CRISPR-aided genome engineering for secondary metabolite biosynthesis in Streptomyces.

The demand for discovering novel microbial secondary metabolites is growing to address the limitations in bioactivities such as antibacterial, antifungal, anticancer, anthelmintic, and immunosuppressive functions. Among microbes, the genus Streptomyces holds particular significance for secondary metabolite discovery. Each Streptomyces species typically encodes approximately 30 secondary metabolite biosynthetic gene clusters (smBGCs) within its genome, which are mostly uncharacterized in terms of their products and bioactivities. The development of next-generation sequencing has enabled the identification of a large number of potent smBGCs for novel secondary metabolites that are imbalanced in number compared with discovered secondary metabolites. The clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated (Cas) system has revolutionized the translation of enormous genomic potential into the discovery of secondary metabolites as the most efficient genetic engineering tool for Streptomyces. In this review, the current status of CRISPR/Cas applications in Streptomyces is summarized, with particular focus on the identification of secondary metabolite biosynthesis gene clusters and their potential applications.This review summarizes the broad range of CRISPR/Cas applications in Streptomyces for natural product discovery and production. ONE-SENTENCE SUMMARY: This review summarizes the broad range of CRISPR/Cas applications in Streptomyces for natural product discovery and production.

An optimized Nurr1 agonist provides disease-modifying effects in Parkinson's disease models.

The nuclear receptor, Nurr1, is critical for both the development and maintenance of midbrain dopamine neurons, representing a promising molecular target for Parkinson's disease (PD). We previously identified three Nurr1 agonists (amodiaquine, chloroquine and glafenine) that share an identical chemical scaffold, 4-amino-7-chloroquinoline (4A7C), suggesting a structure-activity relationship. Herein we report a systematic medicinal chemistry search in which over 570 4A7C-derivatives were generated and characterized. Multiple compounds enhance Nurr1's transcriptional activity, leading to identification of an optimized, brain-penetrant agonist, 4A7C-301, that exhibits robust neuroprotective effects in vitro. In addition, 4A7C-301 protects midbrain dopamine neurons in the MPTP-induced male mouse model of PD and improves both motor and non-motor olfactory deficits without dyskinesia-like behaviors. Furthermore, 4A7C-301 significantly ameliorates neuropathological abnormalities and improves motor and olfactory dysfunctions in AAV2-mediated α-synuclein-overexpressing male mouse models. These disease-modifying properties of 4A7C-301 may warrant clinical evaluation of this or analogous compounds for the treatment of patients with PD.

A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables.

The identification and understanding of gene-environment interactions can provide insights into the pathways and mechanisms underlying complex diseases. However, testing for gene-environment interaction remains a challenge since a.) statistical power is often limited and b.) modeling of environmental effects is nontrivial and such model misspecifications can lead to false positive interaction findings. To address the lack of statistical power, recent methods aim to identify interactions on an aggregated level using, for example, polygenic risk scores. While this strategy can increase the power to detect interactions, identifying contributing genes and pathways is difficult based on these relatively global results. Here, we propose RITSS (Robust Interaction Testing using Sample Splitting), a gene-environment interaction testing framework for quantitative traits that is based on sample splitting and robust test statistics. RITSS can incorporate sets of genetic variants and/or multiple environmental factors. Based on the user's choice of statistical/machine learning approaches, a screening step selects and combines potential interactions into scores with improved interpretability. In the testing step, the application of robust statistics minimizes the susceptibility to main effect misspecifications. Using extensive simulation studies, we demonstrate that RITSS controls the type 1 error rate in a wide range of scenarios, and we show how the screening strategy influences statistical power. In an application to lung function phenotypes and human height in the UK Biobank, RITSS identified highly significant interactions based on subcomponents of genetic risk scores. While the contributing single variant interaction signals are weak, our results indicate interaction patterns that result in strong aggregated effects, providing potential insights into underlying gene-environment interaction mechanisms.

A Pitx3-deficient developmental mouse model for fine motor, olfactory, and gastrointestinal symptoms of Parkinson's disease.

