Molecular Phenotype Research Articles

Understanding the role of temperature in seasonal timing: Effects on behavioural, physiological and molecular phenotypes

AbstractOrganisms adapt to daily and seasonal environmental changes to maximise their metabolic and reproductive fitness. For seasonally breeding animals, photoperiod is considered the most robust cue to drive these changes. It, however, does not explain the interannual variations in different seasonal phenotypes. Several studies have repeatedly shown the influence of ambient temperature on the timing of different seasonal physiologies including the timing of migration, reproduction and its associated behaviours, etc. In the present review, we have discussed the effects of changes in ambient temperature on different seasonal events in endotherms with a focus on migratory birds as they have evolved to draw benefits from distinct but largely predictable seasonal patterns of natural resources. We have further discussed the physiological and molecular mechanisms by which temperature affects seasonal timings. The primary brain area involved in detecting temperature changes is the hypothalamic preoptic area. This area receives thermal inputs via sensory neurons in the peripheral ganglia that measure changes in thermoregulatory tissues such as the skin and spinal cord. For the input signals, several thermal sensory TRP (transient receptor potential ion channels) channels have been identified across different classes of vertebrates. These channels are activated at specific thermal ranges. Once perceived, this information should activate an effector function. However, the link between temperature sensation and the effector pathways is not properly understood yet. Here, we have summarised the available information that may help us understand how temperature information is translated into seasonal timing.

Adaptive digital tissue deconvolution.

The inference of cellular compositions from bulk and spatial transcriptomics data increasingly complements data analyses. Multiple computational approaches were suggested and recently, machine learning techniques were developed to systematically improve estimates. Such approaches allow to infer additional, less abundant cell types. However, they rely on training data which do not capture the full biological diversity encountered in transcriptomics analyses; data can contain cellular contributions not seen in the training data and as such, analyses can be biased or blurred. Thus, computational approaches have to deal with unknown, hidden contributions. Moreover, most methods are based on cellular archetypes which serve as a reference; e.g. a generic T-cell profile is used to infer the proportion of T-cells. It is well known that cells adapt their molecular phenotype to the environment and that pre-specified cell archetypes can distort the inference of cellular compositions. We propose Adaptive Digital Tissue Deconvolution (ADTD) to estimate cellular proportions of pre-selected cell types together with possibly unknown and hidden background contributions. Moreover, ADTD adapts prototypic reference profiles to the molecular environment of the cells, which further resolves cell-type specific gene regulation from bulk transcriptomics data. We verify this in simulation studies and demonstrate that ADTD improves existing approaches in estimating cellular compositions. In an application to bulk transcriptomics data from breast cancer patients, we demonstrate that ADTD provides insights into cell-type specific molecular differences between breast cancer subtypes. A python implementation of ADTD and a tutorial are available at Gitlab and zenodo (doi:10.5281/zenodo.7548362).

Disentangling the splicing factor programs underlying complex molecular phenotypes.

The regulation of exon inclusion through alternative splicing tunes the cell's behavior by increasing the functional diversity of the transcriptome and the proteome. Splicing factors work in concert to generate gene isoform pools that contribute to cell phenotypes yet their activity is controlled by multiple regulatory and signaling layers. This hinders identification of functional, phenotype-specific splicing factors using traditional single-omic measurements, such as their mutational state or expression. To address this challenge, we propose repurposing the virtual inference of protein activity by enriched regulon analysis (VIPER) to measure splicing factor activity solely from their downstream exon transcriptomic inclusion signatures. This approach is effective in assessing the effect of co-occurring splicing factor perturbations, as well as their post-translational regulation. As proof of concept, we dissect recurrent splicing factor programs underlying tumorigenesis including aberrantly activated factors acting as oncogenes and inactivated ones acting as tumor suppressors, which are undetectable by more conventional methodologies. Activation and inactivation of these cancer splicing programs effectively stratifies overall survival, as well as cancer hallmarks such as proliferation and immune evasion. Altogether, repurposing network-based inference of protein activity for splicing factor networks distills common, functionally relevant splicing programs in otherwise heterogeneous molecular contexts.

Brain Chimeroids reveal individual susceptibility to neurotoxic triggers.

