Oral ingestion of the environmental toxicant trichloroethylene in rats induces alterations in the gut microbiome: Relevance to idiopathic Parkinson's disease.

Microbial alterations within the gut microbiome appear to be a common feature of individuals with Parkinson's disease (PD), providing further evidence for the role of the gut-brain axis in PD development. As a major site of contact with the environment, questions have emerged surrounding the cause and effect of alterations to the gut microbiome by environmental contaminants associated with PD risk, such as pesticides, metals, and organic solvents. Recent data from our lab shows that ingestion of the industrial byproduct and environmental pollutant trichloroethylene (TCE) induces key Parkinsonian pathology within aged rats, including the degeneration of dopaminergic neurons, α-synuclein accumulation, neuroinflammation, and endolysosomal deficits. As TCE is the most common organic contaminant within drinking water, we postulated that ingestion of TCE associated with PD-related neurodegeneration may alter the gut microbiome to a similar extent as observed in persons with PD. To assess this, we collected fecal samples from adult rats treated with 200 mg/kg TCE over 6 weeks via oral gavage - the dose that produced nigrostriatal neurodegeneration - and analyzed the gut microbiome via whole genome shotgun sequencing. Our results showed changes in gut microorganisms reflective of the microbial signatures observed in individuals with idiopathic PD, such as decreased abundance of short-chain fatty acid producing Blautia and elevated lactic-acid producing Bifidobacteria, as well as genera who contain species previously reported as opportunistic pathogens such as Clostridium. From these experimental data, we postulate that TCE exposure within contaminated drinking water could induce alterations of the gut microbiome that contributes to chronic disease risk, including idiopathic PD.

Comparison study of differential abundance testing methods using two large Parkinson disease gut microbiome datasets derived from 16S amplicon sequencing.

BACKGROUND: Testing for differential abundance of microbes in disease is a common practice in microbiome studies. Numerous differential abundance (DA) testing methods exist and range from traditional statistical tests to methods designed for microbiome data. Comparison studies of DA testing methods have been performed, but none performed on microbiome datasets collected for the study of real, complex disease. Due to this, DA testing was performed here using various DA methods in two large, uniformly collected gut microbiome datasets on Parkinson disease (PD), and their results compared. RESULTS: Overall, 78-92% of taxa tested were detected as differentially abundant by at least one method, while 5-22% were called differentially abundant by the majority of methods (depending on dataset and filtering of taxonomic data prior to testing). Concordances between method results ranged from 1 to 100%. Average concordance for datasets 1 and 2 were 24% and 28% respectively, and 27% for replicated DA signatures. Concordances increased when removing rarer taxa before testing, increasing average concordances by 2-32%. Certain methods consistently resulted in higher concordances (e.g. ANCOM-BC, LEfSe), while others consistently resulted in lower (e.g. edgeR, fitZIG). Hierarchical clustering revealed three groups of DA signatures that were (1) replicated by the majority of methods on average and included taxa previously associated with PD, (2) replicated by a subset of methods and included taxa largely enriched in PD, and (3) replicated by few to one method(s). CONCLUSIONS: Differential abundance tests yielded varied concordances, and amounts of detected DA signatures. Some methods were more concordant than others on both filtered and unfiltered data, therefore, if consistency with other study methodology is a key goal, one might choose among these methods. Even still, using one method on one dataset may find true associations, but may also detect false positives. To help lower false positives, one might analyze data with two or more DA methods to gauge concordance, and use a built-in replication dataset. This study will hopefully serve to complement previously reported DA method comparison studies by implementing and coalescing a large number of both previously and yet to be compared methods on two real gut microbiome datasets.

Parkinson's disease and Parkinson's disease medications have distinct signatures of the gut microbiome.