Parkinson's disease (PD) is characterized by the selective death of substantia nigra pars compacta (SNpc) dopaminergic neurons and includes both motor and non-motor symptoms. While numerous models exist for the study of typical PD motor deficits, fewer exist for non-motor symptoms. Previous studies have shown that a Pitx3-/- mouse model (aphakia or ak mouse) has specific developmental failure of the dopaminergic neuron population in the SNpc and that it can be used for the study of PD-related gross motor dysfunction as well as cognitive functional deficits. It remains unclear whether the aphakia mouse, both male and female, might also be used to model fine motor deficits and for additional studies of non-motor deficits associated with PD. Here, using an extensive battery of behavioral tests, we demonstrate that the aphakia mouse shows both gross and fine motor functional deficits compared with control mice. Furthermore, aphakia mice show deficits of olfactory function in buried pellet, odor discrimination and odor habituation/dishabituation tests. We also found that aphakia mice suffer from gastrointestinal dysfunction (e.g., longer whole gut transit time and colon motility deficits), suggesting that the mutation also affects function of the gut-brain axis in this animal model. Moreover, our data demonstrate that in the aphakia mouse, L-DOPA, the gold standard PD medication, can rescue both gross and fine motor function deficits but neither olfactory nor gastrointestinal symptoms, a pattern much like that seen in PD patients. Altogether, this suggests that the aphakia mouse is a suitable model for fine motor, olfactory and gastrointestinal behavioral studies of PD as well as for the development of novel disease-modifying therapeutics. SIGNIFICANCE STATEMENT: While several animal models are available to study the major motor symptoms of PD, there are fewer that replicate non-motor symptoms, which constitute a major source of morbidity for patients. Moreover, available models often require manipulations resulting in sudden massive cell loss and inflammation, both of which may interfere with understanding of the direct effects of dopaminergic neuronal loss in the SNpc. We describe a model of congenital SNpc cell deficiency in a Pitx3-/- mouse and characterize it with a battery of behavioral tests suggesting that it closely mimics non-motor as well as motor symptoms of PD, providing a useful insight into the effects of the nigrostriatal dopamine deficit. Taken together, these data suggest that the ak mouse represents a useful model to study dopaminergic system function for both motor and non-motor symptoms of PD.

System-Level Analysis of Transcriptional and Translational Regulatory Elements in Streptomyces griseus.

Bacteria belonging to Streptomyces have the ability to produce a wide range of secondary metabolites through a shift from primary to secondary metabolism regulated by complex networks activated after vegetative growth terminates. Despite considerable effort to understand the regulatory elements governing gene expression related to primary and secondary metabolism in Streptomyces, system-level information remains limited. In this study, we integrated four multi-omics datasets from Streptomyces griseus NBRC 13350: RNA-seq, ribosome profiling, dRNA-seq, and Term-Seq, to analyze the regulatory elements of transcription and translation of differentially expressed genes during cell growth. With the functional enrichment of gene expression in different growth phases, one sigma factor regulon and four transcription factor regulons governing differential gene transcription patterns were found. In addition, the regulatory elements of transcription termination and post-transcriptional processing at transcript 3'-end positions were elucidated, including their conserved motifs, stem-loop RNA structures, and non-terminal locations within the polycistronic operons, and the potential regulatory elements of translation initiation and elongation such as 5'-UTR length, RNA structures at ribosome-bound sites, and codon usage were investigated. This comprehensive genetic information provides a foundational genetic resource for strain engineering to enhance secondary metabolite production in Streptomyces.

Genome-scale analysis of genetic regulatory elements in Streptomyces avermitilis MA-4680 using transcript boundary information.

BACKGROUND: The gram-positive bacterium, Streptomyces avermitilis, holds industrial importance as the producer of avermectin, a widely used anthelmintic agent, and a heterologous expression host of secondary metabolite-biosynthetic gene clusters. Despite its industrial importance, S. avermitilis' genome organization and regulation of gene expression remain poorly understood. In this study, four different types of Next-Generation Sequencing techniques, including dRNA-Seq, Term-Seq, RNA-Seq and ribosome profiling, were applied to S. avermitilis to determine transcription units of S. avermitilis at a genome-wide level and elucidate regulatory elements for transcriptional and translational control of individual transcription units. RESULT: By applying dRNA-Seq and Term-Seq to S. avermitilis MA-4680, a total of 2361 transcription start sites and 2017 transcript 3'-end positions were identified, respectively, leading to determination of 1601 transcription units encoded in S. avermitilis' genome. Cataloguing the transcription units and integrated analysis of multiple high-throughput data types revealed the presence of diverse regulatory elements for gene expression, such as promoters, 5'-UTRs, terminators, 3'-UTRs and riboswitches. The conserved promoter motifs were identified from 2361 transcription start sites as 5'-TANNNT and 5'-BTGACN for the - 10 and - 35 elements, respectively. The - 35 element and spacer lengths between - 10 and - 35 elements were critical for transcriptional regulation of functionally distinct genes, suggesting the involvement of unique sigma factors. In addition, regulatory sequences recognized by antibiotic regulatory proteins were identified from the transcription start site information. Analysis of the 3'-end of RNA transcript revealed that stem structure formation is a major determinant for transcription termination of most transcription units. CONCLUSIONS: The transcription unit architecture elucidated from the transcripts' boundary information provides insights for unique genetic regulatory mechanisms of S. avermitilis. Our findings will elevate S. avermitilis' potential as a production host for a diverse set of secondary metabolites.

