Biology subjects

Su, Z.

Publications and source records attributed to Su, Z..

At least 73 records · Page 4Linked to original sources

bioRxiv2024

Genes in Humans and Mice: Insights from Deep learning of 777K Bulk Transcriptomes

Mice are widely used as animal models in biomedical research, favored for their small size, ease of breeding, and anatomical and physiological similarities to humans1,2. However, discrepancies between mouse gene experimental results and the actual behavior of human genes are not uncommon, despite their shared DNA sequence similarity3-8. This suggests that DNA sequence similarity does not always reliably predict functional similarity. On the other hand, RNA-level gene expression could offer additional information about gene function9,10. In this study, we undertook characterization and inter-species comparison of human and mouse genes by applying innovative deep learning methodologies to a large dataset of 410K human and 366K mouse bulk RNA-seq samples. This was achieved by using gene representations from our Transformer-based GeneRAIN model11,12. These gene representations aggregate information from large gene expression datasets, and provide insights beyond DNA sequence similarity. We identified 2,407 human-mouse homologous genes with high DNA similarity but distinct RNA characteristics, and showed that these genes are more likely to have differing disease/phenotype associations between the two species. Additionally, we found 3,070 homologous genes with low similarity at both the DNA and RNA levels, suggesting the highest risk of discrepancies in study results between the two species. We propose that this approach will support future decision making around whether the mouse will be an appropriate model for studying specific human genes, and whether the results of specific mouse gene studies are likely to be recapitulated in humans. Our methodological innovations offer valuable lessons for future deep learning applications in cross-species omics data. The interspecies gene relationship findings from our study also contribute valuable insights into the gene biology and evolution of the two species.

bioinformatics↗

bioRxiv2024

Multifaceted Representation of Genes via Deep Learning of Gene Expression Networks

Accurate predictive modeling of human gene relationships would fundamentally transform our ability to uncover the molecular mechanisms that underpin key biological and disease processes. Recent studies have employed advanced AI techniques to model the complexities of gene networks using large gene expression datasets1-11. However, the extent and nature of the biological information these models can learn is not fully understood. Furthermore, the potential for improving model performance by using alternative data types, model architectures, and methodologies remains underexplored. Here, we developed GeneRAIN models by training on a large dataset of 410K human bulk RNA-seq samples, rather than single-cell RNA-seq datasets used by most previous studies. We showed that although the models were trained only on gene expression data, they learned a wide range of biological information well beyond gene expression. We introduced GeneRAIN-vec, a state-of-the-art, multifaceted vectorized representation of genes. Further, we demonstrated the capabilities and broad applicability of this approach by making 4,797 biological attribute predictions for each of 13,030 long non-coding RNAs (62.5 million predictions in total). These achievements stem from various methodological innovations, including experimenting with multiple model architectures and a new Binning-By-Gene normalization method. Comprehensive evaluation of our models clearly demonstrated that they significantly outperformed current state-of-the-art models3,12. This study improves our understanding of the capabilities of Transformer and self-supervised deep learning when applied to extensive expression data. Our methodological advancements offer crucial insights into refining these techniques. These innovations are set to significantly advance our understanding and exploration of biology.

bioinformatics↗

bioRxiv2024

Microplastics from disposable paper cups are enriched in the placenta and fetus, leading to metabolic and reproductive toxicity during pregnancy

