dihydrofolate-reductase, 4-amino-4-deoxychorismate synthase, HMG-CoA-reductase, PG-isomerase and hexokinase could act as potential drug targets. Further, molecular docking and MM-GBSA analysis were performed with ligands chosen from DrugBank, and PubChem, and validated by experimental evidence and existing literature based on results from kinetic modeling and PPI network analysis. Based on docking scores and MM-GBSA results, molecular simulations were carried out for 1AJ2-dapsone, 1DIS-sulfamethazine, 1T02-lovastatin and 70YL-3-bromopyruvic acid complexes, which validated our findings. Our study provides a deeper insight into the mechanisms of A. fumigatus's metabolism to reveal dapsone, sulfamethazine, lovastatin and 3-bromopyruvic acid as potential drugs for the treatment of Aspergillosis.

Common approaches for deciphering biological networks involve network embedding algorithms. These approaches strictly focus on clustering the genes' embedding vectors and interpreting such clusters to reveal the hidden information of the networks. However, the difficulty in interpreting the genes' clusters and the limitations of the functional annotations' resources hinder the identification of the currently unknown cell's functioning mechanisms. We propose a new approach that shifts this functional exploration from the embedding vectors of genes in space to the axes of the space itself. Our methodology better disentangles biological information from the embedding space than the classic gene-centric approach. Moreover, it uncovers new data-driven functional interactions that are unregistered in the functional ontologies, but biologically coherent. Furthermore, we exploit these interactions to define new higher-level annotations that we term Axes-Specific Functional Annotations and validate them through literature curation. Finally, we leverage our methodology to discover evolutionary connections between cellular functions and the evolution of species.

Data and source code can be accessed at https://gitlab.bsc.es/sdoria/axes-of-biology.git.

The success of checkpoint inhibitors (CPIs) for cancer has been tempered by immune-related adverse effects including colitis. CPI-induced colitis is hallmarked by expansion of resident mucosal IFNγ cytotoxic CD8+ T cells, but how these arise is unclear. Here, we track CPI-bound T cells in intestinal tissue using multimodal single-cell and subcellular spatial transcriptomics (ST). Target occupancy was increased in inflamed tissue, with drug-bound T cells located in distinct microdomains distinguished by specific intercellular signaling and transcriptional gradients. CPI-bound cells were largely CD4+ T cells, including enrichment in CPI-bound peripheral helper, follicular helper, and regulatory T cells. IFNγ CD8+ T cells emerged from both tissue-resident memory (TRM) and peripheral populations, displayed more restricted target occupancy profiles, and co-localized with damaged epithelial microdomains lacking effective regulatory cues. Our multimodal analysis identifies causal pathways and constitutes a resource to inform novel preventive strategies.