Genetics and functional genomics of multiple sclerosis.

Multiple sclerosis (MS) is an inflammatory neurodegenerative disease with genetic predisposition. Over the last decade, genome-wide association studies with increasing sample size led to the discovery of robustly associated genetic variants at an exponential rate. More than 200 genetic loci have been associated with MS susceptibility and almost half of its heritability can be accounted for. However, many challenges and unknowns remain. Definitive studies of disease progression and endophenotypes are yet to be performed, whereas the majority of the identified MS variants are not yet functionally characterized. Despite these shortcomings, the unraveling of MS genetics has opened up a new chapter on our understanding MS causal mechanisms.

A higher burden of multiple sclerosis genetic risk confers an earlier onset.

BACKGROUND: Age at onset of multiple sclerosis (MS) is an objective, influential predictor of the evolution of MS independent of disease duration. OBJECTIVES: Determine the influence of MS genetic predisposition on age of onset. METHODS: We conducted a comprehensive investigation of MS risk variants and age at onset in 3495 non-Latinx white individuals, including for combinations of HLA-DRB1*15:01 alleles and quintiles of an unweighted genetic risk score (GRS) for 198 of 200 autosomal MS risk variants that reside outside the major histocompatibility complex. RESULTS: The mean age at onset was 32 years, 29% were male, and 46% were HLA-DRB1*15:01 carriers. For those with the greatest genetic risk burden (the highest GRS quintile with two HLA-DRB1*15:01 alleles) were on average 5 years younger at onset (p = 0.002) than those with the lowest genetic risk burden (the lowest GRS quintile with no HLA-DRB1*15:01 alleles). There was a strong inverse relationship between the MS genetic risk burden and age at onset of MS (p < 5 × 10-8). CONCLUSION: We demonstrate a significant gradient between elevated MS genetic risk burden and an earlier onset of MS, suggesting that a higher MS genetic risk burden accelerates onset of the disease.

Interaction of Cigarette Smoking and Polygenic Risk Score on Reduced Lung Function.