The health implications of microplastics (MPs), especially those originating from hot drinks in disposable paper cups (DPCs), are increasingly alarming. We investigated the accumulation and metabolic and reproductive toxicological effects of MPs from DPCs filled with hot water in various tissues in a pregnant mouse model. Simulating human intake of 0.3, 3.3, and 33.3 cups daily, we found MPs exposure-induced dose-responsive harmful effects on murine fetal development and maternal physiology. MPs were detected in all 13 examined tissues, with the highest accumulation in the cecal contents, followed by significant depositions in the fetus, placenta, kidney, spleen, lung, and heart. A higher proportion of smaller MPs (90.35% < 10 m) was identified in brain tissues. Dose-responsive changes in functional microbiome and gene pathways were observed. Moderate MPs intake of 3.3 cups daily significantly altered cecal microbiome composition and metabolic functions. The transcriptomic functional variations in maternal blood, placenta, and mammary gland underscore the significant impacts of realistic MPs exposure on metabolic and immune health and posing neurodegenerative and miscarriage risks. The benchmark dose framework analysis using tissue-specific gene biomarkers revealed safe exposure limits at 2 to 4 cups/day during pregnancy. Our results indicate selective tissue accumulation and potential metabolic and reproductive toxicities of MPs at exposure levels presumed non-hazardous. Such risks remain unaddressed within current food safety regulations, impacting vulnerable groups such as pregnant women and fetuses. Research HighlightsO_LIMicroplastics released from disposable paper cups filled with hot water showed preferential accumulations in the murine fetus, placenta, kidney, spleen, lung, and heart, with significant adverse impacts on fetal development. C_LIO_LIMicroplastic exposure led to dose-responsive maternal microbiome changes associated with increased fatty acid biosynthesis and elevated expressions of genes related to viral infections, neurodegenerative diseases, oxidative stress, and miscarriage risk. C_LIO_LIA consumption level of 3.3 cups/day was sufficient to elicit systemic metabolic and reproductive toxicity, with a predicted safe exposure limit of 2 to 4 cups/day during pregnancy by benchmark dose framework analysis with molecular biomarkers. C_LI

molecular biology↗

bioRxiv2024

Monocyte-derived fibrocytes contribute to nucleus pulposus fibrosis and intervertebral disc degeneration

Fibrotic remodeling of nucleus pulposus (NP) leads to structural and mechanical anomalies of intervertebral discs that prone to degeneration, leading to low back pain incidence and disability. Emergence of fibroblastic cells in disc degeneration has been reported, yet their nature and origin remain elusive. In this study, we performed an integrative analysis of multiple single-cell RNA sequencing datasets to interrogate the cellular heterogeneity and fibroblast-like entities in degenerative human NP specimens. We found that disc degeneration severity is associated with an enrichment of fibrocyte-like phenotype, characterized by CD45 and collagen I dual positivity, and expression of myofibroblast marker -smooth muscle actin. Refined clustering and classification distinguished the fibrocyte-like populations as subtypes in the NP cells - and immunocytes-clusters, expressing disc degeneration markers HTRA1 and ANGPTL4 and genes related to response to TGF-{beta}. In injury-induced mouse disc degeneration model, fibrocyte-like cells were found recruited into the NP undergoing fibrosis and adopted a myofibroblast phenotype. Depleting the fibrocyte-like cells in CD11b-DTR mice in which monocytic lineages were ablated by diphtheria toxin could markedly attenuate fibrous modeling and myofibroblast formation in the NP of the degenerative discs, and prevent disc height loss and histomorphological abnormalities. Marker analysis supports that disc degeneration progression is dependent on a function of CD45+COL1A1+ and SMA+ cells. Our findings reveal that fibrocyte-like cells play a pivotal role in NP fibrosis and may therefore be a target for modifying disc degeneration and promoting its repair.

physiology↗

bioRxiv2024

Annotations of four high-quality indigenous chicken genomes identify more than one thousand missing genes in sub-telomeric regions with high G/C contents