Interindividual genetic variation affects the susceptibility to and progression of many diseases1,2. However, efforts to study how individual human brains differ in normal development and disease phenotypes are limited by the paucity of faithful cellular human models, and the difficulty of scaling current systems to represent multiple people. Here we present human brain Chimeroids, a highly reproducible, multidonor human brain cortical organoid model generated by the co-development of cells from a panel of individual donors in a single organoid. By reaggregating cells from multiple single-donor organoids at the neural stem cell or neural progenitor cell stage, we generate Chimeroids in which each donor produces all cell lineages of the cerebral cortex, even when using pluripotent stem cell lines with notable growth biases. We used Chimeroids to investigate interindividual variation in the susceptibility to neurotoxic triggers that exhibit high clinical phenotypic variability: ethanol and the antiepileptic drug valproic acid. Individual donors varied in both the penetrance of the effect on target cell types, and the molecular phenotype within each affected cell type. Our results suggest that human genetic background may be an important mediator of neurotoxin susceptibility and introduce Chimeroids as a scalable system for high-throughput investigation of interindividual variation in processes of brain development and disease.

Gravidity influences distinct transcriptional profiles of maternal and fetal placental macrophages at term.

Maternal intervillous monocytes (MIMs) and fetal Hofbauer cells (HBCs) are myeloid-derived immune cells at the maternal-fetal interface. Maternal reproductive history is associated with differential risk of pregnancy complications. The molecular phenotypes and roles of these distinct monocyte/macrophage populations and the influence of gravidity on these phenotypes has not been systematically investigated. Here, we used RNA sequencing to study the transcriptional profiles of MIMs and HBCs in normal term pregnancies. Our analyses revealed distinct transcriptomes of MIMs and HBCs. Genes involved in differentiation and cell organization pathways were more highly expressed in MIMs vs. HBCs. In contrast, HBCs had higher expression of genes involved in inflammatory responses and cell surface receptor signaling. Maternal gravidity influenced monocyte programming, as expression of pro-inflammatory molecules was significantly higher in MIMs from multigravidae compared to primigravidae. In HBCs, multigravidae displayed enrichment of gene pathways involved in cell-cell signaling and differentiation. Our results demonstrated that MIMs and HBCs have highly divergent transcriptional signatures, reflecting their distinct origins, locations, functions, and roles in inflammatory responses. Furthermore, maternal gravidity influences the gene signatures of MIMs and HBCs, potentially modulating the interplay between tolerance and trained immunity. The phenomenon of reproductive immune memory may play a novel role in the differential susceptibility of primigravidae to pregnancy complications.

Integrating metabolic profiling of pancreatic juice with transcriptomic analysis of pancreatic cancer tissue identifies distinct clinical subgroups.

Metabolic reprogramming is a hallmark feature of pancreatic ductal adenocarcinoma (PDAC). A pancreatic juice (PJ) metabolic signature has been reported to be prognostic of oncological outcome for PDAC. Integration of PJ profiling with transcriptomic and spatial characterization of the tumor microenvironment would help in identifying PDACs with peculiar vulnerabilities. We performed a transcriptomic analysis of 26 PDAC samples grouped into 3 metabolic clusters (M_CL) according to their PJ metabolic profile. We analyzed molecular subtypes and transcriptional differences. Validation was performed by multidimensional imaging on tumor slides. Pancreatic juice metabolic profiling was associated with PDAC transcriptomic molecular subtypes (p=0.004). Tumors identified as M_CL1 exhibited a non-squamous molecular phenotype and demonstrated longer survival. Enrichment analysis revealed the upregulation of immune genes and pathways in M_CL1 samples compared to M_CL2, the group with worse prognosis, a difference confirmed by immunofluorescence on tissue slides. Enrichment analysis of 39 immune signatures by xCell confirmed decreased immune signatures in M_CL2 compared to M_CL1 and allowed a stratification of patients associated with longer survival. PJ metabolic fingerprints reflect PDAC molecular subtypes and the immune microenvironment, confirming PJ as a promising source of biomarkers for personalized therapy.

Plasminogen missense variants and their involvement in cardiovascular and inflammatory disease.