Importance: The risk of airflow limitation and chronic obstructive pulmonary disease (COPD) is influenced by combinations of cigarette smoking and genetic susceptibility, yet it remains unclear whether gene-by-smoking interactions are associated with quantitative measures of lung function. Objective: To assess the interaction of cigarette smoking and polygenic risk score in association with reduced lung function. Design, Setting, and Participants: This UK Biobank cohort study included UK citizens of European ancestry aged 40 to 69 years with genetic and spirometry data passing quality control metrics. Data was analyzed from July 2020 to March 2021. Exposures: PRS of combined forced expiratory volume in 1 second (FEV1) and percent of forced vital capacity exhaled in the first second (FEV1/FVC), self-reported pack-years of smoking, ever- vs never-smoking status, and current- vs former- or never-smoking status. Main Outcomes and Measures: FEV1/FVC was the primary outcome. Models were used to test for interactions with models, including the main effects of PRS, different smoking variables, and their cross-product terms. The association between pack-years of smoking and FEV1/FVC were compared for those in the highest vs lowest decile of estimated genetic risk for low lung function. Results: We included 319 730 individuals, of whom 24 915 (8%) had moderate-to-severe COPD cases, and 44.4% were men. Participants had a mean (SD) age 56.5 of (8.02) years. The PRS and pack-years were significantly associated with lower FEV1/FVC (PRS: ß, -0.03; 95% CI, -0.031 to -0.03; pack-years: ß, -0.0064; 95% CI, -0.0064 to -0.0063) and the interaction term (ß, -0.0028; 95% CI, -0.0029 to -0.0026). A stepwise increment in estimated effect sizes for these interaction terms was observed per 10 pack-years of smoking exposure. The interaction of PRS with 11 to 20, 31 to 40, and more than 50 pack-years categories were ß (interaction) -0.0038 (95% CI, -0.0046 to -0.0031); -0.013 (95% CI, -0.014 to -0.012); and -0.017 (95% CI, -0.019 to -0.016), respectively. There was evidence of significant interaction between PRS with ever- or never- smoking status (ß, interaction; -0.0064; 95% CI, -0.0068 to -0.0060) and current or not-current smoking (ß, interaction; -0.0091; 95% CI, -0.0097 to -0.0084). For any given level of pack-years of smoking exposure, FEV1/FVC was significantly lower for individuals in the tenth decile (ie, highest risk) than the first decile (ie, lowest risk) of genetic risk. For every 20 pack-years of smoking, those in the tenth decile compared with the first decile of genetic risk showed nearly a 2-fold reduction in FEV1/FVC. Conclusions and Relevance: COPD is characterized by diminished lung function, and our analyses suggest there is substantial interaction between genome-wide PRS and smoking exposures. While smoking was associated with decreased lung function across all genetic risk categories, the associations were strongest in individuals with higher estimated genetic risk.

Re-classification of Streptomyces venezuelae strains and mining secondary metabolite biosynthetic gene clusters.

Streptomyces species have attracted considerable interest as a reservoir of medically important secondary metabolites, which are even diverse and different between strains. Here, we reassess ten Streptomyces venezuelae strains by presenting the highly resolved classification, using 16S rRNA sequencing, MALDI-TOF MS protein profiling, and whole-genome sequencing. The results revealed that seven of the ten strains were misclassified as S. venezuelae species. Secondary metabolite biosynthetic gene cluster (smBGC) mining and targeted LC-MS/MS based metabolite screening of S. venezuelae and misclassified strains identified in total 59 secondary metabolites production. In addition, a comparison of pyrrolamide-type antibiotic BGCs of four misclassified strains, followed by functional genomics, revealed that athv28 is critical in the synthesis of the anthelvencin precursor, 5-amino-3,4-dihydro-2H-pyrrole-2-carboxylate (ADPC). Our findings illustrate the importance of the accurate classification and better utilization of misclassified Streptomyces strains to discover smBGCs and their secondary metabolite products.

Elucidating the Regulatory Elements for Transcription Termination and Posttranscriptional Processing in the Streptomyces clavuligerus Genome.

Identification of transcriptional regulatory elements in the GC-rich Streptomyces genome is essential for the production of novel biochemicals from secondary metabolite biosynthetic gene clusters (smBGCs). Despite many efforts to understand the regulation of transcription initiation in smBGCs, information on the regulation of transcription termination and posttranscriptional processing remains scarce. In this study, we identified the transcriptional regulatory elements in ß-lactam antibiotic-producing Streptomyces clavuligerus ATCC 27064 by determining a total of 1,427 transcript 3'-end positions (TEPs) using the term-seq method. Termination of transcription was governed by three classes of TEPs, of which each displayed unique sequence features. The data integration with transcription start sites and transcriptome data generated 1,648 transcription units (TUs) and 610 transcription unit clusters (TUCs). TU architecture showed that the transcript abundance in TU isoforms of a TUC was potentially affected by the sequence context of their TEPs, suggesting that the regulatory elements of TEPs could control the transcription level in additional layers. We also identified TU features of a xenobiotic response element (XRE) family regulator and DUF397 domain-containing protein, particularly showing the abundance of bidirectional TEPs. Finally, we found that 189 noncoding TUs contained potential cis- and trans-regulatory elements that played a major role in regulating the 5' and 3' UTR. These findings highlight the role of transcriptional regulatory elements in transcription termination and posttranscriptional processing in Streptomyces sp.IMPORTANCE Streptomyces sp. is a great source of bioactive secondary metabolites, including antibiotics, antifungal agents, antiparasitic agents, immunosuppressant compounds, and other drugs. Secondary metabolites are synthesized via multistep conversions of the precursor molecules from primary metabolism, governed by multicomplex enzymes from secondary metabolite biosynthetic gene clusters. As their production is closely related with the growth phase and dynamic cellular status in response to various intra- and extracellular signals, complex regulatory systems tightly control the gene expressions related to secondary metabolism. In this study, we determined genome-wide transcript 3'-end positions and transcription units in the ß-lactam antibiotic producer Streptomyces clavuligerus ATCC 27064 to elucidate the transcriptional regulatory elements in transcription termination and posttranscriptional processing by integration of multiomics data. These unique features, such as transcript 3'-end sequence, potential riboregulators, and potential 3'-untranslated region (UTR) cis-regulatory elements, can be potentially used to design engineering tools that can regulate the transcript abundance of genes for enhancing secondary metabolite production.