BackgroundAlthough multiple chicken genomes have been assembled and annotated, the number of protein-coding genes in chicken genomes is still uncertain due to the low quality of these genome assemblies and limited resources used in gene annotations. ResultsTo fill the gap, we annotated our four recently assembled high-quality genomes of four indigenous chickens with distinct traits using a combination of RNA-seq- and homology-based approach. Our annotated genes in the four chickens recovered 51 of the 274 "missing" genes in birds in general and 36 of the 174 "missing" genes in chickens in particular. Intriguingly, based on deeply sequenced RNA-seq data collected in multiple tissues in each chicken breed, we found a total of 1,420 new protein-coding genes in the four chicken genomes, which were missed in the reference chicken genome annotations. These newly annotated genes (NAGs) tend to have high G/C contents and be located in sub-telomeric regions of almost all assembled chromosomes and some unplaced contigs. The NAGs showed tissue-specific expression and we were able to verify 39 (92.9%) of 42 randomly selected ones in various tissues of the four chicken breeds using RT-qPCR experiments. We found that most of the NAGs also are encoded in previously assembled chicken genomes. The NAGs form functional modules with homology-supported genes that are involved in many important biological pathways. We also identified numerous unique genes in each indigenous chicken genome that might be related to the unique traits of each breed. ConclusionThe ubiquitous presence of the NAGs in various chicken genomes indicate that they might play critical roles in chicken physiology. Counting these new genes, chicken genomes harbor more genes than originally thought.

bioinformatics↗

bioRxiv2023

GAPS: Geometric Attention-based Networks for Peptide Binding Sites Identification by the Transfer Learning Approach

The identification of protein-peptide binding sites significantly advances our understanding of their interaction. Recent advancements in deep learning have profoundly transformed the prediction of protein-peptide binding sites. In this work, we describe the Geometric Attention-based networks for Peptide binding Sites identification (GAPS). The GAPS constructs atom representations using geometric feature engineering and employs various attention mechanisms to update pertinent biological features. In addition, the transfer learning strategy is implemented for leveraging the pre-trained protein-protein binding sites information to enhance training of the protein-peptide binding sites recognition, taking into account the similarity of proteins and peptides. Consequently, GAPS demonstrates state-of-the-art (SOTA) performance in this task. Our model also exhibits exceptional performance across several expanded experiments including predicting the apo protein-peptide, the protein-cyclic peptide, and the predicted protein-peptide binding sites. Overall, the GAPS is a powerful, versatile, stable method suitable for diverse binding site predictions.

bioinformatics↗

bioRxiv2023

Neurexins control the strength and precise timing of glycinergic inhibition in the auditory brainstem

Neurexins play diverse functions as presynaptic organizers in various glutamatergic and GABAergic synapses. However, it remains unknown whether and how neurexins are involved in shaping functional properties of the glycinergic synapses, which mediate prominent inhibition in the brainstem and spinal cord. To address these issues, we examined the role of neurexins in a model glycinergic synapse between the principal neuron in the medial nucleus of the trapezoid body (MNTB) and the principal neuron in the lateral superior olive (LSO) in the auditory brainstem. Combining RNAscope with stereotactic injection of AAV-Cre in the MNTB of neurexin1/2/3 conditional triple knockout mice, we showed that MNTB neurons highly express all isoforms of neurexins although their expression levels vary remarkably. Selective ablation of all neurexins in MNTB neurons not only reduced the amplitude but also altered the kinetics of the glycinergic synaptic transmission at LSO neurons. The synaptic dysfunctions primarily resulted from an impaired Ca2+ sensitivity of release and a loosened coupling between voltage-gated Ca2+ channels and synaptic vesicles. Together, our current findings demonstrate that neurexins are essential in controlling the strength and temporal precision of the glycinergic synapse, which therefore corroborates the role of neurexins as key presynaptic organizers in all major types of fast chemical synapses.

neuroscience↗

bioRxiv2023

Machine-learning-based Structural Analysis of Interactions between Antibodies and Antigens

Computational analysis of paratope-epitope interactions between antibodies and their corresponding antigens can facilitate our understanding of the molecular mechanism underlying humoral immunity and boost the design of new therapeutics for many diseases. The recent breakthrough in artificial intelligence has made it possible to predict protein-protein interactions and model their structures. Unfortunately, detecting antigen-binding sites associated with a specific antibody is still a challenging problem. To tackle this challenge, we implemented a deep learning model to characterize interaction patterns between antibodies and their corresponding antigens. With high accuracy, our model can distinguish between antibody-antigen complexes and other types of protein-protein complexes. More intriguingly, we can identify antigens from other common protein binding regions with an accuracy of higher than 70% even if we only have the epitope information. This indicates that antigens have distinct features on their surface that antibodies can recognize. Additionally, our model was unable to predict the partnerships between antibodies and their particular antigens. This result suggests that one antigen may be targeted by more than one antibody and that antibodies may bind to previously unidentified proteins. Taken together, our results support the precision of antibody-antigen interactions while also suggesting positive future progress in the prediction of specific pairing.

