Reactome: a database of reactions, pathways and biological processes

Knowledge-guided learning methods for integrative analysis of multi-omics data

Integrative analysis of multi-omics data has the potential to yield valuable and comprehensive insights into the molecular mechanisms underlying complex diseases such as cancer and Alzheimer's disease. However, a number of analytical challenges complicate multi-omics data integration. For instance, -omics data are usually high-dimensional, and sample sizes in multi-omics studies tend to be modest. Furthermore, when genes in an important pathway have relatively weak signal, it can be difficult to detect them individually. There is a growing body of literature on knowledge-guided learning methods that can address these challenges by incorporating biological knowledge such as functional genomics and functional proteomics into multi-omics data analysis. These methods have been shown to outperform their counterparts that do not utilize biological knowledge in tasks including prediction, feature selection, clustering, and dimension reduction. In this review, we survey recently developed methods and applications of knowledge-guided multi-omics data integration methods and discuss future research directions.

Target repositioning using multi-layer networks and machine learning: The case of prostate cancer

The discovery of novel therapeutic targets, defined as proteins which drugs can interact with to induce therapeutic benefits, typically represent the first and most important step of drug discovery. One solution for target discovery is target repositioning, a strategy which relies on the repurposing of known targets for new diseases, leading to new treatments, less side effects and potential drug synergies. Biological networks have emerged as powerful tools for integrating heterogeneous data and facilitating the prediction of biological or therapeutic properties. Consequently, they are widely employed to predict new therapeutic targets by characterizing potential candidates, often based on their interactions within a Protein-Protein Interaction (PPI) network, and their proximity to genes associated with the disease. However, over-reliance on PPI networks and the assumption that potential targets are necessarily near known genes can introduce biases that may limit the effectiveness of these methods. This study addresses these limitations in two ways. First, by exploiting a multi-layer network which incorporates additional information such as gene regulation, metabolite interactions, metabolic pathways, and several disease signatures such as Differentially Expressed Genes, mutated genes, Copy Number Alteration, and structural variants. Second, by extracting relevant features from the network using several approaches including proximity to disease-associated genes, but also unbiased approaches such as propagation-based methods, topological metrics, and module detection algorithms. Using prostate cancer as a case study, the best features were identified and utilized to train machine learning algorithms to predict 5 novel promising therapeutic targets for prostate cancer: IGF2R, C5AR, RAB7, SETD2 and NPBWR1.

Supervised discovery of interpretable gene programs from single-cell data

Factor analysis decomposes single-cell gene expression data into a minimal set of gene programs that correspond to processes executed by cells in a sample. However, matrix factorization methods are prone to technical artifacts and poor factor interpretability. We address these concerns with Spectra, an algorithm that combines user-provided gene programs with the detection of novel programs that together best explain expression covariation. Spectra incorporates existing gene sets and cell-type labels as prior biological information, explicitly models cell type and represents input gene sets as a gene-gene knowledge graph using a penalty function to guide factorization toward the input graph. We show that Spectra outperforms existing approaches in challenging tumor immune contexts, as it finds factors that change under immune checkpoint therapy, disentangles the highly correlated features of CD8+ T cell tumor reactivity and exhaustion, finds a program that explains continuous macrophage state changes under therapy and identifies cell-type-specific immune metabolic programs.

Functional transcriptome analysis revealed major changes in pathways affecting systems biology of Tharparkar cattle under seasonal heat stress

Heat stress significantly disturbs the production, reproduction, and systems biology of dairy cattle. A complex interaction among biological systems helps to combat and overcome heat stress. Indicine cattle breed Tharparkar has been well known for its thermal adaptability. Therefore, present investigation considered RNA-seq technology to explore the functional transcriptomics of Tharparkar cattle with the help of samples collected in spring and summer season. Among differentially expressed genes, about 3280 genes were highly dysregulated, in which 1207 gene were upregulated and 2073 genes were downregulated (|log2fold change|≥ 1 and p ≤ 0.05). Upregulated genes were related to insulin activation, interferons, and potassium ion transport. In contrast, downregulated genes were related to RNA processing, translation, and ubiquitination. Functional annotation revealed that the pathways associated with nervous system (NPFFR1, ROBO3) and metal ion transport (KCNG2, ATP1A2) were highly activated while mRNA processing and translation (EIF4A, EIF4B) and protein processing pathway (VPS4B, PEX13) were highly downregulated. Protein-protein interactions identified hub genes such as ATP13A3, IFNGR2, UBXN7, EIF4A2, SLC12A8 found to play an important role in immune, ubiquitination, translation and transport function. Co-expression network includes LYZ, PNRC1, SQSTM1, EIF4AB and DDX17 genes which are involved in lysosomal activity, tumor inhibition, ubiquitination, and translation initiation. Chemokine signaling pathway associated with immune response was highly upregulated in cluster analysis. The findings of this study provide insights into transcriptome expression and regulation which may better explain complex thermal resilience mechanism of Tharparkar cattle in heat stress under natural conditions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-024-04018-2.

Discovering Potential in Non-Cancer Medications: A Promising Breakthrough for Multiple Myeloma Patients

MM is a common type of cancer that unfortunately leads to a significant number of deaths each year. The majority of the reported MM cases are detected in the advanced stages, posing significant challenges for treatment. Additionally, all MM patients eventually develop resistance or experience relapse; therefore, advances in treatment are needed. However, developing new anti-cancer drugs, especially for MM, requires significant financial investment and a lengthy development process. The study of drug repurposing involves exploring the potential of existing drugs for new therapeutic uses. This can significantly reduce both time and costs, which are typically a major concern for MM patients. The utilization of pre-existing non-cancer drugs for various myeloma treatments presents a highly efficient and cost-effective strategy, considering their prior preclinical and clinical development. The drugs have shown promising potential in targeting key pathways associated with MM progression and resistance. Thalidomide exemplifies the success that can be achieved through this strategy. This review delves into the current trends, the challenges faced by conventional therapies for MM, and the importance of repurposing drugs for MM. This review highlights a noncomprehensive list of conventional therapies that have potentially significant anti-myeloma properties and anti-neoplastic effects. Additionally, we offer valuable insights into the resources that can help streamline and accelerate drug repurposing efforts in the field of MM.

CAR'TCR-T cells co-expressing CD33-CAR and dNPM1-TCR as superior dual-targeting approach for AML treatment

Acute myeloid leukemia (AML), a fast-progressing hematological malignancy affecting myeloid cells, is typically treated with chemotherapy or hematopoietic stem cell transplantation. However, approximately half of the patients face relapses and 5-year survival rates are poor. With the goal to facilitate dual-specificity, boosting anti-tumor activity, and minimizing the risk for antigen escape, this study focused on combining chimeric antigen receptor (CAR) and T cell receptor (TCR) technologies. CAR'TCR-T cells, co-expressing a CD33-CAR and a transgenic dNPM1-TCR, revealed increased and prolonged anti-tumor activity in vitro, particularly in case of low target antigen expression. The distinct transcriptomic profile suggested enhanced formation of immunological synapses, activation, and signaling. Complete elimination of AML xenografts in vivo was only achieved with a cell product containing CAR'TCR-T, CAR-T, and TCR-T cells, representing the outcome of co-transduction with two lentiviral vectors encoding either CAR or TCR. A mixture of CAR-T and TCR-T cells, without CAR'TCR-T cells, did not prevent progressive tumor outgrowth and was comparable to treatment with CAR-T and TCR-T cells individually. Overall, our data underscore the efficacy of co-expressing CAR and transgenic TCR in one T cell, and might open a novel therapeutic avenue not only for AML but also other malignancies.

Decoding the hallmarks of allograft dysfunction with a comprehensive pan-organ transcriptomic atlas

The pathogenesis of allograft (dys)function has been increasingly studied using 'omics'-based technologies, but the focus on individual organs has created knowledge gaps that neither unify nor distinguish pathological mechanisms across allografts. Here we present a comprehensive study of human pan-organ allograft dysfunction, analyzing 150 datasets with more than 12,000 samples across four commonly transplanted solid organs (heart, lung, liver and kidney, n = 1,160, 1,241, 1,216 and 8,853 samples, respectively) that we leveraged to explore transcriptomic differences among allograft dysfunction (delayed graft function, acute rejection and fibrosis), tolerance and stable graft function. We identified genes that correlated robustly with allograft dysfunction across heart, lung, liver and kidney transplantation. Furthermore, we developed a transfer learning omics prediction framework that, by borrowing information across organs, demonstrated superior classifications compared to models trained on single organs. These findings were validated using a single-center prospective kidney transplant cohort study (a collective 329 samples across two timepoints), providing insights supporting the potential clinical utility of our approach. Our study establishes the capacity for machine learning models to learn across organs and presents a transcriptomic transplant resource that can be employed to develop pan-organ biomarkers of allograft dysfunction.