Human plasminogen (PLG), the zymogen of the fibrinolytic protease, plasmin, is a polymorphic protein with two widely distributed codominant alleles, PLG/Asp453 and PLG/Asn453. About 15 other missense or non-synonymous single nucleotide polymorphisms (nsSNPs) of PLG show major, yet different, relative abundances in world populations. Although the existence of these relatively abundant allelic variants is generally acknowledged, they are often overlooked or assumed to be non-pathogenic. In fact, at least half of those major variants are classified as having conflicting pathogenicity, and it is unclear if they contribute to different molecular phenotypes. From those, PLG/K19E and PLG/A601T are examples of two relatively abundant PLG variants that have been associated with PLG deficiencies (PD), but their pathogenic mechanisms are unclear. On the other hand, approximately 50 rare and ultra-rare PLG missense variants have been reported to cause PD as homozygous or compound heterozygous variants, often leading to a debilitating disease known as ligneous conjunctivitis. The true abundance of PD-associated nsSNPs is unknown since they can remain undetected in heterozygous carriers. However, PD variants may also contribute to other diseases. Recently, the ultra-rare autosomal dominant PLG/K311E has been found to be causative of hereditary angioedema (HAE) with normal C1 inhibitor. Two other rare pathogenic PLG missense variants, PLG/R153G and PLG/V709E, appear to affect platelet function and lead to HAE, respectively. Herein, PLG missense variants that are abundant and/or clinically relevant due to association with disease are examined along with their world distribution. Proposed molecular mechanisms are discussed when known or can be reasonably assumed.

Suppression of astrocyte BMP signaling improves fragile X syndrome molecular signatures and functional deficits.

Fragile X syndrome (FXS) is a monogenic neurodevelopmental disorder with manifestations spanning molecular, neuroanatomical, and behavioral changes. Astrocytes contribute to FXS pathogenesis and show hundreds of dysregulated genes and proteins; targeting upstream pathways mediating astrocyte changes in FXS could therefore be a point of intervention. To address this, we focused on the bone morphogenetic protein (BMP) pathway, which is upregulated in FXS astrocytes. We generated a conditional KO (cKO) of Smad4 in astrocytes to suppress BMP signaling, and found this lessens audiogenic seizure severity in FXS mice. To ask how this occurs on a molecular level, we performed in vivo transcriptomic and proteomic profiling of cortical astrocytes, finding upregulation of metabolic pathways, and downregulation of secretory machinery and secreted proteins in FXS astrocytes, with these alterations no longer present when BMP signaling is suppressed. Functionally, astrocyte Smad4 cKO restores deficits in inhibitory synapses present in FXS auditory cortex. Thus, astrocytes contribute to FXS molecular and functional phenotypes, and targeting astrocytes can mitigate FXS symptoms.

Precise measurement of molecular phenotypes with barcode-based CRISPRi systems.

Genome-wide CRISPR-Cas9 screens have untangled regulatory networks and revealed the genetic underpinnings of diverse biological processes. Their success relies on experimental designs that interrogate specific molecular phenotypes and distinguish key regulators from background effects. Here, we realize these goals with a generalizable platform for CRISPR interference with barcoded expression reporter sequencing (CiBER-seq) that dramatically improves the sensitivity and scope of genome-wide screens. We systematically address technical factors that distort phenotypic measurements by normalizing expression reporters against closely-matched control promoters, integrated together into the genome at single copy. To test our ability to capture post-transcriptional and post-translational regulation through sequencing, we screened for genes that affected nonsense-mediated mRNA decay and Doa10-mediated cytosolic protein decay. Our optimized CiBER-seq screens accurately capture the known components of well-studied RNA and protein quality control pathways with minimal background. These results demonstrate the precision and versatility of CiBER-seq for dissecting the genetic networks controlling cellular behaviors.

HumanIslets: An integrated platform for human islet data access and analysis.

