Comprehensive analysis of the interaction of antigen presentation during anti-tumour immunity and establishment of AIDPS systems in ovarian cancer

There are hundreds of prognostic models for ovarian cancer. These genes are based on different gene classes, and there are many ways to construct the models. Therefore, this paper aims to build the most stable prognostic evaluation system known to date through 101 machine learning strategies. We combined 101 algorithm combinations with 10 machine learning algorithms to create antigen presentation-associated genetic markers (AIDPS) with outstanding precision and steady performance. The inclusive set of algorithms comprises the elastic network (Enet), Ridge, stepwise Cox, Lasso, generalized enhanced regression model (GBM), random survival forest (RSF), supervised principal component (SuperPC), Cox partial least squares regression (plsRcox), survival support vector machine (Survival-SVM). Then, in the train cohort, the prediction model was fitted using a leave-one cross-validation (LOOCV) technique, which involved 101 different possible combinations of prognostic genes. Seven validation data sets (GSE26193, GSE26712, GSE30161, GSE63885, GSE9891, GSE140082 and ICGC_OV_AU) were compared and analysed, and the C-index was calculated. Finally, we collected 32 published ovarian cancer prognostic models (including mRNA and lncRNA). All data sets and prognostic models were subjected to a univariate Cox regression analysis, and the C-index was calculated to demonstrate that the antigen presentation process should be the core criterion for evaluating ovarian cancer prognosis. In a univariate Cox regression analysis, 22 prognostic genes were identified based on the expression profiles of 283 genes involved in antigen presentation and the intersection of genes (p < 0.05). AIDPS were developed by our machine learning-based integration method, which was applied to these 22 genes. One hundred and one prediction models are fitted using the LOOCV framework, and the C-index is calculated for each model across all validation sets. Interestingly, RSF + Lasso was the best model overall since it had the greatest average C-index and the highest C-index of any combination of models tested on the validated data sets. In comparing external cohorts, we found that the C-index correlated AIDPS method using the RSF + Lasso method in 101 prediction models was in contrast to other features. Notably, AIDPS outperformed the vast majority of models across all data sets. Antigen-presenting anti-tumour immune pathways can be used as a representative gene set of ovarian cancer to track the prognosis of patients with cancer. The antigen-presenting model obtained by the RSF + Lasso method has the best C-INDEX, which plays a key role in developing antigen-presenting targeted drugs in ovarian cancer and improving the treatment outcome of patients.

TIGD1 Is an Independent Prognostic Factor that Promotes the Progression of Colon Cancer

Background: Trigger transposable element-derived 1 (TIGD1) is a human-specific gene, but no studies have been conducted to determine its mechanism of action. Our aim is to ascertain the function and mode of action of TIGD1 in the development of colon cancer. Materials and Methods: The authors used bioinformatics to analyze the relationship between TIGD1 and the clinical characteristics of colon cancer, as well as its prognosis. A series of cell assays were conducted to assess the function of TIGD1 in the proliferation and migration of colon cancer, and flow cytometry was used to explore its effects on apoptosis and the cell cycle. Results: The authors discovered that the expression of TIGD1 was remarkably elevated in colon cancer. Clinical correlation analysis demonstrated that TIGD1 expression was elevated in the tissues of advanced-stage patients, and it was remarkably elevated in individuals with both lymph node and distant metastasis. Further, the authors found that individuals showing elevated TIGD1 expression levels had a shortened survival time. Univariate and multivariate Cox regression analyses revealed that TIGD1 was an independent prognostic factor. Overexpression of the TIGD1 gene remarkedly enhances the proliferation and metastasis of colon cancer cells and suppresses apoptosis. In addition, the overexpression of TIGD1 can enhance the transition of tumor cells from the G1 toward the S phase. Western blot results suggested that TIGD1 may promote the malignant activity of colon cancer cells via the Wnt/β-catenin signaling pathway, Bcl-2, N-cadherin, BAX, E-cadherin, CDK6, and CyclinD1. Conclusions: TIGD1 may be an independent prognostic factor in the advancement of colon cancer, and therefore function as a therapeutic target.

Oktoberfest: Open-source spectral library generation and rescoring pipeline based on Prosit

Machine learning (ML) and deep learning (DL) models for peptide property prediction such as Prosit have enabled the creation of high quality in silico reference libraries. These libraries are used in various applications, ranging from data-independent acquisition (DIA) data analysis to data-driven rescoring of search engine results. Here, we present Oktoberfest, an open source Python package of our spectral library generation and rescoring pipeline originally only available online via ProteomicsDB. Oktoberfest is largely search engine agnostic and provides access to online peptide property predictions, promoting the adoption of state-of-the-art ML/DL models in proteomics analysis pipelines. We demonstrate its ability to reproduce and even improve our results from previously published rescoring analyses on two distinct use cases. Oktoberfest is freely available on GitHub (https://github.com/wilhelm-lab/oktoberfest) and can easily be installed locally through the cross-platform PyPI Python package.

Computational approaches to identify sites of phosphorylation

Due to their oftentimes ambiguous nature, phosphopeptide positional isomers can present challenges in bottom-up mass spectrometry-based workflows as search engine scores alone are often not enough to confidently distinguish them. Additional scoring algorithms can remedy this by providing confidence metrics in addition to these search results, reducing ambiguity. Here we describe challenges to interpreting phosphoproteomics data and review several different approaches to determine sites of phosphorylation for both data-dependent and data-independent acquisition-based workflows. Finally, we discuss open questions regarding neutral losses, gas-phase rearrangement, and false localization rate estimation experienced by both types of acquisition workflows and best practices for managing ambiguity in phosphosite determination.

Analysis of ceRNA Network and Identification of Potential Treatment Target and Biomarkers of Endothelial Cell Injury in Sepsis

Background: Sepsis is a complex clinical syndrome caused by a dysregulated host immune response to infection. This study aimed to identify a competing endogenous RNA (ceRNA) network that can greatly contribute to understanding the pathophysiological process of sepsis and determining sepsis biomarkers. Methods: The GSE100159, GSE65682, GSE167363, and GSE94717 datasets were obtained from the Gene Expression Omnibus (GEO) database. Weighted gene coexpression network analysis was performed to find modules possibly involved in sepsis. A long noncoding RNA-microRNA-messenger RNA (lncRNA-miRNA-mRNA) network was constructed based on the findings. Single-cell analysis was performed. Human umbilical vein endothelial cells were treated with lipopolysaccharide (LPS) to create an in vitro model of sepsis for network verification. Reverse transcription-polymerase chain reaction, fluorescence in situ hybridization, and luciferase reporter genes were used to verify the bioinformatic analysis. Result: By integrating data from three GEO datasets, we successfully constructed a ceRNA network containing 18 lncRNAs, 7 miRNAs, and 94 mRNAs based on the ceRNA hypothesis. The lncRNA ZFAS1 was found to be highly expressed in LPS-stimulated endothelial cells and may thus play a role in endothelial cell injury. Univariate and multivariate Cox analyses showed that only SLC26A6 was an independent predictor of prognosis in sepsis. Overall, our findings indicated that the ZFAS1/hsa-miR-449c-5p/SLC26A6 ceRNA regulatory axis may play a role in the progression of sepsis. Conclusion: The sepsis ceRNA network, especially the ZFAS1/hsa-miR-449c-5p/SLC26A6 regulatory axis, is expected to reveal potential biomarkers and therapeutic targets for sepsis management.

Construction of a diagnostic model based on random forest and artificial neural network for peri-implantitis

OBJECTIVES

This study aimed to reveal critical genes regulating peri-implantitis during its development and construct a diagnostic model by using random forest (RF) and artificial neural network (ANN).

METHODS

GSE-33774, GSE106090, and GSE57631 datasets were obtained from the GEO database. The GSE33774 and GSE106090 datasets were analyzed for differential expression and functional enrichment. The protein-protein interaction networks (PPI) and RF screened vital genes. A diagnostic model for peri-implantitis was established using ANN and validated on the GSE33774 and GSE57631 datasets. A transcription factor-gene interaction network and a transcription factor-micro-RNA (miRNA) regulatory network were also established.

RESULTS

A total of 124 differentially expressed genes (DEGs) involved in the regulation of peri-implantitis were screened. Enrichment analysis showed that DEGs were mainly associated with immune receptor activity and cytokine receptor activity and were mainly involved in processes such as leukocyte and neutrophil migration. The PPI and RF screened six essential genes, namely, CD38, CYBB, FCGR2A, SELL, TLR4, and CXCL8. The receiver operating characteristic curve (ROC) indicated that the ANN model had an excellent diagnostic performance. FOXC1, GATA2, and NF-κB1 may be essential transcription factors in peri-implantitis, and hsa-miR-204 may be a key miRNA.

