Omics Studies Research Articles

Emerging importance of stool preservation methods in OMICS studies with special focus on cancer biology.

The intricate consortium of microorganisms in the human gut plays a crucial role in different physiological functions. The complex known-unknown elements of the gut microbiome are perplexing and the absence of standardized procedures for collecting and preserving samples has hindered continuous research in comprehending it. The technological bias produced because of lack of standard protocols has affected the reproducibility of results. The complex nature of diseases like colorectal cancer, gastric cancer, hepatocellular carcinoma and breast cancer require a thorough understanding of its etiology for an efficient and timely diagnosis. The designated protocols for collection and preservation of stool specimens have great variance, hence generate inconsistencies in OMICS studies. Due to the complications associated to the nature of sample, it is important to preserve the sample to be studied later in a laboratory or to be used in the future research purpose. Stool preservation is gaining importance due to the increased use of treatment options like fecal microbiota transplantation to cure conditions like recurrent Clostridium difficile infections and for OMICS studies including metagenomics, metabolomics and culturomics. This review provides an insight into the importance of omics studies for the identification and development of novel biomarkers for quick and noninvasive diagnosis of various diseases.

Open science in biomedicine: Overcoming barriers in omics studies

Open science in biomedicine: Overcoming barriers in omics studies

Construction of a Full-Length Transcriptome of Western Honeybee Midgut Tissue and Improved Genome Annotation.

Honeybees are an indispensable pollinator in nature with pivotal ecological, economic, and scientific value. However, a full-length transcriptome for Apis mellifera, assembled with the advanced third-generation nanopore sequencing technology, has yet to be reported. Here, nanopore sequencing of the midgut tissues of uninoculated and Nosema ceranae-inoculated A. mellifera workers was conducted, and the full-length transcriptome was then constructed and annotated based on high-quality long reads. Next followed improvement of sequences and annotations of the current reference genome of A. mellifera. A total of 5,942,745 and 6,664,923 raw reads were produced from midguts of workers at 7 days post-inoculation (dpi) with N. ceranae and 10 dpi, while 7,100,161 and 6,506,665 raw reads were generated from the midguts of corresponding uninoculated workers. After strict quality control, 6,928,170, 6,353,066, 5,745,048, and 6,416,987 clean reads were obtained, with a length distribution ranging from 1 kb to 10 kb. Additionally, 16,824, 17,708, 15,744, and 18,246 full-length transcripts were respectively detected, including 28,019 nonredundant ones. Among these, 43,666, 30,945, 41,771, 26,442, and 24,532 full-length transcripts could be annotated to the Nr, KOG, eggNOG, GO, and KEGG databases, respectively. Additionally, 501 novel genes (20,326 novel transcripts) were identified for the first time, among which 401 (20,255), 193 (13,365), 414 (19,186), 228 (12,093), and 202 (11,703) were respectively annotated to each of the aforementioned five databases. The expression and sequences of three randomly selected novel transcripts were confirmed by RT-PCR and Sanger sequencing. The 5' UTR of 2082 genes, the 3' UTR of 2029 genes, and both the 5' and 3' UTRs of 730 genes were extended. Moreover, 17,345 SSRs, 14,789 complete ORFs, 1224 long non-coding RNAs (lncRNAs), and 650 transcription factors (TFs) from 37 families were detected. Findings from this work not only refine the annotation of the A. mellifera reference genome, but also provide a valuable resource and basis for relevant molecular and -omics studies.

Differences between psoriatic arthritis and psoriasis in multi-omics.

We aim to systemically review the genomics, transcriptomics, epigenetics, proteomics, metabonomics and microbiota of psoriatic arthritis and psoriasis, illustrating the differences of these two diseases, broadening our understanding of the pathogenesis of them and providing important clues for valuable biomarkers of earlier diagnosis and treatments. To our knowledge, this is the first study that combine all omics studies from genomics to microbiota and may serve as a reference for future studies to identify the key underlying pathways in psoriatic arthritis.

