Integrated Data Analysis Research Articles

LTBR acts as a novel immune checkpoint of tumor-associated macrophages for cancer immunotherapy.

Tumor-associated macrophages (TAMs) greatly contribute to immune checkpoint inhibitor (ICI) resistance of cancer. However, its underlying mechanisms and whether TAMs can be promising targets to overcome ICI resistance remain to be unveiled. Through integrative analysis of immune multiomics data and single-cell RNA-seq data (iMOS) in lung adenocarcinoma (LUAD), lymphotoxin β receptor (LTBR) is identified as a potential immune checkpoint of TAMs, whose high expression, duplication, and low methylation are correlated with unfavorable prognosis. Immunofluorescence staining shows that the infiltration of LTBR+ TAMs is associated with LUAD stages, immunotherapy failure, and poor prognosis. Mechanistically, LTΒR maintains immunosuppressive activity and M2 phenotype of TAMs by noncanonical nuclear factor kappa B and Wnt/β-catenin signaling pathways. Macrophage-specific knockout of LTBR hinders tumor growth and prolongs survival time via blocking TAMimmunosuppressive activity and M2 phenotype. Moreover, TAM-targeted delivery of LTΒR small interfering RNA improves the therapeutic effect of ICI via reversing TAM-mediated immunosuppression, such as boosting cytotoxic CD8+ T cells and inhibiting granulocytic myeloid-derived suppressor cells infiltration. Taken together, we bring forth an immune checkpoint discovery pipeline iMOS, identify LTBR as a novel immune checkpoint of TAMs, and propose a new immunotherapy strategy by targeting LTBR+ TAMs.

The psychological distance effect in impact between online review-based tourism destination image and recommendation intention: a data mining approach

ABSTRACT A total of 8,725 reviews of three East Asian travel cities were collected from TripAdvisor, and, after extracting and quantifying the words corresponding to the tourism destination image (TDI), the causal relationship with the recommendation intention (RI) was verified. An integrated data analysis on the data from the three cities revealed that all images except cleanliness among TDI had a significant influence on RI. The Seoul review data analysis revealed the environment, culture/history, and travel plan factors at the low-level influenced RI, while the Seoul Palace review data analysis revealed that society, culture/history, transportation, and travel plan factors at the low-level influenced RI.

A new species of karst-dwelling bent-toed gecko of the Cyrtodactylusintermedius group (Squamata, Gekkonidae) from eastern Thailand and the phylogenetic placement of C.intermedius.

A new karst-dwelling bent-toed gecko of the Cyrtodactylusintermedius group is described from Khlong Hat District, Sa Kaeo Province, eastern Thailand, based on an integrative taxonomic analysis of genetic data and morphological characteristics. Phylogenetic analyses using the mitochondrial NADH dehydrogenase subunit 2 (ND2) gene revealed that topotypes of C.intermedius were sister to a clade containing C.kulenensis from Cambodia, an unnamed lineage from Sakaerat Biosphere Reserve in Nakhon Ratchasima Province, Thailand, and the Khlong Hat lineage described here as Cyrtodactyluskhlonghatensis sp. nov. Multivariate analyses of morphometric and meristic characters showed that C.khlonghatensis sp. nov. is morphologically distinct from all other species in the group by having the combination of SVL 76.5-82.8 mm in adult males and 88.5 mm in an adult female; eight supralabial and nine infralabial scales; 30-32 paravertebral tubercles; 20 or 21 longitudinal rows of dorsal tubercles; 43 or 44 ventral scales; seven or eight expanded subdigital lamellae on the 4th toe; 12 unmodified subdigital lamellae on the 4th toe; 19 or 20 total subdigital lamellae on the 4th toe; 31 or 32 total number of enlarged femoral scales; enlarged femoral and precloacal scales continuous; 6-8 pore-bearing precloacal scales in males; three or four rows of enlarged post-precloacal scales; 1-3 postcloacal tubercles; proximal femoral scales less than one-half the size of distal femoral scales; absence of interdigital pocketing between digits of forefeet and hindfeet; and posterior border of the nuchal loop rounded. Uncorrected pairwise genetic divergences (p-distances) between the new species and other species of the intermedius group ranged from 4.73-22.55%. The discovery of this new species exclusively in isolated karst formations from the Thai-Cambodia border suggests that there may be further undiscovered Cyrtodactylus in unexplored karst landscapes along the border of eastern Thailand and western Cambodia.