CONCLUSIONS

The diagnostic model of peri-implantitis constructed by RF and ANN has high confidence, and CD38, CYBB, FCGR2A, SELL, TLR4, and CXCL8 are potential diagnostic markers. FOXC1, GATA2, and NF-κB1 may be essential transcription factors in peri-implantitis, and hsa-miR-204 plays a vital role as a critical miRNA.

Exploring the impact of TGF-β family gene mutations and expression on skin wound healing and tissue repair

Transforming Growth Factor-Beta (TGF-β) signalling pathway is of paramount importance in the processes of wound healing, epidermal integrity maintenance and development of skin cancer. The objective of this research endeavour was to clarify the impact of gene mutations and variations in expression within TGF-β family on mechanisms of tissue repair, as well as to identify potential targets for therapeutic purposes in non-melanoma skin cancer (NMSC). The methods utilized in this study involved obtaining RNA-seq data from 224 NMSC patients and paired normal skin tissues from the PRJNA320473 and PRJEB27606 databases. The purpose of the differential gene expression analysis was to identify genes whose expression had changed significantly. In order to evaluate the effects and interrelationships of identified gene variants, structural analysis with AlphaFold and PDB data and network analysis with the STRING database were both utilized. Critical gene expression was externally validated through the utilization of the GEPIA database. Tumour tissues exhibited a notable upregulation of genes associated with the TGF-β pathway, specifically MMP1, MMP3, MMP9, EGF, COL3A1 and COL1A2, in comparison with normal tissues. As indicated by the central node status of these genes in the network analysis, they play a crucial role in the progression of NMSCs. The results of the structural analysis suggested that mutations might cause functional disruptions. External validation of the upregulation confirmed the expression trends and emphasized the biomarker potential of the upregulated genes. In conclusion, this research offered thorough examination of molecular modifications that occur in TGF-β family genes, which are linked to cutaneous wound healing and NMSC. The modified expression of the identified hub genes may represent innovative targets for therapeutic intervention.

Exploration of SERPINA family functions and prognostic value in breast cancer based on transcriptome and in vitro analysis

Breast cancer poses a significant risk to women worldwide, yet specific role of SERPINA gene family in breast cancer remains unclarified. Data were collected from online databases. SERPINA family gene expression was presented, and prognosis value was evaluated. Multi-omics methods were employed to explore the SERPINA-related biological processes, followed by comprehensive analyses of their roles in breast cancer. Single-cell data were analyzed to characterize the SERPINA family gene expression in different cell clusters. We selected SERPINA5 as the target gene. Via pan-cancer analysis, SERPINA5 was also investigated in various cancers. The experimental validation was conducted in MDA-MB-231 cell line eventually. SERPINA family showed differential expression in breast cancer, which were mainly expressed in myeloid cells, epithelial cells, and dendritic cells. SERPINA5 expression was upregulated in breast cancer, which was associated with a better prognosis. Immune infiltration illustrated the positive correlativity between SERPINA5 intensity and eosinophilic recruitment. Pan-cancer analysis indicated the function of SERPINA5 as a potential biomarker in other cancers. Finally, experimental validation demonstrated that SERPINA5 contributes to lower invasion and metastatic potential of breast cancer cells. With bioinformatics analysis, the significant role SERPINA family genes functioned in breast cancer was comprehensively explored, with SERPINA5 emerging as a key gene in suppressing breast cancer progression.

A single-cell atlas enables mapping of homeostatic cellular shifts in the adult human breast

Here we use single-cell RNA sequencing to compile a human breast cell atlas assembled from 55 donors that had undergone reduction mammoplasties or risk reduction mastectomies. From more than 800,000 cells we identified 41 cell subclusters across the epithelial, immune and stromal compartments. The contribution of these different clusters varied according to the natural history of the tissue. Age, parity and germline mutations, known to modulate the risk of developing breast cancer, affected the homeostatic cellular state of the breast in different ways. We found that immune cells from BRCA1 or BRCA2 carriers had a distinct gene expression signature indicative of potential immune exhaustion, which was validated by immunohistochemistry. This suggests that immune-escape mechanisms could manifest in non-cancerous tissues very early during tumor initiation. This atlas is a rich resource that can be used to inform novel approaches for early detection and prevention of breast cancer.

Transcriptome analysis of polyploid giant cancer cells and their progeny reveals a functional role for p21 in polyploidization and depolyploidization

Polyploid giant cancer cells (PGCC) are frequently detected in tumors and are increasingly recognized for their roles in chromosomal instability and associated genome evolution that leads to cancer recurrence. We previously reported that therapy stress promotes polyploidy, and that acid ceramidase plays a role in depolyploidization. In this study, we used an RNA-seq approach to gain a better understanding of the underlying transcriptomic changes that occur as cancer cells progress through polyploidization and depolyploidization. Our results revealed gene signatures that are associated with disease-free and/or overall survival in several cancers and identified the cell cycle inhibitor CDKN1A/p21 as the major hub in PGCC and early progeny. Increased expression of p21 in PGCC was limited to the cytoplasm. We previously demonstrated that the sphingolipid enzyme acid ceramidase is dispensable for polyploidization upon therapy stress but plays a crucial role in depolyploidization. The current study demonstrates that treatment of cells with ceramide is not sufficient for p53-independent induction of p21 and that knockdown of acid ceramidase, which hydrolyzes ceramide, does not interfere with upregulation of p21. In contrast, blocking the expression of p21 with UC2288 prevented the induction of acid ceramidase and inhibited both the formation of PGCC from parental cells as well as the generation of progeny from PGCC. Taken together, our data suggest that p21 functions upstream of acid ceramidase and plays an important role in polyploidization and depolyploidization.

Construction of a novel mRNA-miRNA-lncRNA/circRNA triple subnetwork associated with immunity and aging in intervertebral disc degeneration

OBJECTIVE

Intervertebral disk degeneration (IVDD) is one of the most common causes of low back pain. However, in the etiology of IVDD, the specific method by which nucleus pulposus (NP) cell senescence and the immune response induce disease is uncertain.

METHODS

Gene Expression Omnibus database was used to find differentially expressed genes (DEGs), differentially expressed miRNAs (DE miRNAs), differentially expressed lncRNAs (DE lncRNAs), and differentially expressed circRNAs (DE circRNAs). Functional enrichment analysis was performed through Enrichr database. Potential regulatory miRNAs, lncRNAs and circRNAs of mRNAs were predicted by ENCORI and circBank, respectively.

RESULTS

We identified 198 upregulated and 131 downregulated genes, 39 upregulated and 22 downregulated miRNAs, 2152 upregulated and 564 downregulated lncRNAs, and 352 upregulated and 279 downregulated circRNAs as DEGs, DE miRNAs, DE lncRNAs, DE circRNAs, respectively. Functional enrichment analysis revealed that they were significantly enriched in Toll-like receptor signaling route and the NF-kappa B signaling pathway. An mRNA-miRNA-lncRNA/circRNA network linked to the pathogenesis of NP cells in IVDD was constructed based on node degree and differential expression level. Eight immune-related DEGs (6 upregulated and 2 downregulated genes) and five aging-related DEGs (3 upregulated and 2 downregulated genes) were identified in the critical network.

CONCLUSION

We established a novel immune-related and aging-related triple regulatory network of mRNA-miRNA-lncRNA/circRNA ceRNA, among which all RNAs may be utilized as the pathogenesis biomarker of NP cells in IVDD.

CB-0821, a novel CC chemokine receptor 5 (CCR5) inhibitor with improved binding efficacy proposed as anti-HIV candidate: Computational and in vitro approach

The CC chemokine receptor 5 (CCR5) serves a pivotal role in human immunodeficiency virus 1 (HIV-1) infection by acting as a co-receptor and facilitating the binding of the viral envelope glycoprotein (env). Maraviroc (MVC), a Food and Drug Administration-approved monocarboxylic acid amide, is one of the CCR5 inhibitors employed in HIV treatment. Despite the existence of approved drugs, the emergence of drug resistance underscores the necessity for novel compounds to combat resistance and enhance therapeutic efficacy. In this study, CB-0821, identified from the ChemBridge library, emerged as a promising CCR5 inhibitor. Molecular dynamics simulations indicate comparable dynamic properties for CB-0821 and MVC. In silico comparisons with other CCR5 inhibitors emphasize CB-0821's superior binding affinity, positioning it as a potential lead compound. Evaluations of the dissociation constant (Ki) and absorption, distribution, metabolism, and excretion predictions suggest CB-0821 as a well-tolerated drug. Furthermore, the dose-dependent inhibition of CCR5 by CB-0821 in Peripheral blood mononuclear cells (PBMCs) (ranging from 10 to 200 nM) demonstrates efficacy, coupled with nontoxicity to Vero cells at concentrations up to 500 nM. These results underscore the potential of CB-0821 in HIV antiviral therapy, calling for additional preclinical validations before advancing to clinical considerations.

