Exaggerated false positives by popular differential expression methods when analyzing human population samples

ZBP1 synchronized with periodontopathogenesis as the essential pattern recognition receptor

BACKGROUND

Periodontitis is a chronic inflammatory disease impacting quality of life. Understanding its pathogenesis is key to developing effective treatments. This study aimed to identify key pattern recognition receptors (PRRs) involved in periodontitis and elucidate their roles in disease progression.

METHODS

Periodontal tissues from healthy individuals and those with periodontitis were analyzed using RNA-sequencing, quantitative real-time PCR(qRT-PCR), and immunohistochemical analysis. Paired tissues collected before and after non-surgical treatment were analyzed via 4D-microDIA proteomics and Western blot.

RESULTS

RNA-sequencing showed significantly higher expression of Z-DNA binding protein 1(ZBP1) and absent in melanoma 2(AIM2) in periodontitis tissues compared to healthy controls, confirmed by qRT-PCR. Post-treatment proteomics indicated significant downregulation of ZBP1, with a non-significant trend for AIM2. Immunohistochemical staining localized ZBP1 to the middle and superficial layers of the gingival epithelium and around deep pockets in periodontitis, while AIM2 was detected in the junctional epithelium and extended throughout the pocket epithelium in periodontitis.

CONCLUSIONS

ZBP1 is highlighted as a key PRR in periodontitis, with significant regulatory potential. AIM2 may play a secondary role. Their distinct spatial distributions suggest involvement in specific microenvironments within periodontal tissues, mediating responses to microbial and inflammatory challenges. ZBP1 may be a critical receptor initiating periodontitis.

Identification and analysis of extracellular matrix and epithelial-mesenchymal transition-related genes in idiopathic pulmonary fibrosis by bioinformatics analysis and experimental validation

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disorder that is characterized by the disruption of lung architecture and respiratory failure. Notwithstanding the advent of novel therapeutic agents such as pirfenidone and nintedanib, there remains a pressing need for the development of innovative diagnostic and therapeutic strategies. Next-generation sequencing allows for the analysis of gene expression and the discovery of biomarkers. The objective of our study was to identify IPF-specific gene signatures, construct a diagnostic nomogram, and explore the role of the extracellular matrix (ECM) and epithelial-to-mesenchymal transition (EMT) in IPF pathogenesis. Utilizing data from the Gene Expression Omnibus (GEO) database, we identified differentially expressed genes (DEGs), performed weighted correlation network analysis (WGCNA), and constructed a nomogram. The present study has identified a group of key genes that are associated with IPF. The identified genes include GREM1, ITLN2, MAP3K15, RGS9BP, and SLCO1A2. The results of the immunohistochemical analysis indicated a significant correlation between these central genes and immune cell infiltration. Furthermore, Gene Set Enrichment Analysis (GSEA) revealed that these genes play a critical role in the pathogenesis of IPF. To validate the diagnostic potential of these core genes, we performed confirmatory analyses in independent Gene Expression Omnibus (GEO) datasets. We observed a significant upregulation of GREM1 expression in IPF animal and cellular models. These findings provide new insights into the molecular mechanisms of IPF and suggest potential targets for future diagnostic and therapeutic strategies.

Worm Perturb-Seq: massively parallel whole-animal RNAi and RNA-seq

Transcriptomes provide highly informative molecular phenotypes that, combined with gene perturbation, can connect genotype to phenotype. An ultimate goal is to perturb every gene and measure transcriptome changes, however, this is challenging, especially in whole animals. Here, we present 'Worm Perturb-Seq (WPS)', a method that provides high-resolution RNA-sequencing profiles for hundreds of replicate perturbations at a time in living animals. WPS introduces multiple experimental advances combining strengths of Caenhorhabditis elegans genetics and multiplexed RNA-sequencing with a novel analytical framework, EmpirDE. EmpirDE leverages the unique power of large transcriptomic datasets and improves statistical rigor by using gene-specific empirical null distributions to identify DEGs. We apply WPS to 103 nuclear hormone receptors (NHRs) and find a striking 'pairwise modularity' in which pairs of NHRs regulate shared target genes. We envision the advances of WPS to be useful not only for C. elegans, but broadly for other models, including human cells.

Downregulation of spermatogenesis-associated transcripts in the spermatozoa of idiopathic infertile men

BACKGROUND

Approximately half of male factor infertility cases are idiopathic, indicating a need for new methods to supplement male fertility assessment.

OBJECTIVES

The objective of this study was to identify differences in the sperm transcriptomes of men with different clinical fertility status. We hypothesized that sperm mRNA profiling could distinguish men presenting for fertility assessment from proven fertile men.

MATERIALS AND METHODS

We compared two groups of study participants: men who presented for infertility assessment (n = 53, "infertility"), and men without a history of infertility who had fathered a child and were presenting for vasectomy (n = 14, "proven fertile" control). Study participants provided a semen sample for semen analysis and sperm mRNA sequencing. Differentially abundant genes were identified, and a gene expression summary score was constructed to test the ability of RNA-seq data to differentiate between study populations.

RESULTS

The semen parameter that best differentiated between study populations was motility (area under the ROC curve = 0.746). In RNA-seq analysis, 1885 total differentially abundant transcripts were identified (q < 0.05, fold difference ≥ 2), 1004 (53.3%) of which were downregulated in infertility study participants. The Gene Ontology term, spermatogenesis, was enriched, with 40 out of 44 differentially abundant genes downregulated in infertility study participants. A gene expression summary score consisting of 100 upregulated and 100 downregulated genes was able to differentiate between the two groups of study participants.

DISCUSSION

Sperm mRNAs differed between proven fertile and infertility study men. Known fertility-associated genes, including PRM1 and PRM2, and potentially novel fertility markers, including HOOK1 and SPATA6, were downregulated in infertility study samples. Future studies should test these results for reproducibility and test whether novel biomarker candidates can provide mechanistic information about etiologies of idiopathic male infertility.

CONCLUSION

Our results support the hypothesis that sperm mRNA abundance differs by clinical fertility status.

Prognostic role and tumor-suppressive effects of CADM family members and the potential molecular mechanisms of CADM1 in neuroblastoma

BACKGROUND

The exact role of cell adhesion molecule (CADM) family members in neuroblastoma is still being explored. Here we uncovered the survival association and the possible mechanisms of CADMs in neuroblastoma through comprehensive bioinformatic analyses. Then the results of CADM1 were verified in neuroblastoma cell lines.

METHODS

CADMs expression was examined by cBioPortal and TARGET databases and verified in several GEO datasets. Kaplan-Meier plot, log-rank test, the ROC curve, and Cox regression analysis were utilized to assess the prognostic value of CADMs in neuroblastoma. Through functional enrichment analysis and interaction network construction, hub genes were screened to explore the molecular mechanism of CADMs in neuroblastoma. We tested the abilities of cell growth and migration in neuroblastoma cells when CADM1 was silenced and overexpressed respectively. We then used western blot to verify the phosphorylation levels of AKT/GSK-3β pathways.

RESULTS

The expression of CADM1-4 was significantly down-regulated in neuroblastoma patients with unfavorable prognostic factors. Moreover, CADM1 and CADM3 increased the accuracy of classical clinical indicators for predicting survival rate. The top 10 KEGG pathways for CADMs and their co-expression genes were mainly enriched in the mitotic cell cycle and the process of chromosomal duplication. Furthermore, our study showed that CADM1 inhibited neuroblastoma cells proliferation, migration and the phosphorylation of GSK-3β.

CONCLUSIONS

Decreased expression of CADM1 and CADM3 was significantly associated with poor outcomes in neuroblastoma. CADM1 may suppress neuroblastoma cell proliferation and migration through regulating the phosphorylation of GSK-3β.