Integrative genomic and transcriptomic profiling of pulmonary sarcomatoid carcinoma identifies molecular subtypes associated with distinct immune features and clinical outcomes

Background

Pulmonary sarcomatoid carcinoma (PSC) is a rare and aggressive subtype of non-small cell lung cancer (NSCLC), characterized by the presence of epithelial and sarcoma-like components. The molecular and immune landscape of PSC has not been well defined.

Methods

Multiomics profiling of 21 pairs of PSCs with matched normal lung tissues was performed through targeted high-depth DNA panel, whole-exome, and RNA sequencing. We describe molecular and immune features that define subgroups of PSC with disparate genomic and immunogenic features as well as distinct clinical outcomes.

Results

In total, 27 canonical cancer gene mutations were identified, with TP53 the most frequently mutated gene, followed by KRAS. Interestingly, most TP53 and KRAS mutations were earlier genomic events mapped to the trunks of the tumors, suggesting branching evolution in most PSC tumors. We identified two distinct molecular subtypes of PSC, driven primarily by immune infiltration and signaling. The Immune High (IM-H) subtype was associated with superior survival, highlighting the impact of immune infiltration on the biological and clinical features of localized PSCs.

Conclusions

We provided detailed insight into the mutational landscape of PSC and identified two molecular subtypes associated with prognosis. IM-H tumors were associated with favorable recurrence-free survival and overall survival, highlighting the importance of tumor immune infiltration in the biological and clinical features of PSCs.

HIV-1 infection is associated with depletion of germinal center B cells and a decrease in IgA + plasma cells in the GI tract

Gastrointestinal (GI) B cells and plasma cells (PCs), critical to mucosal homeostasis, play an important role in the host response to HIV-1 infection. Here, high resolution mapping of human B cells and PCs from colon and ileum during both viremic and suppressed HIV-1 infection identified a significant reduction in germinal center (GC) B cells and Follicular Dendritic Cells (FDCs) during HIV-1 viremia. Further, IgA + PCs, the major cellular output of intestinal GCs were significantly reduced during viremic HIV-1 infection. PC-associated transcriptional perturbations, including type I interferon signaling persisted in antiretroviral therapy (ART) treated individuals, suggesting ongoing disruption of the intestinal immune milieu during ART. GI humoral immune perturbations associated with changes in intestinal microbiome composition and systemic inflammation. Herein, we highlight a key immune defect in the GI mucosa due to HIV-1 viremia, with major implications.

One Sentence Summary

Major perturbations in intestinal GC dynamics in viremic HIV-1 infection relate to reduced IgA + plasma cells, systemic inflammation and microbiota changes.

Peripheral Blood Gene Expression Profiling Reveals Molecular Pathways Associated with Cervical Artery Dissection

Cervical artery dissection (CeAD) is the primary cause of ischemic stroke in young adults. Monogenic heritable connective tissue diseases account for fewer than 5% of cases of CeAD. The remaining sporadic cases have known risk factors. The clinical, radiological, and histological characteristics of systemic vasculopathy and undifferentiated connective tissue dysplasia are present in up to 70% of individuals with sporadic CeAD. Genome-wide association studies identified CeAD-associated genetic variants in the non-coding genomic regions that may impact the gene transcription and RNA processing. However, global gene expression profile analysis has not yet been carried out for CeAD patients. We conducted bulk RNA sequencing and differential gene expression analysis to investigate the expression profile of protein-coding genes in the peripheral blood of 19 CeAD patients and 18 healthy volunteers. This was followed by functional annotation, heatmap clustering, reports on gene-disease associations and protein-protein interactions, as well as gene set enrichment analysis. We found potential correlations between CeAD and the dysregulation of genes linked to nucleolar stress, senescence-associated secretory phenotype, mitochondrial malfunction, and epithelial-mesenchymal plasticity.

Pathformer: a biological pathway informed transformer for disease diagnosis and prognosis using multi-omics data

MOTIVATION

Multi-omics data provide a comprehensive view of gene regulation at multiple levels, which is helpful in achieving accurate diagnosis of complex diseases like cancer. However, conventional integration methods rarely utilize prior biological knowledge and lack interpretability.

RESULTS

To integrate various multi-omics data of tissue and liquid biopsies for disease diagnosis and prognosis, we developed a biological pathway informed Transformer, Pathformer. It embeds multi-omics input with a compacted multi-modal vector and a pathway-based sparse neural network. Pathformer also leverages criss-cross attention mechanism to capture the crosstalk between different pathways and modalities. We first benchmarked Pathformer with 18 comparable methods on multiple cancer datasets, where Pathformer outperformed all the other methods, with an average improvement of 6.3%-14.7% in F1 score for cancer survival prediction, 5.1%-12% for cancer stage prediction, and 8.1%-13.6% for cancer drug response prediction. Subsequently, for cancer prognosis prediction based on tissue multi-omics data, we used a case study to demonstrate the biological interpretability of Pathformer by identifying key pathways and their biological crosstalk. Then, for cancer early diagnosis based on liquid biopsy data, we used plasma and platelet datasets to demonstrate Pathformer's potential of clinical applications in cancer screening. Moreover, we revealed deregulation of interesting pathways (e.g. scavenger receptor pathway) and their crosstalk in cancer patients' blood, providing potential candidate targets for cancer microenvironment study.

AVAILABILITY AND IMPLEMENTATION

Pathformer is implemented and freely available at https://github.com/lulab/Pathformer.

Advances in machine intelligence-driven virtual screening approaches for big-data

Virtual screening (VS) is an integral and ever-evolving domain of drug discovery framework. The VS is traditionally classified into ligand-based (LB) and structure-based (SB) approaches. Machine intelligence or artificial intelligence has wide applications in the drug discovery domain to reduce time and resource consumption. In combination with machine intelligence algorithms, VS has emerged into revolutionarily progressive technology that learns within robust decision orders for data curation and hit molecule screening from large VS libraries in minutes or hours. The exponential growth of chemical and biological data has evolved as "big-data" in the public domain demands modern and advanced machine intelligence-driven VS approaches to screen hit molecules from ultra-large VS libraries. VS has evolved from an individual approach (LB and SB) to integrated LB and SB techniques to explore various ligand and target protein aspects for the enhanced rate of appropriate hit molecule prediction. Current trends demand advanced and intelligent solutions to handle enormous data in drug discovery domain for screening and optimizing hits or lead with fewer or no false positive hits. Following the big-data drift and tremendous growth in computational architecture, we presented this review. Here, the article categorized and emphasized individual VS techniques, detailed literature presented for machine learning implementation, modern machine intelligence approaches, and limitations and deliberated the future prospects.

Homeobox regulator Wilms Tumour 1 is displaced by androgen receptor at cis-regulatory elements in the endometrium of PCOS patients

Decidualisation, the process whereby endometrial stromal cells undergo morphological and functional transformation in preparation for trophoblast invasion, is often disrupted in women with polycystic ovary syndrome (PCOS) resulting in complications with pregnancy and/or infertility. The transcription factor Wilms tumour suppressor 1 (WT1) is a key regulator of the decidualization process, which is reduced in patients with PCOS, a complex condition characterized by increased expression of androgen receptor in endometrial cells and high presence of circulating androgens. Using genome-wide chromatin immunoprecipitation approaches on primary human endometrial stromal cells, we identify key genes regulated by WT1 during decidualization, including homeobox transcription factors which are important for regulating cell differentiation. Furthermore, we found that AR in PCOS patients binds to the same DNA regions as WT1 in samples from healthy endometrium, suggesting dysregulation of genes important to decidualisation pathways in PCOS endometrium due to competitive binding between WT1 and AR. Integrating RNA-seq and H3K4me3 and H3K27ac ChIP-seq metadata with our WT1/AR data, we identified a number of key genes involved in immune response and angiogenesis pathways that are dysregulated in PCOS patients. This is likely due to epigenetic alterations at distal enhancer regions allowing AR to recruit cofactors such as MAGEA11, and demonstrates the consequences of AR disruption of WT1 in PCOS endometrium.

