Timing of standard chow exposure determines the variability of mouse phenotypic outcomes and gut microbiota profile.

Standard chow diet contributes to reproducibility in animal model experiments since chows differ in nutrient composition, which can independently influence phenotypes. However, there is little evidence of the role of timing in the extent of variability caused by chow exposure. Here, we measured the impact of diet (5V5M, 5V0G, 2920X, and 5058) and timing of exposure (adult exposure (AE), lifetime exposure (LE), and developmental exposure (DE)) on growth & development, metabolic health indicators, and gut bacterial microbiota profiles across genetically identical C57BL6/J mice. Diet drove differences in macro- and micronutrient intake for all exposure models. AE had no effect on measured outcomes. However, LE mice exhibited significant sex-dependent diet effects on growth, body weight, and body composition. LE effects were mostly absent in the DE model, where mice were exposed to chow differences from conception to weaning. Both AE and LE models exhibited similar diet-driven beta diversity profiles for the gut bacterial microbiota, with 5058 diet driving the most distinct profile. Diet-induced beta diversity profiles were sex-dependent for LE mice. Compared to AE, LE drove 9X more diet-driven differentially abundant genera, majority of which were the result of inverse effects of 2920X and 5058. Our findings demonstrate that lifetime exposure to different chow diets has the greatest impact on reproducibility of experimental measures that are common components of preclinical mouse model studies. Importantly, weaning DE mice onto a uniform diet is likely an effective way to reduce unwanted phenotypic variability among experimental models.

Comparative proteomic analysis of tail regeneration in the green anole lizard, Anolis carolinensis.

As amniote vertebrates, lizards are the most closely related organisms to humans capable of appendage regeneration. Lizards can autotomize, or release their tails as a means of predator evasion, and subsequently regenerate a functional replacement. Green anoles (Anolis carolinensis) can regenerate their tails through a process that involves differential expression of hundreds of genes, which has previously been analyzed by transcriptomic and microRNA analysis. To investigate protein expression in regenerating tissue, we performed whole proteomic analysis of regenerating tail tip and base. This is the first proteomic data set available for any anole lizard. We identified a total of 2,646 proteins - 976 proteins only in the regenerating tail base, 796 only in the tail tip, and 874 in both tip and base. For over 90% of these proteins in these tissues, we were able to assign a clear orthology to gene models in either the Ensembl or NCBI databases. For 13 proteins in the tail base, 9 proteins in the tail tip, and 10 proteins in both regions, the gene model in Ensembl and NCBI matched an uncharacterized protein, confirming that these predictions are present in the proteome. Ontology and pathways analysis of proteins expressed in the regenerating tail base identified categories including actin filament-based process, ncRNA metabolism, regulation of phosphatase activity, small GTPase mediated signal transduction, and cellular component organization or biogenesis. Analysis of proteins expressed in the tail tip identified categories including regulation of organelle organization, regulation of protein localization, ubiquitin-dependent protein catabolism, small GTPase mediated signal transduction, morphogenesis of epithelium, and regulation of biological quality. These proteomic findings confirm pathways and gene families activated in tail regeneration in the green anole as well as identify uncharacterized proteins whose role in regrowth remains to be revealed. This study demonstrates the insights that are possible from the integration of proteomic and transcriptomic data in tail regrowth in the green anole, with potentially broader application to studies in other regenerative models.

Moderate intrinsic phenotypic alterations in C9orf72 ALS/FTD iPSC-microglia despite the presence of C9orf72 pathological features.