MosGraphFlow: a novel integrative graph AI model mining disease targets from multi-omic data.

Multi-omic data can better characterize complex cellular signaling pathways from multiple views compared to individual omic data. However, integrative multi-omic data analysis to rank key disease biomarkers and infer core signaling pathways remains an open problem. In this study, our novel contributions are that we developed a novel graph AI model, mosGraphFlow, for analyzing multi-omic signaling graphs (mosGraphs), 2) analyzed multi-omic mosGraph datasets of AD, and 3) identified, visualized and evaluated a set of AD associated signaling biomarkers and network. The comparison results show that the proposed model not only achieves the best classification accuracy but also identifies important AD disease biomarkers and signaling interactions. Moreover, the signaling sources are highlighted at specific omic levels to facilitate the understanding of the pathogenesis of AD. The proposed model can also be applied and expanded for other studies using multi-omic data. Model code is accessible via GitHub: https://github.com/FuhaiLiAiLab/mosGraphFlow.

LMNA-Related Dilated Cardiomyopathy: Single-Cell Transcriptomics during Patient-Derived iPSC Differentiation Support Cell Type and Lineage-Specific Dysregulation of Gene Expression and Development for Cardiomyocytes and Epicardium-Derived Cells with Lamin A/C Haploinsufficiency.

LMNA-related dilated cardiomyopathy (DCM) is an autosomal-dominant genetic condition with cardiomyocyte and conduction system dysfunction often resulting in heart failure or sudden death. The condition is caused by mutation in the Lamin A/C (LMNA) gene encoding Type-A nuclear lamin proteins involved in nuclear integrity, epigenetic regulation of gene expression, and differentiation. The molecular mechanisms of the disease are not completely understood, and there are no definitive treatments to reverse progression or prevent mortality. We investigated possible mechanisms of LMNA-related DCM using induced pluripotent stem cells derived from a family with a heterozygous LMNA c.357-2A>G splice-site mutation. We differentiated one LMNA-mutant iPSC line derived from an affected female (Patient) and two non-mutant iPSC lines derived from her unaffected sister (Control) and conducted single-cell RNA sequencing for 12 samples (four from Patients and eight from Controls) across seven time points: Day 0, 2, 4, 9, 16, 19, and 30. Our bioinformatics workflow identified 125,554 cells in raw data and 110,521 (88%) high-quality cells in sequentially processed data. Unsupervised clustering, cell annotation, and trajectory inference found complex heterogeneity: ten main cell types; many possible subtypes; and lineage bifurcation for cardiac progenitors to cardiomyocytes (CMs) and epicardium-derived cells (EPDCs). Data integration and comparative analyses of Patient and Control cells found cell type and lineage-specific differentially expressed genes (DEGs) with enrichment, supporting pathway dysregulation. Top DEGs and enriched pathways included 10 ZNF genes and RNA polymerase II transcription in pluripotent cells (PP); BMP4 and TGF Beta/BMP signaling, sarcomere gene subsets and cardiogenesis, CDH2 and EMT in CMs; LMNA and epigenetic regulation, as well as DDIT4 and mTORC1 signaling in EPDCs. Top DEGs also included XIST and other X-linked genes, six imprinted genes (SNRPN, PWAR6, NDN, PEG10, MEG3, MEG8), and enriched gene sets related to metabolism, proliferation, and homeostasis. We confirmed Lamin A/C haploinsufficiency by allelic expression and Western blot. Our complex Patient-derived iPSC model for Lamin A/C haploinsufficiency in PP, CM, and EPDC provided support for dysregulation of genes and pathways, many previously associated with Lamin A/C defects, such as epigenetic gene expression, signaling, and differentiation. Our findings support disruption of epigenomic developmental programs, as proposed in other LMNA disease models. We recognized other factors influencing epigenetics and differentiation; thus, our approach needs improvement to further investigate this mechanism in an iPSC-derived model.