Drug repurposing for cancer therapy

Cancer, a complex and multifactorial disease, presents a significant challenge to global health. Despite significant advances in surgical, radiotherapeutic and immunological approaches, which have improved cancer treatment outcomes, drug therapy continues to serve as a key therapeutic strategy. However, the clinical efficacy of drug therapy is often constrained by drug resistance and severe toxic side effects, and thus there remains a critical need to develop novel cancer therapeutics. One promising strategy that has received widespread attention in recent years is drug repurposing: the identification of new applications for existing, clinically approved drugs. Drug repurposing possesses several inherent advantages in the context of cancer treatment since repurposed drugs are typically cost-effective, proven to be safe, and can significantly expedite the drug development process due to their already established safety profiles. In light of this, the present review offers a comprehensive overview of the various methods employed in drug repurposing, specifically focusing on the repurposing of drugs to treat cancer. We describe the antitumor properties of candidate drugs, and discuss in detail how they target both the hallmarks of cancer in tumor cells and the surrounding tumor microenvironment. In addition, we examine the innovative strategy of integrating drug repurposing with nanotechnology to enhance topical drug delivery. We also emphasize the critical role that repurposed drugs can play when used as part of a combination therapy regimen. To conclude, we outline the challenges associated with repurposing drugs and consider the future prospects of these repurposed drugs transitioning into clinical application.

From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.

Experimental capture of miRNA targetomes: disease-specific 3'UTR library-based miRNA targetomics for Parkinson's disease

The identification of targetomes remains a challenge given the pleiotropic effect of miRNAs, the limited effects of miRNAs on individual targets, and the sheer number of estimated miRNA-target gene interactions (MTIs), which is around 44,571,700. Currently, targetome identification for single miRNAs relies on computational evidence and functional studies covering smaller numbers of targets. To ensure that the targetome analysis could be experimentally verified by functional assays, we employed a systematic approach and explored the targetomes of four miRNAs (miR-129-5p, miR-129-1-3p, miR-133b, and miR-873-5p) by analyzing 410 predicted target genes, both of which were previously associated with Parkinson's disease (PD). After performing 13,536 transfections, we validated 442 of the 705 putative MTIs (62,7%) through dual luciferase reporter assays. These analyses increased the number of validated MTIs by at least 2.1-fold for miR-133b and by a maximum of 24.3-fold for miR-873-5p. Our study contributes to the experimental capture of miRNA targetomes by addressing i) the ratio of experimentally verified MTIs to predicted MTIs, ii) the sizes of disease-related miRNA targetomes, and iii) the density of MTI networks. A web service to support the analyses on the MTI level is available online ( https://ccb-web.cs.uni-saarland.de/utr-seremato ), and all the data have been added to the miRATBase database ( https://ccb-web.cs.uni-saarland.de/miratbase ).

Temperature-driven coordination of circadian transcriptional regulation

The circadian clock is an evolutionarily-conserved molecular oscillator that enables species to anticipate rhythmic changes in their environment. At a molecular level, the core clock genes induce circadian oscillations in thousands of genes in a tissue-specific manner, orchestrating myriad biological processes. While previous studies have investigated how the core clock circuit responds to environmental perturbations such as temperature, the downstream effects of such perturbations on circadian regulation remain poorly understood. By analyzing bulk-RNA sequencing of Drosophila fat bodies harvested from flies subjected to different environmental conditions, we demonstrate a highly condition-specific circadian transcriptome: genes are cycling in a temperature-specific manner, and the distributions of their phases also differ between the two conditions. Further employing a reference-based gene regulatory network (Reactome), we find evidence of increased gene-gene coordination at low temperatures and synchronization of rhythmic genes that are network neighbors. We report that the phase differences between cycling genes increase as a function of geodesic distance in the low temperature condition, suggesting increased coordination of cycling on the gene regulatory network. Our results suggest a potential mechanism whereby the circadian clock mediates the fly's response to seasonal changes in temperature.

Experimentally Induced Peripheral Venous Congestion Exacerbates Inflammation, Oxidative Stress, and Neurohormonal and Endothelial Cell Activation in Patients With Systolic Heart Failure

BACKGROUND

Venous congestion (VC) is a hallmark of symptomatic heart failure (HF) requiring hospitalization; however, its role in the pathogenesis of HF progression remains unclear. We investigated whether peripheral VC exacerbates inflammation, oxidative stress and neurohormonal and endothelial cell (EC) activation in patients with HF with reduced ejection fraction (HFrEF).

METHODS AND RESULTS

Two matched groups of patients with HFrEF and with no peripheral VC vs without recent HF hospitalization were studied. We modeled peripheral VC by inflating a cuff around the dominant arm, targeting ∼ 30 mmHg increase in venous pressure (venous stress test [VST]). Blood and ECs were sampled before and after 90 minutes of VST. We studied 44 patients (age 53 ± 12 years, 32% female). Circulating endothelin-1, tumor necrosis factor-α, interleukin-6, isoprostane, angiotensin II (ang-2), angiopoietin-2, vascular cell adhesion molecule-1, and CD146 significantly increased after the VST. Enhanced endothelin-1 and angiopoietin-2 responses to the VST were present in patients with vs without recent hospitalization and were prospectively associated with incident HF-related events; 6698 messenger ribonucleic acid (mRNA probe sets were differentially expressed in ECs after VST.

CONCLUSIONS

Experimental VC exacerbates inflammation, oxidative stress, neurohormonal and EC activation and promotes unfavorable transcriptome remodeling in ECs of patients with HFrEF. A distinct biological sensitivity to VC appears to be associated with high risk for HF progression.

A systems-biology approach connects aging mechanisms with Alzheimer's disease pathogenesis

Age is the strongest risk factor for developing Alzheimer's disease, the most common neurodegenerative disorder. However, the mechanisms connecting advancing age to neurodegeneration in Alzheimer's disease are incompletely understood. We conducted an unbiased, genome-scale, forward genetic screen for age-associated neurodegeneration in Drosophila to identify the underlying biological processes required for maintenance of aging neurons. To connect genetic screen hits to Alzheimer's disease pathways, we measured proteomics, phosphoproteomics, and metabolomics in Drosophila models of Alzheimer's disease. We further identified Alzheimer's disease human genetic variants that modify expression in disease-vulnerable neurons. Through multi-omic, multi-species network integration of these data, we identified relationships between screen hits and tau-mediated neurotoxicity. Furthermore, we computationally and experimentally identified relationships between screen hits and DNA damage in Drosophila and human iPSC-derived neural progenitor cells. Our work identifies candidate pathways that could be targeted to attenuate the effects of age on neurodegeneration and Alzheimer's disease.

Topical Ruxolitinib in the Treatment of Necrobiosis Lipoidica: A Prospective, Open-Label Study

Necrobiosis lipoidica (NL) is a rare granulomatous disease. There are few effective treatments for NL. We sought to investigate the efficacy and safety of the Jak1/2 inhibitor, ruxolitnib, in the treatment of NL and identify the biomarkers associated with the disease and treatment response. We conducted an open-label, phase 2 study of ruxolitinib in 12 patients with NL. We performed transcriptomic analysis of tissue samples before and after treatment. At week 12, the mean NL lesion score decreased by 58.2% (SD = 28.7%, P = .003). Transcriptomic analysis demonstrated enrichment of type I and type II IFN pathways in baseline disease. Weighted gene coexpression network analysis demonstrated post-treatment changes in IFN pathways with key hub genes IFNG and signal transducer and activator of transcription 1 gene STAT1. Limitations include small sample size and a study group limited to patients with <10% body surface area. In conclusion, ruxolitinib is an effective treatment for NL and targets the key pathogenic mediators of the disease.

Proteomics and Its Current Application in Biomedical Area: Concise Review

Biomedical researchers tirelessly seek cutting-edge technologies to advance disease diagnosis, drug discovery, and therapeutic interventions, all aimed at enhancing human and animal well-being. Within this realm, proteomics stands out as a pivotal technology, focusing on extensive studies of protein composition, structure, function, and interactions. Proteomics, with its subdivisions of expression, structural, and functional proteomics, plays a crucial role in unraveling the complexities of biological systems. Various sophisticated techniques are employed in proteomics, including polyacrylamide gel electrophoresis, mass spectrometry analysis, NMR spectroscopy, protein microarray, X-ray crystallography, and Edman sequencing. These methods collectively contribute to the comprehensive understanding of proteins and their roles in health and disease. In the biomedical field, proteomics finds widespread application in cancer research and diagnosis, stem cell studies, and the diagnosis and research of both infectious and noninfectious diseases. In addition, it plays a pivotal role in drug discovery and the emerging frontier of personalized medicine. The versatility of proteomics allows researchers to delve into the intricacies of molecular mechanisms, paving the way for innovative therapeutic approaches. As infectious and noninfectious diseases continue to emerge and the field of biomedical research expands, the significance of proteomics becomes increasingly evident. Keeping abreast of the latest developments in proteomics applications becomes paramount for the development of therapeutics, translational research, and study of diverse diseases. This review aims to provide a comprehensive overview of proteomics, offering a concise outline of its current applications in the biomedical domain. By doing so, it seeks to contribute to the understanding and advancement of proteomics, emphasizing its pivotal role in shaping the future of biomedical research and therapeutic interventions.