While motor and cortical neurons are affected in C9orf72 amyotrophic lateral sclerosis and frontotemporal dementia (ALS/FTD), it remains largely unknown if and how non-neuronal cells induce or exacerbate neuronal damage. We differentiated C9orf72 ALS/FTD patient-derived induced pluripotent stem cells into microglia (iPSC-MG) and examined their intrinsic phenotypes. Similar to iPSC motor neurons, C9orf72 ALS/FTD iPSC-MG mono-cultures form G4C2 repeat RNA foci, exhibit reduced C9orf72 protein levels, and generate dipeptide repeat proteins. Healthy control and C9orf72 ALS/FTD iPSC-MG equally express microglial specific genes and perform microglial functions, including inflammatory cytokine release and phagocytosis of extracellular cargos, such as synthetic amyloid beta peptides and healthy human brain synaptoneurosomes. RNA sequencing analysis revealed select transcriptional changes of genes associated with neuroinflammation or neurodegeneration in diseased microglia yet no significant differentially expressed microglial-enriched genes. Moderate molecular and functional differences were observed in C9orf72 iPSC-MG mono-cultures despite the presence of C9orf72 pathological features suggesting that a diseased microenvironment may be required to induce phenotypic changes in microglial cells and the associated neuronal dysfunction seen in C9orf72 ALS/FTD neurodegeneration.

The Foundational Data Initiative for Parkinson Disease: Enabling efficient translation from genetic maps to mechanism.

The Foundational Data Initiative for Parkinson Disease (FOUNDIN-PD) is an international collaboration producing fundamental resources for Parkinson disease (PD). FOUNDIN-PD generated a multi-layered molecular dataset in a cohort of induced pluripotent stem cell (iPSC) lines differentiated to dopaminergic (DA) neurons, a major affected cell type in PD. The lines were derived from the Parkinson's Progression Markers Initiative study, which included participants with PD carrying monogenic PD variants, variants with intermediate effects, and variants identified by genome-wide association studies and unaffected individuals. We generated genetic, epigenetic, regulatory, transcriptomic, and longitudinal cellular imaging data from iPSC-derived DA neurons to understand molecular relationships between disease-associated genetic variation and proximate molecular events. These data reveal that iPSC-derived DA neurons provide a valuable cellular context and foundational atlas for modeling PD genetic risk. We have integrated these data into a FOUNDIN-PD data browser as a resource for understanding the molecular pathogenesis of PD.

Dynamic transcriptomic responses to divergent acute exercise stimuli in young adults.

Acute exercise elicits dynamic transcriptional changes that, when repeated, form the fundamental basis of health, resilience, and performance adaptations. While moderate-intensity endurance training combined with conventional resistance training (traditional, TRAD) is often prescribed and recommended by public health guidance, high-intensity training combining maximal-effort intervals with intensive, limited-rest resistance training is a time-efficient alternative that may be used tactically (HITT) to confer similar benefits. Mechanisms of action of these distinct stimuli are incompletely characterized and have not been directly compared. We assessed transcriptome-wide responses in skeletal muscle and circulating extracellular vesicles (EVs) to a single exercise bout in young adults randomized to TRAD (n = 21, 12 M/9 F, 22 ± 3 yr) or HITT (n = 19, 11 M/8 F, 22 ± 2 yr). Next-generation sequencing captured small, long, and circular RNA in muscle and EVs. Analysis identified differentially expressed transcripts (|log2FC|>1, FDR ≤ 0.05) immediately (h0, EVs only), h3, and h24 postexercise within and between exercise protocols. In aaddition, all apparently responsive transcripts (FDR < 0.2) underwent singular value decomposition to summarize data structures into latent variables (LVs) to deconvolve molecular expression circuits and interregulatory relationships. LVs were compared across time and exercise protocol. TRAD, a longer but less intense stimulus, generally elicited a stronger transcriptional response than HITT, but considerable overlap and key differences existed. Findings reveal shared and unique molecular responses to the exercise stimuli and lay groundwork toward establishing relationships between protein-coding genes and lesser-understood transcripts that serve regulatory roles following exercise. Future work should advance the understanding of these circuits and whether they repeat in other populations or following other types of exercise/stress.NEW & NOTEWORTHY We examined small and long transcriptomics in skeletal muscle and serum-derived extracellular vesicles before and after a single exposure to traditional combined exercise (TRAD) and high-intensity tactical training (HITT). Across 40 young adults, we found more consistent protein-coding gene responses to TRAD, whereas HITT elicited differential expression of microRNA enriched in brain regions. Follow-up analysis revealed relationships and temporal dynamics across transcript networks, highlighting potential avenues for research into mechanisms of exercise response and adaptation.