bioinformatics↗

bioRxiv2023

Protein-lipid interactions drive presynaptic assembly upstream of cell adhesion molecules

Textbook models of synaptogenesis position cell adhesion molecules such as neurexin as initiators of synapse assembly. Here we discover a mechanism for presynaptic assembly that occurs prior to neurexin recruitment, while supporting a role for neurexin in synapse maintenance. We find that the cytosolic active zone scaffold SYD-1 interacts with membrane phospholipids to promote active zone protein clustering at the plasma membrane, and subsequently recruits neurexin to stabilize those clusters. Employing molecular dynamics simulations to model intrinsic interactions between SYD-1 and lipid bilayers followed by in vivo tests of these predictions, we find that PIP2-interacting residues in SYD-1s C2 and PDZ domains are redundantly necessary for proper active zone assembly. Finally, we propose that the uncharacterized yet evolutionarily conserved short {gamma} isoform of neurexin represents a minimal neurexin sequence that can stabilize previously assembled presynaptic clusters, potentially a core function of this critical protein.

neuroscience↗

bioRxiv2023

High-quality genome assembly of a white eared pheasant individual and related functional and genetics data resources

White eared pheasant (WT), (Crossoptilon crossoptilon), inhibiting at high altitudes (3000[~]4,300 m), is a Galliformes bird native to the Qinghai, Sichuan, Yunnan and Tibet Province of China. Due to the difficulty of sequencing the precious species, there is no high-quality genome assembly for the species, hampering the understanding of their genetic mechanisms. To fill the gap, we sequenced and assembled a WT individual using Illumina short reads, PacBio long reads and Hi-C reads. With a contig N50 of 19.63 Mb, scaffold N50 of 29.59 Mb, total length of 1.02 Gb and BUSCO completeness of 97.2%, the assembly is highly complete. Evaluation shows that the assembly is at chromosome-level with only six gaps. Thus, our assembly provides a valuable genetic resource for the crossoptilon species. To further provide resources for gene annotation and population genetics analysis, we also sequenced transcriptomes of 20 tissues of the WT individual and re-sequenced another 10 individuals of WT. Our assembled WT genome and the sequencing data can be valuable resources to study the crossoptilon species.

bioinformatics↗

bioRxiv2023

Metabolic interactions of a minimal bacterial consortium drive robust nitritation at acidic pH

Microbial communities efficiently mediate aerobic ammonia oxidation even at acidic pH. However, little is known about the adaptations and interactions that allow these communities to withstand challenges such as acidic stress, reactive nitrogen species, and resource deprivation under such conditions. Here we combined metagenomic analysis and biogeochemical measurements to infer the composition, metabolic interactions, and stress adaptation mechanisms of microbial consortia in three acidic nitritation bioreactors, operating at pH 5 to 2. This resulted in the recovery of 70 high-quality and mostly novel metagenome-assembled genomes (MAGs). The dominant ammonia oxidiser across all three bioreactors was a novel proteobacterium, herein named Candidatus (Ca.) Nitrosoglobus kelleri, that we enriched to a relative abundance of 55%. Also present were several heterotrophic bacteria that are predicted to engage in metabolically cross-feeding with the Nitrosoglobus. Particularly abundant were mycobacteria, including the novel actinobacterium Ca. Mycobacterium cookii, which are predicted to use organic carbon, hydrogen, carbon monoxide, sulfide and possibly nitrite as energy sources to drive aerobic respiration and denitrification. Remarkably, we observed efficient conversion of ammonia to nitrate even at pH 2, by a minimalistic community comprising the Nitrosoglobus and Mycobacterium as its only core members. Genomic analysis suggests these bacteria each use multiple strategies to maintain intracellular pH homeostasis, detoxify reactive nitrogen species, and scavenge nutrients at this pH. Altogether, these findings reveal that minimal communities can drive a key biogeochemical process even at acidic pH, and have implications for understanding nitrogen cycling and enhancing wastewater treatment.