An atlas of cell-type-specific interactome networks across 44 human tumor types

BACKGROUND

Biological processes are controlled by groups of genes acting in concert. Investigating gene-gene interactions within different cell types can help researchers understand the regulatory mechanisms behind human complex diseases, such as tumors.

METHODS

We collected extensive single-cell RNA-seq data from tumors, involving 563 patients with 44 different tumor types. Through our analysis, we identified various cell types in tumors and created an atlas of different immune cell subsets across different tumor types. Using the SCINET method, we reconstructed interactome networks specific to different cell types. Diverse functional data was then integrated to gain biological insights into the networks, including somatic mutation patterns and gene functional annotation. Additionally, genes with prognostic relevance within the networks were also identified. We also examined cell-cell communications to investigate how gene interactions modulate cell-cell interactions.

RESULTS

We developed a data portal called CellNetdb for researchers to study cell-type-specific interactome networks. Our findings indicate that these networks can be used to identify genes with topological specificity in different cell types. We also found that prognostic genes can deconvolved into cell types through analyzing network connectivity. Additionally, we identified commonalities and differences in cell-type-specific networks across different tumor types. Our results suggest that these networks can be used to prioritize risk genes.

CONCLUSIONS

This study presented CellNetdb, a comprehensive repository featuring an atlas of cell-type-specific interactome networks across 44 human tumor types. The findings underscore the utility of these networks in delineating the intricacies of tumor microenvironments and advancing the understanding of molecular mechanisms underpinning human tumors.

Targeted HBx gene editing by CRISPR/Cas9 system effectively reduces epithelial to mesenchymal transition and HBV replication in hepatoma cells

BACKGROUND AND AIMS

Hepatitis B virus X protein (HBx) play a key role in pathogenesis of HBV-induced hepatocellular carcinoma (HCC) by promoting epithelial to mesenchymal transition (EMT). In this study, we hypothesized that inhibition of HBx is an effective strategy to combat HCC.

METHODOLOGY AND RESULTS

We designed and synthesized novel HBx gene specific single guide RNA (sgRNA) with CRISPR/Cas9 system and studied its in vitro effects on tumour properties of HepG2-2.15. Full length HBx gene was excised using HBx-CRISPR that resulted in significant knockdown of HBx expression in hepatoma cells. HBx-CRISPR also decreased levels of HBsAg and HBV cccDNA expression. A decreased expression of mesenchymal markers, proliferation and tumorigenic properties was observed in HBx-CRISPR treated cells as compared to controls in both two- and three- dimensional (2D and 3D) tumour models. Transcriptomics data showed that out of 1159 differentially expressed genes in HBx-CRISPR transfected cells as compared to controls, 70 genes were upregulated while 1089 genes associated with cell proliferation and EMT pathways were downregulated.

CONCLUSION

Thus, targeting of HBx by CRISPR/Cas9 gene editing system reduces covalently closed circular DNA (cccDNA) levels, HBsAg production and mesenchymal characteristics of HBV-HCC cells. We envision inhibition of HBx by CRISPR as a novel therapeutic approach for HBV-induced HCC.

Single-cell RNA-seq reveals the metabolic status of immune cells response to immunotherapy in triple-negative breast cancer

Immune checkpoint blockade (ICB) therapy offers promise in the treatment of triple-negative breast cancer (TNBC); however, its limited efficacy in certain TNBC patients poses a challenge. In this study, we elucidated the metabolic mechanism at 'sub-subtype' resolution underlying the non-response to ICB therapy in TNBC. Here, an analytic pipeline was developed to reveal the metabolic heterogeneity, which is correlated with the ICB outcomes, within each immune cell subtype. First, we identified metabolic 'sub-subtypes' within certain cell subtypes, predominantly T cell subsets, which are enriched in ICB non-responders and named as non-responder-enriched (NR-E) clusters. Notably, most of NR-E T metabolic cells exhibit globally higher metabolic activities compared to other cells within the same individual subtype. Further, we investigated the extra-cellular signals that trigger the metabolic status of NR-E T cells. In detail, the prediction of cell-to-cell communication indicated that NR-E T cells are regulated by plasmatic dendritic cells (pDCs) through TNFSF9, as well as by macrophages expressing SIGLEC9. In addition, we also validate the communication between TNFSF9+ pDCs and NR-E T cells utilizing deconvolution of spatial transcriptomics analysis. In summary, our research identified specific metabolic 'sub-subtypes' associated with ICB non-response and uncovered the mechanisms of their regulation in TNBC. And the proposed analytical pipeline can be used to examine metabolic heterogeneity within cell types that correlate with diverse phenotypes.

Benchmarking enrichment analysis methods with the disease pathway network

Enrichment analysis (EA) is a common approach to gain functional insights from genome-scale experiments. As a consequence, a large number of EA methods have been developed, yet it is unclear from previous studies which method is the best for a given dataset. The main issues with previous benchmarks include the complexity of correctly assigning true pathways to a test dataset, and lack of generality of the evaluation metrics, for which the rank of a single target pathway is commonly used. We here provide a generalized EA benchmark and apply it to the most widely used EA methods, representing all four categories of current approaches. The benchmark employs a new set of 82 curated gene expression datasets from DNA microarray and RNA-Seq experiments for 26 diseases, of which only 13 are cancers. In order to address the shortcomings of the single target pathway approach and to enhance the sensitivity evaluation, we present the Disease Pathway Network, in which related Kyoto Encyclopedia of Genes and Genomes pathways are linked. We introduce a novel approach to evaluate pathway EA by combining sensitivity and specificity to provide a balanced evaluation of EA methods. This approach identifies Network Enrichment Analysis methods as the overall top performers compared with overlap-based methods. By using randomized gene expression datasets, we explore the null hypothesis bias of each method, revealing that most of them produce skewed P-values.

Modulation of mitochondrial activity by sugarcane (Saccharum officinarum L.) top extract and its bioactive polyphenols: a comprehensive transcriptomics analysis in C2C12 myotubes and HepG2 hepatocytes

Age-related mitochondrial dysfunction leads to defects in cellular energy metabolism and oxidative stress defense systems, which can contribute to tissue damage and disease development. Among the key regulators responsible for mitochondrial quality control, peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α) is an important target for mitochondrial dysfunction. We have previously reported that bioactive polyphenols extracted from sugarcane top (ST) ethanol extract (STEE) could activate neuronal energy metabolism and increase astrocyte PGC-1α transcript levels. However, their potential impact on the mitochondria activity in muscle and liver cells has not yet been investigated. To address this gap, our current study examined the effects of STEE and its polyphenols on cultured myotubes and hepatocytes in vitro. Rhodamine 123 assay revealed that the treatment with STEE and its polyphenols resulted in an increase in mitochondrial membrane potential in C2C12 myotubes. Furthermore, a comprehensive examination of gene expression patterns through transcriptome-wide microarray analysis indicated that STEE altered gene expressions related to mitochondrial functions, fatty acid metabolism, inflammatory cytokines, mitogen-activated protein kinase (MAPK) signaling, and cAMP signaling in both C2C12 myotubes and HepG2 hepatocytes. Additionally, protein-protein interaction analysis identified the PGC-1α interactive-transcription factors-targeted regulatory network of the genes regulated by STEE, and the quantitative polymerase chain reaction results confirmed that STEE and its polyphenols upregulated the transcript levels of PGC-1α in both C2C12 and HepG2 cells. These findings collectively suggest the potential beneficial effects of STEE on muscle and liver tissues and offer novel insights into the potential nutraceutical applications of this material.