Replicability of bulk RNA-Seq differential expression and enrichment analysis results for small cohort sizes

The high-dimensional and heterogeneous nature of transcriptomics data from RNA sequencing (RNA-Seq) experiments poses a challenge to routine downstream analysis steps, such as differential expression analysis and enrichment analysis. Additionally, due to practical and financial constraints, RNA-Seq experiments are often limited to a small number of biological replicates. In light of recent studies on the low replicability of preclinical cancer research, it is essential to understand how the combination of population heterogeneity and underpowered cohort sizes affects the replicability of RNA-Seq research. Using 18'000 subsampled RNA-Seq experiments based on real gene expression data from 18 different data sets, we find that differential expression and enrichment analysis results from underpowered experiments are unlikely to replicate well. However, low replicability does not necessarily imply low precision of results, as data sets exhibit a wide range of possible outcomes. In fact, 10 out of 18 data sets achieve high median precision despite low recall and replicability for cohorts with more than five replicates. To assist researchers constrained by small cohort sizes in estimating the expected performance regime of their data sets, we provide a simple bootstrapping procedure that correlates strongly with the observed replicability and precision metrics. We conclude with practical recommendations to alleviate problems with underpowered RNA-Seq studies.

Lymphovascular Invasion Is Associated With Doxorubicin Resistance in Breast Cancer

Lymphovascular invasion (LVI), the invasion of tumor cells into the lymphatic or vascular space, is an early indicator of potential metastasis, with its presence in breast cancer independently predicting poorer outcomes even after neoadjuvant chemotherapy. However, a major limitation is that LVI detection currently relies on postsurgical evaluation. To address this, we determined whether LVI+ breast tumors contain a unique gene signature that could facilitate earlier detection. Here, we conducted an integrative analysis of the gene profile between LVI+ and LVI- primary breast tumors from various sources, including published data and our own research, using both microarray and RNA-seq data. Our analysis revealed protein binding and vesicle-related genes to be the most enriched categories in LVI+ vs LVI- tumors. Furthermore, LVI+ tumors showed enrichment for xenobiotic metabolism genes, particularly drug metabolism enzymes, such as cytochrome P450 and uridine 5'-diphospho-glucuronosyltransferases. An elastic net regression model containing 13 of these uridine 5'-diphospho-glucuronosyltransferases and cytochrome P450 genes can predict LVI status with 92% accuracy. This suggests a potential link to drug resistance, which was further confirmed by the finding that patients with LVI+ tumors had a significantly lower clinical response rate than individuals with LVI- tumors. We also observed this resistance in patient-derived organoids, with LVI+ organoids exhibiting lower sensitivity to doxorubicin, implying that doxorubicin might be less effective for LVI+ breast cancer, potentially contributing to poorer outcomes. Overall, our study unlocked an exciting opportunity for personalized medicine, in that, therapy efficacy and patient outcomes can be improved by incorporating the LVI-associated gene signature into treatment plans.

Heterogeneity-preserving discriminative feature selection for disease-specific subtype discovery

Disease-specific subtype identification can deepen our understanding of disease progression and pave the way for personalized therapies, given the complexity of disease heterogeneity. Large-scale transcriptomic, proteomic, and imaging datasets create opportunities for discovering subtypes but also pose challenges due to their high dimensionality. To mitigate this, many feature selection methods focus on selecting features that distinguish known diseases or cell states, yet often miss features that preserve heterogeneity and reveal new subtypes. To overcome this gap, we develop Preserving Heterogeneity (PHet), a statistical methodology that employs iterative subsampling and differential analysis of interquartile range, in conjunction with Fisher's method, to identify a small set of features that enhance subtype clustering quality. Here, we show that this method can maintain sample heterogeneity while distinguishing known disease/cell states, with a tendency to outperform previous differential expression and outlier-based methods, indicating its potential to advance our understanding of disease mechanisms and cell differentiation.

Deep sequencing reveals distinct microRNA-mRNA signatures that differentiate pancreatic neuroendocrine tumor from non-diseased pancreas tissue

BACKGROUND

Only a limited number of biomarkers guide personalized management of pancreatic neuroendocrine tumors (PanNETs). Transcriptome profiling of microRNA (miRs) and mRNA has shown value in segregating PanNETs and identifying patients more likely to respond to treatment. Because miRs are key regulators of mRNA expression, we sought to integrate expression data from both RNA species into miR-mRNA interaction networks to advance our understanding of PanNET biology.

METHODS

We used deep miR/mRNA sequencing on six low-grade/high-risk, well-differentiated PanNETs compared with seven non-diseased tissues to identify differentially expressed miRs/mRNAs. Then we crossed a list of differentially expressed mRNAs with a list of in silico predicted mRNA targets of the most and least abundant miRs to generate high probability miR-mRNA interaction networks.

RESULTS

Gene ontology and pathway analyses revealed several miR-mRNA pairs implicated in cellular processes and pathways suggesting perturbed neuroendocrine function (miR-7 and Reg family genes), cell adhesion (miR-216 family and NLGN1, NCAM1, and CNTN1; miR-670 and the claudins, CLDN1 and CLDN2), and metabolic processes (miR-670 and BCAT1/MPST; miR-129 and CTH).

CONCLUSION

These novel miR-mRNA interaction networks identified dysregulated pathways not observed when assessing mRNA alone and provide a foundation for further investigation of their utility as diagnostic and predictive biomarkers.

Cytogenetic signatures favoring metastatic organotropism in colorectal cancer

Colorectal carcinoma (CRC) exhibits metastatic organotropism, primarily targeting liver, lung, and rarely the brain. Here, we study chromosomal imbalances (CIs) in cohorts of primary CRCs and metastases. Brain metastases show the highest burden of CIs, including aneuploidies and focal CIs, with enrichment of +12p encoding KRAS. Compared to liver and lung metastases, brain metastases present with increased co-occurrence of KRAS mutation and amplification. CRCs with concurrent KRAS mutation and amplification display significant metabolic reprogramming with upregulation of glycolysis, alongside upregulation of cell cycle pathways, including copy number gains of MDM2 and CDK4. Evolutionary modeling suggests early acquisition of many organotropic CIs enriched in both liver and brain metastases, while brain-enriched CIs preferentially emerge later. Collectively, this study supports a model where cytogenetic events in CRCs favor site-specific metastatic colonization. These site-enriched CI patterns may serve as biomarkers for metastatic potential in precision oncology.

The Role of Coagulation-Related Genes in Glioblastoma: A Comprehensive Analysis of the Tumor Microenvironment, Prognosis, and Treatment

The influence of coagulation on glioma biology has not been comprehensively elucidated. This study explores the role of coagulation-related genes (CRGs) in glioblastoma (GBM) from the perspectives of the tumor microenvironment (TME), differences in coagulation function among GBM patients, treatment, and prognosis. Somatic mutation analysis was performed on single nucleotide polymorphism (SNP) and copy number variation data from GBM patients in the TCGA cohort. Publicly available single-cell RNA sequencing data were used to analyze the role of coagulation in the GBM TME and its underlying biological mechanisms. Unsupervised clustering of GBM patients from the CGGA693 cohort was conducted, and coagulation function for each patient was assessed using ssGSEA scoring. Prognosis was assessed with Kaplan-Meier survival analysis, and immune infiltration was analyzed through ESTIMATE. A risk signature based on five CRGs (CFI, GNG12, MMP2, LEFTY2, and SERPINC1) was constructed and validated using LASSO regression and random survival forest analyses to predict responses to immunotherapy and identify potential sensitive drugs. Finally, the roles of LEFTY2 and SERPINC1 in GBM progression was verified by immunohistochemistry, cell counting kit-8 (CCK8) assay and wound healing assay, and the anti-GBM effect of the drug PLX4720 was verified by CCK8 assay, wound healing assay, and colony formation assay. Somatic mutation analysis revealed SNP events of CRG mutations in 117 out of 461 GBM cases (25.38%). Single-cell analysis of the GBM TME revealed significant activation of the coagulation pathway in endothelial cells, with intercellular communication mediated via the SPP1-integrin pathway (p < 0.01). Clustering analysis and ssGSEA identified two coagulation-related subtypes in GBM: coagulation-activated and coagulation-inhibited subtypes. Patients in the coagulation-activated subtype exhibited shorter overall survival and poorer prognosis compared to those in the coagulation-inhibited subtype (p = 0.0085). Immune infiltration analysis showed lower tumor purity and higher levels of immune-suppressive cells in the coagulation-activated subtype (p < 0.001). The CRG-based risk signature accurately predicted prognosis (p < 0.0001) and responses to immunotherapy in the IMvigor210 cohort (p = 0.0062). Based on the risk model, PLX4720 was identified as a potential sensitive drug (p < 0.001), and drug validation experiments demonstrated that PLX4720 inhibited the proliferation and migration of glioma cells (p < 0.0001). In vitro experiments demonstrated that LEFTY2 and SERPINC1 were significantly overexpressed in GBM compared to normal brain tissue, and knockdown of LEFTY2 and SERPINC1 inhibited glioma cell proliferation and migration (p < 0.05). The CRG-based risk signature model effectively predicts the prognosis of GBM patients and aids in assessing the efficacy of ICI therapy and chemotherapy. Furthermore, the genes LEFTY2, SERPINC1 and the drug PLX4720 offer potential directions for the development of novel therapeutic strategies for GBM.