Plasma extracellular vesicle transcriptome as a dynamic liquid biopsy in acute heart failure.

Background: Acute decompensation is associated with increased mortality in heart failure (HF) patients, though the underlying etiology remains unclear. Extracellular vesicles (EVs) and their cargo may mark specific cardiovascular physiologic states. We hypothesized that EV transcriptomic cargo, including long non-coding RNAs (lncRNAs) and mRNAs, is dynamic from the decompensated to recompensated HF state, reflecting molecular pathways relevant to adverse remodeling. Methods: We examined differential RNA expression from circulating plasma extracellular RNA in acute HF patients at hospital admission and discharge alongside healthy controls. We leveraged different exRNA carrier isolation methods, publicly available tissue banks, and single nuclear deconvolution of human cardiac tissue to identify cell and compartment specificity of the topmost significantly differentially expressed targets. EV-derived transcript fragments were prioritized by fold change (-1.5 to + 1.5) and significance (<5% false discovery rate), and their expression in EVs was subsequently validated in 182 additional patients (24 control; 86 HFpEF; 72 HFrEF) by qRT-PCR. We finally examined the regulation of EV-derived lncRNA transcripts in human cardiac cellular stress models. Results: We identified 138 lncRNAs and 147 mRNAs (present mostly as fragments in EVs) differentially expressed between HF and control. Differentially expressed transcripts between HFrEF vs. control were primarily cardiomyocyte derived, while those between HFpEF vs. control originated from multiple organs and different (non-cardiomyocyte) cell types within the myocardium. We validated 5 lncRNAs and 6 mRNAs to differentiate between HF and control. Of those, 4 lncRNAs (AC092656.1, lnc-CALML5-7, LINC00989, RMRP) were altered by decongestion, with their levels independent of weight changes during hospitalization. Further, these 4 lncRNAs dynamically responded to stress in cardiomyocytes and pericytes in vitro , with a directionality mirroring the acute congested state. Conclusion: Circulating EV transcriptome is significantly altered during acute HF, with distinct cell and organ specificity in HFpEF vs. HFrEF consistent with a multi-organ vs. cardiac origin, respectively. Plasma EV-derived lncRNA fragments were more dynamically regulated with acute HF therapy independent of weight change (relative to mRNAs). This dynamicity was further demonstrated with cellular stress in vitro . Prioritizing transcriptional changes in plasma circulating EVs with HF therapy may be a fruitful approach to HF subtype-specific mechanistic discovery. CLINICAL PERSPECTIVE: What is new?: We performed extracellular transcriptomic analysis on the plasma of patients with acute decompensated heart failure (HFrEF and HFpEF) before and after decongestive efforts.Long non-coding RNAs (lncRNAs) within extracellular vesicles (EVs) changed dynamically upon decongestion in concordance with changes within human iPSC-derived cardiomyocytes under stress.In acute decompensated HFrEF, EV RNAs are mainly derived from cardiomyocytes, whereas in HFpEF, EV RNAs appear to have broader, non-cardiomyocyte origins.What are the clinical implications?: Given their concordance between human expression profiles and dynamic in vitro responses, lncRNAs within EVs during acute HF may provide insight into potential therapeutic targets and mechanistically relevant pathways. These findings provide a "liquid biopsy" support for the burgeoning concept of HFpEF as a systemic disorder extending beyond the heart, as opposed to a more cardiac-focused physiology in HFrEF.

Muscle transcriptomic circuits linked to periarticular physiology in end-stage osteoarthritis.