Simulating neuronal development: exploring potential mechanisms for central nervous system metastasis in acute lymphoblastic leukemia

Background

Acute lymphoblastic leukemia (ALL) is prone to metastasize to the central nervous system (CNS), which is an important cause of poor treatment outcomes and unfavorable prognosis. However, the pathogenesis of CNS metastasis of ALL cells has not been fully illuminated. Recent reports have shed some light on the correlation between neural mechanisms and ALL CNS metastasis. These progressions prompt us to study the relationship between ALL central nervous system metastasis and neuronal development, exploring potential biomarkers and therapeutic targets of CNS metastasis.

Materials and methods

ALL central nervous system metastasis- and neuronal development-related differentially expressed genes (DEGs) were identified by analyzing gene expression datasets GSE60926 and GSE13715. Target prediction and network analysis methods were applied to assess protein-protein interaction networks. Gene Ontology (GO) terms and pathway enrichment for DEGs were assessed. Co-expressed differentially expressed genes (co-DEGs) coupled with corresponding predicted microRNAs (miRNAs) were studied as well. Reverse transcription-polymerase chain reaction (RT-PCR) and flow cytometry were employed for the validation of key co-DEGs in primary ALL cells. Furthermore, ALL cells were treated with a vascular endothelial growth factor (VEGF) inhibitor to block neuronal development and assess changes in the co-DEGs.

Results

We identified 216, 208, and 204 DEGs in ALL CNS metastasis specimens and neuronal development samples (GSE60926 and GSE13715). CD2, CD3G, CD3D, and LCK may be implicated in ALL CNS metastasis. LAMB1, MATN3, IGFBP3, LGALS1, and NEUROD1 may be associated with neuronal development. Specifically, four co-DEGs (LGALS1, TMEM71, SHISA2, and S100A11) may link ALL central nervous system metastasis and neuronal development process. The miRNAs for each co-DEG could be potential biomarkers or therapeutic targets for ALL central nervous system metastasis, especially hsa-miR-22-3p, hsa-miR-548t-5p, and hsa-miR-6134. Additionally, four co-DEGs (LGALS1, TMEM71, SHISA2, and S100A11) were validated in CNS-infiltrated ALL cells. The VEGF inhibitor demonstrated a suppressive effect on mRNA and protein expression of key co-DEGs.

Conclusion

The bioinformatic survey and key gene validation suggest a possible correlation between ALL CNS metastasis and the neuronal development process. Simulating the neuronal development process might be a possible strategy for CNS metastasis in ALL. LGALS1, TMEM71, SHISA2, and S100A11 genes are promising and novel biomarkers and targets in ALL CNS metastasis.

Integrated multi-omics approach reveals novel associations in the rapeseed diet-microbiota-host axis in pigs

Diet-mediated host-microbiota interplay is a key factor in optimizing the gut function and overall health of the host. Gaining insight into the biological mechanisms behind this relationship is fundamental to finding sustainable, environment-friendly feed solutions in livestock production systems. Here, we apply a multi-omics integration approach to elucidate sustainable diet-associated host-gut microbiota interactions in pigs and we demonstrate novel and biologically relevant host-microbe associations in the gut, driven by a rapeseed meal-based feed (RSF). Interestingly, RSF-diet promoted the abundance of segmented filamentous bacteria Candidatus Arthromitus that was associated with the maintenance of mucosal immunity in the ileum of pigs. In the colon, RSF diet affected host mRNA splicing functions, which may result in different host gene products, through host-microbiota associations, particularly with the Faecalibacterium population, and through the interaction of dietary components such as sinapic acid with the host cells. Moreover, telomere maintenance and organization functions that may determine the overall health of the host were upregulated and notably associated with Subdoligranulum population in the colon of RSF diet-fed pigs. This integrative multi-omics approach provides more insight into the diet-microbiota-host axis, and a better understanding of mechanisms and opportunities to find new strategies for modulating host health and potentially improving caloric and nutritional efficiency in animal production.

High Expression of THY1 in Intestinal Gastric Cancer as a Key Factor in Tumor Biology: A Poor Prognosis-Independent Marker Related to the Epithelial-Mesenchymal Transition Profile

Gastric cancer (GC) is an important cancer-related death worldwide. Among its histological subtypes, intestinal gastric cancer (IGC) is the most common. A previous work showed that increased expression of the THY1 gene was associated with poor overall survival in IGC. Furthermore, it was shown that IGC tumor cells with high expression of THY1 have a greater capacity for tumorigenesis and metastasis in vitro. This study aimed to identify molecular differences between IGC with high and low expression of THY1. Using a feature selection method, a group of 35 genes were found to be the most informative gene set for THY1high IGC tumors. Through a classification model, these genes differentiate THY1high from THY1low tumors with 100% of accuracy both in the test subset and the independent test set. Additionally, this group of 35 genes correctly clustered 100% of the samples. An extensive validation of this potential molecular signature in multiple cohorts successfully segregated between THY1high and THY1low IGC tumors (>95%), proving to be independent of the gene expression quantification methodology. These genes are involved in central processes to tumor biology, such as the epithelial-mesenchymal transition (EMT) and remodeling of the tumor tissue composition. Moreover, patients with THY1high IGC demonstrated poor survival and a more advanced clinicopathological staging. Our findings revealed a molecular signature for IGC with high THY1 expression. This signature showed EMT and remodeling of the tumor tissue composition potentially related to the biology of IGC. Altogether, our results indicate that THY1high IGC tumors are a particular subset of tumors with a specific molecular and prognosis profile.

A time-resolved meta-analysis of consensus gene expression profiles during human T-cell activation

BACKGROUND

The coordinated transcriptional regulation of activated T-cells is based on a complex dynamic behavior of signaling networks. Given an external stimulus, T-cell gene expression is characterized by impulse and sustained patterns over the course. Here, we analyze the temporal pattern of activation across different T-cell populations to develop consensus gene signatures for T-cell activation.

RESULTS

Here, we identify and verify general biomarker signatures robustly evaluating T-cell activation in a time-resolved manner. We identify time-resolved gene expression profiles comprising 521 genes of up to 10 disjunct time points during activation and different polarization conditions. The gene signatures include central transcriptional regulators of T-cell activation, representing successive waves as well as sustained patterns of induction. They cover sustained repressed, intermediate, and late response expression rates across multiple T-cell populations, thus defining consensus biomarker signatures for T-cell activation. In addition, intermediate and late response activation signatures in CAR T-cell infusion products are correlated to immune effector cell-associated neurotoxicity syndrome.

CONCLUSION

This study is the first to describe temporally resolved gene expression patterns across T-cell populations. These biomarker signatures are a valuable source, e.g., monitoring transcriptional changes during T-cell activation with a reasonable number of genes, annotating T-cell states in single-cell transcriptome studies, or assessing dysregulated functions of human T-cell immunity.

Dark kinase annotation, mining, and visualization using the Protein Kinase Ontology

The Protein Kinase Ontology (ProKinO) is an integrated knowledge graph that conceptualizes the complex relationships among protein kinase sequence, structure, function, and disease in a human and machine-readable format. In this study, we have significantly expanded ProKinO by incorporating additional data on expression patterns and drug interactions. Furthermore, we have developed a completely new browser from the ground up to render the knowledge graph visible and interactive on the web. We have enriched ProKinO with new classes and relationships that capture information on kinase ligand binding sites, expression patterns, and functional features. These additions extend ProKinO's capabilities as a discovery tool, enabling it to uncover novel insights about understudied members of the protein kinase family. We next demonstrate the application of ProKinO. Specifically, through graph mining and aggregate SPARQL queries, we identify the p21-activated protein kinase 5 (PAK5) as one of the most frequently mutated dark kinases in human cancers with abnormal expression in multiple cancers, including a previously unappreciated role in acute myeloid leukemia. We have identified recurrent oncogenic mutations in the PAK5 activation loop predicted to alter substrate binding and phosphorylation. Additionally, we have identified common ligand/drug binding residues in PAK family kinases, underscoring ProKinO's potential application in drug discovery. The updated ontology browser and the addition of a web component, ProtVista, which enables interactive mining of kinase sequence annotations in 3D structures and Alphafold models, provide a valuable resource for the signaling community. The updated ProKinO database is accessible at https://prokino.uga.edu.