Robust and Adaptive Non-Parametric Tests for Detecting General Distributional Shifts in Gene Expression

Differential expression analysis is crucial in genomics, yet existing methods primarily focus on detecting mean shifts. Variance shifts in gene expression are well-documented in studies of cellular signaling pathways, and more recently they have characterized aging, thus motivating the need for flexible detection approaches that include tests of expression variance changes. In this work, we present QRscore (Quantile Rank Score), a general method for detecting distributional shifts in gene expression by extending the Mann-Whitney test into a flexible family of rank-based tests. Here, we focus on implementing QRscore to detect shifts in mean and variance in gene expression, using weights designed from negative binomial (NB) and zero-inflated negative binomial (ZINB) models to combine the strengths of parametric and non-parametric approaches. We show through simulations that QRscore not only achieves high statistical power while controlling the false discovery rate (FDR), but also outperforms existing methods in detecting variance shifts and mean shifts. Applying QRscore to bulk RNA-seq data from the Genotype-Tissue Expression (GTEx) project, we identified numerous differentially dispersed genes and differentially expressed genes across 33 tissues. Notably, many genes have significant variance shifts but non-significant mean shifts. QRscore augments the genome bioinformatics toolkit by offering a powerful and flexible approach for differential expression analysis. QRscore is available in R, at https://github.com/songlab-cal/QRscore.

Cell type transcriptomics reveal shared genetic mechanisms in Alzheimer's and Parkinson's disease

Historically, Alzheimer's disease (AD) and Parkinson's disease (PD) have been investigated as two distinct disorders of the brain. However, a few similarities in neuropathology and clinical symptoms have been documented over the years. Traditional single gene-centric genetic studies, including GWAS and differential gene expression analyses, have struggled to unravel the molecular links between AD and PD. To address this, we tailor a pattern-learning framework to analyze synchronous gene co-expression at sub-cell-type resolution. Utilizing recently published single-nucleus AD (70,634 nuclei) and PD (340,902 nuclei) datasets from postmortem human brains, we systematically extract and juxtapose disease-critical gene modules. Our findings reveal extensive molecular similarities between AD and PD gene cliques. In neurons, disrupted cytoskeletal dynamics and mitochondrial stress highlight convergence in key processes; glial modules share roles in T-cell activation, myelin synthesis, and synapse pruning. This multi-module sub-cell-type approach offers insights into the molecular basis of shared neuropathology in AD and PD.

Genome-wide transcriptome differences associated with perceived discrimination in an urban, community-dwelling middle-aged cohort

Discrimination is a social adversity that is linked to several age-related outcomes. However, the molecular drivers of these observations are poorly understood. Social adverse factors are associated with proinflammatory and interferon gene expression, but little is known about whether additional genes are associated with discrimination among both African American and White adults. In this study, we examined how perceived discrimination in African American and White adults was associated with genome-wide transcriptome differences using RNA sequencing. Perceived discrimination was measured based on responses to self-reported lifetime discrimination and racial discrimination. Differential gene expression and pathway analysis were conducted in a cohort (N = 59) stratified by race, sex, and overall discrimination level. We found 28 significantly differentially expressed genes associated with race among those reporting high discrimination. Several of the upregulated genes for African American versus White adults reporting discrimination were related to immune function IGLV2-11, S100B, IGKV3-20, and IGKV4-1; the most significantly downregulated genes were associated with immune modulation and cancer, LUCAT1, THBS1, and ARPIN. The most enriched gene ontology biological process between African American and White men reporting high discrimination was the regulation of cytokine biosynthetic processes. The immune response biological process was significantly lower for African American women compared to White women reporting high discrimination. Discrimination was associated with the expression of small nucleolar RNAs, long noncoding RNAs, and microRNAs associated with energy homeostasis, cancer, and actin. Understanding the pathways through which adverse social factors like discrimination are associated with gene expression is crucial in advancing knowledge of age-related health disparities.

Identifying Essential Hub Genes and circRNA-Regulated ceRNA Networks in Hepatocellular Carcinoma

Competitive endogenous RNAs (ceRNAs) absorb microRNAs and subsequently promote corresponding mRNA and long noncoding RNA (lncRNA) expression, which may alter cancer cell malignancy. Thus, dissecting ceRNA networks may reveal novel targets in cancer therapies. In this study, we analyzed differentially expressed genes (DEGs) of mRNAs and lncRNAs, and differentially expressed microRNAs (DE-miRNAs) and circular RNAs (DE-circRNAs) extracted from high-throughput sequencing datasets of hepatocellular carcinoma patients. Based on these data, we identified 26 gene modules using weighted gene co-expression network analysis (WGCNA), of which 5 were associated with tumor differentiation. In these modules, 269 genes were identified by GO and KEGG enrichment and patient's survival correlation analyses. Next, 40 DE-miRNAs, each of which potentially bound a pair of DE-circRNA and hub gene, were discovered. Together with 201 circRNAs and 24 hub genes potentially bound by these miRNAs, 1151 ceRNA networks were constructed. Among them, 75 ceRNA networks consisting of 24 circRNAs, 28 miRNAs and 17 hub genes showed a positive circRNA-hub gene correlation. For validation, we carried out experiments for 4 randomly selected circRNAs regulating 19 potential ceRNA networks and verified 5 of them. This study represents a powerful strategy to identify essential gene networks and provides insights into designing effective therapeutic strategies.

Worm Perturb-Seq: massively parallel whole-animal RNAi and RNA-seq

The transcriptome provides a highly informative molecular phenotype to connect genotype to phenotype and is most frequently measured by RNA-sequencing (RNA-seq). Therefore, an ultimate goal is to perturb every gene and measure changes in the transcriptome. However, this remains challenging, especially in intact organisms due to different experimental and computational challenges. Here, we present 'Worm Perturb-Seq (WPS)', which provides high-resolution RNA-seq profiles for hundreds of replicate perturbations at a time in a living animal. WPS introduces multiple experimental advances that combine strengths of bulk and single cell RNA-seq, and that further provides an analytical framework, EmpirDE, that leverages the unique power of the large WPS datasets. EmpirDE identifies differentially expressed genes (DEGs) by using gene-specific empirical null distributions, rather than control conditions alone, thereby systematically removing technical biases and improving statistical rigor. We applied WPS to 103 Caenhorhabditis elegans nuclear hormone receptors (NHRs) to delineate a Gene Regulatory Network (GRN) and found that this GRN presents a striking 'pairwise modularity' where pairs of NHRs regulate shared target genes. We envision that the experimental and analytical advances of WPS should be useful not only for C. elegans, but will be broadly applicable to other models, including human cells.

Identifying diagnostic markers and establishing prognostic model for lung cancer based on lung cancer-derived exosomal genes

Background: Lung cancer (LC) is the most common malignancy and the leading cause of cancer death. LC-derived exosomes have been found to play a critical role in tumor initiation, progression, metastasis and drug resistance. Therefore, the objective of this study is to identify prognostic markers based on lung cancer-derived exosomes in patients with different subtypes of lung cancer, including small cell lung cancer (SCLC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) and large cell carcinoma (LCC). Additionally, we aim to develop corresponding prognostic models to predict the outcomes of these patients. Methods: In this study, the mRNAs information about LC-derived exosomes was collected from Vesiclependia database, and the mRNAs data of LCC, LUAD, LUSC and LCC tumors and paracancerous tissues was obtained from the GEO database and UCSC database. The prognostic models based on exosomes-related differential expression genes (ExoDEGs) by univariate Cox, LASSO, and multivariate Cox regression analyses. The independent prognostic value of the risk model was systematically analyzed. Results: A LUAD prognostic risk model of 12 ExoDEGs (CDH17, DAAM2, FKBP3, FLNC, GSTM2, PGAM4, HPCAL1, FERMT2, LYPD1, SNRNP70, KIR3DL2 and GPX3) and a LUSC prognostic risk model of 7 ExoDEGs (FGA, ERH, HID1, CSNK2A1, SLC7A5, ACOT7 and FUNDC1) were constructed. Kaplan-Meier curve, ROC curve and stratification survival analysis confirmed that the LUAD and LUSC risk models both possessed reliable predictive value for the prognosis of LUAD and LUSC patients. The expression level of ExoDEGs for building the LUAD and LUSC risk models is significantly correlated with immunosuppressive activity of patients, and the immunosuppressive activity is lower in the high-risk groups. Conclusions: We established a LUAD prognostic model with 12 ExoDEGs and a LUSC prognostic model with 7 ExoDEGs, which can be used as independent prognostic indicators for patients LUAD and LUSC. The identified ExoDEGs have the potential to be as prognostic markers and may also serve as novel candidate targets for the treatment of LUAD and LUSC.

Categorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics data

In the analysis of spatially resolved transcriptomics data, detecting spatially variable genes (SVGs) is crucial. Numerous computational methods exist, but varying SVG definitions and methodologies lead to incomparable results. We review 34 state-of-the-art methods, classifying SVGs into three categories: overall, cell-type-specific, and spatial-domain-marker SVGs. Our review explains the intuitions underlying these methods, summarizes their applications, and categorizes the hypothesis tests they use in the trade-off between generality and specificity for SVG detection. We discuss challenges in SVG detection and propose future directions for improvement. Our review offers insights for method developers and users, advocating for category-specific benchmarking.

Combination adjuvant improves influenza virus immunity by downregulation of immune homeostasis genes in lymphocytes

Adjuvants play a central role in enhancing the immunogenicity of otherwise poorly immunogenic vaccine antigens. Combining adjuvants has the potential to enhance vaccine immunogenicity compared with single adjuvants, although the cellular and molecular mechanisms of combination adjuvants are not well understood. Using the influenza virus hemagglutinin H5 antigen, we define the immunological landscape of combining CpG and MPLA (TLR-9 and TLR-4 agonists, respectively) with a squalene nanoemulsion (AddaVax) using immunologic and transcriptomic profiling. Mice immunized and boosted with recombinant H5 in AddaVax, CpG+MPLA, or AddaVax plus CpG+MPLA (IVAX-1) produced comparable levels of neutralizing antibodies and were equally well protected against the H5N1 challenge. However, after challenge with H5N1 virus, H5/IVAX-1-immunized mice had 100- to 300-fold lower virus lung titers than mice receiving H5 in AddaVax or CpG+MPLA separately. Consistent with enhanced viral clearance, unsupervised expression analysis of draining lymph node cells revealed the combination adjuvant IVAX-1 significantly downregulated immune homeostasis genes, and induced higher numbers of antibody-producing plasmablasts than either AddaVax or CpG+MPLA. IVAX-1 was also more effective after single-dose administration than either AddaVax or CpG+MPLA. These data reveal a novel molecular framework for understanding the mechanisms of combination adjuvants, such as IVAX-1, and highlight their potential for the development of more effective vaccines against respiratory viruses.

The left amygdala is genetically sexually-dimorphic: multi-omics analysis of structural MRI volumes

Brain anatomy plays a key role in complex behaviors and mental disorders that are sexually divergent. While our understanding of the sex differences in the brain anatomy remains relatively limited, particularly of the underlying genetic and molecular mechanisms that contribute to these differences. We performed the largest study of sex differences in brain volumes (N = 33,208) by examining sex differences both in the raw brain volumes and after controlling the whole brain volumes. Genetic correlation analysis revealed sex differences only in the left amygdala. We compared transcriptome differences between males and females using data from GTEx and characterized cell-type compositions using GTEx bulk amygdala RNA-seq data and LIBD amygdala single-cell reference profiles. We also constructed polygenic risk scores (PRS) to investigate sex-specific genetic correlations between left amygdala volume and mental disorders (N = 25,576~105,318) of Psychiatric Genomics Consortium and other traits of UKB (N = 347,996). Although there were pronounced sex differences in brain volumes, there was no difference in the heritability between sexes. There was a significant sex-specific genetic correlation between male and female left amygdala. We identified sex-differentiated genetic effects of PRSs for schizophrenia on left amygdala volume, as well as significant sex-differentiated genetic correlations between PRSs of left amygdala and six traits in UKB. We also found several sex-differentially expressed genes in the amygdala. These findings not only advanced the current knowledge of genetic basis of sex differences in brain anatomy, but also presented an important clue for future research on the mechanism of sex differences in mental disorders and targeted treatments.

Compositional data analysis enables statistical rigor in comparative glycomics

Comparative glycomics data are compositional data, where measured glycans are parts of a whole, indicated by relative abundances. Applying traditional statistical analyses to these data often results in misleading conclusions, such as spurious "decreases" of glycans when other structures increase in abundance, or high false-positive rates for differential abundance. Our work introduces a compositional data analysis framework, tailored to comparative glycomics, to account for these data dependencies. We employ center log-ratio and additive log-ratio transformations, augmented with a scale uncertainty/information model, to introduce a statistically robust and sensitive data analysis pipeline. Applied to comparative glycomics datasets, including known glycan concentrations in defined mixtures, this approach controls false-positive rates and results in reproducible biological findings. Additionally, we present specialized analysis modalities: alpha- and beta-diversity analyze glycan distributions within and between samples, while cross-class glycan correlations shed light on previously undetected interdependencies. These approaches reveal insights into glycome variations that are critical to understanding roles of glycans in health and disease.

Spermine Synthase : A Potential Prognostic Marker for Lower-Grade Gliomas

OBJECTIVE

The objective of this study was to assess the relationship between spermine synthase (SMS) expression, tumor occurrence, and prognosis in lower-grade gliomas (LGGs).

METHODS

A total of 523 LGG patients and 1152 normal brain tissues were included as controls. Mann-Whitney U test was performed to evaluate SMS expression in the LGG group. Functional annotation analysis was conducted to explore the biological processes associated with high SMS expression. Immune cell infiltration analysis was performed to examine the correlation between SMS expression and immune cell types. The association between SMS expression and clinical and pathological features was assessed using Spearman correlation analysis. In vitro experiments were conducted to investigate the effects of overexpressing or downregulating SMS on cell proliferation, apoptosis, migration, invasion, and key proteins in the protein kinase B (AKT)/epithelialmesenchymal transition signaling pathway.

RESULTS

The study revealed a significant upregulation of SMS expression in LGGs compared to normal brain tissues. High SMS expression was associated with certain clinical and pathological features, including older age, astrocytoma, higher World Health Organization grade, poor disease-specific survival, disease progression, non-1p/19q codeletion, and wild-type isocitrate dehydrogenase. Cox regression analysis identified SMS as a risk factor for overall survival. Bioinformatics analysis showed enrichment of eosinophils, T cells, and macrophages in LGG samples, while proportions of dendritic (DC) cells, plasmacytoid DC (pDC) cells, and CD8+ T cells were decreased.

CONCLUSION

High SMS expression in LGGs may promote tumor occurrence through cellular proliferation and modulation of immune cell infiltration. These findings suggest the prognostic value of SMS in predicting clinical outcomes for LGG patients.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Progression from healthy periodontium to gingivitis and periodontitis: Insights from bioinformatics-driven proteomics - A systematic review with meta-analysis

AIM

The current study aimed to: (1) systematically review the published literature regarding the proteomics analyses of saliva and gingival crevicular fluid (GCF) in healthy humans and gingivitis and/or periodontitis patients; and (2) to identify the differentially expressed proteins (DEPs) based on the systematic review, and comprehensively conduct meta-analyses and bioinformatics analyses.

METHODS

An online search of Web of Science, Scopus, and PubMed was performed without any restriction on the year and language of publication. After the identification of the DEPs reported by the included human primary studies, gene ontology (GO), the Kyoto encyclopedia of genes and genomes pathway (KEGG), protein-protein interaction (PPI), and meta-analyses were conducted. The risk of bias among the included studies was evaluated using the modified Newcastle-Ottawa quality assessment scale.

