Biology subjects

Huang, T.

Publications and source records attributed to Huang, T..

At least 55 records · Page 3Linked to original sources

bioRxiv2025

Novel Pipelines to Extract Differences in Proteome Dynamics Based on Health Status

Understanding dynamics and co-regulatory patterns in the human proteome is a promising path for unraveling the molecular basis of health and disease. Nevertheless, there remains an open challenge in extracting concise information from high-throughput proteomic data that can effectively characterize and predict health. We develop novel statistical and computational pipelines to tackle this problem in a longitudinal saliva proteomics data set collected throughout the awakening response in six healthy controls and six subjects with severe mitochondrial disease (MitoD), a clinical condition caused by genetic mitochondrial defects that affects cellular energy transformation and alters multiple dimensions of health. We undertook three independent unsupervised approaches to characterize proteome dynamics and assessed their ability to separate MitoD individuals from controls. First, we designed a permutation test to detect the global difference in the proteomic co-regulation structure between healthy and unhealthy subjects. Second, we performed non-linear embedding and cluster analysis on elasticity to capture a more complicated relationship between health and the proteome. Third, we developed a machine learning algorithm to extract low-dimensional representations of the proteome dynamic and use them to cluster subjects into healthy and unhealthy groups without any knowledge of their true status. All three methods showed clear differences between MitoD individuals and controls. Our results revealed a significant and consistent association between MitoD status and the saliva proteome at multiple levels during the awakening response, including its dynamic change, co-regulation structure, and elasticity. This connection is not restricted to a few MitoD-specific proteins but spreads over a wide range of proteins from many body functions and pathways. Pipelines such as those shown here are the first step toward establishing interpretable and accurate prediction rules for health based on proteome dynamics.

bioinformatics↗

bioRxiv2025

UNICORN: Towards Universal Cellular Expression Prediction with an Explainable Multi-Task Learning Framework

Sequence-to-function analysis is a challenging task in human genetics, especially in predicting cell-type-specific multi-omic phenotypes from biological sequences such as individualized gene expression. Here, we present UNICORN, a new method with improved prediction performances than the existing methods. UNICORN takes the embeddings from biological sequences as well as external knowledge from pre-trained foundation models as inputs and optimizes the predictor with carefully-designed loss functions. We demonstrate that UNICORN outperforms the existing methods in both gene expression prediction and multiomic phenotype prediction at the cellular level and the cell-type level, and it can also generate uncertainty scores of the predictions. Moreover, UNICORN is able to link personalized gene expression profiles with corresponding genome information. Finally, we show that UNICORN is capable of characterizing complex biological systems for different disease states or perturbations. Overall, embeddings from foundation models can facilitate the understanding of the role of biological sequences in the prediction task, and incorporating multi-omic information can enhance prediction performances.

bioinformatics↗

bioRxiv2025

spEMO: Exploring the Capacity of Foundation Models for Analyzing Spatial Multi-Omic Data

Recent advances in pathology foundation models (PFMs), which are pretrained on large-scale histopathological images, have significantly accelerated progress in disease-centered applications. In parallel, spatial multi-omic technologies collect gene and protein expression levels at high spatial resolution, offering rich understanding of tissue context. However, current models fall short in effectively integrating these complementary data modalities. To fill in this gap, we introduce spEMO, a novel computational system that unifies embeddings from pathology foundation models and large language models (LLMs) to analyze spatial multi-omic data. By incorporating multimodal representations and information, spEMO outperforms models trained on single-modality data across a broad range of downstream tasks, including spatial domain identification, spot-type classification, whole-slide disease-state prediction and interpretation, inference of multicellular interactions, and automated medical report generation. The outstanding performances of spEMO in these tasks demonstrate its strength in both biological and clinical applications. Additionally, we propose a new evaluation task, known as multi-modal alignment, to assess the information retrieval capabilities of pathology foundation models. This task provides a principled benchmark for evaluating and improving model architectures. Collectively, spEMO represents a step forward in building holistic, interpretable, and generalizable AI systems for spatial biology and pathology.

bioinformatics↗

bioRxiv2025

HeteroRepur: Efficient Modeling of Heterogeneous Disease Graph for Depression Drug Repurposing using Heterogeneous Graph Neural Networks