Exposure-Response-Based Multiattribute Clinical Utility Score Framework to Facilitate Optimal Dose Selection for Oncology Drugs.

The advent of new therapeutic modalities highlighted deficiencies in the traditional maximum tolerated dose approach for oncology drug dose selection and prompted the Food and Drug Administration (FDA)'s Project Optimus initiative, which suggests that sponsors take a holistic approach, including efficacy, safety, and pharmacokinetic (PK) and pharmacodynamic data, in conjunction with integrated exposure-response (ER) analyses. However, this method comes with an inherent challenge of the collation of the multisource data. To address this issue, an ER-based clinical utility score (CUS) framework, combining benefit and risk into a single measurement, was developed. Model-predicted outcomes for each clinically relevant end point, informed by ER modeling, are converted to a CUS using a user-defined utility function. Thereafter, individual CUS is integrated into a single score with user-defined weighting for each end point. The user-defined weighting feature allows the user to incorporate expert knowledge/understanding into weighing the product's benefit versus risk profile. To validate the framework, data were leveraged from over 50 oncology programs from 2019 to 2023 on the basis of FDA new drug application/biologics license application review packages and/or related literature studies. Five representative cases were selected for in-depth evaluation. Results showed that the optimal benefit-risk ratio (highest CUS) was consistently observed at PK exposures synonymous with recommended doses. A recurring theme across cases was a greater emphasis on safety over efficacy in oncology drug dose determination. The ER-based CUS framework offers a strategic tool to navigate the complexities of dose selection in oncology programs. It serves as a pillar to the importance of integrative data analysis, aligning with the vision of Project Optimus, and demonstrates its potential in guiding dose optimization by balancing therapeutic benefits against risk.

Enhancing Pharmaceutical Product Quality With a Comprehensive Corrective and Preventive Actions (CAPA) Framework: From Reactive to Proactive.

Corrective and preventive actions (CAPA) are crucial components of quality assurance (QA) within the pharmaceutical industry, essential for maintaining product quality, safety, and regulatory compliance. The review explores the multifaceted role of CAPA in pharmaceutical manufacturing, emphasizing its structured approach to detecting, addressing, and preventing quality issues. CAPA systems are integral to the broader quality management system (QMS), functioning as a dual-loop mechanism that is reactive and proactive approach aligned with continuous improvement principles outlined by the International Organization for Standardization (ISO) 9001:2000. It details the three distinct phases of CAPA: correction or remedial action, corrective action (CA), and preventive action (PA). It highlights the importance of root cause analysis and the necessity for immediate corrections and long-term preventive measures to avoid recurring issues. Regulatory expectations, such as those from the Food and Drug Administration (FDA) under the Code of Federal Regulations (CFR) title 21 part 820 and theInternational Council for Harmonisationof Technical Requirements for Pharmaceuticals for Human Use (ICH) Q10, are discussed, underscoring the need for a comprehensive CAPA plan that integrates data analysis and ongoing process enhancements. Additionally, the paper introduces the 8D methodology as a structured problem-solving approach to complement CAPA efforts. By providing an in-depth examination of CAPA procedures and their implementation, this article aims to contribute to the understanding and effectiveness of quality systems in pharmaceutical manufacturing.

Integrative analysis of long isoform sequencing and functional data identifies distinct cortical layer neuronal subtypes derived from human iPSCs.

Generation of human induced pluripotent stem cells (iPSCs) through reprogramming was a transformational change in the field of regenerative medicine that led to new possibilities for drug discovery and cell replacement therapy. Several protocols have been established to differentiate hiPSCs into neuronal lineages. However, low differentiation efficiency is one of the major drawbacks of these approaches. Here, we compared the efficiency of two methods of neuronal differentiation from iPSCs cultured in two different culture media, StemFlex Medium (SFM) and Essential 8 Medium (E8M). The results indicated that iPSCs cultured in E8M efficiently generated different types of neurons in a shorter time and without the growth of undifferentiated nonneuronal cells in the culture as compared with those generated from iPSCs in SFM. Furthermore, these neurons were validated as functional units immunocytochemically by confirming the expression of mature neuronal markers (i.e., NeuN, β tubulin, and Synapsin I) and whole cell patch-clamp recordings. Long-read single-cell RNA sequencing confirms the presence of upper and deep layer cortical layer excitatory and inhibitory neuronal subtypes in addition to small populations of GABAergic neurons in day 30 neuronal cultures. Pathway analysis indicated that our protocol triggers the signaling transcriptional networks important for the process of neuronal differentiation in vivo.NEW & NOTEWORTHY Low differentiation efficiency is one of the major drawbacks of the existing protocols to differentiate iPSCs into neuronal lineages. Here, we present time-efficient and robust approach of neuronal differentiation leading to the generation of functional brain units, cortical layer neurons. We found iPSCs cultured in Essential 8 media (E8M) resulted in neuronal differentiation without the signs of growth of spontaneously differentiated cells in culture at any point in 35 days compared with Stemflex media (SFM).