RESULTS

The review identified significant differences in protein expression between healthy individuals and those with gingivitis and periodontitis. In GCF, 247 proteins were upregulated and 128 downregulated in periodontal diseases. Saliva analysis revealed 79 upregulated and 70 downregulated proteins. There were distinct protein profiles between gingivitis and periodontitis, with 159 and 31 unique upregulated proteins in GCF, respectively. Meta-analyses confirmed significant upregulation of various proteins in periodontitis, including ALB and MMP9, while CSTB and GSTP1 were downregulated. AMY1A and SERPINA1 were upregulated in periodontitis saliva. HBD was upregulated in gingivitis GCF, while DEFA3 was downregulated. PPI analysis revealed complex networks of interactions among DEPs. GO and KEGG pathway analyses provided insights into biological processes and pathways associated with periodontal diseases.

CONCLUSION

The ongoing MS-based proteomics studies emphasize the need for a highly sensitive and specific diagnostic tool for periodontal diseases. Clinician acceptance of the eventual diagnostic method relies on its ability to provide superior or complementary information to current clinical assessment procedures. Future research should prioritize the multiplex measurement of multiple biomarkers simultaneously to enhance diagnostic accuracy and large study cohorts are necessary to ensure the validity and reliability of research findings.

Mechanistic Modeling the Role of MicroRNAs and Transcription Factors in Disease Progression

In this chapter, we illustrate the utilization of network analysis and mechanistic modeling, two potent branches of systems biology, to simplify the representation of intricate biological processes such as cell signaling, gene regulation, and metabolic pathways. Specifically, we demonstrate the application of a well-established method to generate a microRNA-transcription factor-gene regulatory feed-forward loop network extracted from the GEO dataset GSE163877. Furthermore, we outline a method for constructing a deterministic model using the LSODA method based on the sub-network. This model furnishes insights into the roles of crucial differentially expressed microRNAs and transcription factors in gene expression associated with Alzheimer's disease progression. Our analysis of the model reveals elevated kinetics of synthesis for EGR1, miR-6891, miR-4786, and LTBP1. The model suggests the linear upregulation of miR-8080, miR-3921, HSPB6, and downregulation MX2 gene. The rest of the miRNA, TFs, and genes shows a momentary variation in expression and if the system is undisturbed, they attain equilibrium. Thus, we elucidate how mechanistic modeling, along with perturbation studies and network analysis of expression data, can yield diverse insights into the trajectory of disease progression.

Attenuated sex-related DNA methylation differences in cancer highlight the magnitude bias mediating existing disparities

BACKGROUND

DNA methylation (DNAm) influences both sex differences and cancer development, yet the mechanisms connecting these factors remain unclear.

METHODS

Utilizing data from The Cancer Genome Atlas, we conducted a comprehensive analysis of sex-related DNAm effects in nine non-reproductive cancers, compared to paired normal adjacent tissues (NATs), and validated the results using independent datasets. First, we assessed the extent of sex differential DNAm between cancers and NATs to explore how sex-related DNAm differences change in cancerous tissues. Next, we employed a multivariate adaptive shrinkage approach to model the covariance of cancer-related DNAm effects between sexes, aiming to elucidate how sex impacts aberrant DNAm patterns in cancers. Finally, we investigated correlations between the methylome and transcriptome to identify key signals driving sex-biased DNAm regulation in cancers.

RESULTS

Our analysis revealed a significant attenuation of sex differences in DNAm within cancerous tissues compared to baseline differences in normal tissues. We identified 3,452 CpGs (Pbonf < 0.05) associated with this reduction, with 72% of the linked genes involved in X chromosome inactivation. Through covariance analysis, we demonstrated that sex differences in cancer are predominantly driven by variations in the magnitude of shared DNAm signals, referred to as "amplification." Based on these patterns, we classified cancers into female- and male-biased groups and identified key CpGs exhibiting sex-specific amplification. These CpGs were enriched in binding sites of critical transcription factors, including P53, SOX2, and CTCF. Integrative multi-omics analyses uncovered 48 CpG-gene-cancer trios for females and 380 for males, showing similar magnitude differences in DNAm and gene expression, pointing to a sex-specific regulatory role of DNAm in cancer risk. Notably, several genes regulated by these trios were previously identified as drug targets for cancers, highlighting their potential as sex-specific therapeutic targets.

CONCLUSIONS

These findings advance our understanding of how sex, DNAm, and gene expression interact in cancer, offering insights into the development of sex-specific biomarkers and precision medicine.

Protocol for identifying key genes using network-based approach as an alternative to differential expression analysis

In a variety of biological contexts, characterizing genes associated with disease etiology and mediating global transcriptomic change is a key initial step. Here, we present a protocol to identify such key genes using our tool "PathExt," a tool that implements a network-based approach. We describe steps for installing libraries, preparing input data and detailed procedures for running PathExt, and characterizing differential pathways and key genes based on ripple centrality scores. For complete details on the use and execution of this protocol, please refer to Agrawal et al.1,2.

Transcriptomic profiling of gill biopsies to define predictive markers for seawater survival in farmed Atlantic salmon

Wild Atlantic salmon migrate to sea following completion of a developmental process known as parr-smolt transformation (PST), which establishes a seawater (SW) tolerant phenotype. Effective imitation of this aspect of anadromous life history is a crucial aspect of commercial salmon production, with current industry practice being marred by significant losses during transition from the freshwater (FW) to SW phase of production. The natural photoperiodic control of PST can be mimicked by exposing farmed juvenile fish to a reduced duration photoperiod for at least 6 weeks before increasing the photoperiod in the last 1-2 months before SW transfer. While it is known that variations in this general protocol affect subsequent SW performance, there is no uniformly accepted industry standard; moreover, reliable prediction of SW performance from fish attributes in the FW phase remains a major challenge. Here we describe an experiment in which we took gill biopsies 1 week prior to SW transfer from 3000 individually tagged fish raised on three different photoperiod regimes during the FW phase. Biopsies were subjected to RNA profiling by Illumina sequencing, while individual fish growth and survival was monitored over 300 days in a SW cage environment, run as a common garden experiment. Using a random forest machine learning algorithm, we developed gene expression-based predictive models for initial survival and stunted growth in SW. Stunted growth phenotypes could not be predicted based on gill transcriptomes, but survival the first 40 days in SW could be predicted with moderate accuracy. While several previously identified marker genes contribute to this model, a surprisingly low weighting is ascribed to sodium potassium ATPase subunit genes, contradicting advocacy for their use as SW readiness markers. However, genes with photoperiod-history sensitive regulation were highly enriched among the genes with highest importance in the prediction model. This work opens new avenues for understanding and exploiting developmental changes in gill physiology during smolt development.

Personalized Game-Based Content and Performance: A Pilot Study on a Digital Intervention for Children with ADHD

Mobile-based digital interventions for children with attention-deficit hyperactivity disorder (ADHD) have been developed to alleviate their symptoms. When developing mobile game-based digital interventions for ADHD treatment, it is important to research how the emotional responses of the target audience members-based on flashy visuals or difficulty adjustments to motivate the user-affect their content manipulation ability. This study performed a correlation analysis to examine the impact of perceived difficulty and enjoyment (interest) on the performance of children diagnosed with ADHD while engaging in game-based digital content. Statistically significant differences were observed in the following variables based on the enjoyment level: correct rate (p = 0.0040), decision time (p = 0.0302), difficulty (p < 0.0001), and touch time (p = 0.0249). Considering difficulty level, statistically significant differences were observed for correct rate (p = 0.0011), decision time (p = 0.0158), and difficulty (p < 0.0001). Correlation analysis between the variables correct rate, decision time, difficulty, touch, time limit, and touch time based on enjoyment and difficulty did not reveal significant correlations. Therefore, for children with ADHD, digital interventions should focus on the therapeutic goals rather than on flashy visuals or difficulty adjustments aimed at enhancing interest. Based on these results, further research exploring how psychological states affect performance regarding digital content is necessary.