ObjectiveMajor Depressive Disorder (MDD) is one of the most prevalent psychiatric disorders, yet existing treatments often result in suboptimal responses and encounter high recurrence rate. Repositioning existing medications, which often significantly reduces the required time and budget, has been demonstrated to be a promising strategy for drug discovery of multiple central nervous system (CNS) disorders. However, an efficient modeling strategy of heterogeneous semantic information among biological networks has yet to be developed for drug repurposing. Material and MethodsIn this study, we proposed HeteroRepur, by integrating heterogeneous graph neural networks to learn interaction features of Depression Drug Repurposing Graph (DDRG) and then classify drugs. ResultsThe DDRG embeddings learned by HeteroRepur accurately capture graph information, including 89 types of interaction or associations among drugs, genes, pathways, and GOs, as well as disease genetic network of MDD. In drug classification task, HeteroRepur outperforms various baseline models, including existing graph neural network approaches that do not consider heterogeneous network information. Finally, HeteroRepur predicts new candidates for treating MDD, including CNS drugs that have the potential to be extended to MDD, and dietary supplements that might benefit depression patients by elevating essential nutrient levels. ConclusionHeteroRepur outperforms traditional graph-based approaches, and can be used to rank promising drugs among complex disease networks with heterogeneous topology.

bioinformatics↗

bioRxiv2024

Effects of Forefoot versus Rearfoot Landing on Biomechanical Risk Factors for Lower Limb Injuries and Performance During Stop-Jump Tasks

BackgroundLower limb injuries commonly occur during sudden deceleration movements. Instructing landing with forefoot or rearfoot is hard to answer, especially considering the injury prevention and keep the performance. Therefore, the purpose of this study is to evaluate the effect of the forefoot and rearfoot landing on the biomechanical risk of lower limb injury prevention and performance during the stop-jumping task. MethodTwenty-three male health subjects were recruited for this study. During a stop-jumping task, three-dimensional kinematic and kinetic, and performance data were collected under two conditions: forefoot landing at initial ground contact and rearfoot landing at initial ground contact. Statistical parametric mapping analysis was used to compare the differences between different landing strategies. ResultSignificant differences were found in ankle internal rotation angle and ankle joint moment at foot initial contact with ground between different landing strategies. Landing with forefoot has shorter stance time compared with landing with rearfoot. In the rearfoot strike, posterior ground reaction force (GRF) and GRF inclination angle were smaller than forefoot strike in 0-14% of the stance phase. ConclusionLanding with forefoot may decrease the risk of non-contact anterior cruciate ligament injuries and have an advancement in quick reaction time, as indicated by decreased stance time, but the risk of lateral ankle sprain may increase for the stop-jump task.

bioengineering↗

bioRxiv2024

Investigating the effect of mechanical adaptation on mid-air ultrasound vibrotactile stimuli

Gesture control systems based on mid-air haptics are increasingly used in infotainment systems in cars, where they can provide rich haptic feedback to improve human-computer interactions. Laboratory studies show that mid-air haptic feedback reduces drivers distractions and improve safety. However, it is unclear how the perception of mid-air ultrasound stimuli is affected by prolonged exposure to vibrational noise, e.g., from the steering wheel of a moving vehicle. Studies on vibrotactile adaptation show that perception of mechanical vibration is impaired by prior exposure to stimuli of the same frequency. Here, we investigated the effect of mechanical adaptation on the perception of mid-air ultrasound stimuli. We measured participants detection threshold for ultrasound stimuli of different frequencies both before and after exposure to 30 s mechanical vibrations. Across two experiments, we systematically manipulated the frequency and amplitude of the adapting stimulus. We found that exposure to low-frequency mechanical vibrations significantly impaired the detection of low-frequency ultrasound stimuli. In contrast, exposure to high-frequency mechanical vibrations equally impaired perception of both low- and high-frequency ultrasound stimuli. This effect was mediated by the amplitude of the adapting stimulus, with stronger mechanical vibrations producing a larger increase in participants detection threshold. Overall, these findings show that perception of mid-air ultrasound stimuli is affected by specific sources of mechanical noise. Crucially, frequency-specificity in the low-frequency band also points toward possible mitigating solutions that could help minimising unwanted desensitization of mechanoreceptor channels during mid-air haptic interactions.

neuroscience↗

bioRxiv2024

High-fidelity in vitro packaging of diverse synthetic cargo into encapsulin protein cages