ecology↗

bioRxiv2023

Ambient Temperature Bacterial Large Ribosomal Subunit Structure Enabled by Serial Femtosecond X-ray Crystallography

Ribosomes are the supramolecular complexes responsible for protein synthesis. The large 50S ribosomal subunit catalyzes the peptidyl transferase reaction and peptide bond formation between amino acids. The 50S is targeted by many known clinically effective antibiotics. Available structures, obtained at cryogenic temperatures (CT), are used for drug discovery despite that active or important target sites may display a structural configuration that is CT-induced. The introduction of ultrafast and ultrabright X-ray free electron laser (XFEL) pulses has enabled the structural observation of biological macro- and supramolecules at previously unattainable, near-physiological temperatures. In this study, we use ultrafast and ultrabright XFEL pulses to solve the apo form of 50S ribosomal subunit isolated from the extremely thermophilic bacterium Thermus thermophilus at ambient temperature (AT). The dimeric structure of the 50S subunit presented in this work is among the largest ([~]3 megadalton) structures determined using an XFEL source to date. This study demonstrates the ability to obtain new information about ribosome structural dynamics at AT through serial femtosecond X-ray crystallography (SFX). This allowed us to capture previously unobserved dynamics of ribosomal protein uL23 and coordination by hexahydrated magnesium cations at a hitherto unseen resolution at near-physiological temperature. Also, residue A2602, at the core of the peptidyl transferase center (PTC), shows a rather different orientation of the sugar moiety if compared to CT structures. In addition, our structure highlights the importance of flexible residues at both the PTC and in the binding sites for antibiotics erythromycin and chloramphenicol. The method implemented here may also serve as a starting point for future structural research involving the 50S subunit complexes by employing time-resolved mix-inject and probe kineto-crystallography experiments at XFELs. Unveiling ligand-dependent 50S dynamics at physiological temperatures shall guide further development of next-generation antibiotics that target the translation machinery.

biophysics↗

bioRxiv2023

Integrating large-scale meta-GWAS and PigGTEx resources to decipher the genetic basis of complex traits in pig.

Understanding the molecular and cellular mechanisms that underlie complex traits in pigs is crucial for enhancing their genetic improvement program and unleashing their substantial potentials in human biomedicine research. Here, we conducted a meta-GWAS analysis for 232 complex traits with 28.3 million imputed whole-genome sequence variants in 70,328 individuals from 14 pig breeds. We identified a total of 6,878 genomic regions associated with 139 complex traits. By integrating with the Pig Genotype-Tissue Expression (PigGTEx) resource, we systemically explored the biological context and regulatory circuits through which these trait-associated variants act and finally prioritized 16,664 variant-gene-tissue-trait circuits. For instance, rs344053754 regulates the expression of UGT2B31 in the liver by affecting the activity of regulatory elements and ultimately influences litter weight at weaning. Furthermore, we investigated the conservation of genetic and regulatory mechanisms underlying 136 human traits and 232 pig traits. Overall, our multi-breed meta-GWAS in pigs provides invaluable resources and novel insights for understanding the regulatory and evolutionary mechanisms of complex traits in both pigs and humans.

genetics↗

bioRxiv2023

Evolution of wheat blast resistance gene Rmg8 accompanied by differentiation of variants recognizing the powdery mildew fungus

