Melanocyte lineage dynamics in development, growth and disease.

Melanocytes evolved to produce the melanin that gives colour to our hair, eyes and skin. The melanocyte lineage also gives rise to melanoma, the most lethal form of skin cancer. The melanocyte lineage differentiates from neural crest cells during development, and most melanocytes reside in the skin and hair, where they are replenished by melanocyte stem cells. Because the molecular mechanisms necessary for melanocyte specification, migration, proliferation and differentiation are co-opted during melanoma initiation and progression, studying melanocyte development is directly relevant to human disease. Here, through the lens of advances in cellular omic and genomic technologies, we review the latest findings in melanocyte development and differentiation, and how these developmental pathways become dysregulated in disease.

PanIN and CAF transitions in pancreatic carcinogenesis revealed with spatial data integration.

This study introduces a new imaging, spatial transcriptomics (ST), and single-cell RNA-sequencing integration pipeline to characterize neoplastic cell state transitions during tumorigenesis. We applied a semi-supervised analysis pipeline to examine premalignant pancreatic intraepithelial neoplasias (PanINs) that can develop into pancreatic ductal adenocarcinoma (PDAC). Their strict diagnosis on formalin-fixed and paraffin-embedded (FFPE) samples limited the single-cell characterization of human PanINs within their microenvironment. We leverage whole transcriptome FFPE ST to enable the study of a rare cohort of matched low-grade (LG) and high-grade (HG) PanIN lesions to track progression and map cellular phenotypes relative to single-cell PDAC datasets. We demonstrate that cancer-associated fibroblasts (CAFs), including antigen-presenting CAFs, are located close to PanINs. We further observed a transition from CAF-related inflammatory signaling to cellular proliferation during PanIN progression. We validate these findings with single-cell high-dimensional imaging proteomics and transcriptomics technologies. Altogether, our semi-supervised learning framework for spatial multi-omics has broad applicability across cancer types to decipher the spatiotemporal dynamics of carcinogenesis.

Super-enhancer omics in stem cell.

The hallmarks of stem cells, such as proliferation, self-renewal, development, differentiation, and regeneration, are critical to maintain stem cell identity which is sustained by genetic and epigenetic factors. Super-enhancers (SEs), which consist of clusters of active enhancers, play a central role in maintaining stemness hallmarks by specifically transcriptional model. The SE-navigated transcriptional complex, including SEs, non-coding RNAs, master transcriptional factors, Mediators and other co-activators, forms phase-separated condensates, which offers a toggle for directing diverse stem cell fate. With the burgeoning technologies of multiple-omics applied to examine different aspects of SE, we firstly raise the concept of "super-enhancer omics", inextricably linking to Pan-omics. In the review, we discuss the spatiotemporal organization and concepts of SEs, and describe links between SE-navigated transcriptional complex and stem cell features, such as stem cell identity, self-renewal, pluripotency, differentiation and development. We also elucidate the mechanism of stemness and oncogenic SEs modulating cancer stem cells via genomic and epigenetic alterations hijack in cancer stem cell. Additionally, we discuss the potential of targeting components of the SE complex using small molecule compounds, genome editing, and antisense oligonucleotides to treat SE-associated organ dysfunction and diseases, including cancer. This review also provides insights into the future of stem cell research through the paradigm of SEs.

Mesenchymal stromal/stem cell tissue source and in vitro expansion impact extracellular vesicle protein and miRNA compositions as well as angiogenic and immunomodulatory capacities.