Cargo-filled protein cages are powerful tools in biotechnology with demonstrated potential as catalytic nanoreactors and vehicles for targeted drug delivery. While endogenous biomolecules can be packaged into protein cages during their expression and self-assembly inside cells, synthetic cargo molecules are typically incompatible with live cells and must be packaged in vitro. Here we report a fusion-based in vitro assembly method for packaging diverse synthetic cargo into encapsulin protein cages that outperforms standard in cellulo assembly, producing cages with superior uniformity and thermal stability. Fluorescent dyes, proteins and cytotoxic drug molecules can all be selectively packaged with high efficiency via a peptide-mediated targeting process. The exceptional fidelity and broad compatibility of our in vitro assembly platform enables generalisable access to cargo-filled protein cages that host novel synthetic functionality for diverse biotechnological applications.

biochemistry↗

bioRxiv2024

The Interaction of UBR4, LRP1, and OPHN1 in Refractory Epilepsy: Drosophila Model to Investigate the Oligogenic Effect on Epilepsy

Refractory epilepsy is an intractable neurological disorder that is currently difficult to achieve effective pharmacological control in clinical practice and can result in poor quality of life as well as increased mortality. Genetic factors are important causes of epilepsy, especially idiopathic epilepsy. In the clinical gene sequencing work, we identified one refractory epileptic patient who carried three epileptogenic candidate genes: UBR4, LRP1, and OPHN1 variants. To clarify the epileptogenicity and interactions of UBR4, LRP1, and OPHN1 variants, as well as explore the role of each mutant gene in eliciting epilepsy, we established single-knockdown, double-knockdown, and triple-knockdown Drosophila models by suppressing the gene expression of these three epileptogenic candidate genes. After conducting behavioral testing for epilepsy in the seven Drosophila knockdown models, regression equations illustrating the causal connection between genotype and phenotype were developed. The mutations of the three epileptogenic candidate genes: UBR4, LRP1, and OPHN1, were proved to be epileptogenic at the animal level both in seizure rates and logistic regression results. Moreover, significant negative interactions in UBR4-OPHN1 KD and LRP1-OPHN1 KD were found in the trigenic KD system as well as the UBR4-OPHN1 and LRP1-OPHN1 digenic KD system according to the logistic regression analysis result. However, despite the existence of negative interaction, three groups of digenic KD flies and one group of trigenic KD flies presented higher seizure rates than that of the corresponding monogenic KD flies. LRP1-OPHN1 KD together with its negative interaction was regarded as the main causative factors for seizure in the UBR4-LRP1-OPHN1 KD.

neuroscience↗

bioRxiv2024

Intra-genomic genes-to-genes correlation enables genome representaion

The bacterial pan-genome consists of core genes shared by all members of a taxonomy and accessory genes found in only a subset. The correlation among genes within the pan-genome could advance our understanding of evolution and tackle medical challenges. Here, we discovered a strong intra-genomic correlation among bacterial pan-genes within each of Escherichia coli, Listeria monocytogenes, Staphylococcus aureus, and Campylobacter jejuni. With a convolutional neural network assisted core genome knock-out simulation, we found that different combinations of fewer than 20 highly variable core genes could recover the sub-species type classified by complete core genome with accuracy >95%. This observation led us to test the genes-to-genes predictability: with more than 52,000 assemblies from each species, combinations of highly variable core genes could predict the sequence variants of other core genes (average accuracy >94%) within the same genome and could also predict sequence variants (average accuracy >91%) as well as the presence (average AUROC >0.91) of some accessory genes. Furthermore, combinations of highly variable core genes could also predict multiple antibiotic resistances (AUROC >0.80) in large published datasets of E. coli, S. aureus, and Mycobacterium tuberculosis. Collectively, we propose that genes within the same genome can strongly correlate with each other. Therefore, the strain phylogeny and the status of other genes could be uniformly represented by combinations of highly variable core genes, which could further represent certain phenotypes including in vitro resistance.

bioinformatics↗

bioRxiv2024

Cyclic di-GMP as an Antitoxin Regulates Bacterial Genome Stability and Antibiotic Persistence in Biofilms

Biofilms are complex bacterial communities characterized by a high persister prevalence, which contributes to chronic and relapsing infections. Historically, persister formation in biofilms has been linked to constraints imposed by their dense structures. However, we observed an elevated persister frequency accompanying the stage of cell adhesion, marking the onset of biofilm development. Subsequent mechanistic studies uncovered a comparable type of toxin-antitoxin (TA) module (TA-like system) triggered by cell adhesion, which is responsible for this elevation. In this module, the toxin HipH acts as a genotoxic deoxyribonuclease, inducing DNA double strand breaks and genome instability. While the second messenger c-di-GMP functions as the antitoxin, exerting control over HipH expression and activity. The dynamic interplay between c-di-GMP and HipH levels emerges as a crucial determinant governing genome stability and persister generation within biofilms. These findings unveil a unique TA system, where small molecules act as the antitoxin, outlining a biofilm-specific molecular mechanism influencing genome stability and antibiotic persistence, with potential implications for treating biofilm infections.