BACKGROUND: There is mounting evidence for a connection between the gut and Parkinson's disease (PD). Dysbiosis of gut microbiota could explain several features of PD. OBJECTIVE: The objective of this study was to determine if PD involves dysbiosis of gut microbiome, disentangle effects of confounders, and identify candidate taxa and functional pathways to guide research. METHODS: A total of 197 PD cases and 130 controls were studied. Microbial composition was determined by 16S rRNA gene sequencing of DNA extracted from stool. Metadata were collected on 39 potential confounders including medications, diet, gastrointestinal symptoms, and demographics. Statistical analyses were conducted while controlling for potential confounders and correcting for multiple testing. We tested differences in the overall microbial composition, taxa abundance, and functional pathways. RESULTS: Independent microbial signatures were detected for PD (P = 4E-5), participants' region of residence within the United States (P = 3E-3), age (P = 0.03), sex (P = 1E-3), and dietary fruits/vegetables (P = 0.01). Among patients, independent signals were detected for catechol-O-methyltransferase-inhibitors (P = 4E-4), anticholinergics (P = 5E-3), and possibly carbidopa/levodopa (P = 0.05). We found significantly altered abundances of the Bifidobacteriaceae, Christensenellaceae, [Tissierellaceae], Lachnospiraceae, Lactobacillaceae, Pasteurellaceae, and Verrucomicrobiaceae families. Functional predictions revealed changes in numerous pathways, including the metabolism of plant-derived compounds and xenobiotics degradation. CONCLUSION: PD is accompanied by dysbiosis of gut microbiome. Results coalesce divergent findings of prior studies, reveal altered abundance of several taxa, nominate functional pathways, and demonstrate independent effects of PD medications on the microbiome. The findings provide new leads and testable hypotheses on the pathophysiology and treatment of PD. © 2017 International Parkinson and Movement Disorder Society.

The Impact of Prior Single-Gene Testing on Comprehensive Genomic Profiling Results for Patients with Non-Small Cell Lung Cancer.

INTRODUCTION: Tissue-based broad molecular profiling of guideline-recommended biomarkers is advised for the therapeutic management of patients with non-small cell lung cancer (NSCLC). However, practice variation can affect whether all indicated biomarkers are tested. We aimed to evaluate the impact of common single-gene testing (SGT) on subsequent comprehensive genomic profiling (CGP) test outcomes and results in NSCLC. METHODS: Oncologists who ordered SGT for guideline-recommended biomarkers in NSCLC patients were prospectively contacted (May-December 2022) and offered CGP (DNA and RNA sequencing), either following receipt of negative SGT findings, or instead of SGT for each patient. We describe SGT patterns and compare CGP completion rates, turnaround time, and recommended biomarker detection for NSCLC patients with and without prior negative SGT results. RESULTS: Oncologists in > 80 community practices ordered CGP for 561 NSCLC patients; 135 patients (27%) first had negative results from 30 different SGT combinations; 84% included ALK, EGFR and PD-L1, while only 3% of orders included all available SGTs for guideline-recommended genes. Among patients with negative SGT results, CGP was attempted using the same tissue specimen 90% of the time. There were also significantly more CGP order cancellations due to tissue insufficiency (17% vs. 7%), DNA sequencing failures (13% vs. 8%), and turnaround time > 14 days (62% vs. 29%) than among patients who only had CGP. Forty-six percent of patients with negative prior SGT had positive CGP results for recommended biomarkers, including targetable genomic variants in genes beyond ALK and EGFR, such as ERBB2, KRAS (non-G12C), MET (exon 14 skipping), NTRK2/3, and RET . CONCLUSION: For patients with NSCLC, initial use of SGT increases subsequent CGP test cancellations, turnaround time, and the likelihood of incomplete molecular profiling for guideline-recommended biomarkers due to tissue insufficiency.

Patients with non-small cell lung cancer (NSCLC) should have their tumor tissue tested for all recommended biomarkers that can help identify their best treatment options. Traditional tests look at gene biomarkers one by one (single-gene testing), and doctors can order some or all these tests individually or in a group. However, some recommended biomarkers cannot be tested by traditional single-gene tests at all. Newer technology (next-generation sequencing) covers all current recommended treatment biomarkers in one test (comprehensive genomic profiling), but this testing is more expensive and can take more time. Our study shows that NSCLC patients do not get all recommended treatment biomarkers tested when a single-gene testing approach is taken. Single-gene testing also used up some patients' tumor tissue entirely, such that further testing by comprehensive genomic profiling could not be done at all (17% vs. 7% for patients with no prior single-gene tests), resulted in more sequencing failures (13% vs. 8%), and had turnaround time for results greater than 14 days for more patients (62% vs. 29%). When comprehensive genomic profiling was completed, 46% of patients with negative results from prior single-gene testing had positive results for recommended treatment biomarkers that were not included in the initial single-gene tests. To ensure that NSCLC patients receive testing for all recommended biomarkers, comprehensive genomic profiling must be performed first.

Real-world comprehensive genomic and immune profiling reveals distinct age- and sex-based genomic and immune landscapes in tumors of patients with non-small cell lung cancer.