Statistical methods for comparing two independent exponential-gamma means with application to single cell protein data

In genomic study, log transformation is a common prepossessing step to adjust for skewness in data. This standard approach often assumes that log-transformed data is normally distributed, and two sample t-test (or its modifications) is used for detecting differences between two experimental conditions. However, recently it was shown that two sample t-test can lead to exaggerated false positives, and the Wilcoxon-Mann-Whitney (WMW) test was proposed as an alternative for studies with larger sample sizes. In addition, studies have demonstrated that the specific distribution used in modeling genomic data has profound impact on the interpretation and validity of results. The aim of this paper is three-fold: 1) to present the Exp-gamma distribution (exponential-gamma distribution stands for log-transformed gamma distribution) as a proper biological and statistical model for the analysis of log-transformed protein abundance data from single-cell experiments; 2) to demonstrate the inappropriateness of two sample t-test and the WMW test in analyzing log-transformed protein abundance data; 3) to propose and evaluate statistical inference methods for hypothesis testing and confidence interval estimation when comparing two independent samples under the Exp-gamma distributions. The proposed methods are applied to analyze protein abundance data from a single-cell dataset.

Gradient boosting reveals spatially diverse cholesterol gene signatures in colon cancer

Colon cancer (CC) is the second most common cause of cancer deaths and the fourth most prevalent cancer in the United States. Recently cholesterol metabolism has been identified as a potential therapeutic avenue due to its consistent association with tumor treatment effects and overall prognosis. We conducted differential gene analysis and KEGG pathway analysis on paired tumor and adjacent-normal samples from the TCGA Colon Adenocarcinoma project, identifying that bile secretion was the only significantly downregulated pathway. To evaluate the relationship between cholesterol metabolism and CC prognosis, we used the genes from this pathway in several statistical models like Cox proportional Hazard (CPH), Random Forest (RF), Lasso Regression (LR), and the eXtreme Gradient Boosting (XGBoost) to identify the genes which contributed highly to the predictive ability of all models, ADCY5, and SLC2A1. We demonstrate that using cholesterol metabolism genes with XGBoost models improves stratification of CC patients into low and high-risk groups compared with traditional CPH, RF and LR models. Spatial transcriptomics (ST) revealed that SLC2A1 (glucose transporter 1, GLUT1) colocalized with small blood vessels. ADCY5 localized to stromal regions in both the ST and protein immunohistochemistry. Interestingly, both these significant genes are expressed in tissues other than the tumor itself, highlighting the complex interplay between the tumor and microenvironment, and that druggable targets may be found in the ability to modify how "normal" tissue interacts with tumors.

DElite: a tool for integrated differential expression analysis

One of the fundamental aspects of genomic research is the identification of differentially expressed (DE) genes between two conditions. In the past decade, numerous DE analysis tools have been developed, employing various normalization methods and statistical modelling approaches. In this article, we introduce DElite, an R package that leverages the capabilities of four state-of-the-art DE tools: edgeR, limma, DESeq2, and dearseq. DElite returns the outputs of the four tools with a single command line, thus providing a simplified way for non-expert users to perform DE analysis. Furthermore, DElite provides a statistically combined output of the four tools, and in vitro validations support the improved performance of these combination approaches for the detection of DE genes in small datasets. Finally, DElite offers comprehensive and well-documented plots and tables at each stage of the analysis, thus facilitating result interpretation. Although DElite has been designed with the intention of being accessible to users without extensive expertise in bioinformatics or statistics, the underlying code is open source and structured in such a way that it can be customized by advanced users to meet their specific requirements. DElite is freely available for download from https://gitlab.com/soc-fogg-cro-aviano/DElite.

Y and mitochondrial chromosomes in the heterogeneous stock rat population

Genome-wide association studies typically evaluate the autosomes and sometimes the X Chromosome, but seldom consider the Y or mitochondrial (MT) Chromosomes. We genotyped the Y and MT Chromosomes in heterogeneous stock (HS) rats (Rattus norvegicus), an outbred population created from 8 inbred strains. We identified 8 distinct Y and 4 distinct MT Chromosomes among the 8 founders. However, only 2 types of each nonrecombinant chromosome were observed in our modern HS rat population (generations 81-97). Despite the relatively large sample size, there were virtually no significant associations for behavioral, physiological, metabolome, or microbiome traits after correcting for multiple comparisons. However, both Y and MT Chromosomes were strongly associated with the expression of a few genes located on those chromosomes, which provided a positive control. Our results suggest that within modern HS rats there are no Y and MT Chromosomes differences that strongly influence behavioral or physiological traits. These results do not address other ancestral Y and MT Chromosomes that do not appear in modern HS rats, nor do they address effects that may exist in other rat populations, or in other species.

Stratified analysis identifies HIF-2α as a therapeutic target for highly immune-infiltrated melanomas

While immune-checkpoint blockade (ICB) has revolutionized treatment of metastatic melanoma over the last decade, the identification of broadly applicable robust biomarkers has been challenging, driven in large part by the heterogeneity of ICB regimens and patient and tumor characteristics. To disentangle these features, we performed a standardized meta-analysis of eight cohorts of patients treated with anti-PD-1 (n=290), anti-CTLA-4 (n=175), and combination anti-PD-1/anti-CTLA-4 (n=51) with RNA sequencing of pre-treatment tumor and clinical annotations. Stratifying by immune-high vs -low tumors, we found that surprisingly, high immune infiltrate was a biomarker for response to combination ICB, but not anti-PD-1 alone. Additionally, hypoxia-related signatures were associated with non-response to anti-PD-1, but only amongst immune infiltrate-high melanomas. In a cohort of scRNA-seq of patients with metastatic melanoma, hypoxia also correlated with immunosuppression and changes in tumor-stromal communication in the tumor microenvironment (TME). Clinically actionable targets of hypoxia signaling were also uniquely expressed across different cell types. We focused on one such target, HIF-2α, which was specifically upregulated in endothelial cells and fibroblasts but not in immune cells or tumor cells. HIF-2α inhibition, in combination with anti-PD-1, enhanced tumor growth control in pre-clinical models, but only in a more immune-infiltrated melanoma model. Our work demonstrates how careful stratification by clinical and molecular characteristics can be leveraged to derive meaningful biological insights and lead to the rational discovery of novel clinical targets for combination therapy.

Winsorization greatly reduces false positives by popular differential expression methods when analyzing human population samples

A recent study found severely inflated type I error rates for DESeq2 and edgeR, two dominant tools used for differential expression analysis of RNA-seq data. Here, we show that by properly addressing the outliers in the RNA-Seq data using winsorization, the type I error rate of DESeq2 and edgeR can be substantially reduced, and the power is comparable to Wilcoxon rank-sum test for large datasets. Therefore, as an alternative to Wilcoxon rank-sum test, they may still be applied for differential expression analysis of large RNA-Seq datasets.

Neglecting the impact of normalization in semi-synthetic RNA-seq data simulations generates artificial false positives

A recent study reported exaggerated false positives by popular differential expression methods when analyzing large population samples. We reproduce the differential expression analysis simulation results and identify a caveat in the data generation process. Data not truly generated under the null hypothesis led to incorrect comparisons of benchmark methods. We provide corrected simulation results that demonstrate the good performance of dearseq and argue against the superiority of the Wilcoxon rank-sum test as suggested in the previous study.

Response to "Neglecting normalization impact in semi-synthetic RNA-seq data simulation generates artificial false positives" and "Winsorization greatly reduces false positives by popular differential expression methods when analyzing human population samples"

Two correspondences raised concerns or comments about our analyses regarding exaggerated false positives found by differential expression (DE) methods. Here, we discuss the points they raise and explain why we agree or disagree with these points. We add new analysis to confirm that the Wilcoxon rank-sum test remains the most robust method compared to the other five DE methods (DESeq2, edgeR, limma-voom, dearseq, and NOISeq) in two-condition DE analyses after considering normalization and winsorization, the data preprocessing steps discussed in the two correspondences.