Elucidating Cancer Subtypes by Using the Relationship between DNA Methylation and Gene Expression.

Advancements in the field of next generation sequencing (NGS) have generated vast amounts of data for the same set of subjects. The challenge that arises is how to combine and reconcile results from different omics studies, such as epigenome and transcriptome, to improve the classification of disease subtypes. In this study, we introduce sCClust (sparse canonical correlation analysis with clustering), a technique to combine high-dimensional omics data using sparse canonical correlation analysis (sCCA), such that the correlation between datasets is maximized. This stage is followed by clustering the integrated data in a lower-dimensional space. We apply sCClust to gene expression and DNA methylation data for three cancer genomics datasets from the Cancer Genome Atlas (TCGA) to distinguish between underlying subtypes. We evaluate the identified subtypes using Kaplan-Meier plots and hazard ratio analysis on the three types of cancer-GBM (glioblastoma multiform), lung cancer and colon cancer. Comparison with subtypes identified by both single- and multi-omics studies implies improved clinical association. We also perform pathway over-representation analysis in order to identify up-regulated and down-regulated genes as tentative drug targets. The main goal of the paper is twofold: the integration of epigenomic and transcriptomic datasets followed by elucidating subtypes in the latent space. The significance of this study lies in the enhanced categorization of cancer data, which is crucial to precision medicine.

Omics Insights into Epicardial Adipose Tissue: Unravelling Its Molecular Landscape

Epicardial adipose tissue (EAT) is a unique fat depot located around the heart, intimately associated with the myocardium and coronary arteries. The secretion of bioactive molecules and their interaction with neighbouring cardiac tissues makes it an active organ with important implications in cardiovascular health and disease. In recent years, omics technologies have provided valuable insights into the molecular characteristics and functional relevance of EAT in patients with coronary atherosclerosis, myocardial infarction, atrial fibrillation and associated metabolic disorders. This review aims to summarize the current understanding of EAT biology through the lens of genomics, transcriptomics, proteomics and lipidomics approaches. We discuss key findings from omics studies on EAT, including gene expression profiles, metabolic activity, signalling pathways and regulatory network, in order to explore in depth the disease mechanisms, possible therapeutic strategies, and cardiovascular risk assessment. Further advances in this field and integrating data from multiple omics platforms hold promise for unlocking personalized cardiometabolic profiles with EAT as a possible biomarker and therapeutic target in cardiovascular disease.

'Emerging into the World' - the regulation and control of dormancy and sprouting in geophytes.

Geophytic plants synchronize growth and quiescence with the external environment to survive and thrive under changing seasons. Besides seasonal growth adaptation, dormancy and sprouting are critical factors determining crop yield and market supply as various geophytes also serve as major food, floriculture, and ornamental crops. Dormancy in such crops decides crop availability in the market, as most of such crops are consumed during the dormant stage. On the other hand, uniform/maximal sprouting is crucial for maximum yield. Thus, dormancy and sprouting regulation have great economic importance. Dormancy-sprouting cycles in geophytes are regulated by genetic, exogenous (environmental), and endogenous (genetic, metabolic and hormonal, etc.) factors. Comparatively, the temperature is more dominant in regulating dormancy and sprouting in geophytes, unlike aboveground tissues, where both photoperiod and temperature control are involved. Despite huge economic importance, studies concerning the regulation of dormancy and sprouting are scarce in the majority of geophytes. To date, only a few molecular factors involved in the process have been suggested. Recently, omics studies on molecular and metabolic factors involved in dormancy and growth regulations of underground vegetative tissues have provided more insight into the mechanism. Here, we discuss current knowledge of the environmental and molecular regulation and control of dormancy and sprouting in geophytes and discuss challenges/questions that need to be addressed in the future for crop improvement.