Abstract P215: The reninness score: integrative analysis of multi-omic data to define renin cell identity.

Renin cell (RC) descendants regain the endocrine renin phenotype to overcome homeostatic threats, a process known as recruitment. The determinants that control the identity, fate, and recruitment of RCs are not well understood. We aim to 1) define the chromatin pattern that determines RC identity; and 2) develop a computational tool to score samples by similarity to that pattern. We compared open chromatin regions (ATAC-seq) between non-renin-expressing (n = 184) and renin-expressing samples (n = 19) from three different sources and stimulation states: 1) primary RCs in basal state (WT); 2) primary RCs chronically stimulated to produce renin (Recruited); and 3) constitutively renin-expressing tumoral cells. Differential analyses between the three types of RCs and the non-RCs revealed 1,525 unique open chromatin regions shared in at least two RC groups, including regions associated with RC-specific genes: Ren1 and Akr1b7. Increased accessibility regions in RC groups are cell type specific and enriched in renin-related GO terms ( e.g., cAMP and Notch signaling pathway). The bZip family ( e.g. , Atf3, p < 10 -2147 ) was the most enriched motifs in tumoral cells, and MEF2 family ( e.g. , MEF2a, p < 10 -91 ) in primary cells. We did not find a unique set of TFs specific to the Recruited group, indicating that renin recruitment may not be regulated by a recruitment-specific factor family, but could instead be driven by post-translational modification, differential co-factors, or epigenetic modifications. Next, we used a machine-learning approach to calculate a "reninness" score by training a genomic region model with renin and non-renin bulkATAC-seq data. We identified a cluster of regions (n = 112,992) around the trained renin label, which included 89% (21,820 of 24,612) of the regions with increased accessibility from the differential analyses. We calculated the reninness score based on the distance between the trained label and the predicted bulkATA sample embeddings using the trained model. Homogeneous WT RCs had the highest score, followed by renin-expressing tumoral and primary cells. Non-RCs had the lowest scores. We also adapted bulkATAC region-embedding models to pseudo-bulk ATAC- and scATAC-seq data. The observed reninness scores show that the model can also capture differences in renin recruitment potential. In conclusion, we identified the chromatin configurations defining the identity and recruitment of RCs and developed a computational score to quantify it.

Investigating the correlation between phenotypes, adrenal transcriptome, and serum metabolism in laying ducks exhibiting varying behaviours under the same stressor