Microbial dynamics and pulmonary immune responses in COVID-19 secondary bacterial pneumonia

Secondary bacterial pneumonia (2°BP) is associated with significant morbidity following respiratory viral infection, yet remains incompletely understood. In a prospective cohort of 112 critically ill adults intubated for COVID-19, we comparatively assess longitudinal airway microbiome dynamics and the pulmonary transcriptome of patients who developed 2°BP versus controls who did not. We find that 2°BP is significantly associated with both mortality and corticosteroid treatment. The pulmonary microbiome in 2°BP is characterized by increased bacterial RNA mass and dominance of culture-confirmed pathogens, detectable days prior to 2°BP clinical diagnosis, and frequently also present in nasal swabs. Assessment of the pulmonary transcriptome reveals suppressed TNFα signaling in patients with 2°BP, and sensitivity analyses suggest this finding is mediated by corticosteroid treatment. Further, we find that increased bacterial RNA mass correlates with reduced expression of innate and adaptive immunity genes in both 2°BP patients and controls. Taken together, our findings provide fresh insights into the microbial dynamics and host immune features of COVID-19-associated 2°BP, and suggest that suppressed immune signaling, potentially mediated by corticosteroid treatment, permits expansion of opportunistic bacterial pathogens.

Targeting circadian transcriptional programs through a cis-regulatory mechanism in triple negative breast cancer

Circadian clock genes are emerging targets in many types of cancer, but their mechanistic contributions to tumor progression are still largely unknown. This makes it challenging to stratify patient populations and develop corresponding treatments. In this work, we show that in breast cancer, the disrupted expression of circadian genes has the potential to serve as biomarkers. We also show that the master circadian transcription factors (TFs) BMAL1 and CLOCK are required for the proliferation of metastatic mesenchymal stem-like (mMSL) triple-negative breast cancer (TNBC) cells. Using currently available small molecule modulators, we found that a stabilizer of cryptochrome 2 (CRY2), the direct repressor of BMAL1 and CLOCK transcriptional activity, synergizes with inhibitors of proteasome, which is required for BMAL1 and CLOCK function, to repress a transcriptional program comprising circadian cycling genes in mMSL TNBC cells. Omics analyses on drug-treated cells implied that this repression of transcription is mediated by the transcription factor binding sites (TFBSs) features in the cis-regulatory elements (CRE) of clock-controlled genes. Through a massive parallel reporter assay, we defined a set of CRE features that are potentially repressed by the specific drug combination. The identification of cis -element enrichment might serve as a new concept of defining and targeting tumor types through the modulation of cis -regulatory programs, and ultimately provide a new paradigm of therapy design for cancer types with unclear drivers like TNBC.

Categorization of 33 computational methods to detect spatially variable genes from spatially resolved transcriptomics data

In the analysis of spatially resolved transcriptomics data, detecting spatially variable genes (SVGs) is crucial. Numerous computational methods exist, but varying SVG definitions and methodologies lead to incomparable results. We review 33 state-of-the-art methods, categorizing SVGs into three types: overall, cell-type-specific, and spatial-domain-marker SVGs. Our review explains the intuitions underlying these methods, summarizes their applications, and categorizes the hypothesis tests they use in the trade-off between generality and specificity for SVG detection. We discuss challenges in SVG detection and propose future directions for improvement. Our review offers insights for method developers and users, advocating for category-specific benchmarking.

Single-plant-omics reveals the cascade of transcriptional changes during the vegetative-to-reproductive transition

Plants undergo rapid developmental transitions, which occur contemporaneously with gradual changes in physiology. Moreover, individual plants within a population undergo developmental transitions asynchronously. Single-plant-omics has the potential to distinguish between transcriptional events that are associated with these binary and continuous processes. Furthermore, we can use single-plant-omics to order individual plants by their intrinsic biological age, providing a high-resolution transcriptional time series. We performed RNA-seq on leaves from a large population of wild-type Arabidopsis (Arabidopsis thaliana) during the vegetative-to-reproductive transition. Though most transcripts were differentially expressed between bolted and unbolted plants, some regulators were more closely associated with leaf size and biomass. Using a pseudotime inference algorithm, we determined that some senescence-associated processes, such as the reduction in ribosome biogenesis, were evident in the transcriptome before a bolt was visible. Even in this near-isogenic population, some variants are associated with developmental traits. These results support the use of single-plant-omics to uncover rapid transcriptional dynamics by exploiting developmental asynchrony.

GNAS knockout potentiates HDAC3 inhibition through viral mimicry-related interferon responses in lymphoma

Despite selective HDAC3 inhibition showing promise in a subset of lymphomas with CREBBP mutations, wild-type tumors generally exhibit resistance. Here, using unbiased genome-wide CRISPR screening, we identify GNAS knockout (KO) as a sensitizer of resistant lymphoma cells to HDAC3 inhibition. Mechanistically, GNAS KO-induced sensitization is independent of the canonical G-protein activities but unexpectedly mediated by viral mimicry-related interferon (IFN) responses, characterized by TBK1 and IRF3 activation, double-stranded RNA formation, and transposable element (TE) expression. GNAS KO additionally synergizes with HDAC3 inhibition to enhance CD8+ T cell-induced cytotoxicity. Moreover, we observe in human lymphoma patients that low GNAS expression is associated with high baseline TE expression and upregulated IFN signaling and shares common disrupted biological activities with GNAS KO in histone modification, mRNA processing, and transcriptional regulation. Collectively, our findings establish an unprecedented link between HDAC3 inhibition and viral mimicry in lymphoma. We suggest low GNAS expression as a potential biomarker that reflects viral mimicry priming for enhanced response to HDAC3 inhibition in the clinical treatment of lymphoma, especially the CREBBP wild-type cases.

Lysine lactylation-based insight to understanding the characterization of cervical cancer

Lysine lactylation (Kla), a recently discovered post-translational modification (PTM), is not only present in histone proteins but also widely distributed among non-histone proteins in tumor cells and immunocytes. However, the precise characterization and functional implications of these non-histone Kla proteins remain to be explored. Herein, a comprehensive proteomic analysis of Kla was conducted in HeLa cells. As a result, a total of 3633 Kla sites on 1637 proteins were identified. Subsequently, the stable Kla substrates were obtained and sorted to investigate the characterization and function of Kla proteins. Moreover, we characterized the Kla-related features of cervical cancers through integrative analyses of multiple datasets with proteomes, transcriptomes and single-cell transcriptome profiling. Kla-related genes (KRGs) were used to stratify cervical cancers into two clusters (C1 and C2). C2 cluster display inhibition in glycosylation and increased oxidative phosphorylation activity with high survival rate. In addition, we constructed a prognostic model based on two lactate signature genes, namely ISY1 and PPP1R14B. Interestingly, our findings revealed a negative correlation between PPP1R14B expression and the infiltration of CD8+ T cells, as well as a lower survival rate. This observation was further validated at the single-cell resolution. Simultaneously, we found that K140R mutant of PPP1R14B resulted in the decrease of Kla level and enhanced the proliferation and migration capabilities of cervical cancer cell lines, suggesting PPP1R14B-K140la has an effect on tumor behaviors. Collectively, we provides a Kla-based insight to understanding the characterization of cervical cancer, offering a potential avenue for therapeutic approaches.

5-Hydroxymethylcytosines in circulating cell-free DNA as a diagnostic biomarker for nasopharyngeal carcinoma

OBJECTIVE

To evaluate the diagnostic value of 5-hydroxymethylcytosines (5hmC) in circulating cell-free DNA (cfDNA) for nasopharyngeal carcinoma (NPC) and to develop a diagnostic model.

METHODS

Genome-wide 5hmC profiles in cfDNA from 174 NPC patients and 146 non-cancer individuals were analyzed using the 5hmC-Seal technique. A cfDNA 5hmC-based diagnostic model to identify NPC patients was developed using least absolute shrinkage and selection operator (LASSO) logistic regression, and performance was evaluated with receiver operating characteristic (ROC) curves and confusion matrices.

RESULTS

The 5hmC-Seal data from patients with NPC showed a different genome-wide distribution than non-tumor samples. Our initial analysis revealed a 12-gene-based 5hmC marker panel to be an accurate diagnostic model effectively distinguishing between NPC samples and non-cancerous samples (training set: area under curve (AUC)= 0.97 [95 % CI: 0.94-0.99]; and test set: AUC= 0.93 [95 % CI: 0.88-0.98]) superior to EBV DNA testing. The diagnostic score performed well in differentiating the non-cancer subjects from early-stage NPC (training set: AUC=0.99 [95 % CI: 0.98-1]; test set: AUC=0.98 [95 % CI: 0.95-1]), and advanced-stage NPC (training set: AUC=0.96 [95 % CI: 0.93-0.99]; test set: AUC=0.93 [95 % CI: 0.88-0.98]). Notably, in EBV-negative patients, the diagnostic scores showed excellent capacity for distinguishing EBV-negative patients with NPC from non-cancer subjects in both the training set (AUC= 0.94 [95 % CI: 0.88-1]) and test set (AUC=0.91 [95 % CI: 0.81-1]).