Spatial transcriptomics reveals the interplay between cancer and immune cells directed by MXene quantum dots

Nanomedicine plays an essential role in the development of tumor treatment modalities. However, tumors have a complex structure that includes a variety of immune cell types, which, along with tumor cells, comprise the heterogeneous tumor microenvironment (TME). Although nanoparticles that overcome the limitations of other classical therapeutics and navigate heterogeneous biological barriers have been developed, many unknown players within the TME still exist. The role of immune cell populations and signaling pathways in the TME, which can affect the nanoparticle distribution in the tumor or the overall outcome of nanotherapeutic treatments, are poorly described. In this study, we used spatial transcriptomics to determine in situ gene expression and identified immune cells, other than tumor-associated macrophages, that play vital roles following nanoparticle exposure. In this proof-of-concept study, a recently explored type of nanoparticle, Ti3C2Tx MXene quantum dots (MQDs), with a single particle diameter of ≤10 nm, were administered to orthotopic breast cancer model mice. Thanks to their red-emitting fluorescence properties, we could track the distribution of MQDs in the tumor. Whole-transcriptome analysis and immunofluorescence staining suggested that the heterogeneous distribution of MQDs results in different tumor and immune cell responses in situ. We observed a more tumor-suppressive phenotype at tumor regions with high MQD accumulation compared to regions with decreased MQD accumulation or naïve tumors. Based on pathway analysis and cell deconvolution, we also identified other immune cell types altered by MQDs, including B cells and neutrophils. Specifically, the application of MQDs recruited and activated B cells and resulted in neutrophil degranulation and NETosis in vivo. Using spatial transcriptomics technology, we defined the fundamental molecular and cellular changes that occur in situ in the TME following MQD administration. Future spatial omics studies involving different nanoparticles with other material characteristics will help design more effective nanotherapeutics.

Omics-driven advances in the understanding of regulatory landscape of peanut seed development.

Peanuts (Arachis hypogaea) are an essential oilseed crop known for their unique developmental process, characterized by aerial flowering followed by subterranean fruit development. This crop is polyploid, consisting of A and B subgenomes, which complicates its genetic analysis. The advent and progression of omics technologies-encompassing genomics, transcriptomics, proteomics, epigenomics, and metabolomics-have significantly advanced our understanding of peanut biology, particularly in the context of seed development and the regulation of seed-associated traits. Following the completion of the peanut reference genome, research has utilized omics data to elucidate the quantitative trait loci (QTL) associated with seed weight, oil content, protein content, fatty acid composition, sucrose content, and seed coat color as well as the regulatory mechanisms governing seed development. This review aims to summarize the advancements in peanut seed development regulation and trait analysis based on reference genome-guided omics studies. It provides an overview of the significant progress made in understanding the molecular basis of peanut seed development, offering insights into the complex genetic and epigenetic mechanisms that influence key agronomic traits. These studies highlight the significance of omics data in profoundly elucidating the regulatory mechanisms of peanut seed development. Furthermore, they lay a foundational basis for future research on trait-related functional genes, highlighting the pivotal role of comprehensive genomic analysis in advancing our understanding of plant biology.

Current advances on single or multi-omics analysis of esophageal cancer

Esophageal cancer is associated with high mortality rates and is one of the cancers with the worst prognosis. Its incidence has significant regional specificity, particularly in China where it is much higher than in other countries. Moreover, effective diagnostic markers, therapeutic targets, and molecular subtyping biomarkers are currently lacking for esophageal cancer. Nevertheless, large-scale omics studies have identified dozens of robust genetic risk loci and prognosis-related loci, drawn genomic, epigenomic, and transcriptomic maps of esophageal cancer at multiple molecular levels, and described significant differences between esophageal squamous cell carcinoma and adenocarcinoma. These studies are of great significance for exploring the occurrence and development mechanism of esophageal cancer, guiding clinical treatment, and improving patient prognosis. This review, from the perspective of multi-omics, discusses the analytical strategies employed in these studies and summarizes their core findings. It emphasizes that the integration and analysis of multi-omics data is a key focus and development trend in the precise medical research of esophageal cancer, and has broad research and application prospects.Keywords: Esophageal cancer; GWAS; Precision medicine; Biomarker

OmicNavigator: open-source software for the exploration, visualization, and archival of omic studies