Laying ducks in cage environments face various stressors, including the fear of novelty, which negatively affects their behaviour and performance. The reasons behind the variation in behaviour under identical stress conditions are not well understood. This study investigated how different behaviours affect production performance, immune response, antioxidant capabilities, adrenal gene expression, and serum metabolite profiles in caged laying ducks subjected to the same stressor. Overall, 42-week-old laying ducks (N = 300) were selected, fed for 60 days, and simultaneously underwent behavioural tests. Based on their behavioural responses, 24 ducks were chosen and categorised into two groups: high-active avoidance (HAA) and low-active avoidance (LAA). The study utilised phenotypic, genetic, and metabolomic analyses, coupled with bioinformatics, to identify crucial biological processes, genes, and metabolites. The results indicated that ΔW (BW gain) and average daily egg weight (ADEW) were significantly lower in the HAA group compared to the LAA group (P < 0.05). By contrast, the feed-to-egg ratio was higher in the HAA group than in the LAA group (P < 0.05). Levels of serum immunoglobulin A, total antioxidant capacity, and the activities of enzymes like superoxide dismutase and catalase (CAT) were significantly lower in the HAA than in the LAA group (P < 0.05), whereas serum ACTH levels were significantly higher in HAA than in the LAA group (P < 0.05). The adrenal transcriptome analysis revealed 148 differentially expressed genes in the HAA group, with 97 up-regulated and 51 down-regulated. Moreover, enrichment analysis highlighted significant differences in two metabolic pathways: neuroactive ligand-receptor interaction and oxidative phosphorylation (P < 0.05). Serum metabolomics identified 11 differentially accumulated metabolites between the groups, with variations in up and down-regulation. Integrative analysis of phenotype, transcriptome, and metabolome data showed a strong correlation between the exosome component 3 (EXOSC3) gene, phenotypic traits, and differential metabolites. Thus, we deduced that the differences in average daily egg weight among ducks could be linked to variations in gabapentin and EXOSC3 gene expressions, affecting serum CAT levels.

Integration of single-cell sequencing and bulk transcriptome data develops prognostic markers based on PCLAF+ stem-like tumor cells using artificial neural network in gastric cancer

BackgroundGastric cancer stem cells (GCSCs) are important tumour cells involved in tumourigenesis and gastric cancer development. However, their clinical value remains unclear due to the limitations of the available technologies. This study aims to explore the clinical significance of GCSCs, their connection to the tumour microenvironment, and their underlying molecular mechanisms. MethodsStem-like tumour cells were identified by mining single-cell transcriptomic data from multiple samples. Integrated analysis of single-cell and bulk transcriptome data was performed to analyse the role of stem-like tumour cells in predicting clinical outcomes by introducing the intermediate variable mRNA stemness degree (SD). Consensus clustering analysis was performed to develop an SD-related molecular classification strategy to assess the clinical characteristics in gastric cancer. A prognostic model was constructed using a customized approach that comprehensively considered SD-related gene signatures based on an artificial neural network. ResultsBy analysing single-cell data and validating immunofluorescence results, we identified a PCLAF+ stem-like tumour cell population in GC. By calculating SD, we observed that PCLAF+ stem-like tumour cells were associated with poor prognosis and certain clinical features. The SD was negatively correlated with the abundance of most immune cell types. Furthermore, we proposed an SD-related classification method and prognostic model. In addition, the customised prognostic model can be used to predict whether a patient respond to PD-1/PD-L1 immunotherapy. ConclusionWe identified a cluster of stem-like cells and elucidated their clinical significance, highlighting the possibility of their use as immunotherapeutic targets.

Identifying epithelial-mesenchymal transition-related genes as prognostic biomarkers and therapeutic targets of hepatocellular carcinoma by integrated analysis of single-cell and bulk-RNA sequencing data.

Hepatocellular carcinoma (HCC) remains one of the most lethal cancers globally. Patients with advanced HCC tend to have poor prognoses and shortened survival. Recently, data from bulk RNA sequencing have been employed to discover prognostic markers for various cancers. However, they fall short in precisely identifying core molecular and cellular activities within tumor cells. In our present study, we combined bulk-RNA sequencing (bulk RNA-seq) data with single-cell RNA sequencing (scRNA-seq) to develop a prognostic model for HCC. The goal of our research is to uncover new biomarkers and enhance the accuracy of HCC prognosis prediction. Integrating single-cell sequencing data with transcriptomics were used to identify epithelial-mesenchymal transition (EMT)-related genes (ERGs) implicated in HCC progression and their clinical significance was elucidated. Utilizing marker genes derived from core cells and ERGs, we constructed a prognostic model using univariate Cox analysis, exploring a multitude of algorithmic combinations, and further refining it through multivariate Cox analysis. Additionally, we conducted an in-depth investigation into the disparities in clinicopathological features, immune microenvironment composition, immune checkpoint expression, and chemotherapeutic drug sensitivity profiles between high- and low-risk patient cohorts. We developed a prognostic model predicated on the expression profiles of eight signature genes, namely HSP90AA1, CIRBP, CCR7, S100A9, ADAM17, ENG, PGF, and INPP4B, aiming at predicting overall survival (OS) outcomes. Notably, patients classified with high-risk scores exhibited a propensity towards diminished OS rates, heightened frequencies of stage III-IV disease, increased tumor mutational burden (TMB), augmented immune cell infiltration, and diminished responsiveness to immunotherapeutic interventions. This study presented a novel prognostic model for predicting the survival of HCC patients by integrating scRNA-seq and bulk RNA-seq data. The risk score emerges as a promising independent prognostic factor, showing a correlation with the immune microenvironment and clinicopathological features. It provided new clinical tools for predicting prognosis and aided future research into the pathogenesis of HCC.