Bioinformatics-based clinical study

INTRODUCTION

At the beginning in July 2023, there has been a significant increase in daily hospital admissions attributed to the new variant of COVID-19. Aim of this study is to explore the clinical benefits and outcomes of using linezolid in the management of pneumonic COVID-19 patients.

METHODOLOGY

The study included 230 patients with SARS-CoV-2 infection confirmed by RT-PCR. Group 1: 118 patients were managed with Linazolid alongside steroids. Group 2: (control group) patients treated according to the Protocol for Egyptian COVID-19 management outlines and WHO guidelines (112 patients). Each patient group was categorized into 3 age groups: 20-40 years, 41-65 years, and over 65 years. Patients were carefully followed up until recovery or mortality. A docking analysis was carried out to investigate the potential of linezolid to act as an Mpro inhibitor.

RESULTS

Group 1's average recovery time was 15.1 days in contrast to 18.7 days for Group 2 (control). There were no deaths reported. In silico investigations revealed that Linezolid was able to achieve a binding mode comparable to that of the co-crystalized inhibitor.

CONCLUSIONS

Linazolid is considered an effective antiviral weapon against SARS-COV-2. It could be used in the management plan of pneumonic individuals due to SARS-COV-2 infection. We recommend using it to combat the current wave caused by Omicron EG-5 Variant.

Unravelling the Genetic Landscape of Hemiplegic Migraine: Exploring Innovative Strategies and Emerging Approaches

Migraine is a severe, debilitating neurovascular disorder. Hemiplegic migraine (HM) is a rare and debilitating neurological condition with a strong genetic basis. Sequencing technologies have improved the diagnosis and our understanding of the molecular pathophysiology of HM. Linkage analysis and sequencing studies in HM families have identified pathogenic variants in ion channels and related genes, including CACNA1A, ATP1A2, and SCN1A, that cause HM. However, approximately 75% of HM patients are negative for these mutations, indicating there are other genes involved in disease causation. In this review, we explored our current understanding of the genetics of HM. The evidence presented herein summarises the current knowledge of the genetics of HM, which can be expanded further to explain the remaining heritability of this debilitating condition. Innovative bioinformatics and computational strategies to cover the entire genetic spectrum of HM are also discussed in this review.

Modeling of new markers for the diagnosis and prognosis of pancreatic cancer based on the transition from inflammation to cancer

Background

Pancreatic adenocarcinoma (PAAD) is a lethal disease with a poor prognosis. Genes involved in acute pancreatitis (AP) or chronic pancreatitis (CP) might be important for PAAD development. This study sought to identify potential PAAD diagnosis markers and to establish a PAAD prognosis prediction model based on AP- and CP-related genes.

Methods

The significantly differentially expressed genes in both AP or CP and PAAD were obtained by a bioinformatics analysis. A risk-score model for predicting survival was constructed based on The Cancer Genome Atlas (TCGA) data and validated using an International Cancer Genome Consortium (ICGC) cohort. Protein expression and the effects of the genes in the risk models were validated by immunohistochemistry, or Cell Counting Kit-8 (CCK-8) and transwell assays. The study sample data included six AP tissue samples and five normal pancreatic tissue samples, six CP tissue samples and six normal pancreatic tissue samples from the Gene Expression Omnibus (GEO) expression profiling microarrays GSE109227 and GSE41418 data sets, respectively, and fragments per kilobase per million mapped fragments (FPKM) data from four normal controls and 150 PAAD cases from TCGA database, and 182 cancer patient samples with complete survival prognostic data from the ICGC database.

Results

In total, 508 significantly differentially expressed genes were found in both AP or CP and PAAD. Trefoil factor 2 (TFF2), tubulointerstitial nephritis antigen (TINAG), trefoil factor 1 (TFF1), aquaporin 5 (AQP5), SAM pointed domain containing ETS transcription factor (SPDEF), anterior gradient protein 2 (AGR2), apolipoprotein B messenger RNA editing enzyme catalytic subunit 1 (APOBEC1), kallikrein-related peptidase 6 (KLK6), dopa decarboxylase (DDC), mucin 13 (MUC13), claudin 18 (CLDN18), annexin A10 (ANXA10), and tetraspanin 1 (TSPAN1) were found to be present in PAAD and had the largest fold change. A risk-score model, comprising 19 genes, was constructed for prognostic prediction. A high-risk score indicated a poor prognosis. TINAG, DDC, SPDEF, and APOBEC1 proteins were increased in PAAD, while TINAG and DDC were correlated with the pathologic grade. Decreased TINAG, APOBEC1, transmembrane protein 94 (TMEM94), and kelch like family member 36 (KLHL36) expression inhibited PAAD cell proliferation, while decreased SPDEF, TMEM94, and KLHL36 expression significantly inhibited PAAD cell migration.

Conclusions

The AP and CP co-related genes were significantly correlated with PAAD. TINAG, DDC, SPDEF, and APOBEC1 could serve as new PAAD predictors. The risk model developed in this study could be used to predict the prognosis of PAAD patients.

Gastric cancer prognosis: unveiling autophagy-related signatures and immune infiltrates

Background

Autophagy played a crucial regulatory role in tumor initiation and progression. Therefore, we aimed to comprehensively analyze autophagy-related genes (ARGs) in gastric cancer, focusing on their expression, prognostic value, and potential functions.

Methods

The gastric cancer gene chip datasets (GSE79973 and GSE54129) were collected from the Gene Expression Omnibus (GEO) database. Subsequently, the Limma package was employed to identify differentially expressed genes (DEGs) between the normal and disease groups. The selected ARGs were further authenticated using the Human Protein Atlas (HPA) database, The Cancer Genome Atlas (TCGA) database, and GSE19826 database.

Results

A total of 15 autophagy-related DEGs, eight of which were upregulated [FKBP1A, IL24, PEA15, HSP90AB1, cathepsin B (CTSB), ITGB1, SPHK1, HIF1A], while seven were downregulated (DAPK2, EIF2AK3, FKBP1B, PTK6, NKX2-3, NFE2L2, PRKCD). Analysis revealed that CTSB was specifically associated with the prognosis of gastric cancer patients. Gene set enrichment analysis (GSEA) showcased a significant enrichment of CTSB-related genes within immune-related pathways. Moreover, correlation analysis demonstrated a clear association between the expression of CTSB and immune infiltration. The upregulation of CTSB in gastric cancer was linked to poor survival and increased immune infiltration.

Conclusions

We conjectured that CTSB likely played a critical role in regulating immunity and autophagy in gastric cancer.

Open-source large language models in action: A bioinformatics chatbot for PRIDE database

We here present a chatbot assistant infrastructure (https://www.ebi.ac.uk/pride/chatbot/) that simplifies user interactions with the PRIDE database's documentation and dataset search functionality. The framework utilizes multiple Large Language Models (LLM): llama2, chatglm, mixtral (mistral), and openhermes. It also includes a web service API (Application Programming Interface), web interface, and components for indexing and managing vector databases. An Elo-ranking system-based benchmark component is included in the framework as well, which allows for evaluating the performance of each LLM and for improving PRIDE documentation. The chatbot not only allows users to interact with PRIDE documentation but can also be used to search and find PRIDE datasets using an LLM-based recommendation system, enabling dataset discoverability. Importantly, while our infrastructure is exemplified through its application in the PRIDE database context, the modular and adaptable nature of our approach positions it as a valuable tool for improving user experiences across a spectrum of bioinformatics and proteomics tools and resources, among other domains. The integration of advanced LLMs, innovative vector-based construction, the benchmarking framework, and optimized documentation collectively form a robust and transferable chatbot assistant infrastructure. The framework is open-source (https://github.com/PRIDE-Archive/pride-chatbot).