The ability of individuals with end-stage osteoarthritis (OA) to functionally recover from total joint arthroplasty is highly inconsistent. The molecular mechanisms driving this heterogeneity have yet to be elucidated. Furthermore, OA disproportionately impacts females, suggesting a need for identifying female-specific therapeutic targets. We profiled the skeletal muscle transcriptome in females with end-stage OA (n = 20) undergoing total knee or hip arthroplasty using RNA-Seq. Single-gene differential expression (DE) analyses tested for DE genes between skeletal muscle overlaying the surgical (SX) joint and muscle from the contralateral (CTRL) leg. Network analyses were performed using Pathway-Level Information ExtractoR (PLIER) to summarize genes into latent variables (LVs), i.e., gene circuits, and link them to biological pathways. LV differences in SX versus CTRL muscle and across sources of muscle tissue (vastus medialis, vastus lateralis, or tensor fascia latae) were determined with ANOVA. Linear models tested for associations between LVs and muscle phenotype on the SX side (inflammation, function, and integrity). DE analysis revealed 360 DE genes (|Log2 fold-difference| ≥ 1, FDR ≤ 0.05) between the SX and CTRL limbs, many associated with inflammation and lipid metabolism. PLIER analyses revealed circuits associated with protein degradation and fibro-adipogenic cell gene expression. Muscle inflammation and function were linked to an LV associated with endothelial cell gene expression highlighting a potential regulatory role of endothelial cells within skeletal muscle. These findings may provide insight into potential therapeutic targets to improve OA rehabilitation before and/or following total joint replacement.

Phase 2 of extracellular RNA communication consortium charts next-generation approaches for extracellular RNA research.

The extracellular RNA communication consortium (ERCC) is an NIH-funded program aiming to promote the development of new technologies, resources, and knowledge about exRNAs and their carriers. After Phase 1 (2013-2018), Phase 2 of the program (ERCC2, 2019-2023) aims to fill critical gaps in knowledge and technology to enable rigorous and reproducible methods for separation and characterization of both bulk populations of exRNA carriers and single EVs. ERCC2 investigators are also developing new bioinformatic pipelines to promote data integration through the exRNA atlas database. ERCC2 has established several Working Groups (Resource Sharing, Reagent Development, Data Analysis and Coordination, Technology Development, nomenclature, and Scientific Outreach) to promote collaboration between ERCC2 members and the broader scientific community. We expect that ERCC2's current and future achievements will significantly improve our understanding of exRNA biology and the development of accurate and efficient exRNA-based diagnostic, prognostic, and theranostic biomarker assays.

Multi-modality machine learning predicting Parkinson's disease.

Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson's disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug-gene interactions. We performed automated ML on multimodal data from the Parkinson's progression marker initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson's Disease Biomarker Program (PDBP) dataset. Our initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data (PDBP, AUC 85.03%). Optimizing thresholds for classification increased the diagnosis prediction accuracy and other metrics. Finally, networks were built to identify gene communities specific to PD. Combining data modalities outperforms the single biomarker paradigm. UPSIT and PRS contributed most to the predictive power of the model, but the accuracy of these are supplemented by many smaller effect transcripts and risk SNPs. Our model is best suited to identifying large groups of individuals to monitor within a health registry or biobank to prioritize for further testing. This approach allows complex predictive models to be reproducible and accessible to the community, with the package, code, and results publicly available.

A Novel Tissue Atlas and Online Tool for the Interrogation of Small RNA Expression in Human Tissues and Biofluids.