Integrative bioinformatics analysis of transcriptomic data from CD8+ T cells in Systemic Lupus Erythematosus

IntroductionSystemic Lupus Erythematosus (SLE) is a complex, multisystem autoimmune disorder characterized by extensive inflammation that affects nearly all organ systems in the body. It is primarily mediated by auto-antibodies and immune complexes, and it predominantly affects women more than men. This study employs an in-silico approach to identify key genes potentially involved in the pathogenesis of SLE. ObjectivesTo identify key genes potentially involved in SLE pathogenesis using in-silico approach. MethodsHigh-throughput sequencing dataset GSE97264, from the Gene Expression Omnibus (GEO) database, which contains RNA transcriptome data from CD8+ T-cells of 18 SLE patients and 14 healthy controls was utilized for the analysis. Differentially expressed genes (DEGs) were identified using the Bioconductor DESeq2 package in R platform. Gene Ontology (GO) and pathway enrichment analyses were performed using the ToppGene suite. Motif analysis of the genes’ promoter regions was conducted using HOMER software. Protein-protein interaction (PPI) and Reactome functional interaction (FI) networks were created using Cytoscape plugins StringApp and ReactomeFIViz, and analysed to identify hub genes. ResultsOur analysis identified 931 DEGs, with 577 upregulated and 354 downregulated. GO and pathway enrichment analyses indicated that upregulated genes were associated with immune responses, including cytokine production and receptor activation. Motif analysis identified key regulatory motifs linked to immune regulation in upregulated genes and T-cell activation in downregulated genes. PPI and FI networks analyses revealed 29 cell cycle-associated hub genes, with 10 genes—CDK1, TPX2, BIRC5, CCNA2, BUB1, BUB1B, AURKA, KIF2C, PLK1, and CDCA8—common to both biological networks, suggesting their crucial role in SLE pathogenesis. ConclusionThis study suggests that dysregulation of the identified 10 genes may impact immune responses and contribute to the autoimmune-like conditions observed in SLE. Several of these genes are also implicated in other autoimmune diseases, highlighting their potential as SLE biomarkers. Despite their known roles in other immune-related diseases involving CD8+ T cells, their direct association with SLE had not been previously established. This novel finding underscores the potential of these genes as therapeutic targets and may contribute to the development of diagnostic tools.

The role of artificial intelligence in revolutionizing personalized medicine: A comprehensive review of techniques and applications

This comprehensive review explores the transformative role of Artificial Intelligence (AI) in revolutionizing personalized medicine, focusing on techniques, applications, and future directions. Personalized medicine, which tailors medical interventions to individual characteristics, has significantly advanced through the integration of AI technologies. AI's capabilities in data integration, genomic and molecular data analysis, predictive modeling, and personalized treatment development are highlighted. The review also addresses the challenges and limitations of AI in personalized medicine, such as data privacy, bias, and the need for seamless clinical integration. Furthermore, it discusses future trends, including advances in AI technologies, the integration of multi-omics data, and the importance of ethical and regulatory considerations. The historical evolution of personalized medicine is traced, emphasizing key milestones that have led to current innovations. This review underscores AI's pivotal role in enhancing the precision and effectiveness of personalized healthcare, offering insights into how AI-driven approaches can shape the future of medicine.