BackgroundThe results of high-throughput biology (‘omic’) experiments provide insight into biological mechanisms but can be challenging to explore, archive and share. The scale of these challenges continues to grow as omic research volume expands and multiple analytical technologies, bioinformatic pipelines, and visualization preferences have emerged. Multiple software applications exist that support omic study exploration and/or archival. However, an opportunity remains for open-source software that can archive and present the results of omic analyses with broad accommodation of study-specific analytical approaches and visualizations with useful exploration features.ResultsWe present OmicNavigator, an R package for the archival, visualization and interactive exploration of omic studies. OmicNavigator enables bioinformaticians to create web applications that interactively display their custom visualizations and analysis results linked with app-derived analytical tools, graphics, and tables. Studies created with OmicNavigator can be viewed within an interactive R session or hosted on a server for shared access.ConclusionsOmicNavigator can be found at https://github.com/abbvie-external/OmicNavigator

Unraveling the dynamic transcriptomic changes during the dimorphic transition of Talaromyces marneffei through time-course analysis.

Systemic dimorphic fungi pose a significant public health challenge, causing over one million new infections annually. The dimorphic transition between saprophytic mycelia and pathogenic yeasts is strongly associated with the pathogenesis of dimorphic fungi. However, despite the dynamic nature of dimorphic transition, the current omics studies focused on dimorphic transition primarily employ static strategies, partly due to the lack of suitable dynamic analytical methods. We conducted time-course transcriptional profiling during the dimorphic transition of Talaromyces marneffei, a model organism for thermally dimorphic fungi. To capture non-uniform and nonlinear transcriptional changes, we developed DyGAM-NS (dynamic optimized generalized additive model with natural cubic smoothing). The performance of DyGAM-NS was evaluated by comparison with seven other commonly used time-course analysis methods. Based on dimorphic transition induced genes (DTIGs) identified by DyGAM-NS, cluster analysis was utilized to discern distinct gene expression patterns throughout dimorphic transitions of T. marneffei. Simultaneously, a gene expression regulatory network was constructed to probe pivotal regulatory elements governing the dimorphic transitions. By using DyGAM-NS, model, we identified 5,223 DTIGs of T. marneffei. Notably, the DyGAM-NS model showcases performance on par with or superior to other commonly used models, achieving the highest F1 score in our assessment. Moreover, the DyGAM-NS model also demonstrates potential in predicting gene expression levels throughout temporal processes. The cluster analysis of DTIGs suggests divergent gene expression patterns between mycelium-to-yeast and yeast-to-mycelium transitions, indicating the asymmetrical nature of two transition directions. Additionally, leveraging the identified DTIGs, we constructed a regulatory network for the dimorphic transition and identified two zinc finger-containing transcription factors that potentially regulate dimorphic transition in T. marneffei. Our study elucidates the dynamic transcriptional profile changes during the dimorphic transition of T. marneffei. Furthermore, it offers a novel perspective for unraveling the underlying mechanisms of fungal dimorphism, emphasizing the importance of dynamic analytical methods in understanding complex biological processes.

Ploidy inference from single-cell data: application to human and mouse cell atlases.

Ploidy is relevant to numerous biological phenomena, including development, metabolism, and tissue regeneration. Single-cell RNA-seq and other omics studies are revolutionizing our understanding of biology, yet they have largely overlooked ploidy. This is likely due to the additional assay step required for ploidy measurement. Here, we developed a statistical method to infer ploidy from single-cell ATAC-seq data, addressing this gap. When applied to data from human and mouse cell atlases, our method enabled systematic detection of polyploidy across diverse cell types. This method allows for the integration of ploidy analysis into single-cell studies. Additionally, this method can be adapted to detect the proliferating stage in the cell cycle and copy number variations in cancer cells. The software is implemented as the scPloidy package of the R software and is freely available from CRAN.