One promising goal for utilizing the molecular information circulating in biofluids is the discovery of clinically useful biomarkers. Extracellular RNAs (exRNAs) are one of the most diverse classes of molecular cargo, easily assayed by sequencing and with expressions that rapidly change in response to subject status. Despite diverse exRNA cargo, most evaluations from biofluids have focused on small RNA sequencing and analysis, specifically on microRNAs (miRNAs). Another goal of characterizing circulating molecular information, is to correlate expression to injuries associated with specific tissues of origin. Biomarker candidates are often described as being specific, enriched in a particular tissue or associated with a disease process. Likewise, miRNA data is often reported to be specific, enriched for a tissue, without rigorous testing to support the claim. Here we provide a tissue atlas of small RNAs from 30 different tissues and three different blood cell types. We analyzed the tissues for enrichment of small RNA sequences and assessed their expression in biofluids: plasma, cerebrospinal fluid, urine, and saliva. We employed published data sets representing physiological (resting vs. acute exercise) and pathologic states (early- vs. late-stage liver fibrosis, and differential subtypes of stroke) to determine differential tissue-enriched small RNAs. We also developed an online tool that provides information about exRNA sequences found in different biofluids and tissues. The data can be used to better understand the various types of small RNA sequences in different tissues as well as their potential release into biofluids, which should help in the validation or design of biomarker studies.

Nasal Feeding Tubes Are Associated with Fewer Adverse Events than Feeding via Ostomy in Hospitalized Patients Receiving Enteral Nutrition.

BACKGROUND: Surgical feeding ostomies (eg, gastrostomy) have become required by many nursing facilities for all patients receiving enteral nutrition, whether for short- or long-term use. These policies lack supportive evidence. Comparisons of adverse event rates between surgical and natural orifice tubes are few and lacking in the inpatient setting. Additionally, we hypothesize that adverse events related to feeding tubes are underreported. We sought to quantify adverse events to test the relative safety of surgical feeding ostomies and natural orifice (eg. nasogastric or orogastric) feeding tubes in hospitalized patients. METHODS: This was a prospective observational cohort study of enterally fed inpatients using semiweekly focused physical examination, scripted survey, and chart review. RESULTS: All tube-fed patients admitted to a large, urban, academic hospital received semiweekly bedside evaluation and chart review over a 9-week period (n = 226 unique patients, mean 6.25 visits each, total 1118 observations). Demographics were comparable between 148 subjects with natural orifice and 113 subjects with surgical feeding tubes. A higher incidence of adverse events was observed with surgical tubes (3.34 vs 1.25 events per 100 subject days, P < .001). Only 50% of all adverse events were documented in the medical record. More patients with surgical tubes were discharged to skilled nursing facilities (58% vs 24%). CONCLUSIONS: Surgical feeding tubes are associated with significantly higher in-hospital adverse event rates when compared with natural orifice (nasal or oral) feeding tubes. Policies requiring surgical feeding ostomies should be reevaluated.

Extracellular circular RNA profiles in plasma and urine of healthy, male college athletes.

Circular RNA (circRNA) are a recently discovered class of RNA characterized by a covalently-bonded back-splice junction. As circRNAs are inherently more stable than other RNA species, they may be detected extracellularly in peripheral biofluids and provide novel biomarkers. While circRNA have been identified previously in peripheral biofluids, there are few datasets for circRNA junctions from healthy controls. We collected 134 plasma and 114 urine samples from 54 healthy, male college athlete volunteers, and used RNASeq to determine circRNA content. The intersection of six bioinformatic tools identified 965 high-confidence, characteristic circRNA junctions in plasma and 72 in urine. Highly-expressed circRNA junctions were validated by qRT-PCR. Longitudinal samples were collected from a subset, demonstrating circRNA expression was stable over time. Lastly, the ratio of circular to linear transcripts was higher in plasma than urine. This study provides a valuable resource for characterization of circRNA in plasma and urine from healthy volunteers, one that can be developed and reassessed as researchers probe the circRNA contents of biofluids across physiological changes and disease states.

HDL-enriched miR-30a-5p is associated with HDL-cholesterol levels and glucose metabolism in healthy men and women.