Maternal immune cell gene expression associates with maternal gut microbiome, milk composition and infant gut microbiome

Background– Pre-pregnancy overweight and obesity promote deleterious health impacts on both mothers during pregnancy and the offspring. Significant changes in the maternal peripheral blood mononuclear cells (PBMCs) gene expression due to obesity are well-known. However, the impact of pre-pregnancy overweight on immune cell gene expression during pregnancy and its association with maternal and infant outcomes is not well explored. Methods– Blood samples were collected from healthy normal weight (NW, pre-pregnancy BMI 18.5-24.9) or overweight (OW, pre-pregnancy BMI 25-29.9) 2nd parity pregnant women at 12, 24 and 36 weeks of pregnancy. PBMCs were isolated from the blood and subjected to mRNA sequencing. Maternal and infant microbiota were analyzed by 16S rRNA gene sequencing. Integrative multi-omics data analysis was performed to evaluate the association of gene expression with maternal diet, gut microbiota, milk composition, and infant gut microbiota. ResultsGene expression analysis revealed that 453 genes were differentially expressed in the OW women compared to NW women at 12 weeks of pregnancy, out of which 354 were upregulated and 99 were downregulated. Several up-regulated genes in the OW group were enriched in inflammatory, chemokine-mediated signaling and regulation of interleukin-8 production-related pathways. At 36 weeks of pregnancy healthy eating index score was positively associated with several genes that include, DTD1, ELOC, GALNT8, ITGA6-AS1, KRT17P2, NPW, POT1-AS1 and RPL26. In addition, at 36 weeks of pregnancy, genes involved in adipocyte functions, such as NG2 and SMTNL1, were negatively correlated to human milk 2’FL and total fucosylated oligosaccharides content collected at 1 month postnatally. Furthermore, infant Akkermansia was positively associated with maternal PBMC anti-inflammatory genes that include CPS1 and RAB7B, at 12 and 36 weeks of pregnancy. Conclusions– These findings suggest that prepregnancy overweight impacts the immune cell gene expression profile, particularly at 12 weeks of pregnancy. Furthermore, deciphering the complex association of PBMC’s gene expression levels with maternal gut microbiome and milk composition and infant gut microbiome may aid in developing strategies to mitigate obesity-mediated effects.

MosGraphGen: a novel tool to generate multi-omics signaling graphs to facilitate integrative and interpretable graph AI model development.

Multi-omics data, i.e., genomics, epigenomics, transcriptomics, proteomics, characterize cellular complex signaling systems from multi-level and multi-view and provide a holistic view of complex cellular signaling pathways. However, it remains challenging to integrate and interpret multi-omics data for mining key disease targets and signaling pathways. Graph AI models have been widely used to analyze graph-structure datasets, and are ideal for integrative multi-omics data analysis because they can naturally integrate and represent multi-omics data as a biologically meaningful multi-level signaling graph and interpret multi-omics data via graph node and edge ranking analysis. However, it is non-trivial for graph-AI model developers to pre-analyze multi-omics data and convert the data into biologically meaningful graphs, which can be directly fed into graph-AI models. To resolve this challenge, we developed mosGraphGen (multi-omics signaling graph generator), generating Multi-omics Signaling graphs (mos-graph) of individual samples by mapping multi-omics data onto a biologically meaningful multi-level background signaling network with data normalization by aggregating measurements and aligning to the reference genome. With mosGraphGen, AI model developers can directly apply and evaluate their models using these mos-graphs. In the results, mosGraphGen was used and illustrated using two widely used multi-omics datasets of TCGA and Alzheimer's disease (AD) samples. The code of mosGraphGen is open-source and publicly available via GitHub: https://github.com/FuhaiLiAiLab/mosGraphGen.

Targeting Galectin-1 Overcomes Paclitaxel Resistance in Esophageal Squamous Cell Carcinoma.