Recently, therapies utilizing extracellular vesicles (EVs) derived from mesenchymal stromal/stem cells (MSCs) have begun to show promise in clinical trials. However, EV therapeutic potential varies with MSC tissue source and in vitro expansion through passaging. To find the optimal MSC source for clinically translatable EV-derived therapies, this study aims to compare the angiogenic and immunomodulatory potentials and the protein and miRNA cargo compositions of EVs isolated from the two most common clinical sources of adult MSCs, bone marrow and adipose tissue, across different passage numbers. Primary bone marrow-derived MSCs (BMSCs) and adipose-derived MSCs (ASCs) were isolated from adult female Lewis rats and expanded in vitro to the indicated passage numbers (P2, P4, and P8). EVs were isolated from the culture medium of P2, P4, and P8 BMSCs and ASCs and characterized for EV size, number, surface markers, protein content, and morphology. EVs isolated from different tissue sources showed different EV yields per cell, EV sizes, and protein yield per EV. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of proteomics data and miRNA seq data identified key proteins and pathways associated with differences between BMSC-EVs and ASC-EVs, as well as differences due to passage number. In vitro tube formation assays employing human umbilical vein endothelial cells suggested that both tissue source and passage number had significant effects on the angiogenic capacity of EVs. With or without lipopolysaccharide (LPS) stimulation, EVs more significantly impacted expression of M2-macrophage genes (IL-10, Arg1, TGFß) than M1-macrophage genes (IL-6, NOS2, TNFα). By correlating the proteomics analyses with the miRNA seq analysis and differences observed in our in vitro immunomodulatory, angiogenic, and proliferation assays, this study highlights the trade-offs that may be necessary in selecting the optimal MSC source for development of clinical EV therapies.

Combined transcriptome and metabolome analysis reveals the toxic effects of antimony on the earthworm.

Antimony (Sb) poses a significant ecological threat. This study combines biochemical, pathological, transcriptome, and metabolome analyses to assess the short-term (14-day) toxic impact of two Sb levels (25 mg/kg and 125 mg/kg) on earthworms (Eisenia fetida). Higher Sb concentration caused severe intestinal damage, elevated metallothionein (MT) levels, and reduced antioxidant capacity. Metabolome analysis identifies 404 and 1698 significantly differential metabolites in the two groups. Metabolites such as S(-)-cathinone, N-phenyl-1-naphthylamine, serotonin, 4-hydroxymandelonitrile, and 5-fluoropentylindole contributed to the metabolic responses to Sb stress. Transcriptome analysis shows increased chitin synthesis as a protective response, impacting amino sugar and nucleotide sugar metabolism for cell wall synthesis and damage repair. Integrated analysis indicated that 5 metabolite-gene pairs were found in two Sb levels and 11 enriched pathways were related to signal transduction, carbohydrate metabolism, immune system, amino acid metabolism, digestive system, and nervous system. Therefore, the integration of multiomics approaches enhanced our comprehension of the molecular mechanisms underlying the toxicity of Sb in E. fetida.

Integrated multi-omics analysis reveals molecular changes associated with chronic lipid accumulation following contusive spinal cord injury.

Functional and pathological recovery after spinal cord injury (SCI) is often incomplete due to the limited regenerative capacity of the central nervous system (CNS), which is further impaired by several mechanisms that sustain tissue damage. Among these, the chronic activation of immune cells can cause a persistent state of local CNS inflammation and damage. However, the mechanisms that sustain this persistent maladaptive immune response in SCI have not been fully clarified yet. In this study, we integrated histological analyses with proteomic, lipidomic, transcriptomic, and epitranscriptomic approaches to study the pathological and molecular alterations that develop in a mouse model of cervical spinal cord hemicontusion. We found significant pathological alterations of the lesion rim with myelin damage and axonal loss that persisted throughout the late chronic phase of SCI. This was coupled by a progressive lipid accumulation in myeloid cells, including resident microglia and infiltrating monocyte-derived macrophages. At tissue level, we found significant changes of proteins indicative of glycolytic, tricarboxylic acid cycle (TCA), and fatty acid metabolic pathways with an accumulation of triacylglycerides with C16:0 fatty acyl chains in chronic SCI. Following transcriptomic, proteomic, and epitranscriptomic studies identified an increase of cholesterol and m6A methylation in lipid-droplet-accumulating myeloid cells as a core feature of chronic SCI. By characterizing the multiple metabolic pathways altered in SCI, our work highlights a key role of lipid metabolism in the chronic response of the immune and central nervous system to damage.