Exploring the Integrated Role of miRNAs and lncRNAs in Regulating the Transcriptional Response to Amino Acids and Insulin-like Growth Factor 1 in Gilthead Sea Bream (Sparus aurata) Myoblasts

In this study, gilthead sea bream (Sparus aurata) fast muscle myoblasts were stimulated with two pro-growth treatments, amino acids (AA) and insulin-like growth factor 1 (Igf-1), to analyze the transcriptional response of mRNAs, microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) and to explore their possible regulatory network using bioinformatic approaches. AA had a higher impact on transcription (1795 mRNAs changed) compared to Igf-1 (385 mRNAs changed). Both treatments stimulated the transcription of mRNAs related to muscle differentiation (GO:0042692) and sarcomere (GO:0030017), while AA strongly stimulated DNA replication and cell division (GO:0007049). Both pro-growth treatments altered the transcription of over 100 miRNAs, including muscle-specific miRNAs (myomiRs), such as miR-133a/b, miR-206, miR-499, miR-1, and miR-27a. Among 111 detected lncRNAs (>1 FPKM), only 30 were significantly changed by AA and 11 by Igf-1. Eight lncRNAs exhibited strong negative correlations with several mRNAs, suggesting a possible regulation, while 30 lncRNAs showed strong correlations and interactions with several miRNAs, suggesting a role as sponges. This work is the first step in the identification of the ncRNAs network controlling muscle development and growth in gilthead sea bream, pointing out potential regulatory mechanisms in response to pro-growth signals.

PLMSearch: Protein language model powers accurate and fast sequence search for remote homology

Homologous protein search is one of the most commonly used methods for protein annotation and analysis. Compared to structure search, detecting distant evolutionary relationships from sequences alone remains challenging. Here we propose PLMSearch (Protein Language Model), a homologous protein search method with only sequences as input. PLMSearch uses deep representations from a pre-trained protein language model and trains the similarity prediction model with a large number of real structure similarity. This enables PLMSearch to capture the remote homology information concealed behind the sequences. Extensive experimental results show that PLMSearch can search millions of query-target protein pairs in seconds like MMseqs2 while increasing the sensitivity by more than threefold, and is comparable to state-of-the-art structure search methods. In particular, unlike traditional sequence search methods, PLMSearch can recall most remote homology pairs with dissimilar sequences but similar structures. PLMSearch is freely available at https://dmiip.sjtu.edu.cn/PLMSearch .

Genome-Wide Analysis of the LBD Gene Family in Melon and Expression Analysis in Response to Wilt Disease Infection

LBD transcription factors are a class of transcription factors that regulate the formation of lateral organs, establish boundaries, and control secondary metabolism in plants. In this study, we identified 37 melon LBD transcription factors using bioinformatics methods and analyzed their basic information, chromosomal location, collinearity, evolutionary tree, gene structure, and expression patterns. The results showed that the genes were unevenly distributed across the 13 chromosomes of melon plants, with tandem repeats appearing on chromosomes 11 and 12. These 37 transcription factors can be divided into two major categories, Class I and Class II, and seven subfamilies: Ia, Ib, Ic, Id, Ie, IIa, and IIb. Of the 37 included transcription factors, 25 genes each contained between one to three introns, while the other 12 genes did not contain introns. Through cis-acting element analysis, we identified response elements such as salicylic acid, MeJA, abscisic acid, and auxin, gibberellic acid, as well as light response, stress response, and MYB-specific binding sites. Expression pattern analysis showed that genes in the IIb subfamilies play important roles in the growth and development of various organs in melon plants. Expression analysis found that the majority of melon LBD genes were significantly upregulated after infection with wilt disease, with the strongest response observed in the stem.

Computational Modeling and Characterization of Peptides Derived from Nanobody Complementary-Determining Region 2 (CDR2) Targeting Active-State Conformation of the β2-Adrenergic Receptor (β2AR)

This study assessed the suitability of the complementarity-determining region 2 (CDR2) of the nanobody (Nb) as a template for the derivation of nanobody-derived peptides (NDPs) targeting active-state β2-adrenergic receptor (β2AR) conformation. Sequences of conformationally selective Nbs favoring the agonist-occupied β2AR were initially analyzed by the informational spectrum method (ISM). The derived NDPs in complex with β2AR were subjected to protein-peptide docking, molecular dynamics (MD) simulations, and metadynamics-based free-energy binding calculations. Computational analyses identified a 25-amino-acid-long CDR2-NDP of Nb71, designated P4, which exhibited the following binding free-energy for the formation of the β2AR:P4 complex (ΔG = -6.8 ± 0.8 kcal/mol or a Ki = 16.5 μM at 310 K) and mapped the β2AR:P4 amino acid interaction network. In vitro characterization showed that P4 (i) can cross the plasma membrane, (ii) reduces the maximum isoproterenol-induced cAMP level by approximately 40% and the isoproterenol potency by up to 20-fold at micromolar concentration, (iii) has a very low affinity to interact with unstimulated β2AR in the cAMP assay, and (iv) cannot reduce the efficacy and potency of the isoproterenol-mediated β2AR/β-arrestin-2 interaction in the BRET2-based recruitment assay. In summary, the CDR2-NDP, P4, binds preferentially to agonist-activated β2AR and disrupts Gαs-mediated signaling.

Procrustes is a machine-learning approach that removes cross-platform batch effects from clinical RNA sequencing data

With the increased use of gene expression profiling for personalized oncology, optimized RNA sequencing (RNA-seq) protocols and algorithms are necessary to provide comparable expression measurements between exome capture (EC)-based and poly-A RNA-seq. Here, we developed and optimized an EC-based protocol for processing formalin-fixed, paraffin-embedded samples and a machine-learning algorithm, Procrustes, to overcome batch effects across RNA-seq data obtained using different sample preparation protocols like EC-based or poly-A RNA-seq protocols. Applying Procrustes to samples processed using EC and poly-A RNA-seq protocols showed the expression of 61% of genes (N = 20,062) to correlate across both protocols (concordance correlation coefficient > 0.8, versus 26% before transformation by Procrustes), including 84% of cancer-specific and cancer microenvironment-related genes (versus 36% before applying Procrustes; N = 1,438). Benchmarking analyses also showed Procrustes to outperform other batch correction methods. Finally, we showed that Procrustes can project RNA-seq data for a single sample to a larger cohort of RNA-seq data. Future application of Procrustes will enable direct gene expression analysis for single tumor samples to support gene expression-based treatment decisions.

A de novo PKD1 mutation in a Chinese family with autosomal dominant polycystic kidney disease

BACKGROUND

PKD1, which has a relatively high mutation rate, is highly polymorphic, and the role of PKD1 is incompletely defined. In the current study, in order to determine the molecular etiology of a family with autosomal dominant polycystic kidney disease, the pathogenicity of an frameshift mutation in the PKD1 gene, c.9484delC, was evaluated.

METHODS

The family clinical data were collected. Whole exome sequencing analysis determined the level of this mutation in the proband's PKD1, and Sanger sequencing and bioinformatics analysis were performed. SIFT, Polyphen2, and MutationTaster were used to evaluate the conservation of the gene and pathogenicity of the identified mutations. SWISS-MODEL was used to predict and map the protein structure of PKD1 and mutant neonate proteins.

RESULTS

A novel c.9484delC (p.Arg3162Alafs*154) mutation of the PKD1 gene was identified by whole exome sequencing in the proband, which was confirmed by Sanger sequencing in his sister (II7). The same mutation was not detected in the healthy pedigree members. Random screening of 100 normal and end-stage renal disease patients did not identify the c.9484delC mutation. Bioinformatics analysis suggested that the mutation caused the 3162 nd amino acid substitution of arginine by alanine and a shift in the termination codon. As a result, the protein sequence was shortened from 4302 amino acids to 3314 amino acids, the protein structure was greatly changed, and the PLAT/LH2 domain was destroyed. Clustal analysis indicated that the altered amino acids were highly conserved in mammals.

CONCLUSION

A novel mutation in the PKD1 gene has been identified in an affected Chinese family. The mutation is probably responsible for a range of clinical manifestations for which reliable prenatal diagnosis and genetic counseling may be provided.

DELVE: feature selection for preserving biological trajectories in single-cell data

Single-cell technologies can measure the expression of thousands of molecular features in individual cells undergoing dynamic biological processes. While examining cells along a computationally-ordered pseudotime trajectory can reveal how changes in gene or protein expression impact cell fate, identifying such dynamic features is challenging due to the inherent noise in single-cell data. Here, we present DELVE, an unsupervised feature selection method for identifying a representative subset of molecular features which robustly recapitulate cellular trajectories. In contrast to previous work, DELVE uses a bottom-up approach to mitigate the effects of confounding sources of variation, and instead models cell states from dynamic gene or protein modules based on core regulatory complexes. Using simulations, single-cell RNA sequencing, and iterative immunofluorescence imaging data in the context of cell cycle and cellular differentiation, we demonstrate how DELVE selects features that better define cell-types and cell-type transitions. DELVE is available as an open-source python package: https://github.com/jranek/delve .