Restore Veto Property in Low Dose Aspirin/ASA Treated Preeclampsia Placenta Mesenchymal Stem Cells: Insights Into ASA-mediated Clinical Response*

Preeclampsia (PE) is a pregnancy complication characterized by elevated blood pressure, proteinuria, and other laboratory abnormalities. PE affects 2–8% of pregnancies globally and can lead to preterm birth and other complications for the mother and fetus. Successful pregnancy depends on the ability of the mother’s immune system to tolerate the allogeneic fetus. However, in PE, this immune tolerance is exacerbated by inflammation. Transforming growth factor β (TGF-β) is important to retain an immune balance in healthy pregnancy. In PE, TGF-β level is reduced, with an imbalance of the T-cell subset pool to favor inflammation. Omics studies by our group reported an increase in TGF-β signaling when PE-derived placenta mesenchymal stem cells (P-MSCs) were treated with low dose aspirin (ASA). This correlated with increased cycling quiescence and epigenetic changes, resembling healthy P-MSCs. This study tested the hypothesis that ASA could restore the veto property of P-MSCs to mitigate inflammation. ASA (10 mM) treated P-MSCs from PE and healthy placentas increased TGFβ1 and its receptor. The ASA treated MSCs, when added as third-party cells to a one-way mixed lymphocyte reaction, suppressed T-cell proliferation. Prediction studies with omics data indicated that ASA-mediated TGFβ signaling could explain ASA-induced blunting of cell apoptosis. Together, the findings support ASA-mediated expression of TGFβ1 and its receptor on P-MSCs from PE to restore the ability to be licensed as immune suppressor to mitigate PE inflammation. The findings provide new insight into the benefit of ASA treatment for PE.

Single Cell Atlas: a single-cell multi-omics human cell encyclopedia.

Single-cell sequencing datasets are key in biology and medicine for unraveling insights into heterogeneous cell populations with unprecedented resolution. Here, we construct a single-cell multi-omics map of human tissues through in-depth characterizations of datasets from five single-cell omics, spatial transcriptomics, and two bulk omics across 125 healthy adult and fetal tissues. We construct its complement web-based platform, the Single Cell Atlas (SCA, www.singlecellatlas.org ), to enable vast interactive data exploration of deep multi-omics signatures across human fetal and adult tissues. The atlas resources and database queries aspire to serve as a one-stop, comprehensive, and time-effective resource for various omics studies.

Lysine and salt-sensitive hypertension.

Salt-sensitive (SS) hypertension and its associated kidney damage have been extensively studied, yet proper therapeutic strategies are lacking. The interest in altering the metabolome to affect renal and cardiovascular disease has been emerging. Here, we discuss the effect and potential mechanism behind the protective effect of lysine, an essential amino acid, on the progression of SS hypertension. We have recently demonstrated that administering lysine in an SS rodent model can control the progression of hypertension. Both the animal and pilot human studies showed that lysine can efficiently inhibit tubular reabsorption of albumin and protect the kidneys from further damage. In addition, we conducted multilevel omics studies that showed increased lysine conjugation and excretion, leading to the depletion of harmful metabolites and an increase in useful ones. Lysine's twofold action involves both mechanically flushing protein from proximal tubules to shield the kidneys and initiating metabolic adaptations in the kidneys. This results in a net positive impact on SS hypertension. While further research is necessary to apply the current findings in clinical settings, this study offers some evidence suggesting that lysine supplementation holds promise as a therapeutic approach for hypertensive kidney disease.

Glycoproteome-Wide Discovery of Cortical Glycoproteins That May Provide Cognitive Resilience in Older Adults.

