Biology subjects

Saba, L. M.

Publications and source records attributed to Saba, L. M..

11 recordsLinked to original sources

bioRxiv2026

Deep transcriptomic analysis of molecular mediators of complex traits in heterogeneous stock (HS) rats

Rats are a powerful model organism for genome-wide association studies (GWAS) of physiological and behavioral traits. Heterogeneous Stock (HS) rats, which were created by intercrossing 8 inbred rat strains, are heavily used for this purpose because of their genetic diversity and accumulated recombinations due to over 100 generations of outbreeding, which improves mapping precision. However, functional genomics resources for post-GWAS interpretation of genomic findings have been limited. We processed 3,160 RNA-seq samples, 1,932 of which were previously unpublished, from 13 tissues as part of RatGTEx. We used Pantry to quantify six modalities of transcriptome regulation (expression, isoform ratio, intron excision ratio, alternative transcription start site (TSS), alternative poly(A), and RNA stability) in each sample, which allowed us to identify 144,876 independent quantitative trait loci (QTL) for 22,599 genes. We performed a transcriptome-wide association study (TWAS) with 19,796 unique genes across tissues and transcriptional modalities and 308 traits collected from 14 studies using FUSION, which identified 7,473 significant gene-trait associations. This work presents a significant expansion of the RatGTEx resource in terms of samples, modes of transcriptome regulation, and genetic analyses. Our findings provide molecular evidence for the involvement of specific genes in mediating GWAS signals through a range of transcriptome regulatory mechanisms. These gene-to-phenotype relationships can be easily applied to genetic analyses in humans and other species. Further experiments can use this evidence to confirm these molecular mediators and target them for the treatment of behavioral and physiological traits and disorders. To facilitate use of these results, we have updated results on RatGTEx.org and provided a new interactive TWAS web portal (twas.ratgtex.org).

genetics↗

bioRxiv2026

Voluntary oxycodone self-administration produces analgesic tolerance and sex-dependent hyperalgesia across genetically diverse rats

Long-term opioid therapy is limited by analgesic tolerance and opioid-induced hyperalgesia, but the roles of genetic background, sex, and drug exposure remain unclear. We used 20 inbred strains from the Hybrid Rat Diversity Panel to examine thermal sensitivity, oxycodone analgesia, tolerance, and hyperalgesia-like changes following voluntary intravenous oxycodone or saline self-administration. Rats underwent tail-immersion testing before self-administration (Pre-SA) and after self-administration (Post-SA). Oxycodone analgesia was assessed using the percent maximum possible effect time course and the corresponding area under the curve. Pre-SA thermal sensitivity differed across strains and between sexes, and Pre-SA oxycodone analgesia also differed across strains. Oxycodone self-administration produced a sex-dependent increase in thermal sensitivity that was most evident in males. During Post-SA testing, oxycodone self-administering rats showed reduced analgesic responsiveness compared with saline controls, and the magnitude of this difference varied across strains. Within-strain Pre-SA-to-Post-SA comparisons identified tolerance-like reductions in several strains. Across strains and sexes, oxycodone self-administering rats showed a greater Pre-SA-to-Post-SA reduction in analgesic responsiveness than saline controls, consistent with analgesic tolerance. Total oxycodone intake was not associated with tolerance at either the strain-mean or individual-animal level. Heritability estimates were higher for thermal sensitivity and analgesia (H2 {approx} 0.28-0.40) than for changes in thermal sensitivity and tolerance (H2 {approx} 0.18-0.27). These findings demonstrate strain variation in thermal sensitivity and oxycodone analgesia, sex-dependent hyperalgesia-like effects, and reduced analgesic responsiveness following voluntary oxycodone intake.

animal behavior and cognition↗

bioRxiv2026

Genetic Modulation of Oxycodone Self-Administration Trajectories: From Initiation to Escalating Burst Patterns