Potential diagnostic markers shared between non-alcoholic fatty liver disease and atherosclerosis determined by machine learning and bioinformatic analysis

Background

Evidence indicates that chronic non-alcoholic fatty liver disease (NAFLD) can increase the risk of atherosclerosis (AS), but the underlying mechanism remains unclear.

Objective

This study is intended for confirming key genes shared between NAFLD and AS, and their clinical diagnostic value to establish a foundation for searching novel therapeutic targets.

Methods

We downloaded the Gene Expression Omnibus (GEO) datasets, GSE48452 and GSE89632 for NAFLD and GSE100927, GSE40231 and GSE28829 for AS. The progression of NAFLD co-expression gene modules were recognized via weighted gene co-expression network analysis (WGCNA). We screened for differentially expressed genes (DEGs) associated with AS and identified common genes associated with NAFLD and AS using Venn diagrams. We investigated the most significant core genes between NAFLD and AS using machine learning algorithms. We then constructed a diagnostic model by creating a nomogram and evaluating its performance using ROC curves. Furthermore, the CIBERSORT algorithm was utilized to explore the immune cell infiltration between the two diseases, and evaluate the relationship between diagnostic genes and immune cells.

Results

The WGCNA findings associated 1,129 key genes with NAFLD, and the difference analysis results identified 625 DEGs in AS, and 47 genes that were common to both diseases. We screened the core RPS6KA1 and SERPINA3 genes associated with NAFLD and AS using three machine learning algorithms. A nomogram and ROC curves demonstrated that these genes had great clinical meaning. We found differential expression of RPS6KA1 in patients with steatosis and NASH, and of SERPINA3 only in those with NASH compared with normal individuals. Immune infiltration findings revealed that macrophage and mast cell infiltration play important roles in the development of NAFLD and AS. Notably, SERPINA3 correlated negatively, whereas RPS6KA1 correlated positively with macrophages and mast cells.

Conclusion

We identified RPS6KA1 and SERPINA3 as potential diagnostic markers for NAFLD and AS. The most promising marker for a diagnosis of NAFLD and AS might be RPS6KA1, whereas SERPINA3 is the most closely related gene for NASH and AS. We believe that further exploration of these core genes will reveal the etiology and a pathological relationship between NAFLD and AS.

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

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

Bioinformatic validation and machine learning-based exploration of purine metabolism-related gene signatures in the context of immunotherapeutic strategies for nonspecific orbital inflammation

Background

Nonspecific orbital inflammation (NSOI) represents a perplexing and persistent proliferative inflammatory disorder of idiopathic nature, characterized by a heterogeneous lymphoid infiltration within the orbital region. This condition, marked by the aberrant metabolic activities of its cellular constituents, starkly contrasts with the metabolic equilibrium found in healthy cells. Among the myriad pathways integral to cellular metabolism, purine metabolism emerges as a critical player, providing the building blocks for nucleic acid synthesis, such as DNA and RNA. Despite its significance, the contribution of Purine Metabolism Genes (PMGs) to the pathophysiological landscape of NSOI remains a mystery, highlighting a critical gap in our understanding of the disease's molecular underpinnings.

Methods

To bridge this knowledge gap, our study embarked on an exploratory journey to identify and validate PMGs implicated in NSOI, employing a comprehensive bioinformatics strategy. By intersecting differential gene expression analyses with a curated list of 92 known PMGs, we aimed to pinpoint those with potential roles in NSOI. Advanced methodologies, including Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA), facilitated a deep dive into the biological functions and pathways associated with these PMGs. Further refinement through Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) enabled the identification of key hub genes and the evaluation of their diagnostic prowess for NSOI. Additionally, the relationship between these hub PMGs and relevant clinical parameters was thoroughly investigated. To corroborate our findings, we analyzed expression data from datasets GSE58331 and GSE105149, focusing on the seven PMGs identified as potentially crucial to NSOI pathology.

Results

Our investigation unveiled seven PMGs (ENTPD1, POLR2K, NPR2, PDE6D, PDE6H, PDE4B, and ALLC) as intimately connected to NSOI. Functional analyses shed light on their involvement in processes such as peroxisome targeting sequence binding, seminiferous tubule development, and ciliary transition zone organization. Importantly, the diagnostic capabilities of these PMGs demonstrated promising efficacy in distinguishing NSOI from non-affected states.

Conclusions

Through rigorous bioinformatics analyses, this study unveils seven PMGs as novel biomarker candidates for NSOI, elucidating their potential roles in the disease's pathogenesis. These discoveries not only enhance our understanding of NSOI at the molecular level but also pave the way for innovative approaches to monitor and study its progression, offering a beacon of hope for individuals afflicted by this enigmatic condition.

BERT-TFBS: a novel BERT-based model for predicting transcription factor binding sites by transfer learning

Transcription factors (TFs) are proteins essential for regulating genetic transcriptions by binding to transcription factor binding sites (TFBSs) in DNA sequences. Accurate predictions of TFBSs can contribute to the design and construction of metabolic regulatory systems based on TFs. Although various deep-learning algorithms have been developed for predicting TFBSs, the prediction performance needs to be improved. This paper proposes a bidirectional encoder representations from transformers (BERT)-based model, called BERT-TFBS, to predict TFBSs solely based on DNA sequences. The model consists of a pre-trained BERT module (DNABERT-2), a convolutional neural network (CNN) module, a convolutional block attention module (CBAM) and an output module. The BERT-TFBS model utilizes the pre-trained DNABERT-2 module to acquire the complex long-term dependencies in DNA sequences through a transfer learning approach, and applies the CNN module and the CBAM to extract high-order local features. The proposed model is trained and tested based on 165 ENCODE ChIP-seq datasets. We conducted experiments with model variants, cross-cell-line validations and comparisons with other models. The experimental results demonstrate the effectiveness and generalization capability of BERT-TFBS in predicting TFBSs, and they show that the proposed model outperforms other deep-learning models. The source code for BERT-TFBS is available at https://github.com/ZX1998-12/BERT-TFBS.

Leveraging multi-omics data to empower quantitative systems pharmacology in immuno-oncology

Understanding the intricate interactions of cancer cells with the tumor microenvironment (TME) is a pre-requisite for the optimization of immunotherapy. Mechanistic models such as quantitative systems pharmacology (QSP) provide insights into the TME dynamics and predict the efficacy of immunotherapy in virtual patient populations/digital twins but require vast amounts of multimodal data for parameterization. Large-scale datasets characterizing the TME are available due to recent advances in bioinformatics for multi-omics data. Here, we discuss the perspectives of leveraging omics-derived bioinformatics estimates to inform QSP models and circumvent the challenges of model calibration and validation in immuno-oncology.

DSNetax: a deep learning species annotation method based on a deep-shallow parallel framework

Microbial community analysis is an important field to study the composition and function of microbial communities. Microbial species annotation is crucial to revealing microorganisms' complex ecological functions in environmental, ecological and host interactions. Currently, widely used methods can suffer from issues such as inaccurate species-level annotations and time and memory constraints, and as sequencing technology advances and sequencing costs decline, microbial species annotation methods with higher quality classification effectiveness become critical. Therefore, we processed 16S rRNA gene sequences into k-mers sets and then used a trained DNABERT model to generate word vectors. We also design a parallel network structure consisting of deep and shallow modules to extract the semantic and detailed features of 16S rRNA gene sequences. Our method can accurately and rapidly classify bacterial sequences at the SILVA database's genus and species level. The database is characterized by long sequence length (1500 base pairs), multiple sequences (428,748 reads) and high similarity. The results show that our method has better performance. The technique is nearly 20% more accurate at the species level than the currently popular naive Bayes-dominated QIIME 2 annotation method, and the top-5 results at the species level differ from BLAST methods by <2%. In summary, our approach combines a multi-module deep learning approach that overcomes the limitations of existing methods, providing an efficient and accurate solution for microbial species labeling and more reliable data support for microbiology research and application.