Chemosynthetic alphaproteobacterial diazotrophs reside in deep-sea cold-seep bottom waters.

Nitrogen (N)-fixing organisms, also known as diazotrophs, play a crucial role in N-limited ecosystems by controlling the production of bioavailable N. The carbon-dominated cold-seep ecosystems are inherently N-limited, making them hotspots of N fixation. However, the knowledge of diazotrophs in cold-seep ecosystems is limited compared to other marine ecosystems. In this study, we used multi-omics to investigate the diversity and catabolism of diazotrophs in deep-sea cold-seep bottom waters. Our findings showed that the relative abundance of diazotrophs in the bacterial community reached its highest level in the cold-seep bottom waters compared to the cold-seep upper waters and non-seep bottom waters. Remarkably, more than 98% of metatranscriptomic reads aligned on diazotrophs in cold-seep bottom waters belonged to the genus Sagittula, an alphaproteobacterium. Its metagenome-assembled genome, named Seep-BW-D1, contained catalytic genes (nifHDK) for nitrogen fixation, and the nifH gene was actively transcribed in situ. Seep-BW-D1 also exhibited chemosynthetic capability to oxidize C1 compounds (methanol, formaldehyde, and formate) and thiosulfate (S2O32-). In addition, we observed abundant transcripts mapped to genes involved in the transport systems for acetate, spermidine/putrescine, and pectin oligomers, suggesting that Seep-BW-D1 can utilize organics from the intermediates synthesized by methane-oxidizing microorganisms, decaying tissues from cold-seep benthic animals, and refractory pectin derived from upper photosynthetic ecosystems. Overall, our study corroborates that carbon-dominated cold-seep bottom waters select for diazotrophs and reveals the catabolism of a novel chemosynthetic alphaproteobacterial diazotroph in cold-seep bottom waters. IMPORTANCE: Bioavailable nitrogen (N) is a crucial element for cellular growth and division, and its production is controlled by diazotrophs. Marine diazotrophs contribute to nearly half of the global fixed N and perform N fixation in various marine ecosystems. While previous studies mainly focused on diazotrophs in the sunlit ocean and oxygen minimum zones, recent research has recognized cold-seep ecosystems as overlooked N-fixing hotspots because the seeping fluids in cold-seep ecosystems introduce abundant bioavailable carbon but little bioavailable N, making most cold seeps inherently N-limited. With thousands of cold-seep ecosystems detected at continental margins worldwide in the past decades, the significant role of cold seeps in marine N biogeochemical cycling is emphasized. However, the diazotrophs in cold-seep bottom waters remain poorly understood. Through multi-omics, this study identified a novel alphaproteobacterial chemoheterotroph belonging to Sagittula as one of the most active diazotrophs residing in cold-seep bottom waters and revealed its catabolism.

Barramundi (Lates calcarifer) rare coloration patterns: a multiomics approach to understand the "panda" phenotype.

The barramundi (Lates calcarifer), a significant aquaculture species, typically displays silver to bronze coloration. However, attention is now drawn to rare variants like the "panda" phenotype, characterized by blotch-like patterns of black (PB) and golden (PG) patches. This phenotype presents an opportunity to explore the molecular mechanisms underlying color variations in teleosts. Unlike stable color patterns in many fish, the "panda" variant demonstrates phenotypic plasticity, responding dynamically to unknown cues. We propose a complex interplay of genetic factors and epigenetic modifications, focusing on DNA methylation. Through a multiomics approach, we analyze transcriptomic and methylation patterns between PB and PG patches. Our study reveals differential gene expression related to melanosome trafficking and chromatophore differentiation. Although the specific gene responsible for the PB-PG difference remains elusive, candidate genes like asip1, asip2, mlph, and mreg have been identified. Methylation emerges as a potential contributor to the "panda" phenotype, with changes in gene promoters like hand2 and dynamin possibly influencing coloration. This research lays the groundwork for further exploration into rare barramundi color patterns, enhancing our understanding of color diversity in teleosts. Additionally, it underscores the "panda" phenotype's potential as a model for studying adult skin coloration.