microbiology↗

bioRxiv2024

Deep, unbiased and quantitative mass spectrometry-based plasma proteome analysis of individual responses to mRNA COVID-19 vaccine

Global campaign against COVID-19 have vaccinated a significant portion of the world population in recent years. Combating the COVID-19 pandemic with mRNA vaccines played a pivotal role in the global immunization effort. However, individual responses to a vaccine are diverse and lead to varying vaccination efficacy. Despite significant progress, a complete understanding of the molecular mechanisms driving the individual immune response to the COVID-19 vaccine remains elusive. To address this gap, we combined a novel nanoparticle-based proteomic workflow with tandem mass tag (TMT) labeling, to quantitatively assess the proteomic changes in a cohort of 12 volunteers following two doses of the Pfizer-BioNTech mRNA COVID-19 vaccine. This optimized protocol seamlessly integrates comprehensive proteome analysis with enhanced throughput by leveraging the enrichment of low-abundant plasma proteins by engineered nanoparticles. Our data demonstrate the ability of this nanoparticle-based workflow to quantify over 3,000 proteins from 48 human plasma samples, providing the deepest view into COVID-19 vaccine-related plasma proteome study. We identified 69 proteins exhibiting a boosted response to the vaccine after the second dose. Additionally, 74 proteins were differentially regulated between seven volunteers, who contracted COVID-19 despite receiving two doses of the vaccine, and the ones who did not contract COVID-19. These findings offer valuable insights into individual variability in response to vaccination, demonstrating the potential of personalized medicine approaches in vaccine development.

immunology↗

bioRxiv2024

Exploration of the link between COVID-19 and gastric cancer from the perspective of bioinformatics and systems biology

BackgroundCoronavirus disease 2019 (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has caused a global pandemic. Gastric cancer (GC) poses a great threat to peoples health, which is a high- risk factor for COVID-19. Previous studies have found some associations between GC and COVID-19, whereas the underlying molecular mechanisms are not well understood. MethodsWe used a bioinformatics and systems biology approach to investigate the relationship between GC and COVID-19. The gene expression profiles of COVID-19 (GSE196822) and GC (GSE179252) were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the shared differentially expressed genes (DEGs) for GC and COVID-19, functional annotation, protein-protein interaction (PPI) network, hub genes, transcriptional regulatory networks and candidate drugs were analyzed. ResultsA total of 209 shared DEGs were identified to explore the linkages between COVID-19 and GC. Functional analyses showed that Immune-related pathway collectively participated in the development and progression of COVID-19 and GC. In addition, there are selected 10 hub genes including CDK1, KIF20A, TPX2, UBE2C, HJURP, CENPA, PLK1, MKI67, IFI6, and IFIT2. The transcription factor/gene and miRNA/gene interaction networks identified 38 transcription factors (TFs) and 234 miRNAs. More importantly, we identified ten potential therapeutic agents, including ciclopirox, resveratrol, etoposide, methotrexate, trifluridine, enterolactone, troglitazone, calcitriol, dasatinib and deferoxamine, some of which have been reported to improve and treat GC and COVID-19. This study also provides insight into the diseases most associated with mutual DEGs, which may provide new ideas for research on the treatment of COVID-19. ConclusionsThis research has the possibility to be contributed to effective therapeutic in COVID-19 and GC.

bioinformatics↗

bioRxiv2024

EAP: a versatile cloud-based platform for comprehensive and interactive analysis of large-scale ChIP/ATAC-seq data sets

Epigenome profiling techniques such as ChIP-seq and ATAC-seq have revolutionized our understanding of gene expression regulation in tissue development and disease progression. However, increasing amount of ChIP/ATAC-seq data poses challenges in computational resources, and the absence of systematic tools for epigenomic analysis underscores the necessity for an efficient analysis platform. To address these issues, we developed the Epigenomic Analysis Platform (EAP, https://www.biosino.org/epigenetics), a scalable cloud-based tool that efficiently analyzes large-scale ChIP/ATAC-seq data sets. EAP employs advanced computational algorithms to derive biologically meaningful insights from heterogeneous datasets and automatically generates publication-ready figures and tabular results, enabling comprehensive epigenomic analysis and data mining in areas like cancer subtyping and therapeutic target discovery.