Potential Mechanism of Tibetan Medicine Liuwei Muxiang Pills against Colorectal Cancer: Network Pharmacology and Bioinformatics Analyses

This study aimed to explore the mechanism through which Tibetan medicine Liuwei Muxiang (LWMX) pills acts against colorectal cancer (CRC). We firstly retrieved the active ingredients and the correlated targets of LWMX pills from public databases. The CRC-related targets were determined through bioinformatic analysis of a public CRC dataset. By computing the intersection of the drug-specific and disease-related targets, LWMX pill-CRC interaction networks were constructed using the protein-protein interaction (PPI) method and functional enrichment analysis. Subsequently, we determined the hub genes using machine learning tools and further verified their critical roles in CRC treatment via immune infiltration analysis and molecular docking studies. We identified 81 active ingredients in LWMX pills with 614 correlated targets, 1877 differentially expressed genes, and 9534 coexpression module genes related to CRC. A total of 5 target hub genes were identified among the 108 intersecting genes using machine learning algorithms. The immune infiltration analysis results suggested that LWMX pills could affect the CRC immune infiltration microenvironment by regulating the expression of the target hub genes. Finally, the molecular docking outcomes revealed stable binding affinity between all target hub proteins and the primary active ingredients of LWMX pills. Our findings illustrate the anti-CRC potential and the mechanism of action of LWMX pills and provide novel insights into multitarget medication for CRC treatment.

A microbial knowledge graph-based deep learning model for predicting candidate microbes for target hosts

Predicting interactions between microbes and hosts plays critical roles in microbiome population genetics and microbial ecology and evolution. How to systematically characterize the sophisticated mechanisms and signal interplay between microbes and hosts is a significant challenge for global health risks. Identifying microbe-host interactions (MHIs) can not only provide helpful insights into their fundamental regulatory mechanisms, but also facilitate the development of targeted therapies for microbial infections. In recent years, computational methods have become an appealing alternative due to the high risk and cost of wet-lab experiments. Therefore, in this study, we utilized rich microbial metagenomic information to construct a novel heterogeneous microbial network (HMN)-based model named KGVHI to predict candidate microbes for target hosts. Specifically, KGVHI first built a HMN by integrating human proteins, viruses and pathogenic bacteria with their biological attributes. Then KGVHI adopted a knowledge graph embedding strategy to capture the global topological structure information of the whole network. A natural language processing algorithm is used to extract the local biological attribute information from the nodes in HMN. Finally, we combined the local and global information and fed it into a blended deep neural network (DNN) for training and prediction. Compared to state-of-the-art methods, the comprehensive experimental results show that our model can obtain excellent results on the corresponding three MHI datasets. Furthermore, we also conducted two pathogenic bacteria case studies to further indicate that KGVHI has excellent predictive capabilities for potential MHI pairs.

What is new in FungiDB: a web-based bioinformatics platform for omics-scale data analysis for fungal and oomycete species

FungiDB (https://fungidb.org) serves as a valuable online resource that seamlessly integrates genomic and related large-scale data for a wide range of fungal and oomycete species. As an integral part of the VEuPathDB Bioinformatics Resource Center (https://veupathdb.org), FungiDB continually integrates both published and unpublished data addressing various aspects of fungal biology. Established in early 2011, the database has evolved to support 674 datasets. The datasets include over 300 genomes spanning various taxa (e.g. Ascomycota, Basidiomycota, Blastocladiomycota, Chytridiomycota, Mucoromycota, as well as Albuginales, Peronosporales, Pythiales, and Saprolegniales). In addition to genomic assemblies and annotation, over 300 extra datasets encompassing diverse information, such as expression and variation data, are also available. The resource also provides an intuitive web-based interface, facilitating comprehensive approaches to data mining and visualization. Users can test their hypotheses and navigate through omics-scale datasets using a built-in search strategy system. Moreover, FungiDB offers capabilities for private data analysis via the integrated VEuPathDB Galaxy platform. FungiDB also permits genome improvements by capturing expert knowledge through the User Comments system and the Apollo genome annotation editor for structural and functional gene curation. FungiDB facilitates data exploration and analysis and contributes to advancing research efforts by capturing expert knowledge for fungal and oomycete species.

The identification of a novel shared therapeutic target and drug across all insulin-sensitive tissues under insulin resistance

Background

To identify key and shared insulin resistance (IR) molecular signatures across all insulin-sensitive tissues (ISTs), and their potential targeted drugs.

Methods

Three datasets from Gene Expression Omnibus (GEO) were acquired, in which the ISTs (fat, muscle, and liver) were from the same individual with obese mice. Integrated bioinformatics analysis was performed to obtain the differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was carried out to determine the "most significant trait-related genes" (MSTRGs). Enrichment analysis and PPI network were performed to find common features and novel hub genes in ISTs. The shared genes of DEGs and genes between DEGs and MSTRGs across four ISTs were identified as key IR therapeutic target. The Attie Lab diabetes database and obese rats were used to verify candidate genes. A medical drug-gene interaction network was conducted by using the Comparative Toxicogenomics Database (CTD) to find potential targeted drugs. The candidate drug was validated in Hepa1-6 cells.

Results

Lipid metabolic process, mitochondrion, and oxidoreductase activity as common features were enriched from ISTs under an obese context. Thirteen shared genes (Ubd, Lbp, Hp, Arntl, Cfd, Npas2, Thrsp., Tpx2, Pkp1, Sftpd, Mthfd2, Tnfaip2, and Vnn3) of DEGs across ISTs were obtained and confirmed. Among them, Ubd was the only shared gene between DEGs and MSTRGs across four ISTs. The expression of Ubd was significantly upregulated across four ISTs in obese rats, especially in the liver. The IR Hepa1-6 cell models treated with dexamethasone (Dex), palmitic acid (PA), and 2-deoxy-D-ribose (dRib) had elevated expression of Ubd. Knockdown of Ubd increased the level of p-Akt. A lowing Ubd expression drug, promethazine (PMZ) from CTD analysis rescued the decreased p-Akt level in IR Hepa1-6 cells.

Conclusion

This study revealed Ubd, a novel and shared IR molecular signature across four ISTs, as an effective biomarker and provided new insight into the mechanisms of IR. PMZ was a candidate drug for IR which increased p-Akt level and thus improved IR by targeting Ubd and downregulation of Ubd expression. Both Ubd and PMZ merit further clinical translational investigation to improve IR.

Excavation of gene markers associated with pancreatic ductal adenocarcinoma based on interrelationships of gene expression

Pancreatic ductal adenocarcinoma (PDAC) accounts for 95% of all pancreatic cancer cases, posing grave challenges to its diagnosis and treatment. Timely diagnosis is pivotal for improving patient survival, necessitating the discovery of precise biomarkers. An innovative approach was introduced to identify gene markers for precision PDAC detection. The core idea of our method is to discover gene pairs that display consistent opposite relative expression and differential co-expression patterns between PDAC and normal samples. Reversal gene pair analysis and differential partial correlation analysis were performed to determine reversal differential partial correlation (RDC) gene pairs. Using incremental feature selection, the authors refined the selected gene set and constructed a machine-learning model for PDAC recognition. As a result, the approach identified 10 RDC gene pairs. And the model could achieve a remarkable accuracy of 96.1% during cross-validation, surpassing gene expression-based models. The experiment on independent validation data confirmed the model's performance. Enrichment analysis revealed the involvement of these genes in essential biological processes and shed light on their potential roles in PDAC pathogenesis. Overall, the findings highlight the potential of these 10 RDC gene pairs as effective diagnostic markers for early PDAC detection, bringing hope for improving patient prognosis and survival.

Identification and validation of the biomarkers related to ferroptosis in calcium oxalate nephrolithiasis

Ferroptosis is a specific type of programmed cell death characterized by iron-dependent lipid peroxidation. Understanding the involvement of ferroptosis in calcium oxalate (CaOx) stone formation may reveal potential targets for this condition. The publicly available dataset GSE73680 was used to identify 61 differentially expressed ferroptosis-related genes (DEFERGs) between normal kidney tissues and Randall's plaques (RPs) from patients with nephrolithiasis through employing weighted gene co-expression network analysis (WGCNA). The findings were validated through in vitro and in vivo experiments using CaOx nephrolithiasis rat models induced by 1% ethylene glycol administration and HK-2 cell models treated with 1 mM oxalate. Through WGCNA and the machine learning algorithm, we identified LAMP2 and MDM4 as the hub DEFERGs. Subsequently, nephrolithiasis samples were classified into cluster 1 and cluster 2 based on the expression of the hub DEFERGs. Validation experiments demonstrated decreased expression of LAMP2 and MDM4 in CaOx nephrolithiasis animal models and cells. Treatment with ferrostatin-1 (Fer-1), a ferroptosis inhibitor, partially reversed oxidative stress and lipid peroxidation in CaOx nephrolithiasis models. Moreover, Fer-1 also reversed the expression changes of LAMP2 and MDM4 in CaOx nephrolithiasis models. Our findings suggest that ferroptosis may be involved in the formation of CaOx kidney stones through the regulation of LAMP2 and MDM4.