Aim: To investigate the associations between high-density lipoprotein (HDL)-enriched miRNAs and the cardiometabolic profile of healthy men and women. Patients & methods: miRNAs were quantified using next-generation sequencing of miRNAs extracted from purified HDL and plasma from 17 healthy men and women couples. Results: Among the HDL-enriched miRNAs, miR-30a-5p correlated positively with HDL-cholesterol levels, whereas miR-144-5p and miR-30a-5p were negatively associated with fasting insulin levels and Homeostasis model assessment of insulin resistance index. Overall, miR-30a-5p, miR-150-5p and sex contributed to 45% of HDL-cholesterol variance. A model containing only miR-30a-5p, age and sex explained 41% of fasting glucose variance. Conclusion: HDL-enriched miRNAs, notably miR-30a-5p, are associated with cardiometabolic markers. These miRNAs could play a role in HDL's protective functions, particularly regarding glucose-insulin homeostasis.

Analysis of recurrently protected genomic regions in cell-free DNA found in urine.

Cell-free DNA (cfDNA) in urine is a promising analyte for noninvasive diagnostics. However, urine cfDNA is highly fragmented. Whether characteristics of these fragments reflect underlying genomic architecture is unknown. Here, we characterized fragmentation patterns in urine cfDNA using whole-genome sequencing. Size distribution of urine cfDNA fragments showed multiple strong peaks between 40 and 120 base pairs (bp) with a modal size of 81- and sharp 10-bp periodicity, suggesting transient protection from complete degradation. These properties were robust to preanalytical perturbations, such as at-home collection and delay in processing. Genome-wide sequencing coverage of urine cfDNA fragments revealed recurrently protected regions (RPRs) conserved across individuals, with partial overlap with nucleosome positioning maps inferred from plasma cfDNA. The ends of cfDNA fragments clustered upstream and downstream of RPRs, and nucleotide frequencies of fragment ends indicated enzymatic digestion of urine cfDNA. Compared to plasma, fragmentation patterns in urine cfDNA showed greater correlation with gene expression and chromatin accessibility in epithelial cells of the urinary tract. We determined that tumor-derived urine cfDNA exhibits a higher frequency of aberrant fragments that end within RPRs. By comparing the fraction of aberrant fragments and nucleotide frequencies of fragment ends, we identified urine samples from cancer patients with an area under the curve of 0.89. Our results revealed nonrandom genomic positioning of urine cfDNA fragments and suggested that analysis of fragmentation patterns across recurrently protected genomic loci may serve as a cancer diagnostic.

RNA sequencing of whole blood reveals early alterations in immune cells and gene expression in Parkinson's disease.

Changes in the blood-based RNA transcriptome have the potential to inform biomarkers of Parkinson's disease (PD) progression. Here we sequenced a discovery set of whole-blood RNA species in 4,871 longitudinally collected samples from 1,570 clinically phenotyped individuals from the Parkinson's Progression Marker Initiative (PPMI) cohort. Samples were sequenced to an average of 100 million read pairs to create a high-quality transcriptome. Participants with PD in the PPMI had significantly altered RNA expression (>2,000 differentially expressed genes), including an early and persistent increase in neutrophil gene expression, with a concomitant decrease in lymphocyte cell counts. This was validated in a cohort from the Parkinson's Disease Biomarkers Program (PDBP) consisting of 1,599 participants and by alterations in immune cell subtypes. This publicly available transcriptomic dataset, coupled with available detailed clinical data, provides new insights into PD biological processes impacting whole blood and new paths for developing diagnostic and prognostic PD biomarkers.

Deep sequencing of sncRNAs reveals hallmarks and regulatory modules of the transcriptome during Parkinson's disease progression.