Immune cell-specific and common molecular signatures in rheumatoid arthritis through molecular network approaches

Rheumatoid arthritis (RA) is an autoimmune disorder and common symptom of RA is chronic synovial inflammation. The pathogenesis of RA is not fully understood. Therefore, we aimed to identify underlying common and distinct molecular signatures and pathways among ten types of tissue and cells obtained from patients with RA. In this study, transcriptomic data including synovial tissues, macrophages, blood, T cells, CD4+T cells, CD8+T cells, natural killer T (NKT), cells natural killer (NK) cells, neutrophils, and monocyte cells were analyzed with an integrative and comparative network biology perspective. Each dataset yielded a list of differentially expressed genes as well as a reconstruction of the tissue-specific protein-protein interaction (PPI) network. Molecular signatures were identified by a statistical test using the hypergeometric probability density function by employing the interactions of transcriptional regulators and PPI. Reporter metabolites of each dataset were determined by using genome-scale metabolic networks. It was defined as the common hub proteins, novel molecular signatures, and metabolites in two or more tissue types while immune cell-specific molecular signatures were identified, too. Importantly, miR-155-5p is found as a common miRNA in all tissues. Moreover, NCOA3, PRKDC and miR-3160 might be novel molecular signatures for RA. Our results establish a novel approach for identifying immune cell-specific molecular signatures of RA and provide insights into the role of common tissue-specific genes, miRNAs, TFs, receptors, and reporter metabolites. Experimental research should be used to validate the corresponding genes, miRNAs, and metabolites.

Ligand-Independent Activation of Aryl Hydrocarbon Receptor and Attenuation of Glutamine Levels by Natural Deep Eutectic Solvent

Natural deep eutectic solvents (NADESs) are emerging sustainable alternatives to conventional organic solvents. Beyond their role as laboratory solvents, NADESs are increasingly explored in drug delivery and as therapeutics. Their increasing applications notwithstanding, our understanding of how they interact with biomolecules at multiple levels - metabolome, proteome, and transcriptome - within human cell remain poor. Here, we deploy integrated metabolomics, proteomics, and transcriptomics to probe how NADESs perturb the molecular landscape of human cells. In a human cell line model, we found that an archetypal NADES derived from choline and geranic acid (CAGE) significantly altered the metabolome, proteome, and transcriptome. CAGE upregulated indole-3-lactic acid and 4-hydroxyphenyllactic acid levels, resulting in ligand-independent activation of aryl hydrocarbon receptor to signal the transcription of genes with implications for inflammation, immunomodulation, cell development, and chemical detoxification. Further, treating the cell line with CAGE downregulated glutamine biosynthesis, a nutrient rapidly proliferating cancer cells require. CAGE's ability to attenuate glutamine levels is potentially relevant for cancer treatment. These findings suggest that NADESs, even when derived from natural components like choline, can indirectly modulate cell biology at multiple levels, expanding their applications beyond chemistry to biomedicine and biotechnology.

Targeting Translation and the Cell Cycle Inversely Affects CTC Metabolism but Not Metastasis

Melanoma brain metastasis (MBM) is significantly associated with poor prognosis and is diagnosed in 80% of patients at autopsy. Circulating tumor cells (CTCs) are "seeds" of metastasis and the smallest functional units of cancer. Our multilevel approach has previously identified a CTC RPL/RPS gene signature directly linked to MBM onset. We hypothesized that targeting ribogenesis prevents MBM/metastasis in CTC-derived xenografts. We treated parallel cohorts of MBM mice with FDA-approved protein translation inhibitor omacetaxine with or without CDK4/CDK6 inhibitor palbociclib, and monitored metastatic development and cell proliferation. Necropsies and IVIS imaging showed decreased MBM/extracranial metastasis in drug-treated mice, and RNA-Seq on mouse-blood-derived CTCs revealed downregulation of four RPL/RPS genes. However, mitochondrial stress tests and RT-qPCR showed that omacetaxine and palbociclib inversely affected glycolytic metabolism, demonstrating that dual targeting of cell translation/proliferation is critical to suppress plasticity in metastasis-competent CTCs. Equally relevant, we provide the first-ever functional metabolic characterization of patient-derived circulating neoplastic cells/CTCs.

Temperature-driven coordination of circadian transcriptome regulation

The circadian rhythm is an evolutionarily-conserved molecular oscillator that enables species to anticipate rhythmic changes in their environment. At a molecular level, the core clock genes induce a circadian oscillation in thousands of genes in a tissue-specific manner, orchestrating myriad biological processes. While studies have investigated how the core clock circuit responds to environmental perturbations such as temperature, the downstream effects of such perturbations on circadian regulation remain poorly understood. By analyzing bulk-RNA sequencing of Drosophila fat bodies harvested from flies subjected to different environmental conditions, we demonstrate a highly condition-specific circadian transcriptome. Further employing a reference-based gene regulatory network (Reactome), we find evidence of increased gene-gene coordination at low temperatures and synchronization of rhythmic genes that are network neighbors. Our results point to the mechanisms by which the circadian clock mediates the fly's response to seasonal changes in temperature.

Deep neural network for discovering metabolism-related biomarkers for lung adenocarcinoma

Introduction

Lung cancer is a major cause of illness and death worldwide. Lung adenocarcinoma (LUAD) is its most common subtype. Metabolite-mRNA interactions play a crucial role in cancer metabolism. Thus, metabolism-related mRNAs are potential targets for cancer therapy.

Methods

This study constructed a network of metabolite-mRNA interactions (MMIs) using four databases. We retrieved mRNAs from the Tumor Genome Atlas (TCGA)-LUAD cohort showing significant expressional changes between tumor and non-tumor tissues and identified metabolism-related differential expression (DE) mRNAs among the MMIs. Candidate mRNAs showing significant contributions to the deep neural network (DNN) model were mined. Using MMIs and the results of function analysis, we created a subnetwork comprising candidate mRNAs and metabolites.

Results

Finally, 10 biomarkers were obtained after survival analysis and validation. Their good prognostic value in LUAD was validated in independent datasets. Their effectiveness was confirmed in the TCGA and an independent Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset by comparison with traditional machine-learning models.

Conclusion

To summarize, 10 metabolism-related biomarkers were identified, and their prognostic value was confirmed successfully through the MMI network and the DNN model. Our strategy bears implications to pave the way for investigating metabolic biomarkers in other cancers.

The application of multi-omics in the respiratory microbiome: Progresses, challenges and promises

The study of the respiratory microbiome has entered a multi-omic era. Through integrating different omic data types such as metagenome, metatranscriptome, metaproteome, metabolome, culturome and radiome surveyed from respiratory specimens, holistic insights can be gained on the lung microbiome and its interaction with host immunity and inflammation in respiratory diseases. The power of multi-omics have moved the field forward from associative assessment of microbiome alterations to causative understanding of the lung microbiome in the pathogenesis of chronic, acute and other types of respiratory diseases. However, the application of multi-omics in respiratory microbiome remains with unique challenges from sample processing, data integration, and downstream validation. In this review, we first introduce the respiratory sample types and omic data types applicable to studying the respiratory microbiome. We next describe approaches for multi-omic integration, focusing on dimensionality reduction, multi-omic association and prediction. We then summarize progresses in the application of multi-omics to studying the microbiome in respiratory diseases. We finally discuss current challenges and share our thoughts on future promises in the field.

Deconvolution of immune cell composition and biological age of hepatocellular carcinoma using DNA methylation

Hepatocellular carcinoma (HCC) has been an approved indication for the administration of immunotherapy since 2017, but biomarkers that predict therapeutic response have remained limited. Understanding and characterizing the tumor immune microenvironment enables better classification of these tumors and may reveal biomarkers that predict immunotherapeutic efficacy. In this paper, we applied a cell-type deconvolution algorithm using DNA methylation array data to investigate the composition of the tumor microenvironment in HCC. Using publicly available and in-house datasets with a total cohort size of 57 patients, each with tumor and matched normal tissue samples, we identified key differences in immune cell composition. We found that NK cell abundance was significantly decreased in HCC tumors compared to adjacent normal tissue. We also applied DNA methylation "clocks" which estimate phenotypic aging and compared these findings to expression-based determinations of cellular senescence. Senescence and epigenetic aging were significantly increased in HCC tumors, and the degree of age acceleration and senescence was strongly associated with decreased NK cell abundance. In summary, we found that NK cell infiltration in the tumor microenvironment is significantly diminished, and that this loss of NK abundance is strongly associated with increased senescence and age-related phenotype. These findings point to key interactions between NK cells and the senescent tumor microenvironment and offer insights into the pathogenesis of HCC as well as potential biomarkers of therapeutic efficacy.