CONCLUSION

5hmC modifications in cfDNA are promising noninvasive biomarkers for NPC, offering high sensitivity and specificity, particularly for early-stage and EBV-negative NPC.

Unraveling Dynamic Transcriptomic Changes in Sheep's Lactating Mammary Gland Following Escherichia coli Lipopolysaccharide Exposure

Mammary gland infections constitute a significant challenge in dairy sheep, impacting productivity and welfare. Temporal RNA-Seq provide a valuable approach to evaluate the evolution of the host defensive molecular mechanisms triggered by mastitis caused by external agents or events. This study aimed to characterize the transcriptomic response of sheep mammary glands to an intramammary inflammation induced with an Escherichia coli lipopolysaccharide (LPS) inoculation based on RNA-Seq samples generated from milk somatic cells collected at 3 time points: pre-inoculation (0 h), and 6 h and 24 h post-LPS inoculation. The differential expression analyses between the analyzed time points were performed using 2 statistical approaches: one parametric (DESeq2) and one non-parametric (Wilcoxon rank sum test). The differentially expressed genes (DEGs) commonly identified by both approaches encompass 5,872 for the 0 h versus 6 h comparison, 4,063 for the 0 h versus 24 h comparison, and 1,034 for the 6 h versus 24 h comparison. At both 6 h and 24 h, transcriptomic data highlighted a significant decrease in the expression of genes linked to metabolic processes crucial for milk protein and lipid synthesis within the mammary gland. Concurrently, increased expression of genes related to the neutrophil attraction was observed for 6 and 24 h, with differences in gene expression between DEGs with the highest expression at 6 h, related to T cell activation, type I interferon-mediated signaling pathway, and 24 h, related to cell-cell neutrophil adhesion extravasation or epithelial cell proliferation. In summary, this study reveals how the sheep mammary gland transcriptome responds dynamically to an LPS inoculation, providing a comprehensive understanding of how gene expression patterns evolve over time and shedding light on the molecular mechanisms driving the initial defensive response of the mammary gland against potential inflammatory challenges.

Tumor specimen cold ischemia time impacts molecular cancer drug target discovery

Tumor tissue collections are used to uncover pathways associated with disease outcomes that can also serve as targets for cancer treatment, ideally by comparing the molecular properties of cancer tissues to matching normal tissues. The quality of such collections determines the value of the data and information generated from their analyses including expression and modifications of nucleic acids and proteins. These biomolecules are dysregulated upon ischemia and decompose once the living cells start to decay into inanimate matter. Therefore, ischemia time before final tissue preservation is the most important determinant of the quality of a tissue collection. Here we show the impact of ischemia time on tumor and matching adjacent normal tissue samples for mRNAs in 1664, proteins in 1818, and phosphosites in 1800 cases (tumor and matching normal samples) of four solid tumor types (CRC, HCC, LUAD, and LUSC NSCLC subtypes). In CRC, ischemia times exceeding 15 min impacted 12.5% (mRNA), 25% (protein), and 50% (phosphosites) of differentially expressed molecules in tumor versus normal tissues. This hypoxia- and decay-induced dysregulation increased with longer ischemia times and was observed across tumor types. Interestingly, the proteomics analysis revealed that specimen ischemia time above 15 min is mostly associated with a dysregulation of proteins in the immune-response pathway and less so with metabolic processes. We conclude that ischemia time is a crucial quality parameter for tissue collections used for target discovery and validation in cancer research.

Deciphering gene expression patterns using large-scale transcriptomic data and its applications

Gene expression varies stochastically across genders, racial groups, and health statuses. Deciphering these patterns is crucial for identifying informative genes, classifying samples, and understanding diseases like cancer. This study analyzes 11,252 bulk RNA-seq samples to explore expression patterns of 19,156 genes, including 10,512 cancer tissue samples and 740 normal samples. Additionally, 4,884 single-cell RNA-seq samples are examined. Statistical analysis using 16 probability distributions shows that normal samples display a wider range of distributions compared to cancer samples. Cancer samples tend to favor asymmetric distributions such as generalized extreme value, logarithmic normal, and Gaussian mixture distributions. In contrast, certain genes in normal samples exhibit symmetric distributions. Remarkably, more than 95.5% of genes exhibit non-normal distributions, which challenges traditional assumptions. Furthermore, distributions differ significantly between bulk and single-cell RNA-seq data. Many cancer driver genes exhibit distinct distribution patterns across sample types, suggesting potential for gene selection and classification based on distribution characteristics. A novel skewness-based metric is proposed to quantify distribution variation across datasets, showing genes with significant skewness differences have biological relevance. Finally, an improved naïve Bayes method incorporating gene-specific distributions demonstrates superior performance in simulations over traditional methods. This work enhances understanding of gene expression and its application in omics-based gene selection and sample classification.

HYENA detects oncogenes activated by distal enhancers in cancer

Somatic structural variations (SVs) in cancer can shuffle DNA content in the genome, relocate regulatory elements, and alter genome organization. Enhancer hijacking occurs when SVs relocate distal enhancers to activate proto-oncogenes. However, most enhancer hijacking studies have only focused on protein-coding genes. Here, we develop a computational algorithm 'HYENA' to identify candidate oncogenes (both protein-coding and non-coding) activated by enhancer hijacking based on tumor whole-genome and transcriptome sequencing data. HYENA detects genes whose elevated expression is associated with somatic SVs by using a rank-based regression model. We systematically analyze 1146 tumors across 25 types of adult tumors and identify a total of 108 candidate oncogenes including many non-coding genes. A long non-coding RNA TOB1-AS1 is activated by various types of SVs in 10% of pancreatic cancers through altered 3-dimensional genome structure. We find that high expression of TOB1-AS1 can promote cell invasion and metastasis. Our study highlights the contribution of genetic alterations in non-coding regions to tumorigenesis and tumor progression.

Modeling Cortical Versus Hippocampal Network Dysfunction in a Human Brain Assembloid Model of Epilepsy and Intellectual Disability

Neurodevelopmental disorders often impair multiple cognitive domains. For instance, a genetic epilepsy syndrome might cause seizures due to cortical hyperexcitability and present with memory impairments arising from hippocampal dysfunction. This study examines how a single disorder differentially affects distinct brain regions by using human patient iPSC-derived cortical- and hippocampal-ganglionic eminence assembloids to model Developmental and Epileptic Encephalopathy 13 (DEE-13), a condition arising from gain-of-function mutations in the SCN8A gene. While cortical assembloids showed network hyperexcitability akin to epileptogenic tissue, hippocampal assembloids did not, and instead displayed network dysregulation patterns similar to in vivo hippocampal recordings from epilepsy patients. Predictive computational modeling, immunohistochemistry, and single-nucleus RNA sequencing revealed changes in excitatory and inhibitory neuron organization that were specific to hippocampal assembloids. These findings highlight the unique impacts of a single pathogenic variant across brain regions and establish hippocampal assembloids as a platform for studying neurodevelopmental disorders.

A Deep Dive into Statistical Modeling of RNA Splicing QTLs Reveals New Variants that Explain Neurodegenerative Disease

Genome-wide association studies (GWAS) have identified thousands of putative disease causing variants with unknown regulatory effects. Efforts to connect these variants with splicing quantitative trait loci (sQTLs) have provided functional insights, yet sQTLs reported by existing methods cannot explain many GWAS signals. We show current sQTL modeling approaches can be improved by considering alternative splicing representation, model calibration, and covariate integration. We then introduce MAJIQTL, a new pipeline for sQTL discovery. MAJIQTL includes two new statistical methods: a weighted multiple testing approach for sGene discovery and a model for sQTL effect size inference to improve variant prioritization. By applying MAJIQTL to GTEx, we find significantly more sGenes harboring sQTLs with functional significance. Notably, our analysis implicates the novel variant rs582283 in Alzheimer's disease. Using antisense oligonucleotides, we validate this variant's effect by blocking the implicated YBX3 binding site, leading to exon skipping in the gene MS4A3.