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.

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.

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.

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.

PGE1 and PGA1 bind to Nurr1 and activate its transcriptional function.

The orphan nuclear receptor Nurr1 is critical for the development, maintenance and protection of midbrain dopaminergic (mDA) neurons. Here we show that prostaglandin E1 (PGE1) and its dehydrated metabolite, PGA1, directly interact with the ligand-binding domain (LBD) of Nurr1 and stimulate its transcriptional function. We also report the crystallographic structure of Nurr1-LBD bound to PGA1 at 2.05 Å resolution. PGA1 couples covalently to Nurr1-LBD by forming a Michael adduct with Cys566, and induces notable conformational changes, including a 21° shift of the activation function-2 helix (H12) away from the protein core. Furthermore, PGE1/PGA1 exhibit neuroprotective effects in a Nurr1-dependent manner, prominently enhance expression of Nurr1 target genes in mDA neurons and improve motor deficits in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-lesioned mouse models of Parkinson's disease. Based on these results, we propose that PGE1/PGA1 represent native ligands of Nurr1 and can exert neuroprotective effects on mDA neurons, via activation of Nurr1's transcriptional function.

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.

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.

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.

Primary transcriptome and translatome analysis determines transcriptional and translational regulatory elements encoded in the Streptomyces clavuligerus genome.

Determining transcriptional and translational regulatory elements in GC-rich Streptomyces genomes is essential to elucidating the complex regulatory networks that govern secondary metabolite biosynthetic gene cluster (BGC) expression. However, information about such regulatory elements has been limited for Streptomyces genomes. To address this limitation, a high-quality genome sequence of ß-lactam antibiotic-producing Streptomyces clavuligerus ATCC 27 064 is completed, which contains 7163 newly annotated genes. This provides a fundamental reference genome sequence to integrate multiple genome-scale data types, including dRNA-Seq, RNA-Seq and ribosome profiling. Data integration results in the precise determination of 2659 transcription start sites which reveal transcriptional and translational regulatory elements, including -10 and -35 promoter components specific to sigma (σ) factors, and 5'-untranslated region as a determinant for translation efficiency regulation. Particularly, sequence analysis of a wide diversity of the -35 components enables us to predict potential σ-factor regulons, along with various spacer lengths between the -10 and -35 elements. At last, the primary transcriptome landscape of the ß-lactam biosynthetic pathway is analyzed, suggesting temporal changes in metabolism for the synthesis of secondary metabolites driven by transcriptional regulation. This comprehensive genetic information provides a versatile genetic resource for rational engineering of secondary metabolite BGCs in Streptomyces.

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.

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.

System-level understanding of gene expression and regulation for engineering secondary metabolite production in Streptomyces.

The gram-positive bacterium, Streptomyces, is noticed for its ability to produce a wide array of pharmaceutically active compounds through secondary metabolism. To discover novel bioactive secondary metabolites and increase the production, Streptomyces species have been extensively studied for the past decades. Among the cellular components, RNA molecules play important roles as the messengers for gene expression and diverse regulations taking place at the RNA level. Thus, the analysis of RNA-level regulation is critical to understanding the regulation of Streptomyces' metabolism and secondary metabolite production. A dramatic advance in Streptomyces research was made recently, by exploiting high-throughput technology to systematically understand RNA levels. In this review, we describe the current status of the system-wide investigation of Streptomyces in terms of RNA, toward expansion of its genetic potential for secondary metabolite synthesis.

Maternal and Early Postnatal Immune Activation Produce Dissociable Effects on Neurotransmission in mPFC-Amygdala Circuits.