Noncoding RNAs have diagnostic and prognostic importance in Parkinson's disease (PD). We studied circulating small noncoding RNAs (sncRNAs) in two large-scale longitudinal PD cohorts (Parkinson's Progression Markers Initiative (PPMI) and Luxembourg Parkinson's Study (NCER-PD)) and modeled their impact on the transcriptome. Sequencing of sncRNAs in 5,450 blood samples of 1,614 individuals in PPMI yielded 323 billion reads, most of which mapped to microRNAs but covered also other RNA classes such as piwi-interacting RNAs, ribosomal RNAs and small nucleolar RNAs. Dysregulated microRNAs associated with disease and disease progression occur in two distinct waves in the third and seventh decade of life. Originating predominantly from immune cells, they resemble a systemic inflammation response and mitochondrial dysfunction, two hallmarks of PD. Profiling 1,553 samples from 1,024 individuals in the NCER-PD cohort validated biomarkers and main findings by an independent technology. Finally, network analysis of sncRNA and transcriptome sequencing from PPMI identified regulatory modules emerging in patients with progressing PD.

Maternal Liver Metabolic Response to Chronic Vitamin D Deficiency Is Determined by Mouse Strain Genetic Background.

BACKGROUND: Liver metabolite concentrations have the potential to be key biomarkers of systemic metabolic dysfunction and overall health. However, for most conditions we do not know the extent to which genetic differences regulate susceptibility to metabolic responses. This limits our ability to detect and diagnose effects in heterogeneous populations. OBJECTIVES: Here, we investigated the extent to which naturally occurring genetic differences regulate maternal liver metabolic response to vitamin D deficiency (VDD), particularly during perinatal periods when such changes can adversely affect maternal and fetal health. METHODS: We used a panel of 8 inbred Collaborative Cross (CC) mouse strains, each with a different genetic background (72 dams, 3-6/treatment group, per strain). We identified robust maternal liver metabolic responses to vitamin D depletion before and during gestation and lactation using a vitamin-D-deficient (VDD; 0 IU vitamin D3/kg) or -sufficient diet (1000 IU vitamin D3/kg). We then identified VDD-induced metabolite changes influenced by strain genetic background. RESULTS: We detected a significant VDD effect by orthogonal partial least squares discriminant analysis (Q2 = 0.266, pQ2 = 0.002): primarily, altered concentrations of 78 metabolites involved in lipid, amino acid, and nucleotide metabolism (variable importance to projection score ≥1.5). Metabolites in unsaturated fatty acid and glycerophospholipid metabolism pathways were significantly enriched [False Discovery Rate (FDR) <0.05]. VDD also significantly altered concentrations of putative markers of uremic toxemia, acylglycerols, and dipeptides. The extent of the metabolic response to VDD was strongly dependent on genetic strain, ranging from robustly responsive to nonresponsive. Two strains (CC017/Unc and CC032/GeniUnc) were particularly sensitive to VDD; however, each strain altered different pathways. CONCLUSIONS: These novel findings demonstrate that maternal VDD induces different liver metabolic effects in different genetic backgrounds. Strains with differing susceptibility and metabolic response to VDD represent unique tools to identify causal susceptibility factors and further elucidate the role of VDD-induced metabolic changes in maternal and/or fetal health for ultimately translating findings to human populations.

Profiling Extracellular Long RNA Transcriptome in Human Plasma and Extracellular Vesicles for Biomarker Discovery.

The recent discovery of extracellular RNAs in blood, including RNAs in extracellular vesicles (EVs), combined with low-input RNA-sequencing advances have enabled scientists to investigate their role in human disease. To date, most studies have been focusing on small RNAs, and methodologies to optimize long RNAs measurement are lacking. We used plasma RNA to assess the performance of six long RNA sequencing methods, at two different sites, and we report their differences in reads (%) mapped to the genome/transcriptome, number of genes detected, long RNA transcript diversity, and reproducibility. Using the best performing method, we further compare the profile of long RNAs in the EV- and no-EV-enriched RNA plasma compartments. These results provide insights on the performance and reproducibility of commercially available kits in assessing the landscape of long RNAs in human plasma and different extracellular RNA carriers that may be exploited for biomarker discovery.