Interactive molecular causal networks of hypertension using a fast machine learning algorithm MRdualPC.

BACKGROUND: Understanding the complex interactions between genes and their causal effects on diseases is crucial for developing targeted treatments and gaining insight into biological mechanisms. However, the analysis of molecular networks, especially in the context of high-dimensional data, presents significant challenges. METHODS: This study introduces MRdualPC, a computationally tractable algorithm based on the MRPC approach, to infer large-scale causal molecular networks. We apply MRdualPC to investigate the upstream causal transcriptomics influencing hypertension using a comprehensive dataset of kidney genome and transcriptome data. RESULTS: Our algorithm proves to be 100 times faster than MRPC on average in identifying transcriptomics drivers of hypertension. Through clustering, we identify 63 modules with causal driver genes, including 17 modules with extensive causal networks. Notably, we find that genes within one of the causal networks are associated with the electron transport chain and oxidative phosphorylation, previously linked to hypertension. Moreover, the identified causal ancestor genes show an over-representation of blood pressure-related genes. CONCLUSIONS: MRdualPC has the potential for broader applications beyond gene expression data, including multi-omics integration. While there are limitations, such as the need for clustering in large gene expression datasets, our study represents a significant advancement in building causal molecular networks, offering researchers a valuable tool for analyzing big data and investigating complex diseases.

Open-source, high-throughput targeted in-situ transcriptomics for developmental and tissue biology.

Multiplexed spatial profiling of mRNAs has recently gained traction as a tool to explore the cellular diversity and the architecture of tissues. We propose a sensitive, open-source, simple and flexible method for the generation of in-situ expression maps of hundreds of genes. We exploit direct ligation of padlock probes on mRNAs, coupled with rolling circle amplification and hybridization-based in situ combinatorial barcoding, to achieve high detection efficiency, high throughput and large multiplexing. We validate the method across a number of species, and show its use in combination with orthogonal methods such as antibody staining, highlighting its potential value for developmental and tissue biology studies. Finally, we provide an end-to-end computational workflow that covers the steps of probe design, image processing, data extraction, cell segmentation, clustering and annotation of cell types. By enabling easier access to high-throughput spatially resolved transcriptomics, we hope to encourage a diversity of applications and the exploration of a wide range of biological questions.

Kiphynet: an online network simulation tool connecting cellular kinetics and physiological transport.

INTRODUCTION: Human metabolism is sustained by functional networks that operate at diverse scales. Capturing local and global dynamics in the human body by hierarchically bridging multi-scale functional networks is a major challenge in physiological modeling. OBJECTIVES: To develop an interactive, user-friendly web application that facilitates the simulation and visualization of advection-dispersion transport in three-dimensional (3D) microvascular networks, biochemical exchange, and metabolic reactions in the tissue layer surrounding the vasculature. METHODS: To help modelers combine and simulate biochemical processes occurring at multiple scales, KiPhyNet deploys our discrete graph-based modeling framework that bridges functional networks existing at diverse scales. KiPhyNet is implemented in Python based on Apache web server using MATLAB as the simulator engine. KiPhyNet provides the functionality to assimilate multi-omics data from clinical and experimental studies as well as vascular data from imaging studies to investigate the role of structural changes in vascular topology on the functional response of the tissue. RESULTS: With the network topology, its biophysical attributes, values of initial and boundary conditions, parameterized kinetic constants, biochemical species-specific transport properties such as diffusivity as inputs, a user can use our application to simulate and view the simulation results. The results of steady-state velocity and pressure fields and dynamic concentration fields can be interactively examined. CONCLUSION: KiPhyNet provides barrier-free access to perform time-course simulation experiments by building multi-scale models of microvascular networks in physiology, using a discrete modeling framework. KiPhyNet is freely accessible at   http://pallab.cds.iisc.ac.in/kiphynet/ and the documentation is available at   https://deepamahm.github.io/kiphynet_docs/ .

Lubricating gel influence on vaginal microbiome sampling.