Molecular omics studies have identified proteins related to cognitive resilience but unrelated to Alzheimer disease and Alzheimer disease-related dementia (AD/ADRD) pathologies. Posttranslational modifications of proteins with glycans can modify protein function. In this study, we identified glycopeptiforms associated with cognitive resilience. We studied brains from adults with annual cognitive testing with postmortem indices of 10 AD/ADRD pathologies and proteome-wide data from dorsal lateral prefrontal cortex (DLPFC). We quantified 11, 012 glycopeptiforms from DLPFC using liquid chromatography with tandem mass spectrometry. We used linear mixed-effects models to identify glycopeptiforms associated with cognitive decline correcting for multiple comparisons (p < 5 × 10-6). Then, we regressed out the effect of AD/ADRD pathologies to identify glycopeptiforms that may provide cognitive resilience. We studied 366 brains, average age at death 89 years, and 70% female with no cognitive impairment = 152, mild cognitive impairment = 93, and AD = 121 cognitive status at death. In models adjusting for age, sex and education, 11 glycopeptiforms were associated with cognitive decline. In further modeling, 8 of these glycopeptiforms remained associated with cognitive decline after adjusting for AD/ADRD pathologies: NPTX2a (Est., 0.030, SE, 0.005, p = 1 × 10-4); NPTX2b (Est.,0.019, SE, 0.005, p = 2 × 10-4) NECTIN1(Est., 0.029, SE, 0.009, p = 9 × 10-4), NPTX2c (Est., 0.015, SE, 0.004, p = 9 × 10-4), HSPB1 (Est., -0.021, SE, 0.006, p = 2 × 10-4), PLTP (Est., -0.027, SE, 0.009, p = 4.2 × 10-3), NAGK (Est., -0.027, SE, 0.008, p = 1.4 × 10-3), and VAT1 (Est., -0.020, SE, 0.006, p = 1.1 × 10-3). Higher levels of 4 resilience glycopeptiforms derived through glycosylation were associated with slower decline and higher levels of 4 derived through glycation were related to faster decline. Together, these 8 glycopeptiforms accounted for an additional 6% of cognitive decline over the 33% accounted for the 10 brain pathologies and demographics. All 8 resilience glycopeptiforms remained associated with cognitive decline after adjustments for the expression level of their corresponding protein. Exploratory gene ontology suggested that molecular mechanisms of glycopeptiforms associated with cognitive decline may involve metabolic pathways including pyruvate and NADH pathways and highlighted the importance of molecular mechanisms involved in glucose metabolism. Glycopeptiforms in aging brains may provide cognitive resilience. Targeting these glycopeptiforms may lead to therapies that maintain cognition through resilience.

High‐dimensional feature screening for nonlinear associations with survival outcome using restricted mean survival time

SummaryFeature screening is an important tool in analysing ultrahigh‐dimensional data, particularly in the field of Omics and oncology studies. However, most attention has been focused on identifying features that have a linear or monotonic impact on the response variable. Detecting a sparse set of variables that have a nonlinear or nonmonotonic relationship with the response variable is still a challenging task. To fill the gap, this paper proposed a robust model‐free screening approach for right‐censored survival data by providing a new perspective of quantifying the covariate effect on the restricted mean survival time, rather than the routinely used hazard function. The proposed measure, based on the difference between the restricted mean survival time of covariate‐stratified and overall data, is able to identify comprehensive types of associations including linear, nonlinear, nonmonotone and even local dependencies like change points. The sure screening property is established, and a more flexible iterative screening procedure is developed to increase the accuracy of the variable screening. Simulation studies are carried out to demonstrate the superiority of the proposed method in selecting important features with a complex association with the response variable. The potential of applying the proposed method to handle interval‐censored failure time data has also been explored in simulations, and the results have been promising. The method is applied to a breast cancer dataset to identify potential prognostic factors, which reveals potential associations between breast cancer and lymphoma.

Abstract LB250: Dynamics of transcript alternations and differential splicing in the evolution of acquired MAPKi resistance in melanoma