DESP demixes cell-state profiles from dynamic bulk molecular measurements

Understanding the dynamic expression of proteins and other key molecules driving phenotypic remodeling in development and pathobiology has garnered widespread interest, yet the exploration of these systems at the foundational resolution of the underlying cell states has been significantly limited by technical constraints. Here, we present DESP, an algorithm designed to leverage independent estimates of cell-state proportions, such as from single-cell RNA sequencing, to resolve the relative contributions of cell states to bulk molecular measurements, most notably quantitative proteomics, recorded in parallel. We applied DESP to an in vitro model of the epithelial-to-mesenchymal transition and demonstrated its ability to accurately reconstruct cell-state signatures from bulk-level measurements of both the proteome and transcriptome, providing insights into transient regulatory mechanisms. DESP provides a generalizable computational framework for modeling the relationship between bulk and single-cell molecular measurements, enabling the study of proteomes and other molecular profiles at the cell-state level using established bulk-level workflows.

Analysis of the Gene Networks and Pathways Correlated with Tissue Differentiation in Prostate Cancer

Prostate cancer (PCa) is the most prevalent non-cutaneous cancer in men. Early PCa detection has been made possible by the adoption of screening methods based on the serum prostate-specific antigen and Gleason score (GS). The aim of this study was to correlate gene expression with the differentiation level of prostate adenocarcinomas, as indicated by GS. We used data from The Cancer Genome Atlas (TCGA) and included 497 prostate cancer patients, 52 of which also had normal tissue sample sequencing data. Gene ontology analysis revealed that higher GSs were associated with greater responses to DNA damage, telomere lengthening, and cell division. Positive correlation was found with transcription factor activator of the adenovirus gene E2 (E2F) and avian myelocytomatosis viral homolog (MYC) targets, G2M checkpoints, DNA repair, and mitotic spindles. Immune cell deconvolution revealed high M0 macrophage counts and an increase in M2 macrophages dependent on the GS. The molecular pathways most correlated with GSs were cell cycle, RNA transport, and calcium signaling (depleted). A combinatorial approach identified a set of eight genes able to differentiate by k-Nearest Neighbors (kNN) between normal tissues, low-Gleason tissues, and high-Gleason tissues with high accuracy. In conclusion, our study could be a step forward to better understanding the link between gene expression and PCa progression and aggressiveness.

InPACT: a computational method for accurate characterization of intronic polyadenylation from RNA sequencing data

Alternative polyadenylation can occur in introns, termed intronic polyadenylation (IPA), has been implicated in diverse biological processes and diseases, as it can produce noncoding transcripts or transcripts with truncated coding regions. However, a reliable method is required to accurately characterize IPA. Here, we propose a computational method called InPACT, which allows for the precise characterization of IPA from conventional RNA-seq data. InPACT successfully identifies numerous previously unannotated IPA transcripts in human cells, many of which are translated, as evidenced by ribosome profiling data. We have demonstrated that InPACT outperforms other methods in terms of IPA identification and quantification. Moreover, InPACT applied to monocyte activation reveals temporally coordinated IPA events. Further application on single-cell RNA-seq data of human fetal bone marrow reveals the expression of several IPA isoforms in a context-specific manner. Therefore, InPACT represents a powerful tool for the accurate characterization of IPA from RNA-seq data.

Translating genetic findings to epigenetics: identifying the mechanisms associated with aging after high-radiation exposure on earth and in space

Purpose

Exposure to radiation is a health concern within and beyond the Earth's atmosphere for aircrew and astronauts in their respective austere environments. The biological effects of radiation exposure from a multiomics standpoint are relatively unexplored and stand to shed light on tailored monitoring and treatment for those in these career fields. To establish a reference variable for genetic damage, biological age seems to be closely associated with the effect of radiation. Following a genetic-based study, this study explores the epigenetic landscape of radiation exposure along with its associative effects on aging processes.

Methods

We imported the results of the genetics-based study that was a secondary analysis of five publicly available datasets (noted as Data1). The overlap of these genes with new data involving methylation data from two datasets (noted as Data2) following similar secondary analysis procedures is the basis of this study. We performed the standard statistical analysis on these datasets along with supervised and unsupervised learning to create preranked gene lists used for functional analysis in Ingenuity Pathway Analysis (IPA).

Results

There were 664 genes of interest from Data1 and 577 genes from Data2. There were 40 statistically significant methylation probes within 500 base pairs of the gene's transcription start site and 10 probes within 100 base pairs, which are discussed in depth. IPA yielded 21 significant pathways involving metabolism, cellular development, cell death, and diseases. Compared to gold standards for gestational age, we observed relatively low error and standard deviation using newly identified biomarkers.

Conclusion

We have identified 17 methylated genes that exhibited particular interest and potential in future studies. This study suggests that there are common trends in oxidative stress, cell development, and metabolism that indicate an association between aging processes and the effects of ionizing radiation exposure.

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors

Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.

Identification of Shared Signature Genes and Immune Microenvironment Subtypes for Heart Failure and Chronic Kidney Disease Based on Machine Learning

Background

A complex interrelationship exists between Heart Failure (HF) and chronic kidney disease (CKD). This study aims to clarify the molecular mechanisms of the organ-to-organ interplay between heart failure and CKD, as well as to identify extremely sensitive and specific biomarkers.

Methods

Differentially expressed tandem genes were identified from HF and CKD microarray datasets and enrichment analyses of tandem perturbation genes were performed to determine their biological functions. Machine learning algorithms are utilized to identify diagnostic biomarkers and evaluate the model by ROC curves. RT-PCR was employed to validate the accuracy of diagnostic biomarkers. Molecular subtypes were identified based on tandem gene expression profiling, and immune cell infiltration of different subtypes was examined. Finally, the ssGSEA score was used to build the ImmuneScore model and to assess the differentiation between subtypes using ROC curves.

Results

Thirty-three crosstalk genes were associated with inflammatory, immune and metabolism-related signaling pathways. The machine-learning algorithm identified 5 hub genes (PHLDA1, ATP1A1, IFIT2, HLTF, and MPP3) as the optimal shared diagnostic biomarkers. The expression levels of tandem genes were negatively correlated with left ventricular ejection fraction and glomerular filtration rate. The CIBERSORT results indicated the presence of severe immune dysregulation in patients with HF and CKD, which was further validated at the single-cell level. Consensus clustering classified HF and CKD patients into immune and metabolic subtypes. Twelve immune genes associated with immune subtypes were screened based on WGCNA analysis, and an ImmuneScore model was constructed for high and low risk. The model accurately predicted different molecular subtypes of HF or CKD.

Conclusion

Five crosstalk genes may serve as potential biomarkers for diagnosing HF and CKD and are involved in disease progression. Metabolite disorders causing activation of a large number of immune cells explain the common pathogenesis of HF and CKD.

Prognostic Signature in Osteosarcoma Based on Amino Acid Metabolism-Associated Genes

Background: Osteosarcoma (OS) is undeniably a formidable bone malignancy characterized by a scarcity of effective treatment options. Reprogramming of amino acid (AA) metabolism has been associated with OS development. The present study was designed to identify metabolism-associated genes (MAGs) that are differentially expressed in OS and to construct a MAG-based prognostic risk signature for this disease. Methods: Expression profiles and clinicopathological data were downloaded from Gene Expression Omnibus (GEO) and UCSC Xena databases. A set of AA MAGs was obtained from the MSigDB database. Differentially expressed genes (DEGs) in GEO dataset were identified using "limma." Prognostic MAGs from UCSC Xena database were determined through univariate Cox regression and used in the prognostic signature development. This signature was validated using another dataset from GEO database. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, single sample gene set enrichment analysis, and GDSC2 analyses were performed to explore the biological functions of the MAGs. A MAG-based nomogram was established to predict 1-, 3-, and 5-year survival. Real-time quantitative polymerase chain reaction, Western blot, and immunohistochemical staining confirmed the expression of MAGs in primary OS and paired adjacent normal tissues. Results: A total of 790 DEGs and 62 prognostic MAGs were identified. A MAG-based signature was constructed based on four MAGs: PIPOX, PSMC2, SMOX, and PSAT1. The prognostic value of this signature was successfully validated, with areas under the receiver operating characteristic curves for 1-, 3-, and 5-year survival of 0.714, 0.719, and 0.715, respectively. This MAG-based signature was correlated with the infiltration of CD56dim natural killer cells and resistance to several antiangiogenic agents. The nomogram was accurate in predictions, with a C-index of 0.77. The expression of MAGs verified by experiment was consistent with the trends observed in GEO database. Conclusion: Four AA MAGs were prognostic of survival in OS patients. This MAG-based signature has the potential to offer valuable insights into the development of treatments for OS.