bioinformatics↗

bioRxiv2024

Assessing Spns2-dependent S1P Transport as a Prospective Therapeutic Target using a New Chemical Probe

S1P (sphingosine 1-phosphate) receptor modulator (SRM) drugs interfere with lymphocyte trafficking by downregulating lymphocyte S1P receptors. While the immunosuppressive activity of SRM drugs has proved useful in treating autoimmune diseases such as multiple sclerosis, that drug class is beset by on-target liabilities such as initial dose bradycardia. The S1P that binds to cell surface lymphocyte S1P receptors is provided by S1P transporters. Mice born deficient in one of these, spinster homolog 2 (Spns2), are lymphocytopenic and have low lymph S1P concentrations. Such observations suggest that inhibition of Spns2-mediated S1P transport might provide another therapeutically beneficial method to modulate immune cell positioning. We report here results using a novel S1P transport blocker (STB), SLF80821178, to investigate the consequences of S1P transport inhibition in rodents. We found that SLF80821178 is efficacious in a multiple sclerosis model but - unlike the SRM fingolimod - neither decreases heart rate nor compromises lung endothelial barrier function. Notably, although Spns2 null mice have a sensorineural hearing defect, mice treated chronically with SLF80821178 have normal hearing acuity. STBs such as SLF80821178 evoke a dose-dependent decrease in peripheral blood lymphocyte counts, which affords a reliable pharmacodynamic marker of target engagement. However, the maximal reduction in circulating lymphocyte counts in response to SLF80821178 is substantially less than the response to SRMs such as fingolimod (50% vs. 90%) due to a lesser effect on T lymphocyte sub-populations by SLF80821178. Finally, in contrast to results obtained with Spns2 deficient mice, lymph S1P concentrations were not significantly changed in response to administration of STBs at doses that evoke maximal lymphopenia, which indicates that current understanding of the mechanism of action of S1P transport inhibitors is incomplete.

pharmacology and toxicology↗

bioRxiv2024

Loss of an extensive ciliary connectome induces proteostasis and cell fate switching in a severe motile ciliopathy

Motile cilia have essential cellular functions in development, reproduction, and homeostasis. Genetic causes for motile ciliopathies have been identified, but the consequences on cellular functions beyond impaired motility remain unknown. Variants in CCDC39 and CCDC40 cause severe disease not explained by loss of motility. Using human cells with pathological variants in these genes, Chlamydomonas genetics, cryo-electron microscopy, single cell RNA transcriptomics, and proteomics, we identified perturbations in multiple cilia-independent pathways. Absence of the axonemal CCDC39/CCDC40 heterodimer results in loss of a connectome of over 90 proteins. The undocked connectome activates cell quality control pathways, switches multiciliated cell fate, impairs microtubule architecture, and creates a defective periciliary barrier. Both cilia-dependent and independent defects are likely responsible for the disease severity. Our findings provide a foundation for reconsidering the broad cellular impact of pathologic variants in ciliopathies and suggest new directions for therapies.

cell biology↗

bioRxiv2024

An optimized somatic embryo transformation system assisted homozygous edited rubber tree generation method mediated by CRISPR/Cas9

Previously, we have realized the CRISPR/Cas9-RNP and plasmid mediated protoplast transient transformation genome editing in the rubber tree (Hevea brasiliensis), but no gene editing plants were acquired due to the bottleneck of genetic transformation. In present study, antibiotic sensitivity tests against kanamycin, hygromycin and basta were analyzed for embryo screening, the results demonstrated that 10 mg/L hygromycin is the best for transformation. Then Agrobacterium mediated transformation of H. brasiliensis embryos was carried out using a pCAMBIA1300-based CRISPR/Cas9 vector targeting Phytoene desaturase gene (HbPDS). High-throughput sequencing of T0 generation positive embryos which were used as regeneration materials in typical transformation procedure showed that more than 90% T0 edited embryos are chimeric with a 3.2% editing efficiency. A T0 embryo with 9.8% edited cells was sliced into small pieces for one more cycle embryogenesis to produce T1 generation embryos in order to improve the ratio of homozygous embryos. Subsequently, next-generation sequencing (NGS) demonstrated that 29 out of 33 T1 embryos were edited, nearly 50% of which were found homozygous. At last, besides four chimeric plantlets with partial albino leaves, four plantlets with complete albino phenotype were regenerated from the 29 T1 generation edited embryos, among which one is a homozygous mono-allelic mutant and the other three are homozygous bi-allelic mutants. NGS demonstrated that the threshold for the proportion of edited cells with expected albino phenotype is between 70-85%. Additionally, Tail-PCR indicate that the T-DNA was inserted into different genome positions in the four homozygous edited plantlets, combined with the different genotypes are considered, the four homozygous plantlets can be confirmed as independently derived from single transformed cells. Overall, this is the first edited rubber trees with expected phenotype reported publicly, which shows the potential in genetic improvement of H. brasiliensis by CRISPR/Cas9 gene editing, and subculture of T0 positive transformed somatic embryos into T1 generation is proved to be an effective and necessary procedure to produce homozygous transgenic plantlets. This study presents a significant advancement in transgenic and gene editing for rubber tree.