Comprehensive molecular and cellular phenotyping of human islets can enable deep mechanistic insights for diabetes research. We established the Human Islet Data Analysis and Sharing (HI-DAS) consortium to advance goals in accessibility, usability, and integration of data from human islets isolated from donors with and without diabetes at the Alberta Diabetes Institute (ADI) IsletCore. Here we introduce HumanIslets.com, an open resource for the research community. This platform, which presently includes data on 547 human islet donors, allows users to access linked datasets describing molecular profiles, islet function and donor phenotypes, and to perform various statistical and functional analyses at the donor, islet and single-cell levels. As an example of the analytic capacity of this resource we show a dissociation between cell culture effects on transcript and protein expression, and an approach to correct for exocrine contamination found in hand-picked islets. Finally, we provide an example workflow and visualization that highlights links between type 2 diabetes status, SERCA3b Ca2+-ATPase levels at the transcript and protein level, insulin secretion and islet cell phenotypes. HumanIslets.com provides a growing and adaptable set of resources and tools to support the metabolism and diabetes research community.

Proteomic profiling of bronchoalveolar lavage fluid uncovers protein clusters linked to survival in idiopathic forms of interstitial lung disease

BackgroundIdiopathic interstitial pneumonias (IIPs) such as idiopathic pulmonary fibrosis (IPF) and interstitial pneumonia with autoimmune features (IPAF), present diagnostic and therapeutic challenges due to their heterogeneous nature. This study aimed to identify intrinsic molecular signatures within the lung microenvironment of these IIPs through proteomic analysis of bronchoalveolar lavage fluid (BALF).MethodsPatients with IIP (n=23) underwent comprehensive clinical evaluation including pre-treatment bronchoscopy and were compared to controls without lung disease (n=5). Proteomic profiling of BALF was conducted using label-free quantitative methods. Unsupervised cluster analyses identified protein expression profiles which were then analyzed to predict survival outcomes and investigate associated pathways.ResultsProteomic profiling successfully differentiated IIP from controls.k-means clustering, based on protein expression revealed three distinct IIP clusters, which were not associated with age, smoking history, or baseline pulmonary function. These clusters had unique survival trajectories and provided more accurate survival predictions than the Gender Age Physiology (GAP) index (C-index 0.794versus0.709). The cluster with the worst prognosis featured decreased inflammatory signaling and complement activation, with pathway analysis highlighting altered immune response pathways related to immunoglobulin production and B cell-mediated immunity.ConclusionsThe unsupervised clustering of BALF proteomics provided a novel stratification of IIP patients, with potential implications for prognostic and therapeutic targeting. The identified molecular phenotypes underscore the diversity within the IIP classification and the potential importance of personalized treatments for these conditions. Future validation in larger, multi-ethnic cohorts is essential to confirm these findings and to explore their utility in clinical decision-making for patients with IIP.

A novel insertional allele of the CG18135 gene is associated with severe mutant phenotypes in Drosophila melanogaster.

Drosophila melanogaster has been at the forefront of genetic studies and biochemical modeling for over a century. Yet, the functions of many genes are still unknown, mainly because no phenotypic data are available. Herein, we present the first evidence data regarding the particular molecular and other quantifiable phenotypes, such as viability and anatomical anomalies, induced by a novel P{lacW} insertional mutant allele of the CG18135 gene. So far, the CG18135 functions have only been theorized based on electronic annotation and presumptive associations inferred upon high-throughput proteomics or RNA sequencing experiments. The descendants of individuals harboring the CG18135 P{lacW}CG18135 allele were scored in order to assess mutant embryonic, larval, and pupal viability versus Canton Special (CantonS). Our results revealed that the homozygous CG18135 P{lacW}CG18135 /CG18135 P{lacW}CG18135 genotype determines significant lethality both at the inception of the larval stage and during pupal development. The very few imago escapers that either breach or fully exit the puparium exhibit specific eye depigmentation, wing abnormal unfolding, strong locomotor impairment with apparent spasmodic leg movements, and their maximum lifespan is shorter than 2days. Using the quantitative real-time PCR (qRT-PCR) method, we found that CG18135 is upregulated in male flies, but an unexpected gene upregulation was also detected in heterozygous mutants compared to wild-type flies, probably because of regulatory perturbations induced by the P{lacW} transposon. Our work provides the first phenotypic evidence for the essential role of CG18135, a scenario in accordance with the putative role of this gene in carbohydrate-binding processes.

THADA-IGF2BP3 gene fusions in thyroid fine needle aspiration is involved in the pathway to "noninvasive follicular thyroid neoplasm with papillary-like nuclear features".