Wheat blast, a devastating disease having spread recently from South America to Asia and Africa, is caused by Pyricularia oryzae pathotype Triticum which emerged in 1985. Rmg8 and Rmg7, genes for resistance to wheat blast found in common wheat and tetraploid wheat, respectively, recognize the same avirulence gene, AVR-Rmg8. Here, we show an evolutionary process in which resistance gene(s), which had obtained an ability to recognize AVR-Rmg8 before the differentiation of Triticum and Aegilops, has expanded its target pathogens. Molecular cloning revealed that Rmg7 was one of alleles of Pm4 (Pm4a), a gene for resistance to wheat powdery mildew on 2AL, whereas Rmg8 was its homoeolog on 2BL ineffective against wheat powdery mildew. Rmg8 variants with the ability to recognize AVR-Rmg8 were distributed not only in Triticum spp. but also in Aegilops speltoides, Ae. umbellulata, and Ae. comosa. This result suggests that the origin of resistance gene(s) recognizing AVR-Rmg8 dates back to the time before differentiation of A, B, S, U, and M genomes, that is, [~]5 million years before the emergence of its current target, the wheat blast fungus. Phylogenetic analyses suggested that, in the evolutionary process thereafter, some of their variants gained the ability to recognize the wheat powdery mildew fungus and evolved into genes for resistance to wheat powdery mildew.

plant biology↗

bioRxiv2023

Prediction of Interactions between Cell Surface Proteins by Machine Learning

Cells detect changes of external environments or communicate with each other through proteins on their surfaces. These cell surface proteins form a complicated network of interactions in order to fulfill their functions. The interactions between cell surface proteins are highly dynamic and thus challenging to detect using traditional experimental techniques. Here we tackle this challenge by a computational framework. The primary focus of the framework is to develop new tools to identify interactions between domains in immunoglobulin (Ig) fold, which is the most abundant domain family in cell surface proteins. These interactions could be formed between ligands and receptors from different cells, or between proteins on the same cell surface. In practice, we collected all structural data of Ig domain interactions and transformed them into an interface fragment pair library. A high dimensional profile can be then constructed from the library for a given pair of query protein sequences. Multiple machine learning models were used to read this profile, so that the probability of interaction between the query proteins can be predicted. We tested our models to an experimentally derived dataset which contains 564 cell surface proteins in human. The cross-validation results show that we can achieve higher than 70% accuracy in identifying the PPIs within this dataset. We then applied this method to a group of 46 cell surface proteins in C elegans. We screened every possible interaction between these proteins. Many interactions recognized by our machine learning classifiers have been experimentally confirmed in the literatures. In conclusion, our computational platform serves a useful tool to help identifying potential new interactions between cell surface proteins in addition to current state-of-the-art experimental techniques. The tool is freely accessible for use by the scientific community. Moreover, the general framework of the machine learning classification can also be extended to study interactions of proteins in other domain superfamilies.

bioinformatics↗

bioRxiv2023

Encoding the Space of Protein-protein Binding Interfaces by Artificial Intelligence

The physical interactions between proteins are largely determined by the structural properties at their binding interfaces. It was found that the binding interfaces in distinctive protein complexes are highly similar. The structural properties underlying different binding interfaces could be further captured by artificial intelligence. In order to test this hypothesis, we broke protein-protein binding interfaces into pairs of interacting fragments. We employed a generative model to encode these interface fragment pairs in a low-dimensional latent space. After training, new conformations of interface fragment pairs were generated. We found that, by only using a small number of interface fragment pairs that were generated by artificial intelligence, we were able to guide the assembly of protein complexes into their native conformations. These results demonstrate that the conformational space of fragment pairs at protein-protein binding interfaces is highly degenerate. Our study illustrates how artificial intelligence can be used to understand and characterize protein-protein binding interfaces. The method will be potentially useful to search for the conformation of unknown protein-protein interactions. This result demonstrated that the structural space of protein-protein interactions is highly degenerate under the representation of interface fragment pairs. Features in this degenerate space can be well characterized by artificial intelligence. In summary, our machine learning method will be potentially useful to search for and predict the conformations of unknown protein-protein interactions.