Inflammatory processes may be involved in the pathophysiology of neuropsychiatric illnesses including autism spectrum disorder (ASD). Evidence from studies in rodents indicates that immune activation during early development can produce core features of ASD (social interaction deficits, dysregulation of communication, increases in stereotyped behaviors, and anxiety), although the neural mechanisms of these effects are not thoroughly understood. We treated timed-pregnant mice with polyinosinic:polycytidylic acid (Poly I:C), which simulates a viral infection, or vehicle on gestational day 12.5 to produce maternal immune activation (MIA). Male offspring received either vehicle or lipopolysaccharide, which simulates a bacterial infection, on postnatal day 9 to produce postnatal immune activation (PIA). We then used optogenetics to address the possibility that early developmental immune activation causes persistent alterations in the flow of signals within the mPFC to basolateral amygdala (BLA) pathway, a circuit implicated in ASD. We found that our MIA regimen produced increases in synaptic strength in glutamatergic projections from the mPFC to the BLA. In contrast, our PIA regimen produced decreases in feedforward GABAergic inhibitory postsynaptic responses resulting from activation of local circuit interneurons in the BLA by mPFC-originating fibers. Both effects were seen together when the regimens were combined. Changes in the balance between excitation and inhibition were differentially translated into the modified spike output of BLA neurons. Our findings raise the possibility that prenatal and postnatal immune activation may affect different cellular targets within brain circuits that regulate some of the core behavioral signs of conditions such as ASD.SIGNIFICANCE STATEMENT Immune system activation during prenatal and early postnatal development may contribute to the development of autism spectrum disorder (ASD). Combining optogenetic approaches and behavioral assays that reflect core features of ASD (anxiety, decreased social interactions), we uncovered mechanisms by which the ASD-associated behavioral impairments induced by immune activation could be mediated at the level of interactions within brain circuits implicated in control of emotion and motivation (mPFC and BLA, specifically). Here, we present evidence that prenatal and postnatal immune activation can have different cellular targets in the brain, providing support to the notion that the etiology of ASD may be linked to the excitation/inhibition imbalance in the brain affecting the signal flow within relevant behavior-driving neural microcircuits.

DSP variants may be associated with longitudinal change in quantitative emphysema.

BACKGROUND: Emphysema, characterized by lung destruction, is a key component of Chronic Obstructive Pulmonary Disease (COPD) and is associated with increased morbidity and mortality. Genome-wide association studies (GWAS) have identified multiple genetic factors associated with cross-sectional measures of quantitative emphysema, but the genetic determinants of longitudinal change in quantitative measures of emphysema remain largely unknown. Our study aims to identify genetic variants associated with longitudinal change in quantitative emphysema measured by computed tomography (CT) imaging. METHODS: We included current and ex-smokers from two longitudinal cohorts: COPDGene, a study of Non-Hispanic Whites (NHW) and African Americans (AA), and the Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE). We calculated annual change in two quantitative measures of emphysema based on chest CT imaging: percent low attenuation area (≤ - 950HU) (%LAA-950) and adjusted lung density (ALD). We conducted GWAS, separately in 3030 NHW and 1158 AA from COPDGene and 1397 Whites from ECLIPSE. We further explored effects of 360 previously reported variants and a lung function based polygenic risk score on annual change in quantitative emphysema. RESULTS: In the genome-wide association analysis, no variants achieved genome-wide significance (P < 5e-08). However, in the candidate region analysis, rs2076295 in the DSP gene, previously associated with COPD, lung function and idiopathic pulmonary fibrosis, was associated with change in %LAA-950 (ß (SE) = 0.09 (0.02), P = 3.79e-05) and in ALD (ß (SE) = - 0.06 (0.02), P = 2.88e-03). A lung function based polygenic risk score was associated with annual change in %LAA-950 (P = 4.03e-02) and with baseline measures of quantitative emphysema (P < 1e-03) and showed a trend toward association with annual change in ALD (P = 7.31e-02). CONCLUSIONS: DSP variants may be associated with longitudinal change in quantitative emphysema. Additional investigation of the DSP gene are likely to provide further insights into the disease progression in emphysema and COPD. TRIAL REGISTRATION: Clinicaltrials.gov Identifier: NCT00608764 , NCT00292552 .

Effects of Chronic Social Defeat Stress on Sleep and Circadian Rhythms Are Mitigated by Kappa-Opioid Receptor Antagonism.