Opioid Use Disorder (OUD) remains a prominent threat to global health. Genetic background influences the susceptibility of developing OUD, although specific genetic factors remain elusive. Rodent models that differ in susceptibility to escalation and dysregulation of opioid use are valuable tools to facilitate discovery of genetic pathways. Phenotypes associated with the development of OUD were compared in seven classic inbred rat strains (M520/N, WKY/NCrl, F344/NCrl, F344/Stm, LEW/Crl, LEW/SSNHsd, LE/Stm) from the Hybrid Rat Diversity Panel (HRDP). A two-phase self-administration paradigm was utilized to assess characteristics of the acquisition of oxycodone self-administration during daily 2-h sessions, and the escalation of oxycodone use during daily 12-h sessions. Genetic background influenced the acquisition of oxycodone self-administration as indicated by differences in the initiation of responding for oxycodone during each session and different amounts of oxycodone intake. We observed that escalation of oxycodone intake between-sessions was strain dependent, and the within-session distribution of oxycodone intake was strongly influenced by strain. The M520/N strain engaged in a unique pattern of intake, characterized by rapid initiation of oxycodone responding during the acquisition phase and a significant burst-like responding during escalation. Strain-dependent sex differences were also observed in several acquisition and escalation metrics. Of interest, burst responding was more prevalent in females of the M520/N strain compared to males. Together, these data indicate that genetic background influences not only overall oxycodone intake, but specific within- and between-session metrics that capture patterns of consumption across the substance use trajectory.

animal behavior and cognition↗

bioRxiv2024

Sex and genetic background influence intravenous oxycodone self-administration in the Hybrid Rat Diversity Panel

Opioid Use Disorder (OUD) is an ongoing worldwide public health concern. Genetic factors contribute to multiple OUD-related phenotypes, such as opioid-induced analgesia, initiation of opioid use, and opioid dependence. Here, we present findings from a behavioral phenotyping protocol using male and female rats from 15 genetically diverse inbred strains from the Hybrid Rat Diversity Panel (HRDP). We used a self-administration paradigm to measure the acquisition of oxycodone intake during ten 2-hour sessions and escalation of oxycodone use during ten 12-hour sessions. During both the acquisition and escalation phases of self-administration, we observed that genetic background and sex influence oxycodone intake. The heritability of oxycodone intake phenotypes ranged between 0.26 to 0.54, indicating that genetic background plays a major role in the variability of oxycodone consumption. Genetic background and sex also influenced additional phenotypes recorded during oxycodone self-administration including lever discrimination and timeout responding. The genetic contribution to these traits was slightly more moderate, with heritability estimates ranging between 0.25 to 0.42. Measures of oxycodone intake were highly positively correlated between acquisition and escalation phases. Interestingly, the efficacy of oxycodone analgesia was positively correlated with oxycodone intake during the escalation phase, indicating that the initial behavioral responses to oxycodone may predict self-administration phenotypes. Together, these data demonstrate that sex and genetic background are major contributors to oxycodone self-administration phenotypes.

genetics↗

bioRxiv2024

Genome-Wide Association Study and transcriptome analysis reveals a complex gene network that regulates opsin gene expression and cell fate determination in Drosophila R7 photoreceptor cells.

BackgroundAn animals ability to discriminate between differing wavelengths of light (i.e., color vision) is mediated, in part, by a subset of photoreceptor cells that express opsins with distinct absorption spectra. In Drosophila R7 photoreceptors, expression of the rhodopsin molecules, Rh3 or Rh4, is determined by a stochastic process mediated by the transcription factor spineless. The goal of this study was to identify additional factors that regulate R7 cell fate and opsin choice using a Genome Wide Association Study (GWAS) paired with transcriptome analysis via RNA-Seq. ResultsWe examined Rh3 and Rh4 expression in a subset of fully-sequenced inbred strains from the Drosophila Genetic Reference Panel and performed a GWAS to identify 42 naturally-occurring polymorphisms--in proximity to 28 candidate genes--that significantly influence R7 opsin expression. Network analysis revealed multiple potential interactions between the associated candidate genes, spineless and its partners. GWAS candidates were further validated in a secondary RNAi screen which identified 12 lines that significantly reduce the proportion of Rh3 expressing R7 photoreceptors. Finally, using RNA-Seq, we demonstrated that all but four of the GWAS candidates are expressed in the pupal retina at a critical developmental time point and that five are among the 917 differentially expressed genes in sevenless mutants, which lack R7 cells. ConclusionsCollectively, these results suggest that the relatively simple, binary cell fate decision underlying R7 opsin expression is modulated by a larger, more complex network of regulatory factors. Of particular interest are a subset of candidate genes with previously characterized neuronal functions including neurogenesis, neurodegeneration, photoreceptor development, axon growth and guidance, synaptogenesis, and synaptic function.

genomics↗

bioRxiv2024

Appearance of green tea compounds in plasma following green tea consumption is modulated by the gut microbiome in mice