Discovery of novel secondary metabolites encoded in actinomycete genomes through coculture.

Actinomycetes are a rich source of bioactive natural products important for novel drug leads. Recent genome mining approaches have revealed an enormous number of secondary metabolite biosynthetic gene clusters (smBGCs) in actinomycetes. However, under standard laboratory culture conditions, many smBGCs are silent or cryptic. To activate these dormant smBGCs, several approaches, including culture-based or genetic engineering-based strategies, have been developed. Above all, coculture is a promising approach to induce novel secondary metabolite production from actinomycetes by mimicking an ecological habitat where cryptic smBGCs may be activated. In this review, we introduce coculture studies that aim to expand the chemical diversity of actinomycetes, by categorizing the cases by the type of coculture partner. Furthermore, we discuss the current challenges that need to be overcome to support the elicitation of novel bioactive compounds from actinomycetes.

Potent synthetic and endogenous ligands for the adopted orphan nuclear receptor Nurr1.

Until recently, Nurr1 (NR4A2) was known as an orphan nuclear receptor without a canonical ligand-binding domain, featuring instead a narrow and tight cavity for small molecular ligands to bind. In-depth characterization of its ligand-binding pocket revealed that it is highly dynamic, with its structural conformation changing more than twice on the microsecond-to-millisecond timescale. This observation suggests the possibility that certain ligands are able to squeeze into this narrow space, inducing a conformational change to create an accessible cavity. The cocrystallographic structure of Nurr1 bound to endogenous ligands such as prostaglandin E1/A1 and 5,6-dihydroxyindole contributed to clarifying the crucial roles of Nurr1 and opening new avenues for therapeutic interventions for neurodegenerative and/or inflammatory diseases related to Nurr1. This review introduces novel endogenous and synthetic Nurr1 agonists and discusses their potential effects in Nurr1-related diseases.

Systems and synthetic biology to elucidate secondary metabolite biosynthetic gene clusters encoded in Streptomyces genomes.

Covering: 2010 to 2020 Over the last few decades, Streptomyces have been extensively investigated for their ability to produce diverse bioactive secondary metabolites. Recent advances in Streptomyces research have been largely supported by improvements in high-throughput technology 'omics'. From genomics, numerous secondary metabolite biosynthetic gene clusters were predicted, increasing their genomic potential for novel bioactive compound discovery. Additional omics, including transcriptomics, translatomics, interactomics, proteomics and metabolomics, have been applied to obtain a system-level understanding spanning entire bioprocesses of Streptomyces, revealing highly interconnected and multi-layered regulatory networks for secondary metabolism. The comprehensive understanding derived from this systematic information accelerates the rational engineering of Streptomyces to enhance secondary metabolite production, integrated with the exploitation of the highly efficient 'Design-Build-Test-Learn' cycle in synthetic biology. In this review, we describe the current status of omics applications in Streptomyces research to better understand the organism and exploit its genetic potential for higher production of valuable secondary metabolites and novel secondary metabolite discovery.

Streptomyces as Microbial Chassis for Heterologous Protein Expression.

Heterologous production of recombinant proteins is gaining increasing interest in biotechnology with respect to productivity, scalability, and wide applicability. The members of genus Streptomyces have been proposed as remarkable hosts for heterologous production due to their versatile nature of expressing various secondary metabolite biosynthetic gene clusters and secretory enzymes. However, there are several issues that limit their use, including low yield, difficulty in genetic manipulation, and their complex cellular features. In this review, we summarize rational engineering approaches to optimizing the heterologous production of secondary metabolites and recombinant proteins in Streptomyces species in terms of genetic tool development and chassis construction. Further perspectives on the development of optimal Streptomyces chassis by the design-build-test-learn cycle in systems are suggested, which may increase the availability of secondary metabolites and recombinant proteins.