Stress plays a critical role in the neurobiology of mood and anxiety disorders. Sleep and circadian rhythms are affected in many of these conditions. Here we examined the effects of chronic social defeat stress (CSDS), an ethological form of stress, on sleep and circadian rhythms. We exposed male mice implanted with wireless telemetry transmitters to a 10 day CSDS regimen known to produce anhedonia (a depressive-like effect) and social avoidance (an anxiety-like effect). EEG, EMG, body temperature, and locomotor activity data were collected continuously during the CSDS regimen and a 5 day recovery period. CSDS affected numerous endpoints, including paradoxical sleep (PS) and slow-wave sleep (SWS), as well as the circadian rhythmicity of body temperature and locomotor activity. The magnitude of the effects increased with repeated stress, and some changes (PS bouts, SWS time, body temperature, locomotor activity) persisted after the CSDS regimen had ended. CSDS also altered mRNA levels of the circadian rhythm-related gene mPer2 within brain areas that regulate motivation and emotion. Administration of the κ-opioid receptor (KOR) antagonist JDTic (30 mg/kg, i.p.) before CSDS reduced stress effects on both sleep and circadian rhythms, or hastened their recovery, and attenuated changes in mPer2 Our findings show that CSDS produces persistent disruptions in sleep and circadian rhythmicity, mimicking attributes of stress-related conditions as they appear in humans. The ability of KOR antagonists to mitigate these disruptions is consistent with previously reported antistress effects. Studying homologous endpoints across species may facilitate the development of improved treatments for psychiatric illness.SIGNIFICANCE STATEMENT Stress plays a critical role in the neurobiology of mood and anxiety disorders. We show that chronic social defeat stress in mice produces progressive alterations in sleep and circadian rhythms that resemble features of depression as it appears in humans. Whereas some of these alterations recover quickly upon cessation of stress, others persist. Administration of a kappa-opioid receptor (KOR) antagonist reduced stress effects or hastened recovery, consistent with the previously reported antistress effects of this class of agents. Use of endpoints, such as sleep and circadian rhythm, that are homologous across species will facilitate the implementation of translational studies that better predict clinical outcomes in humans, improve the success of clinical trials, and facilitate the development of more effective therapeutics.

On Decoding Grid Cell Population Codes Using Approximate Belief Propagation.

Neural systems are inherently noisy. One well-studied example of a noise reduction mechanism in the brain is the population code, where representing a variable with multiple neurons allows the encoded variable to be recovered with fewer errors. Studies have assumed ideal observer models for decoding population codes, and the manner in which information in the neural population can be retrieved remains elusive. This letter addresses a mechanism by which realistic neural circuits can recover encoded variables. Specifically, the decoding problem of recovering a spatial location from populations of grid cells is studied using belief propagation. We extend the belief propagation decoding algorithm in two aspects. First, beliefs are approximated rather than being calculated exactly. Second, decoding noises are introduced into the decoding circuits. Numerical simulations demonstrate that beliefs can be effectively approximated by combining polynomial nonlinearities with divisive normalization. This approximate belief propagation algorithm is tolerant to decoding noises. Thus, this letter presents a realistic model for decoding neural population codes and investigates fault-tolerant information retrieval mechanisms in the brain.

Baclofen, a GABAB receptor agonist, enhances ubiquitin-proteasome system functioning and neuronal survival in Huntington's disease model mice.

Huntington's disease (HD) is an autosomal neurodegenerative disease. Its manifestations is selective degeneration of medium-sized spiny neurons (MSN) in the striatum. The specificity of the vulnerability of these GABAergic MSNs can be explained by abnormal protein accumulation, excitotoxicity, mitochondrial dysfunction, and failure of trophic control, among other dysfunctions. In this study, we used in vitro and in vivo models of HD to study the effects of GABAergic neuron stimulation on the cellular protein degradation machinery. We administered the GABA(B) receptor agonist, baclofen, to wild-type or mutant huntingtin-expressing striatal cells (HD19 or HD43). Chymotrypsin-like proteasome activity and cell viability were significantly increased in the mutant huntingtin-expressing striatal cells (HD43) after GABA(B) receptor agonist treatment. In addition, we systemically administered baclofen to a HD model containing the entire human huntingtin gene with 128 CAG repeats (YAC128). Chymotrypsin-like proteasome activity was significantly increased in YAC128 transgenic mice after baclofen administration. Baclofen-injected mutant YAC128 mice also showed significantly reduced numbers of ubiquitin-positive neuronal intranuclear inclusions (NIIs) in the striatum. Baclofen markedly improved behavioral abnormalities in mutant YAC128 mice as determined by the rotarod performance test. These data indicate that stimulation of GABAergic neurons with the GABAB receptor agonist, baclofen, enhances ubiquitin-proteasome system (UPS) function and cell survival in in vitro and in vivo models of HD.

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.

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.

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.