Resistance to paclitaxel poses a major obstacle in esophageal squamous cell carcinoma (ESCC) treatment. A better understanding of the mechanisms underlying paclitaxel resistance could help identify prognostic biomarkers and improved therapeutic strategies. In this study, we established a patient-derived xenograft (PDX) model of acquired paclitaxel resistance and used RNA-sequencing to identify galectin-1, encoded by LGALS1, as a key mediator of resistance. Integrative analysis of clinical data and physiological studies indicated that serum galectin-1 levels were elevated in resistant patients and correlated with treatment outcomes before and during taxane therapy. Importantly, exposing cells to serum from resistant patients resulted in increased paclitaxel resistance compared to serum from sensitive patients, which was closely associated with galectin-1 concentrations in the serum. The specific clearance of galectin-1 from resistant patient serum significantly restored paclitaxel sensitivity, and inhibiting galectin-1, through knockdown or the pharmacologic inhibitor OTX008, increased sensitivity to paclitaxel. Galectin-1 inhibition reduced the activity of β-catenin, thereby inhibiting stem cell properties induced by the Wnt/β-catenin pathway. Furthermore, galectin-1 regulated MDR1 transcription through increased nuclear accumulation of β-catenin, thus increasing resistance to paclitaxel. Combining OTX008 with clinical taxane formulations effectively reversed paclitaxel resistance in vitro and in vivo. Elevated galectin-1 levels thus serve as an indicator of response to paclitaxel therapy in ESCC, offering a therapeutic intervention strategy to overcome drug resistance.

Foxg1 regulates translation of neocortical neuronal genes, including the main NMDA receptor subunit gene, Grin1

BackgroundMainly known as a transcription factor patterning the rostral brain and governing its histogenesis, FOXG1 has been also detected outside the nucleus; however, biological meaning of that has been only partially clarified.ResultsPrompted by FOXG1 expression in cytoplasm of pallial neurons, we investigated its implication in translational control. We documented the impact of FOXG1 on ribosomal recruitment of Grin1-mRNA, encoding for the main subunit of NMDA receptor. Next, we showed that FOXG1 increases GRIN1 protein level by enhancing the translation of its mRNA, while not increasing its stability. Molecular mechanisms underlying this activity included FOXG1 interaction with EIF4E and, possibly, Grin1-mRNA. Besides, we found that, within murine neocortical cultures, de novo synthesis of GRIN1 undergoes a prominent and reversible, homeostatic regulation and FOXG1 is instrumental to that. Finally, by integrated analysis of multiple omic data, we inferred that FOXG1 is implicated in translational control of hundreds of neuronal genes, modulating ribosome engagement and progression. In a few selected cases, we experimentally verified such inference.ConclusionsThese findings point to FOXG1 as a key effector, potentially crucial to multi-scale temporal tuning of neocortical pyramid activity, an issue with profound physiological and neuropathological implications.

MorphLink: Bridging Cell Morphological Behaviors and Molecular Dynamics in Multi-modal Spatial Omics.

Multi-modal spatial omics data are invaluable for exploring complex cellular behaviors in diseases from both morphological and molecular perspectives. Current analytical methods primarily focus on clustering and classification, and do not adequately examine the relationship between cell morphology and molecular dynamics. Here, we present MorphLink, a framework designed to systematically identify disease-related morphological-molecular interplays. MorphLink has been evaluated across a wide array of datasets, showcasing its effectiveness in extracting and linking interpretable morphological features with various molecular measurements in multi-modal spatial omics analyses. These linkages provide a transparent depiction of cellular behaviors that drive transcriptomic heterogeneity and immune diversity across different regions within diseased tissues, such as cancer. Additionally, MorphLink is scalable and robust against cross-sample batch effects, making it an efficient method for integrative spatial omics data analysis across samples, cohorts, and modalities, and enhancing the interpretation of results for large-scale studies.

Integrative Data Analysis Where Partial Covariates Have Complex Nonlinear Effects by Using Summary Information from an External Data

A full parametric and linear specification may be insufficient to capture complicated patterns in studies exploring complex features, such as those investigating age-related changes in brain functional abilities. Alternatively, a partially linear model (PLM) consisting of both parametric and nonparametric elements may have a better fit. This model has been widely applied in economics, environmental science, and biomedical studies. In this article, we introduce a novel statistical inference framework that equips PLM with high estimation efficiency by effectively synthesizing summary information from external data into the main analysis. Such an integrative scheme is versatile in assimilating various types of reduced models from the external study. The proposed method is shown to be theoretically valid and numerically convenient, and it ensures a high-efficiency gain compared to classic methods in PLM. Our method is further validated using two data applications by evaluating the risk factors of brain imaging measures and blood pressure.