Studies have suggested that phytochemicals in green tea have systemic anti-inflammatory and neuroprotective effects. However, the mechanisms behind these effects are poorly understood, possibly due to differential metabolism of phytochemicals resulting from variation in gut microbiome composition. To unravel this complex relationship, our team utilized a novel combined microbiome analysis and metabolomics approach applied to low complexity microbiome (LCM) and human colonized (HU) gnotobiotic mice treated with an acute dose of powdered matcha green tea. A total of 20 LCM mice received 10 distinct human fecal slurries for an n=2 mice per human gut microbiome; 9 LCM mice remained un-colonized with human slurries throughout the experiment. We performed untargeted metabolomics on green tea and plasma to identify green tea compounds that were found in plasma of LCM and HU mice that had consumed green tea. 16S ribosomal RNA gene sequencing was performed on feces of all mice at study end to assess microbiome composition. We found multiple green tea compounds in plasma associated with microbiome presence and diversity (including acetylagmatine, lactiflorin, and aspartic acid negatively associated with diversity). Additionally, we detected strong associations between bioactive green tea compounds in plasma and specific gut bacteria, including associations between spiramycin and Gemmiger, and between wildforlide and Anaerorhabdus. Additionally, some of the physiologically relevant green tea compounds are likely derived from plant-associated microbes, highlighting the importance of considering foods and food products as meta-organisms. Overall, we describe a novel workflow for discovering relationships between individual food compounds and composition of the gut microbiome. ImportanceFoods contain thousands of unique and biologically important compounds beyond the macro- and micro-nutrients listed on nutrition facts labels. In mammals, many of these compounds are metabolized by the community of microbes in the colon. These microbes may impact the thousands of biologically important compounds we consume; therefore, understanding microbial metabolism of food compounds will be important for understanding how foods impact health. We used metabolomics to track green tea compounds in plasma of mice with and without complex microbiomes. From this, we can start to recognize certain groups of green tea-derived compounds that are impacted by mammalian microbiomes. This research presents a novel technique for understanding microbial metabolism of food-derived compounds in the gut, which can be applied to other foods.

systems biology↗

bioRxiv2024

Genetic Modulation of Protein Expression in Rat Brain

Genetic variations in protein expression are implicated in a broad spectrum of common diseases and complex traits. However, the fundamental genetic architecture and variation of protein expression have received comparatively less attention than either mRNA or classical phenotypes. In this study, we systematically quantified proteins in the brains of a large family of rats using tandem mass tag (TMT)-based quantitative mass-spectrometry (MS) technology. We identified and quantified a comprehensive proteome of 8,119 proteins from Spontaneously Hypertensive (SHR/Olalpcv), Brown Norway with polydactyly-luxate (BN-Lx/Cub), and 29 of their fully inbred HXB/BXH progeny. Differential expression (DE) analysis identified 597 proteins with significant differences in expression between the parental strains (fold change > 2 and FDR < 0.01). We characterized 95 variant peptides by proteogenomics approach and discovered 464 proteins linked to strong cis-acting quantitative trait loci (pQTLs, FDR < 0.05). We also explored the linkage of pQTLs with behavioral phenotypes in rats and examined the sex-specific pQTLs to reveal both distinct and shared cis-pQTLs between sexes. Furthermore, by creating a novel view of the rat pangenome, we improved the ability to pinpoint candidate genes underlying pQTL. Finally, we explored the connection between the pQTLs in rat and human disorders, underscoring the translational potential of our findings. Collectively, this work demonstrates the value of large and systematic proteo-genetic datasets in understanding protein modulation in the brain and its functional linkage to complex central nervous system (CNS) traits.

genetics↗

bioRxiv2022

Power and precision: Evaluation and recommendations of quantitative trait analysis methods for RNA expression levels in the Hybrid Rat Diversity Panel

The Hybrid Rat Diversity Panel (HRDP) is a stable and well-characterized set of more than 90 inbred rat strains that can be leveraged for systems genetics approaches to understanding the genetic and genomic variation associated with complex disease. The HRDP exhibits substantial between-strain diversity while retaining substantial within-strain isogenicity, allowing for the precise mapping of genetic variation associated with complex phenotypes and providing statistical power to identify associated variants. In order to robustly identify associated genetic variants, it is important to account for the population structure induced by inbreeding. To this end, we investigate the performance of various plausible approaches towards modeling quantitative traits in the HRDP and quantify their operating characteristics. Towards facilitating study planning and design, we conduct extensive simulations to investigate the power of genetic association analyses in the HRDP, and characterize the impressive attained power.

systems biology↗

bioRxiv2022

The regulatory landscape of multiple brain regions in outbred heterogeneous stock rats