The increased usage and adaptation of molecular testing of thyroid fine needle aspirations (FNA) has expanded the variety and number of gene fusions identified. While the identified number of molecular alterations is increasing, the definitive association between preoperative molecular analysis and phenotype has yet to be established. The aim of this study was to examine Thyroid adenoma-associated (THADA)-IGF2BP3 molecular fusions with FNA categorization, surgical pathology diagnosis, and other molecular alterations detected by ThyroSeq Genomic Classifier testing. FNA cytology samples of thyroid nodules from 04/2017 to 01/2023 with the diagnosis of atypia of undetermined significance or follicular lesion of undetermined significance (AUS/FLUS; Bethesda III) or follicular neoplasm suspicious for follicular neoplasm (FN/SFN; Bethesda IV) with associated ThyroSeqV3 testing were reviewed. Parameters including patient demographics, FNA diagnosis, ThyroSeq V3 results, and surgical pathology follow up were examined. 87 out of 249 (35%) FNA specimens of thyroid nodules displayed molecular alterations. 64 cases (74%) had a cytology diagnosis of AUS and 23 (26%) had FN. RAS mutation was observed in 48 cases. On surgical follow-up, 17 (35%) cases showed non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP), while 14 (29%) patients had a malignant diagnosis. THADA-IGF2BP3 fusions were seen in 8 cases, all with NIFTP on surgical pathology follow-up (100%). Analysis of THADA-IGF2BP3 fusion, in our institutional series, shows close association with NIFTP cases. THADA-IGF2BP3 fusion, which seems to be a favorable prognostic indicator in general, may serve as a molecular marker for non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP).

Identifying FUS amyotrophic lateral sclerosis disease signatures in patient dermal fibroblasts

Amyotrophic lateral sclerosis (ALS) is a rapidly progressing, highly heterogeneous neurodegenerative disease, underscoring the importance of obtaining information to personalize clinical decisions quickly after diagnosis. Here, we investigated whether ALS-relevant signatures can be detected directly from biopsied patient fibroblasts. We profiled familial ALS (fALS) fibroblasts, representing a range of mutations in the fused in sarcoma (FUS) gene and ages of onset. To differentiate FUS fALS and healthy control fibroblasts, machine-learning classifiers were trained separately on high-content imaging and transcriptional profiles. "Molecular ALS phenotype" scores, derived from these classifiers, captured a spectrum from disease to health. Interestingly, these scores negatively correlated with age of onset, identified several pre-symptomatic individuals and sporadic ALS (sALS) patients with FUS-like fibroblasts, and quantified "movement" of FUS fALS and "FUS-like" sALS toward health upon FUS ASO treatment. Taken together, these findings provide evidence that non-neuronal patient fibroblasts can be used for rapid, personalized assessment in ALS.

Identification of a novel likely pathogenic TPM1 variant linked to hypertrophic cardiomyopathy in a family with sudden cardiac death.

Hypertrophic cardiomyopathy (HCM) is an autosomal dominant genetic cardiac disorder characterized by unexplained left ventricular hypertrophy. It can cause a wide spectrum of clinical manifestations, ranging from asymptomatic to heart failure and sudden cardiac death (SCD). Approximately half of HCM cases are caused by variants in sarcomeric proteins, including α-tropomyosin (TPM1). In this study, we aimed to characterize the clinical and molecular phenotype of HCM in an Iranian pedigree with SCD. The proband and available family members underwent comprehensive clinical evaluations, including echocardiography, cardiac magnetic resonance (CMR) imaging and electrocardiography (ECG). Whole-exome sequencing (WES) was performed in all available family members to identify the causal variant, which was validated, and segregation analysis was conducted via Sanger sequencing. WES identified a novel missense variant, c.761A>G:p.D254G (NM_001018005.2), in the TPM1 gene, in the proband, his father and one of his sisters. Bioinformatic analysis predicted it to be likely pathogenic. Clinical features in affected individuals were consistent with HCM. The identification of a novel TPM1 variant in a family with HCM and SCD underscores the critical role of genetic screening in at-risk families. Early detection of pathogenic variants can facilitate timely intervention and management, potentially reducing the risk of SCD in individuals with HCM.