Elucidating the Proximal Tubule HNF4A Gene Regulatory Network in Human Kidney Organoids

SIGNIFICANCE STATEMENT

HNF4 genes promote proximal tubule differentiation in mice, but their function in human nephrogenesis is not fully defined. This study uses human pluripotent stem cell (PSC)-derived kidney organoids as a model to investigate HNF4A and HNF4G functions. The loss of HNF4A , but not HNF4G , impaired reabsorption-related molecule expression and microvilli formation in human proximal tubules. Cleavage under targets and release using nuclease (CUT&RUN) sequencing and CRISPR-mediated transcriptional activation (CRISPRa) further confirm that HNF4A directly regulates its target genes. Human kidney organoids provide a good model for studying transcriptional regulation in human kidney development.

BACKGROUND

The proximal tubule plays a major role in electrolyte homeostasis. Previous studies have shown that HNF4A regulates reabsorption-related genes and promotes proximal tubule differentiation during murine kidney development. However, the functions and gene regulatory mechanisms of HNF4 family genes in human nephrogenesis have not yet been investigated.

METHODS

We generated HNF4A -knock out (KO), HNF4G -KO, and HNF4A/4G -double KO human pluripotent stem cell lines, differentiated each into kidney organoids, and used immunofluorescence analysis, electron microscopy, and RNA-seq to analyze them. We probed HNF4A-binding sites genome-wide by cleavage under targets and release using nuclease sequencing in both human adult kidneys and kidney organoid-derived proximal tubular cells. Clustered Regularly Interspaced Short Palindromic Repeats-mediated transcriptional activation validated HNF4A and HNF4G function in proximal tubules during kidney organoid differentiation.

RESULTS

Organoids lacking HNF4A , but not HNF4G , showed reduced expression of transport-related, endocytosis-related, and brush border-related genes, as well as disorganized brush border structure in the apical lumen of the organoid proximal tubule. Cleavage under targets and release using nuclease revealed that HNF4A primarily bound promoters and enhancers of genes that were downregulated in HNF4A -KO, suggesting direct regulation. Induced expression of HNF4A or HNF4G by CRISPR-mediated transcriptional activation drove increased expression of selected target genes during kidney organoid differentiation.

CONCLUSIONS

This study reveals regulatory mechanisms of HNF4A and HNF4G during human proximal tubule differentiation. The experimental strategy can be applied more broadly to investigate transcriptional regulation in human kidney development.

Developing human olfactory network and exploring olfactory receptor-odorant interaction

The Olfactory receptor (OR)-odorant interactions are perplexed. ORs can bind to structurally diverse odorants associated with one or more odor percepts. Various attempts have been made to understand the intricacies of OR-odorant interaction. In this study, experimentally documented OR-odorant interactions are investigated comprehensively to; (a) suggest potential odor percepts for ORs based on the OR-OR network; (b) determine how odorants interacting with specific ORs differ in terms of inherent pharmacophoric features and molecular properties, (c) identify molecular interactions that explained OR-odorant interactions of selective ORs; and (d) predict the probable role of ORs other than olfaction. Human olfactory receptor network (hORnet) is developed to study possible odor percepts for ORs. We identified six molecular properties which showed variation and significant patterns to differentiate odorants binding with five ORs. The pharmacophore analysis revealed that odorants subset of five ORs follow similar pharmacophore hypothesis, (one hydrogen acceptor and two hydrophobic regions) but differ in terms of distance and orientation of pharmacophoric features. To ascertain the binding site residues and key interactions between the selected ORs and their interacting odorants, 3D-structure modelling, docking and molecular dynamics studies were carried out. Lastly, the potential role of ORs beyond olfaction is explored. A human OR-OR network was developed to suggest possible odor percepts for ORs using empirically proven OR-odorant interactions. We sought to find out significant characteristics, molecular properties, and molecular interactions that could explain OR-odorant interactions and add to the understanding of the complex issue of odor perception.Communicated by Ramaswamy H. Sarma.

CCR9 overexpression promotes T-ALL progression by enhancing cholesterol biosynthesis

Introduction: T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematological malignancy of the lymphoid progenitor cells, contributing to ∼ 20% of the total ALL cases, with a higher prevalence in adults than children. Despite the important role of human T-ALL cell lines in understanding the pathobiology of the disease, a detailed comparison of the tumorigenic potentials of two commonly used T-ALL cell lines, MOLT4 and JURKAT cells, is still lacking. Methodology: In the present study, NOD-Prkdc scid IL2rgd ull (NTG) mice were intravenously injected with MOLT4, JURKAT cells, and PBS as a control. The leukemiac cell homing/infiltration into the bone marrow, blood, liver and spleen was investigated for bioluminescence imaging, flow cytometry, and immunohistochemistry staining. Gene expression profiling of the two cell lines was performed via RNA-seq to identify the differentially expressed genes (DEGs). CCR9 identified as a DEG, was further screened for its role in invasion and metastasis in both cell lines in vitro. Moreover, a JURKAT cell line with overexpressed CCR9 (Jurkat-OeCCR9) was investigated for T-ALL formation in the NTG mice as compared to the GFP control. Jurkat-OeCCR9 cells were then subjected to transcriptome analysis to identify the genes and pathways associated with the upregulation of CCR9 leading to enhanced tumirogenesis. The DEGs of the CCR9-associated upregulation were validated both at mRNA and protein levels. Simvastatin was used to assess the effect of cholesterol biosynthesis inhibition on the aggressiveness of T-ALL cells. Results: Comparison of the leukemogenic potentials of the two T-ALL cell lines showed the relatively higher leukemogenic potential of MOLT4 cells, characterized by their enhanced tissue infiltration in NOD-PrkdcscidIL2rgdull (NTG) mice. Transcriptmoe analysis of the two cell lines revealed numerous DEGs, including CCR9, enriched in vital signaling pathways associated with growth and proliferation. Notably, the upregulation of CCR9 also promoted the tissue infiltration of JURKAT cells in vitro and in NTG mice. Transcriptome analysis revealed that CCR9 overexpression facilitated cholesterol production by upregulating the expression of the transcriptional factor SREBF2, and the downstream genes: MSMO1, MVD, HMGCS1, and HMGCR, which was then corroborated at the protein levels. Notably, simvastatin treatment reduced the migration of the CCR9-overexpressing JURKAT cells, suggesting the importance of cholesterol in T-ALL progression. Conclusions: This study highlights the distinct tumorigenic potentials of two T-ALL cell lines and reveals CCR9-regulated enhanced cholesterol biosynthesis in T-ALL.

AutoCore: A network-based definition of the core module of human autoimmunity and autoinflammation

Although research on rare autoimmune and autoinflammatory diseases has enabled definition of nonredundant regulators of homeostasis in human immunity, because of the single gene-single disease nature of many of these diseases, contributing factors were mostly unveiled in sequential and noncoordinated individual studies. We used a network-based approach for integrating a set of 186 inborn errors of immunity with predominant autoimmunity/autoinflammation into a comprehensive map of human immune dysregulation, which we termed "AutoCore." The AutoCore is located centrally within the interactome of all protein-protein interactions, connecting and pinpointing multidisease markers for a range of common, polygenic autoimmune/autoinflammatory diseases. The AutoCore can be subdivided into 19 endotypes that correspond to molecularly and phenotypically cohesive disease subgroups, providing a molecular mechanism-based disease classification and rationale toward systematic targeting for therapeutic purposes. Our study provides a proof of concept for using network-based methods to systematically investigate the molecular relationships between individual rare diseases and address a range of conceptual, diagnostic, and therapeutic challenges.

Phospho-Analyst: An Interactive, Easy-to-Use Web Platform To Analyze Quantitative Phosphoproteomics Data

Phosphoproteomics is nowadays the method of choice to comprehensively identify and quantify thousands of phosphorylated peptides and their associated proteins with the goal of interrogating changes in signal transduction pathways and other cellular processes. One of the most popular software suites to analyze phosphoproteomic data sets is MaxQuant, which converts mass spectrometric raw data into quantitative information on phosphopeptides and proteins. However, despite the increased utilization of phosphoproteomics in biomedical research, simple and user-friendly tools supporting downstream statistical analysis and interpretation of these highly complex outputs are still lacking. We have therefore developed Phospho-Analyst, which─similar to its sibling LFQ-Analyst─is an easy-to-use, interactive web application specifically designed to reproducibly perform differential expression analyses with "one click" and to visualize phosphoproteomic results in a meaningful and practical manner. Furthermore, if quantitative total proteomic information is available for the same samples, Phospho-Analyst automatically normalizes all phosphoproteomic results to underlying protein abundance levels, thereby ensuring that only genuine changes in phosphorylation events are considered. As such, Phospho-Analyst can not only be used by experienced proteomic veterans but also by researchers without any prior knowledge in (phospho)proteomics, statistics, or bioinformatics. Phospho-Analyst, including a detailed manual, is freely available at https://analyst-suites.org/apps/phospho-analyst/.