Predicting immunotherapy response in melanoma using a novel tumor immunological phenotype-related gene index

Introduction

Melanoma is a highly aggressive and recurrent form of skin cancer, posing challenges in prognosis and therapy prediction.

Methods

In this study, we developed a novel TIPRGPI consisting of 20 genes using Univariate Cox regression and the LASSO algorithm. The high and low-risk groups based on TIPRGPI exhibited distinct mutation profiles, hallmark pathways, and immune cell infiltration in the tumor microenvironment.

Results

Notably, significant differences in tumor immunogenicity and TIDE were observed between the risk groups, suggesting a better response to immune checkpoint blockade therapy in the low-TIPRGPI group. Additionally, molecular docking predicted 10 potential drugs that bind to the core target, PTPRC, of the TIPRGPI signature.

Discussion

Our findings highlight the reliability of TIPRGPI as a prognostic signature and its potential application in risk classification, immunotherapy response prediction, and drug candidate identification for melanoma treatment. The "TIP genes" guided strategy presented in this study may have implications beyond melanoma and could be applied to other cancer types.

Mitochondrial energy metabolism-related gene signature as a prognostic indicator for pancreatic adenocarcinoma

Background: Pancreatic adenocarcinoma (PAAD) is a highly malignant gastrointestinal tumor and is associated with an unfavorable prognosis worldwide. Considering the effect of mitochondrial metabolism on the prognosis of pancreatic cancer has rarely been investigated, we aimed to establish prognostic gene markers associated with mitochondrial energy metabolism for the prediction of survival probability in patients with PAAD. Methods: Gene expression data were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases, and the mitochondrial energy metabolism-related genes were obtained from the GeneCards database. Based on mitochondrial energy metabolism score (MMs), differentially expressed MMRGs were established for MMs-high and MMs-low groups using ssGSEA. After the univariate Cox and least absolute and selection operator (LASSO) analyses, a prognostic MMRG signature was used in the multivariate Cox proportional regression model. Survival and immune cell infiltration analyses were performed. In addition, a nomogram based on the risk model was used to predict the survival probability of patients with PAAD. Finally, the expression of key genes was verified using quantitative polymerase chain reaction and immunohistochemical staining. Intro cell experiments were performed to evaluated the proliferation and invasion of pancreatic cancer cells. Results: A prognostic signature was constructed consisting of two mitochondrial energy metabolism-related genes (MMP11, COL10A1). Calibration and receiver operating characteristic (ROC) curves verified the good predictability performance of the risk model for the survival rate of patients with PAAD. Finally, immune-related analysis explained the differences in immune status between the two subgroups based on the risk model. The high-risk score group showed higher estimate, immune, and stromal scores, expression of eight checkpoint genes, and infiltration of M0 macrophages, which might indicate a beneficial response to immunotherapy. The qPCR results confirmed high expression of MMP11 in pancreatic cancer cell lines, and IHC also verified high expression of MMP11 in clinical pancreatic ductal adenocarcinoma tissues. In vitro cell experiments also demonstrated the role of MMP11 in cell proliferation and invasion. Conclusion: Our study provides a novel two-prognostic gene signature-based on MMRGs-that accurately predicted the survival of patients with PAAD and could be used for mitochondrial energy metabolism-related therapies in the future.

A universal molecular control for DNA, mRNA and protein expression

The expression of genes encompasses their transcription into mRNA followed by translation into protein. In recent years, next-generation sequencing and mass spectrometry methods have profiled DNA, RNA and protein abundance in cells. However, there are currently no reference standards that are compatible across these genomic, transcriptomic and proteomic methods, and provide an integrated measure of gene expression. Here, we use synthetic biology principles to engineer a multi-omics control, termed pREF, that can act as a universal molecular standard for next-generation sequencing and mass spectrometry methods. The pREF sequence encodes 21 synthetic genes that can be in vitro transcribed into spike-in mRNA controls, and in vitro translated to generate matched protein controls. The synthetic genes provide qualitative controls that can measure sensitivity and quantitative accuracy of DNA, RNA and peptide detection. We demonstrate the use of pREF in metagenome DNA sequencing and RNA sequencing experiments and evaluate the quantification of proteins using mass spectrometry. Unlike previous spike-in controls, pREF can be independently propagated and the synthetic mRNA and protein controls can be sustainably prepared by recipient laboratories using common molecular biology techniques. Together, this provides a universal synthetic standard able to integrate genomic, transcriptomic and proteomic methods.

Roles of pyroptosis and immune infiltration in aortic dissection

Introduction: Aortic dissection (AD) is often fatal, and its pathogenesis involves immune infiltration and pyroptosis, though the molecular pathways connecting these processes remain unclear. This study aimed to investigate the role of immune infiltration and pyroptosis in AD pathogenesis using bioinformatics analysis. Methods: Two Gene Expression Omnibus datasets and a Gene Cards dataset of pyroptosis-related genes (PRGs) were utilized. Immunological infiltration was assessed using CIBERSORT, and AD diagnostic markers were identified through univariate logistic regression and least absolute shrinkage and selection operator regression. Interaction networks were constructed using STRING, and weighted gene correlation network analysis (WGCNA) was employed to identify important modules and essential genes. Single-sample gene set enrichment analysis determined immune infiltration, and Pearson correlation analysis assessed the association of key genes with infiltrating immune cells. Results: Thirty-one PRGs associated with inflammatory response, vascular epidermal growth factor receptor, and Rap1 signaling pathways were identified. WGCNA revealed seven important genes within a critical module. CIBERSORT detected immune cell infiltration, indicating significant changes in immune cell infiltration and pyroptosis genes in AD and their connections. Discussion: Our findings suggest that key PRGs may serve as indicators for AD or high-risk individuals. Understanding the role of pyroptosis and immune cell infiltration in AD pathogenesis may lead to the development of novel molecular-targeted therapies for AD. Conclusion: This study provides insights into the molecular mechanisms underlying AD pathogenesis, highlighting the importance of immune infiltration and pyroptosis. Identification of diagnostic markers and potential therapeutic targets may improve the management of AD and reduce associated morbidity and mortality.

Applications of transcriptomics in ischemia reperfusion research in lung transplantation

Ischemia-reperfusion (IR) injury contributes to primary graft dysfunction, a major cause of early mortality after lung transplantation. Transcriptomics uses high-throughput techniques to profile the RNA transcripts within a sample and provides a unique view of the mechanisms underlying various biological processes. This review aims to highlight the applications of transcriptomics in lung IR injury studies, which have thus far revealed inflammatory responses to be the major event activated by IR, identified potential biomarkers and therapeutic targets, and investigated the mechanisms of therapeutic interventions. Ex vivo lung perfusion, together with advanced bioinformatic and transcriptomic techniques, including single-cell RNA-sequencing, microRNA profiling, and multi-omics, continue to expand the capabilities of transcriptomics. In the future, the construction of biospecimen banks and the promotion of international collaborations among clinicians and researchers have the potential to advance our understanding of IR injury and improve the management of lung transplant recipients.

Integrative analysis of bioinformatics and machine learning to identify cuprotosis-related biomarkers and immunological characteristics in heart failure

Backgrounds

Cuprotosis is a newly discovered programmed cell death by modulating tricarboxylic acid cycle. Emerging evidence showed that cuprotosis-related genes (CRGs) are implicated in the occurrence and progression of multiple diseases. However, the mechanism of cuprotosis in heart failure (HF) has not been investigated yet.

Methods

The HF microarray datasets GSE16499, GSE26887, GSE42955, GSE57338, GSE76701, and GSE79962 were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed CRGs between HF patients and nonfailing donors (NFDs). Four machine learning models were used to identify key CRGs features for HF diagnosis. The expression profiles of key CRGs were further validated in a merged GEO external validation dataset and human samples through quantitative reverse-transcription polymerase chain reaction (qRT-PCR). In addition, Gene Ontology (GO) function enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and immune infiltration analysis were used to investigate potential biological functions of key CRGs.

Results

We discovered nine differentially expressed CRGs in heart tissues from HF patients and NFDs. With the aid of four machine learning algorithms, we identified three indicators of cuprotosis (DLAT, SLC31A1, and DLST) in HF, which showed good diagnostic properties. In addition, their differential expression between HF patients and NFDs was confirmed through qRT-PCR. Moreover, the results of enrichment analyses and immune infiltration exhibited that these diagnostic markers of CRGs were strongly correlated to energy metabolism and immune activity.

Conclusions

Our study discovered that cuprotosis was strongly related to the pathogenesis of HF, probably by regulating energy metabolism-associated and immune-associated signaling pathways.