bioinformatics↗

bioRxiv2023

Integrative analysis of DNA replication origins and ORC binding sites in human cells reveals a lack of overlap between them

Based on experimentally determined average inter-origin distances of [~]100 kb, DNA replication initiates from [~]50,000 origins on human chromosomes in each cell cycle. The origins are believed to be specified by binding of factors like the Origin Recognition Complex (ORC) or CTCF or other features like G-quadruplexes. We have performed an integrative analysis of 113 genome-wide human origin profiles (from five different techniques) and 5 ORC-binding profiles to critically evaluate whether the most reproducible origins are specified by these features. Out of [~]7.5 million union origins identified by all datasets, only 0.27% were reproducibly obtained in at least 20 independent SNS-seq datasets and contained in initiation zones identified by each of three other techniques (20,250 shared origins), suggesting extensive variability in origin usage and identification. 21% of the shared origins overlap with transcriptional promoters, posing a conundrum. Although the shared origins overlap more than union origins with constitutive CTCF binding sites, G-quadruplex sites and activating histone marks, these overlaps are comparable or less than that of known Transcription Start Sites, so that these features could be enriched in origins because of the overlap of origins with epigenetically open, promoter-like sequences. Only 6.4% of the 20,250 shared origins were within 1 kb from any of the [~]13,000 reproducible ORC binding sites in human cancer cells, and only 4.5% were within 1 kb of the [~]11,000 union MCM2-7 binding sites in contrast to the nearly 100% overlap in the two comparisons in the yeast, S. cerevisiae. Thus, in human cancer cell lines, replication origins appear to be specified by highly variable stochastic events dependent on the high epigenetic accessibility around promoters, without extensive overlap between the most reproducible origins and currently known ORC- or MCM-binding sites.

genomics↗

bioRxiv2023

Hostile attribution bias shapes neural synchrony in the left ventromedial prefrontal cortexduring ambiguous social narratives

Hostile attribution bias refers to the tendency to interpret social situations as intentionally hostile. While previous research has focused on its developmental origins and behavioral consequences, the underlying neural mechanisms remain underexplored. Here, we employed functional near infrared spectroscopy (fNIRS) to investigate the neural correlates of hostile attribution bias. While undergoing fNIRS, participants listened to and provided attribution ratings for 21 hypothetical scenarios where a characters actions resulted in a negative outcome for the listener. Ratings of hostile intentions were averaged to obtain a measure of hostile attribution bias. Using intersubject-representational similarity analysis, we found that participants with similar levels of hostile attribution bias exhibited higher levels of neural synchrony during narrative listening, suggesting shared interpretations of the scenarios. This effect was localized to the left ventromedial prefrontal cortex (VMPFC), and was particularly prominent in scenarios where the characters intentions were highly ambiguous. We then grouped participants into high and low bias groups based on a median split of their hostile attribution bias scores. A similarity-based classifier trained on the neural data classified participants as having high or low bias with 76% accuracy, indicating that the neural time courses during narrative listening was systematically different between the two groups. Furthermore, hostile attribution bias correlated negatively with attributional complexity, a measure of ones tendency to consider multifaceted causes when explaining behavior. Our study sheds light on the neural mechanisms underlying hostile attribution bias and highlights the potential of using fNIRS to develop non-intrusive and cost-effective neural markers of this socio-cognitive bias. Significance StatementInferring the intentions from behavior is crucial for adaptive social functioning. A predisposition towards interpreting intentions as hostile is a significant predictor of interpersonal conflict and aggressive tendencies. Using fNIRS, we found that individual differences in hostile attribution bias shaped neural synchrony in the VMPFC while processing real-world social situations. Additionally, we were able to distinguish between participants with high and low hostile attribution bias from neural activity time courses. These results reveal how subjective interpretations of social situations are influenced by hostile attribution bias and reflected in the temporal dynamics of the VMPFC. Our findings lay the groundwork for future studies aimed at understanding the neurobiological basis of socio-cognitive biases, as well as interventions aimed at mitigating these biases.

neuroscience↗