Heterogeneous Stock (HS) rats are a genetically diverse outbred rat population that is widely used for studying genetics of behavioral and physiological traits. Mapping Quantitative Trait Loci (QTL) associated with transcriptional changes would help to identify mechanisms underlying these traits. We generated genotype and transcriptome data for five brain regions from 88 HS rats. We identified 21,392 cis-QTLs associated with expression and splicing changes across all five brain regions and validated their effects using allele specific expression data. We identified 80 cases where eQTLs were colocalized with genome-wide association study (GWAS) results from nine physiological traits. Comparing our dataset to human data from the Genotype-Tissue Expression (GTEx) project, we found that the HS rat data yields twice as many significant eQTLs as a similarly sized human dataset. We also identified a modest but highly significant correlation between genetic regulatory variation among orthologous genes. Surprisingly, we found less genetic variation in gene regulation in HS rats relative to humans, though we still found eQTLs for the orthologs of many human genes for which eQTLs had not been found. These data are available from the RatGTEx data portal (RatGTEx.org) and will enable new discoveries of the genetic influences of complex traits.

genetics↗

bioRxiv2022

RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks

Inferring gene co-expression networks is a useful process for understanding gene regulation and pathway activity. The networks are usually undirected graphs where genes are represented as nodes and an edge represents a significant co-expression relationship. When gene-expression data from multiple conditions (e.g., treatments, tissues, strains) are available, joint estimation of networks harnessing shared information across them can significantly increase the power of analysis. In addition, examining condition-specific patterns of co-expression can provide insights into the underlying cellular processes activated in a particular condition. Condition adaptive fused graphical lasso (CFGL) is an existing method that incorporates condition specificity in a fused graphical lasso (FGL) model for estimating multiple co-expression networks. However, the current implementation of CFGL is prohibitively slow even for a moderate number of genes and can only be used for a maximum of three conditions. In this paper, we propose a fast alternative of CFGL known as rapid condition adaptive fused graphical lasso (RCFGL). In RCFGL, we incorporate the condition specificity into another popular model for joint network estimation, known as fused multiple graphical lasso (FMGL). We use a more efficient algorithm in the iterative steps compared to CFGL, enabling faster computation and making it easily generalizable for more than three conditions. We also present a novel screening rule to determine if the full network estimation problem can be broken down into estimation of smaller disjoint sub-networks, thereby reducing the complexity further. We demonstrate the computational advantage and superior performance of our method compared to two non-condition adaptive methods, FGL and FMGL, and one condition adaptive method, CFGL in several simulation scenarios. We use RCFGL to jointly estimate the gene co-expression networks of different brain regions (conditions) using a cohort of heterogeneous stock rats. We also provide an accommodating C and Python based package that implements RCFGL. Author summaryInferring gene co-expression networks can be useful for understanding pathway activity and gene regulation. While jointly estimating co-expression networks of multiple conditions, taking into account condition specificity such as information about an edge being present only in a specific condition, substantially increases the power. In this paper, a computationally rapid condition adaptive method for jointly estimating gene co-expression networks of multiple conditions is proposed. The novelty of the method is demonstrated through a broad range of simulation studies and a real data analysis with multiple brain regions from a genetically diverse cohort of rats.

bioinformatics↗

bioRxiv2019

Identification of genes involved in the differentiation of R7y and R7p photoreceptor cells in Drosophila

The R7 and R8 photoreceptor cells of the Drosophila compound eye mediate color vision. Throughout the majority of the eye, these cells occur in two principal types of ommatidia. Approximately 35% of ommatidia are of the pale type and express Rh3 in R7 cells and Rh5 in R8 cells. The remaining 65% are of the yellow type and express Rh4 in R7 cells and Rh6 in R8 cells. The specification of an R8 cell in a pale or yellow ommatidium depends on the fate of the adjacent R7 cell. However, pale and yellow R7 cells are specified by a stochastic process that requires the genes spineless, tango and klumpfuss. To identify additional genes involved in this process we performed a genetic screen using a collection of 480 P{EP} transposon insertion strains. We identified genes that when inactivated and/or ectopically expressed in R7 cells resulted in a significantly altered percentage of Rh3 expressing R7 cells (Rh3%) from wild-type. 53 strains resulted in altered Rh3% in the heterozygous inactivation arm of the screen. 36 strains resulted in altered Rh3% in the ectopic expression arm of the screen, where the P{EP} insertion strains were crossed to a sevEP-GAL4 driver line. 4 strains showed differential effects between the two screens. Analyses of these results suggest that R7 cell fate specification is sensitive to perturbations in transcription, growth inhibition, glycoprotein ligand binding, WNT signaling, ubiquitin protease activity and Ser/Thr kinase activity, among other diverse signaling and cell biological processes.

genetics↗