Genome-Wide Gene-by-Smoking Interaction Study of Chronic Obstructive Pulmonary Disease.

Risk of chronic obstructive pulmonary disease (COPD) is determined by both cigarette smoking and genetic susceptibility, but little is known about gene-by-smoking interactions. We performed a genome-wide association analysis of 179,689 controls and 21,077 COPD cases from UK Biobank subjects of European ancestry recruited from 2006 to 2010, considering genetic main effects and gene-by-smoking interaction effects simultaneously (2-degrees-of-freedom (df) test) as well as interaction effects alone (1-df interaction test). We sought to replicate significant results in COPDGene (United States, 2008-2010) and SpiroMeta Consortium (multiple countries, 1947-2015) data. We considered 2 smoking variables: 1) ever/never and 2) current/noncurrent. In the 1-df test, we identified 1 genome-wide significant locus on 15q25.1 (cholinergic receptor nicotinic ß4 subunit, or CHRNB4) for ever- and current smoking and identified PI*Z allele (rs28929474) of serpin family A member 1 (SERPINA1) for ever-smoking and 3q26.2 (MDS1 and EVI1 complex locus, or MECOM) for current smoking in an analysis of previously reported COPD loci. In the 2-df test, most of the significant signals were also significant for genetic marginal effects, aside from 16q22.1 (sphingomyelin phosphodiesterase 3, or SMPD3) and 19q13.2 (Egl-9 family hypoxia inducible factor 2, or EGLN2). The significant effects at 15q25.1 and 19q13.2 loci, both previously described in prior genome-wide association studies of COPD or smoking, were replicated in COPDGene and SpiroMeta. We identified interaction effects at previously reported COPD loci; however, we failed to identify novel susceptibility loci.

Genome-scale determination of 5´ and 3´ boundaries of RNA transcripts in Streptomyces genomes.

Streptomyces species are gram-positive bacteria with GC-rich linear genomes and they serve as dominant reservoirs for producing clinically and industrially important secondary metabolites. Genome mining of Streptomyces revealed that each Streptomyces species typically encodes 20-50 secondary metabolite biosynthetic gene clusters (smBGCs), emphasizing their potential for novel compound discovery. Unfortunately, most of smBGCs are uncharacterized in terms of their products and regulation since they are silent under laboratory culture conditions. To translate the genomic potential of Streptomyces to practical applications, it is essential to understand the complex regulation of smBGC expression and to identify the underlying regulatory elements. To progress towards these goals, we applied two Next-Generation Sequencing methods, dRNA-Seq and Term-Seq, to industrially relevant Streptomyces species to reveal the 5´ and 3´ boundaries of RNA transcripts on a genome scale. This data provides a fundamental resource to aid our understanding of Streptomyces' regulation of smBGC expression and to enhance their potential for secondary metabolite synthesis.

Toward a Unified Framework for Cognitive Maps.

In this study, we integrated neural encoding and decoding into a unified framework for spatial information processing in the brain. Specifically, the neural representations of self-location in the hippocampus (HPC) and entorhinal cortex (EC) play crucial roles in spatial navigation. Intriguingly, these neural representations in these neighboring brain areas show stark differences. Whereas the place cells in the HPC fire as a unimodal function of spatial location, the grid cells in the EC show periodic tuning curves with different periods for different subpopulations (called modules). By combining an encoding model for this modular neural representation and a realistic decoding model based on belief propagation, we investigated the manner in which self-location is encoded by neurons in the EC and then decoded by downstream neurons in the HPC. Through the results of numerical simulations, we first show the positive synergy effects of the modular structure in the EC. The modular structure introduces more coupling between heterogeneous modules with different periodicities, which provides increased error-correcting capabilities. This is also demonstrated through a comparison of the beliefs produced for decoding two- and four-module codes. Whereas the former resulted in a complete decoding failure, the latter correctly recovered the self-location even from the same inputs. Further analysis of belief propagation during decoding revealed complex dynamics in information updates due to interactions among multiple modules having diverse scales. Therefore, the proposed unified framework allows one to investigate the overall flow of spatial information, closing the loop of encoding and decoding self-location in the brain.