Abstract The primary therapy for melanoma with a BRAF mutation involves Mitogen-Activated Protein Kinase inhibitors (MAPKi), targeting the dysregulated MAPK signaling pathway. Despite the initial response, many patients often develop acquired resistance. Resistance to MAPKi could be linked to various altered signaling pathways, including reactivation of the MAPK pathway, activation of alternative survival pathways like PI3K/PTEN/AKT, engagement of receptor tyrosine kinases (RTKs) such as PDGFRβ and EGFR, and developmental pathways. While numerous omics studies have aimed to understand the mechanism of acquired resistance, how these pathways are altered remains elusive. To gain further insights into the acquired MAPKi resistance in melanoma, we explored the role of RNA splicing events in this context by analyzing publicly available datasets of pre- and post-MAPKi treated melanoma from patients and in vitro studies using cell lines. We investigated the differential transcript usage (DTU) to detect the specific splice variants that are altered during the development of resistance. We find that the genes involving DTU are enriched in various pathways related to MAPKi resistance, such as the MAPKi signaling pathway, PI3K/AKT pathway, signaling by RTKs, etc. Furthermore, the DTU-centered analysis provides better insights into MAPKi resistance mechanisms compared to standard differential gene expression analysis. Next, we analyzed the developmental splicing signatures during MAPKi resistance development, revealing developmental reprogramming as a key factor in MAPKi resistance. Further, we identified the top 10 transcription factors as potential upstream regulators of the perturbed transcripts, among which five have been previously reported to be involved in various stages of embryonic development. This finding suggests a link between the reactivation of developmental pathways and MAPKi resistance development. Further, by analyzing MAPKi-treated melanoma cell-line models at different time points, such as no-treatment, on-treatment, and at the resistant stage, we observed significant alterations in transcript isoforms during the development of resistance. These changes involve lipid metabolic reprogramming and PI3K/Akt/mTOR signaling, providing further insights into the mechanisms of MAPKi resistance. Our study also uncovers a global shift in transcript profiles towards shorter 3’ and 5’ UTRs in response to MAPKi treatment, suggesting the existence of a master regulatory mechanism influencing global splicing changes during therapy resistance. In summary, our study deciphered the post-transcriptional mechanism coordinating the signaling changes, underscoring the complexity of MAPKi resistance in melanoma, as revealed by the joint analysis of both transcript alterations and splicing events. Citation Format: Sumit Mukherjee, Arashdeep Singh, Hyunjeong Joo, Sumeet Patiyal, Hyungsoo Kim, Lipika R. Pal, Kun Wang, Chi-Ping Day, Ze’ev A. Ronai, Eytan Ruppin, Sridhar Hannenhalli. Dynamics of transcript alternations and differential splicing in the evolution of acquired MAPKi resistance in melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB250.

Plasma metabolomic markers underlying skeletal muscle mitochondrial function relationships with cognition and motor function.

Lower skeletal muscle mitochondrial function is associated with future cognitive impairment and mobility decline, but the biological underpinnings for these associations are unclear. We examined metabolomic markers underlying skeletal muscle mitochondrial function, cognition and motor function. We analysed data from 560 participants from the Baltimore Longitudinal Study of Aging (mean age: 68.4years, 56% women, 28% Black) who had data on skeletal muscle oxidative capacity (post-exercise recovery rate of phosphocreatine, kPCr) via 31P magnetic resonance spectroscopy and targeted plasma metabolomics using LASSO model. We then examined which kPCr-related markers were also associated with cognition and motor function in a larger sample (n = 918, mean age: 69.4, 55% women, 27% Black). The LASSO model revealed 24 metabolites significantly predicting kPCr, with the top 5 being asymmetric dimethylarginine, lactic acid, lysophosphatidylcholine a C18:1, indoleacetic acid and triacylglyceride (17:1_34:3), also significant in multivariable linear regression. The kPCr metabolite score was associated with cognitive or motor function, with 2.5-minute usual gait speed showing the strongest association (r = 0.182). Five lipids (lysophosphatidylcholine a C18:1, phosphatidylcholine ae C42:3, cholesteryl ester 18:1, sphingomyelin C26:0, octadecenoic acid) and 2 amino acids (leucine, cystine) were associated with both cognitive and motor function measures. Our findings add evidence to the hypothesis that mitochondrial function is implicated in the pathogenesis of cognitive and physical decline with aging and suggest that targeting specific metabolites may prevent cognitive and mobility decline through their effects on mitochondria. Future omics studies are warranted to confirm these findings and explore mechanisms underlying mitochondrial dysfunction in aging phenotypes.