plant biology↗

bioRxiv2024

FTO-mediated m6A demethylation of ULK1 mRNA promotes autophagy and activation of hepatic stellate cells in liver fibrosis

The activation of hepatic stellate cells (HSCs) is the central link in the occurrence and development of liver fibrosis. Our previous studies showed that autophagy promotes HSCs activation and ultimately accelerates liver fibrosis. Unc-51-like autophagy activating kinase 1 (ULK1) is an autophagic initiator in mammals and N6-methyladenosine (m6A) modification is closely related to autophagy. In this study, we find that m6A demethylase fat mass and obesity-associated protein (FTO) is upregulated during HSCs activation and bile duct ligation (BDL)-induced hepatic fibrosis, which is the m6A methylase with the most significant difference in expression. Importantly, we identify that FTO overexpression aggravates HSCs activation and hepatic fibrosis via autophagy. Mechanistically, compared with other autophagy-related genes, ULK1 is the target of FTO due to FTO mainly mediates the m6A demethylation of ULK1 and upregulates its expression, thereby enhancing autophagy and activation of HSCs. Noteworthy, m6A reader YTH domain-containing protein 2 (YTHDC2) decreases ULK1 mRNA level via recognizing the m6A binding site and ultimately inhibits autophagy and activation of HSCs. Taken together, our findings highlight m6A-dependent ULK1 as an essential regulator of HSCs autophagy and reveal ULK1 as a novel potential therapeutic target for hepatic fibrosis treatment. Graphical Abstractm6A demethylases FTO promoted autophagy via recognizing the ULK1 m6A binding site, thus triggering HSCs activation, and eventually leading to liver fibrosis. In this process, YTHDC2 participated in the translation of ULK1. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/584975v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@4fdd01org.highwire.dtl.DTLVardef@136791forg.highwire.dtl.DTLVardef@149f448org.highwire.dtl.DTLVardef@44ca9c_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2024

Effects of noncanonical genomic imprinting in monoaminergic pathways on the regulation of social behaviors

Genomic imprinting in the brain is theorized to provide parental control over offspring social behaviors. Noncanonical genomic imprinting is a form of epigenetic regulation in which one of a genes alleles, either that of maternal or paternal inheritance, exhibits a bias towards higher expression of one parental allele compared to the other. This bias can differ depending on tissue type, and the degree of the parental allele expression bias can even vary across anatomical domains within the same tissue. Dopa decarboxylase (Ddc) and tyrosine hydroxylase (Th) are both noncanonically imprinted genes that preferentially express their maternal alleles in the brain and Ddc also has a paternal allele expression bias in the periphery. These two genes encode catecholamine synthesis enzymes for the production of dopamine (DA), norepinephrine (NE), and epinephrine (E), and Ddc is also in the serotonin (5-HT) synthesis pathway. These four neurotransmitters are critical regulators of social behavior and disruptions to them are implicated in human mental illnesses. Here we investigated the functional effects of noncanonical imprinting of Ddc and Th on social behavior in mice. By using reciprocal heterozygous mutant mice, we tested the impacts of Ddc and/or Th maternally and paternally inherited alleles on aggression, social recognition, dominance, and social preference behaviors. We found that Ddc paternal-null alleles affect aggression and social recognition behavior, Th maternal-null alleles affect sociability preferences, and compound inheritance of Th and Ddc maternal-null alleles influence preferences for social novelty. These results are consistent with Th and Ddc maternal allele biased expression in central monoaminergic systems regulating sociability, and Ddc paternal allele biased expression in peripheral monoaminergic systems regulating aggression and social recognition.

neuroscience↗