Gel lubrication is routinely used during gynecological examination to prevent or reduce pain, yet its impact on microbial composition during sampling remains unclear. This study aimed to investigate whether lubricating gel affects the microbial composition of vaginal samples. We included 31 pregnant women presenting during their third trimester to clinics or emergency room and collected 143 unique vaginal samples for 16S amplicon microbial analysis. Vaginal samples were obtained using sterile swabs under various conditions: without gel-immediately frozen (n = 30), with gel-immediately frozen, without gel-at room temperature (RT) for 5 h before freezing, with gel-at RT for 5 h before freezing, and additional sampling after 24 h without gel-immediate freezing. We found that sample collection with gel lubrication influenced specimen quality-half of the gel samples failing to meet processing limitation compared to those without gel. The effect of gel on testing quality dissipated after 24 h. However, when samples met post-sequencing filters, gel lubrication did not alter the microbial composition, individual taxa abundance or alpha and beta diversity. We recommend sampling either before gel exposure or 24 h after. These findings underscore the importance of considering sample collection methodologies in vaginal microbiome studies to ensure high-quality microbial data for accurate analysis.

scMaui: a widely applicable deep learning framework for single-cell multiomics integration in the presence of batch effects and missing data.

The recent advances in high-throughput single-cell sequencing have created an urgent demand for computational models which can address the high complexity of single-cell multiomics data. Meticulous single-cell multiomics integration models are required to avoid biases towards a specific modality and overcome sparsity. Batch effects obfuscating biological signals must also be taken into account. Here, we introduce a new single-cell multiomics integration model, Single-cell Multiomics Autoencoder Integration (scMaui) based on variational product-of-experts autoencoders and adversarial learning. scMaui calculates a joint representation of multiple marginal distributions based on a product-of-experts approach which is especially effective for missing values in the modalities. Furthermore, it overcomes limitations seen in previous VAE-based integration methods with regard to batch effect correction and restricted applicable assays. It handles multiple batch effects independently accepting both discrete and continuous values, as well as provides varied reconstruction loss functions to cover all possible assays and preprocessing pipelines. We demonstrate that scMaui achieves superior performance in many tasks compared to other methods. Further downstream analyses also demonstrate its potential in identifying relations between assays and discovering hidden subpopulations.

Review of the microbiome and metabolic pathways associated with psychoneurological symptoms in children with cancer.

Children with cancer often endure a range of psychoneurological symptoms (PNS), including pain, fatigue, cognitive impairment, anxiety, depressive symptoms, and sleep disturbance. Despite their prevalence, the underlying pathophysiology of PNS remains unclear. Hypotheses suggest an interplay between the gut microbiome and the functional metabolome, given the immune, neurological, and inflammatory influences these processes exert. This mini-review aims to provide a synopsis of the literature that examines the relationship between microbiome-metabolome pathways and PNS in children with cancer, drawing insights from the adult population when applicable. While there is limited microbiome research in the pediatric population, promising results in adult cancer patients include an association between lower microbial diversity and compositional changes, including decreased abundance of the beneficial microbes Fusicatenibacter, Ruminococcus, and Odoribacter, and more PNS. In pediatric patients, associations between peptide, tryptophan, carnitine shuttle, and gut microbial metabolism pathways and PNS outcomes were found. Utilizing multi-omics methods that combine microbiome and metabolome analyses provide insights into the functional capacity of microbiomes and their associated microbial metabolites. In children with cancer receiving chemotherapy, increased abundances of Intestinibacter and Megasphaera correlated with six metabolic pathways, notably carnitine shuttle and tryptophan metabolism. Interventions that target the underlying microbiome-metabolome pathway may be effective in reducing PNS, including the use of pre- and probiotics, fecal microbiome transplantation, dietary modifications, and increased physical activity. Future multi-omics research is needed to corroborate the associations between the microbiome, metabolome, and PNS outcomes in the pediatric oncology population.

Effects of long-time and short-time heat stress on the meat quality of geese.