Comparative Proteomics Analysis Reveals Distinct Molecular Phenotype and Biomarkers in Patients with Erythrodermic Atopic Dermatitis and Erythrodermic Psoriasis.

Erythrodermic atopic dermatitis (EAD) and erythrodermic psoriasis (EP) are rare yet debilitating inflammatory skin disorders that propose challenges in diagnosis and discovering effective therapeutic targets. Despite their clinical and histological similarities, the underlying molecular mechanisms and systemic biomarkers of these diseases are substantially unclear. In this study, we sought to investigate the differential serum proteome of EP and EAD patients and identify biomarkers for these two subtypes of erythroderma. We recruited 14 EAD patients, 14 EP patients and 14 healthy controls. Serum samples were collected and analyzed using the Olink high-throughput platform to assess the levels of 269 inflammation-/immune response-/cardiovascular-related biomarkers. Both EAD and EP patients exhibited enhanced immune activation and dysregulated cardiovascular profiles compared to healthy controls. EAD demonstrated a more pronounced inflammation tone, characterized by Th1/Th2/Th22/IL-1-dominant patterns, as well as increased TNF superfamily, Th17, and apoptosis markers. Conversely, EP displayed inflammation with Th1/Th17/TNF-skewing and mild Th2 upregulation, along with notable increases in epidermal-development markers. Disease severity in EAD was strongly correlated with apoptosis/Th2 markers, while correlated with Th17 markers in EP. Furthermore, a panel of eight markers (IL-17A/IL-17C/PI3/CCL20/SH2D1A/SIRT2/DFFA/IL-13) was identified that effectively discriminated between EP and EAD, with an Area Under the Curve greater than 0.8. Our study comprehensively characterizes the circulating molecular profiles in EAD and EP patients, providing insights into the similarities and complexities of their inflammation phenotypes. The identified serum biomarkers have the potential to differentiate between EP and EAD, which could aid in the diagnosis and guiding tailored therapeutics.

Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides

Cancer diagnosis and management depend upon the extraction of complex information from microscopy images by pathologists, which requires time-consuming expert interpretation prone to human bias. Supervised deep learning approaches have proven powerful, but are inherently limited by the cost and quality of annotations used for training. Therefore, we present Histomorphological Phenotype Learning, a self-supervised methodology requiring no labels and operating via the automatic discovery of discriminatory features in image tiles. Tiles are grouped into morphologically similar clusters which constitute an atlas of histomorphological phenotypes (HP-Atlas), revealing trajectories from benign to malignant tissue via inflammatory and reactive phenotypes. These clusters have distinct features which can be identified using orthogonal methods, linking histologic, molecular and clinical phenotypes. Applied to lung cancer, we show that they align closely with patient survival, with histopathologically recognised tumor types and growth patterns, and with transcriptomic measures of immunophenotype. These properties are maintained in a multi-cancer study.

Multimodal framework to resolve variants of uncertain significance in TSC2.

Efforts to resolve the functional impact of variants of uncertain significance (VUS) have lagged behind the identification of new VUS; as such, there is a critical need for scalable VUS resolution technologies. Computational variant effect predictors (VEPs), once trained, can predict pathogenicity for all missense variants in a gene, set of genes, or the exome. Existing tools have employed information on known pathogenic and benign variants throughout the genome to predict pathogenicity of VUS. We hypothesize that taking a gene-specific approach will improve pathogenicity prediction over globally-trained VEPs. We tested this hypothesis using the gene TSC2, whose loss of function results in tuberous sclerosis, a multisystem mTORopathy affecting about 1 in 6,000 individuals born in the United States. TSC2 has been identified as a high-priority target for VUS resolution, with (1) well-characterized molecular and patient phenotypes associated with loss-of-function variants, and (2) more than 2,700 VUS already documented in ClinVar. We developed Tuberous sclerosis classifier to Resolve variants of Uncertain Significance in T SC2 (TRUST), a machine learning model to predict pathogenicity of TSC2 missense VUS. To test whether these predictions are accurate, we further introduce curated loci prime editing (cliPE) as an accessible strategy for performing scalable multiplexed assays of variant effect (MAVEs). Using cliPE, we tested the effects of more than 200 TSC2 variants, including 106 VUS. It is highly likely this functional data alone would be sufficient to reclassify 92 VUS with most being reclassified as likely benign. We found that TRUST's classifications were correlated with the functional data, providing additional validation for the in silico predictions. We provide our pathogenicity predictions and MAVE data to aid with VUS resolution. In the near future, we plan to host these data on a public website and deposit into relevant databases such as MAVEdb as a community resource. Ultimately, this study provides a framework to complete variant effect maps of TSC1 and TSC2 and adapt this approach to other mTORopathy genes.