Does the Necrotic Portion of Metastatic Lymphadenopathy from Squamous Cell Carcinoma Still Have Tumoral Oncologic Information? Differential Diagnosis of Benign Necrotic Lymphadenopathy Using microRNA

Neck necrotic lymph nodes commonly correspond to metastasis or benign inflammatory conditions such as Kikuchi disease and tuberculosis. Ultrasound-guided biopsy can be used for differential diagnosis, but results may be unclear. Therefore, this study aimed to identify target microRNAs (miRNAs) and genes for the differential diagnosis of inflammatory and malignant necrotic lymph nodes. We selected six inflammatory lymphadenitis formalin-fixed paraffin-embedded (FFPE) samples that showed internal necrosis and five cancer necrotic FFPE samples. Tissue microarray (TMA) was performed to separate the necrotic and cancerous portions. Total RNA was extracted from six pairs of separated inflammatory necrosis, five pairs of cancer necrosis, and cancer portions. Differentially expressed miRNAs were analyzed by comparing inflammatory necrosis, cancer, and cancer necrosis. Seventeen miRNAs were upregulated in cancer necrosis compared to inflammatory necrosis, and two miRNAs (hsa-miR-155-5p and hsa-miR-146b-5p) showed lower expression in cancer necrotic cells. Nineteen miRNAs that were differentially expressed between inflammatory and cancer necrosis were analyzed for target gene expression; these transcripts demonstrated a clear relationship with cancer. The differentially expressed miRNAs in inflammatory and tumor necrosis were associated with cancer-related pathways. These preliminary results might help in the differential diagnosis of cervical metastatic necrotic lymphadenopathy and avoiding unnecessary excisional biopsies.

Bile acid-dependent transcription factors and chromatin accessibility determine regional heterogeneity of intestinal antimicrobial peptides

Antimicrobial peptides (AMPs) are important mediators of intestinal immune surveillance. However, the regional heterogeneity of AMPs and its regulatory mechanisms remain obscure. Here, we clarified the regional heterogeneity of intestinal AMPs at the single-cell level, and revealed a cross-lineages AMP regulation mechanism that bile acid dependent transcription factors (BATFs), NR1H4, NR1H3 and VDR, regulate AMPs through a ligand-independent manner. Bile acids regulate AMPs by perturbing cell differentiation rather than activating BATFs signaling. Chromatin accessibility determines the potential of BATFs to regulate AMPs at the pre-transcriptional level, thus shaping the regional heterogeneity of AMPs. The BATFs-AMPs axis also participates in the establishment of intestinal antimicrobial barriers of fetuses and the defects of antibacterial ability during Crohn's disease. Overall, BATFs and chromatin accessibility play essential roles in shaping the regional heterogeneity of AMPs at pre- and postnatal stages, as well as in maintenance of antimicrobial immunity during homeostasis and disease.

Short heat shock has a long-term effect on mesenchymal stem cells' transcriptome

The adverse effects of heat stress (HS) on physiological systems are well documented, yet the underlying molecular mechanisms behind it remain poorly understood. To address this knowledge gap, we conducted a comprehensive investigation into the impact of HS on mesenchymal stem cells (MSCs), focusing on their morphology, phenotype, proliferative capacity, and fate determination. Our in-depth analysis of the MSCs' transcriptome revealed a significant influence of HS on the transcriptional landscape. Notably, even after a short period of stress, we observed a persistent alteration in cell identity, potentially mediated by the activation of bivalent genes. Furthermore, by comparing the differentially expressed genes following short HS with their transcriptional state after recovery, we identified the transient upregulation of MLL and other histone modifiers, providing a potential mechanistic explanation for the stable activation of bivalent genes. This could be used to predict and modify the long-term effect of HS on cell identity.

Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases

Genome-wide association studies (GWASs) are a valuable tool for understanding the biology of complex human traits and diseases, but associated variants rarely point directly to causal genes. In the present study, we introduce a new method, polygenic priority score (PoPS), that learns trait-relevant gene features, such as cell-type-specific expression, to prioritize genes at GWAS loci. Using a large evaluation set of genes with fine-mapped coding variants, we show that PoPS and the closest gene individually outperform other gene prioritization methods, but observe the best overall performance by combining PoPS with orthogonal methods. Using this combined approach, we prioritize 10,642 unique gene-trait pairs across 113 complex traits and diseases with high precision, finding not only well-established gene-trait relationships but nominating new genes at unresolved loci, such as LGR4 for estimated glomerular filtration rate and CCR7 for deep vein thrombosis. Overall, we demonstrate that PoPS provides a powerful addition to the gene prioritization toolbox.

Machine Learning Identifies a Signature of Nine Exosomal RNAs That Predicts Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Although alpha fetoprotein (AFP) remains a commonly used serological marker of HCC, the sensitivity and specificity of AFP in detecting HCC is often limited. Exosomal RNA has emerged as a promising diagnostic tool for various cancers, but its use in HCC detection has yet to be fully explored. Here, we employed Machine Learning on 114,602 exosomal RNAs to identify a signature that can predict HCC. The exosomal expression data of 118 HCC patients and 112 healthy individuals were stratified split into Training, Validation and Unseen Test datasets. Feature selection was then performed on the initial training dataset using permutation importance, and the predictive performance of the selected features were tested on the validation dataset using Support Vector Machine (SVM) Classifier. A minimum of nine features were identified to be predictive of HCC and these nine features were then evaluated across six different models in an unseen test set. These features, mainly in the immune, platelet/neutrophil and cytoskeletal pathways, exhibited good predictive performance with ROC-AUC from 0.79-0.88 in the unseen test set. Hence, these nine exosomal RNAs have potential to be clinically useful minimally invasive biomarkers for HCC.

Mass spectrometry-based "omics" technologies for the study of gestational diabetes and the discovery of new biomarkers

Gestational diabetes (GDM) is one of the most common complications occurring during pregnancy. Diagnosis is performed by oral glucose tolerance test, but harmonized testing methods and thresholds are still lacking worldwide. Short-term and long-term effects include obesity, type 2 diabetes, and increased risk of cardiovascular disease. The identification and validation of sensitidve, selective, and robust biomarkers for early diagnosis during the first trimester of pregnancy are required, as well as for the prediction of possible adverse outcomes after birth. Mass spectrometry (MS)-based omics technologies are nowadays the method of choice to characterize various pathologies at a molecular level. Proteomics and metabolomics of GDM were widely investigated in the last 10 years, and various proteins and metabolites were proposed as possible biomarkers. Metallomics of GDM was also reported, but studies are limited in number. The present review focuses on the description of the different analytical methods and MS-based instrumental platforms applied to GDM-related omics studies. Preparation procedures for various biological specimens are described and results are briefly summarized. Generally, only preliminary findings are reported by current studies and further efforts are required to determine definitive GDM biomarkers.

Proteins and metabolites fingerprints of gestational diabetes mellitus forming protein-metabolite interactomes are its potential biomarkers

Gestational diabetes mellitus (GDM) is a consequence of glucose intolerance with an inadequate production of insulin that happens during pregnancy and leads to adverse health consequences for both mother and fetus. GDM patients are at higher risk for preeclampsia, and developing diabetes mellitus type 2 in later life, while the child born to GDM mothers are more prone to macrosomia, and hypoglycemia. The universally accepted diagnostic criteria for GDM are lacking, therefore there is a need for a diagnosis of GDM that can identify GDM at its early stage (first trimester). We have reviewed the literature on proteins and metabolites fingerprints of GDM. Further, we have performed protein-protein, metabolite-metabolite, and protein-metabolite interaction network studies on GDM proteins and metabolites fingerprints. Notably, some proteins and metabolites fingerprints are forming strong interaction networks at high confidence scores. Therefore, we have suggested that those proteins and metabolites that are forming protein-metabolite interactomes are the potential biomarkers of GDM. The protein-metabolite biomarkers interactome may help in a deep understanding of the prognosis, pathogenesis of GDM, and also detection of GDM. The protein-metabolites interactome may be further applied in planning future therapeutic strategies to promote long-term health benefits in GDM mothers and their children.