This investigation sought to reveal the effects of heat stress on the meat quality of geese. Wuzong geese were subjected to heat stress at 35°C for 25 d or 4 h to examine different heat stress time on meat quality. Short-time heat stress reduced muscle drip loss and meat color L* value while increasing pH value and meat color a* and b* values. Long-time heat stress decreased body weight and increased leg muscle pH value and meat color b* value. Amino acid profile of geese breast muscle revealed that both LHS and SHS can induce L-Cystine but reduced L-Cystathionine, which were positive correlated with cooking loss and meat color lightness, respectively. Lipidome analysis indicated that heat stress would alter the synthesis of unsaturated fatty acids, and the difference between LHS and SHS on lipids mainly focused on Hex1Cer and TG. Non-target metabolome analysis indicated effects of heat stress on Glycerolipid metabolism, Arachidonic acid metabolism, and Pyrimidine metabolism. Proteome analysis showed that heat stress mainly affects cellular respiration metabolism and immune response. These findings highlight the diverse effects of heat stress on meat quality, amino acid composition, lipidome, metabolome, and proteome in geese.

Integrating evolutionary genomics of forest trees to inform future tree breeding amidst rapid climate change.

Global climate change is leading to rapid and drastic shifts in environmental conditions, posing threats to biodiversity and nearly all life forms worldwide. Forest trees serve as foundational components of terrestrial ecosystems and play a crucial and leading role in combating and mitigating the adverse effects of extreme climate events, despite their own vulnerability to these threats. Therefore, understanding and monitoring how natural forests respond to rapid climate change is a key priority for biodiversity conservation. The recent progress of evolutionary genomics, primarily driven by cutting-edge multi-omics technologies, offer powerful new tools to address several key issues. These include the precise delineation of species and evolutionary units, inference of past evolutionary histories and demographic fluctuations, identification of environmental adaptive variants, and measurement of genetic load levels. As the urgency to deal with more extreme environmental stresses grows, understanding the genomics of evolutionary history, local adaptation, future responses to climate change, and the conservation and restoration of natural forest trees will be critical for research at the nexus of global change, population genomics and conservation biology. In this review, we explore the application of evolutionary genomics to assess the effects of global climate change using multi-omics approaches and discuss the outlook for breeding climate-adapted trees.

Multi-omics Integration Analysis: Tools and Applications in Environmental Toxicology.

Nowadays, traditional single-omics study is not enough to explain the causality between molecular alterations and toxicity endpoints for environmental pollutants. With the development of high-throughput sequencing technology and high-resolution mass spectrometry technology, the integrative analysis of multi-omics has become an efficient strategy to understand holistic biological mechanisms and to uncover the regulation network in specific biological processes. This review summarized sample preparation methods, integration analysis tools and the application of multi-omics integration analyses in environmental toxicology field. Currently, omics methods have been widely applied being as the sensitivity of early biological response, especially for low-dose and long-term exposure to environmental pollutants. Integrative omics can reveal the overall changes of genes, proteins, and/or metabolites in the cells, tissues or organisms, which provide new insights into revealing the overall toxicity effects, screening the toxic targets, and exploring the underlying molecular mechanism of pollutants.

Concepts and new developments in droplet-based single cell multi-omics.

Single cell sequencing technologies have become a fixture in the molecular profiling of cells due to their ease, flexibility, and commercial availability. In particular, partitioning individual cells inside oil droplets via microfluidic reactions enables transcriptomic or multi-omic measurements for thousands of cells in parallel. Complementing the multitude of biological discoveries from genomics analyses, the past decade has brought new capabilities from assay baselines to enable a deeper understanding of the complex data from single cell multi-omics. Here, we highlight four innovations that have improved the reliability and understanding of droplet microfluidic assays. We emphasize new developments that further orient principles of technology development and guidelines for the design, benchmarking, and implementation of new droplet-based methodologies.

Serum CD44 levels in early pregnancy and its genetic variants for increased risk of gestational diabetes mellitus in Chinese pregnant women.