Metabolomics Simultaneously Derives Benchmark Dose Estimates and Discovers Metabolic Biotransformations in a Rat Bioassay.

Benchmark dose (BMD) modeling estimates the dose of a chemical that causes a perturbation from baseline. Transcriptional BMDs have been shown to be relatively consistent with apical end point BMDs, opening the door to using molecular BMDs to derive human health-based guidance values for chemical exposure. Metabolomics measures the responses of small-molecule endogenous metabolites to chemical exposure, complementing transcriptomics by characterizing downstream molecular phenotypes that are more closely associated with apical end points. The aim of this study was to apply BMD modeling to in vivo metabolomics data, to compare metabolic BMDs to both transcriptional and apical end point BMDs. This builds upon our previous application of transcriptomics and BMD modeling to a 5-day rat study of triphenyl phosphate (TPhP), applying metabolomics to the same archived tissues. Specifically, liver from rats exposed to five doses of TPhP was investigated using liquid chromatography-mass spectrometry and 1H nuclear magnetic resonance spectroscopy-based metabolomics. Following the application of BMDExpress2 software, 2903 endogenous metabolic features yielded viable dose-response models, confirming a perturbation to the liver metabolome. Metabolic BMD estimates were similarly sensitive to transcriptional BMDs, and more sensitive than both clinical chemistry and apical end point BMDs. Pathway analysis of the multiomics data sets revealed a major effect of TPhP exposure on cholesterol (and downstream) pathways, consistent with clinical chemistry measurements. Additionally, the transcriptomics data indicated that TPhP activated xenobiotic metabolism pathways, which was confirmed by using the underexploited capability of metabolomics to detect xenobiotic-related compounds. Eleven biotransformation products of TPhP were discovered, and their levels were highly correlated with multiple xenobiotic metabolism genes. This work provides a case study showing how metabolomics and transcriptomics can estimate mechanistically anchored points-of-departure. Furthermore, the study demonstrates how metabolomics can also discover biotransformation products, which could be of value within a regulatory setting, for example, as an enhancement of OECD Test Guideline 417 (toxicokinetics).

A systems genomics and genetics approach to identify the genetic regulatory network for lignin content in Brassica napus seeds.

Seed quality traits of oilseed rape, Brassica napus (B. napus), exhibit quantitative inheritance determined by its genetic makeup and the environment via the mediation of a complex genetic architecture of hundreds to thousands of genes. Thus, instead of single gene analysis, network-based systems genomics and genetics approaches that combine genotype, phenotype, and molecular phenotypes offer a promising alternative to uncover this complex genetic architecture. In the current study, systems genetics approaches were used to explore the genetic regulation of lignin traits in B. napus seeds. Four QTL (qLignin_A09_1, qLignin_A09_2, qLignin_A09_3, and qLignin_C08) distributed on two chromosomes were identified for lignin content. The qLignin_A09_2 and qLignin_C08 loci were homologous QTL from the A and C subgenomes, respectively. Genome-wide gene regulatory network analysis identified eighty-three subnetworks (or modules); and three modules with 910 genes in total, were associated with lignin content, which was confirmed by network QTL analysis. eQTL (expression quantitative trait loci) analysis revealed four cis-eQTL genes including lignin and flavonoid pathway genes, cinnamoyl-CoA-reductase (CCR1), and TRANSPARENT TESTA genes TT4, TT6, TT8, as causal genes. The findings validated the power of systems genetics to identify causal regulatory networks and genes underlying complex traits. Moreover, this information may enable the research community to explore new breeding strategies, such as network selection or gene engineering, to rewire networks to develop climate resilience crops with better seed quality.