This study aimed to explore associations of serum cluster of differentiation 44 (CD44) levels and its genetic variants in early pregnancy with gestational diabetes mellitus (GDM). We conducted a 1:1 case-control study (n = 414) nested in a prospective cohort of 22,302 pregnant women recruited from 2010 to 2012 in Tianjin, China. Blood samples were collected at the first antenatal care visit (at a median of 10th gestational week). Binary conditional logistic regressions were performed to examine associations of serum CD44 levels and its genetic variants with increased risk of GDM. In this study, we found that serum CD44 levels in early pregnancy was associated with GDM risk in a U-shaped manner. High serum CD44 levels and its genetic risk score in early pregnancy were associated with markedly increased risk of GDM after adjustment for traditional confounders (OR: 1.95, 95%CI: 1.12-3.40 & 1.95, 1.05-3.61). Furthermore, after adjustment for serum CD44 levels, the OR of CD44 genetic risk score for GDM was slightly attenuated but not significant (1.84, 0.98-3.48). In conclusion, serum CD44 levels and its genetic variants in early pregnancy were associated with GDM risk in Chinese pregnant women, with the effect of CD44 genetic variants being accounted for by serum CD44. SIGNIFICANCE: Recent studies suggested that pregnant women with GDM may have abnormal levels of CD44 and abnormal expression of CD44 gene, but it is uncertain whether abnormal CD44 plays a causal role in occurrence of GDM. Specifically, it remains unknown whether serum CD44 levels in early pregnancy and its genetic variants can predict the later occurrence of GDM. In this study, we found that high serum CD44 levels in early pregnancy and its genetic variants were associated with markedly increased risk of GDM in Chinese pregnant women, with the effect of CD44 genetic variants being largely accounted for by serum CD44 levels. Our study is the first reporting that serum CD44 levels and its genetic variants were associated with markedly increased risk of GDM. These multi-omics risk markers may be useful for identification of women at high risk of GDM in early pregnancy. Our findings also provide new insights into the disease mechanisms.

Transcriptomics and epigenetic data integration learning module on Google Cloud.

Multi-omics (genomics, transcriptomics, epigenomics, proteomics, metabolomics, etc.) research approaches are vital for understanding the hierarchical complexity of human biology and have proven to be extremely valuable in cancer research and precision medicine. Emerging scientific advances in recent years have made high-throughput genome-wide sequencing a central focus in molecular research by allowing for the collective analysis of various kinds of molecular biological data from different types of specimens in a single tissue or even at the level of a single cell. Additionally, with the help of improved computational resources and data mining, researchers are able to integrate data from different multi-omics regimes to identify new prognostic, diagnostic, or predictive biomarkers, uncover novel therapeutic targets, and develop more personalized treatment protocols for patients. For the research community to parse the scientifically and clinically meaningful information out of all the biological data being generated each day more efficiently with less wasted resources, being familiar with and comfortable using advanced analytical tools, such as Google Cloud Platform becomes imperative. This project is an interdisciplinary, cross-organizational effort to provide a guided learning module for integrating transcriptomics and epigenetics data analysis protocols into a comprehensive analysis pipeline for users to implement in their own work, utilizing the cloud computing infrastructure on Google Cloud. The learning module consists of three submodules that guide the user through tutorial examples that illustrate the analysis of RNA-sequence and Reduced-Representation Bisulfite Sequencing data. The examples are in the form of breast cancer case studies, and the data sets were procured from the public repository Gene Expression Omnibus. The first submodule is devoted to transcriptomics analysis with the RNA sequencing data, the second submodule focuses on epigenetics analysis using the DNA methylation data, and the third submodule integrates the two methods for a deeper biological understanding. The modules begin with data collection and preprocessing, with further downstream analysis performed in a Vertex AI Jupyter notebook instance with an R kernel. Analysis results are returned to Google Cloud buckets for storage and visualization, removing the computational strain from local resources. The final product is a start-to-finish tutorial for the researchers with limited experience in multi-omics to integrate transcriptomics and epigenetics data analysis into a comprehensive pipeline to perform their own biological research.This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [16] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.