Introduction: Younger patients with non-small cell lung cancer (NSCLC) (<50 years) represent a significant patient population with distinct clinicopathological features and enriched targetable genomic alterations compared to older patients. However, previous studies of younger NSCLC suffer from inconsistent findings, few studies have incorporated sex into their analyses, and studies targeting age-related differences in the tumor immune microenvironment are lacking. Methods: We performed a retrospective analysis of 8,230 patients with NSCLC, comparing genomic alterations and immunogenic markers of younger and older patients while also considering differences between male and female patients. We defined older patients as those ≥65 years and used a 5-year sliding threshold from <45 to <65 years to define various groups of younger patients. Additionally, in an independent cohort of patients with NSCLC, we use our observations to inform testing of the combinatorial effect of age and sex on survival of patients given immunotherapy with or without chemotherapy. Results: We observed distinct genomic and immune microenvironment profiles for tumors of younger patients compared to tumors of older patients. Younger patient tumors were enriched in clinically relevant genomic alterations and had gene expression patterns indicative of reduced immune system activation, which was most evident when analyzing male patients. Further, we found younger male patients treated with immunotherapy alone had significantly worse survival compared to male patients ≥65 years, while the addition of chemotherapy reduced this disparity. Contrarily, we found younger female patients had significantly better survival compared to female patients ≥65 years when treated with immunotherapy plus chemotherapy, while treatment with immunotherapy alone resulted in similar outcomes. Discussion: These results show the value of comprehensive genomic and immune profiling (CGIP) for informing clinical treatment of younger patients with NSCLC and provides support for broader coverage of CGIP for younger patients with advanced NSCLC.

Population fraction of Parkinson's disease attributable to preventable risk factors.

Parkinson's disease is the fastest-growing neurologic disease with seemingly no means of prevention. Intrinsic risk factors (age, sex, and genetics) are inescapable, but environmental factors are not. We identified repeated blows to the head in sports/combat as a potential new risk factor. 23% of PD cases in females were attributable to pesticide/herbicide exposure, and 30% of PD in males were attributable to pesticides/herbicides, military-related chemical exposures, and repeated blows to the head, and therefore could have potentially been prevented.

Population fraction of Parkinson's disease attributable to preventable risk factors.

Parkinson's disease is the fastest growing neurologic disease with seemingly no means for prevention. Intrinsic risk factors (age, sex, genetics) are inescapable, but environmental factors are not. We studied population attributable fraction and estimated fraction of PD that could be reduced if modifiable risk factors were eliminated. Assessing several known risk factors simultaneously in one study, we demonstrate that all were operative and independent, underscoring etiological heterogeneity within a single population. We investigated repeated blows to head in sports or combat as a potential new risk factor, and found it was associated with two-fold increased risk of PD. Considering modifiable risk factors, 23% of PD cases in females were attributable to pesticides/herbicides exposure, and 30% of PD cases in males was attributable to pesticides/herbicides, Agent Orange/chemical warfare, and repeated blows to the head. Thus, one-in-three cases of PD in males, and one-in-four cases in females could have potentially been prevented.

Metagenomics of Parkinson's disease implicates the gut microbiome in multiple disease mechanisms.

Parkinson's disease (PD) may start in the gut and spread to the brain. To investigate the role of gut microbiome, we conducted a large-scale study, at high taxonomic resolution, using uniform standardized methods from start to end. We enrolled 490 PD and 234 control individuals, conducted deep shotgun sequencing of fecal DNA, followed by metagenome-wide association studies requiring significance by two methods (ANCOM-BC and MaAsLin2) to declare disease association, network analysis to identify polymicrobial clusters, and functional profiling. Here we show that over 30% of species, genes and pathways tested have altered abundances in PD, depicting a widespread dysbiosis. PD-associated species form polymicrobial clusters that grow or shrink together, and some compete. PD microbiome is disease permissive, evidenced by overabundance of pathogens and immunogenic components, dysregulated neuroactive signaling, preponderance of molecules that induce alpha-synuclein pathology, and over-production of toxicants; with the reduction in anti-inflammatory and neuroprotective factors limiting the capacity to recover. We validate, in human PD, findings that were observed in experimental models; reconcile and resolve human PD microbiome literature; and provide a broad foundation with a wealth of concrete testable hypotheses to discern the role of the gut microbiome in PD.

Exploring human-genome gut-microbiome interaction in Parkinson's disease.

The causes of complex diseases remain an enigma despite decades of epidemiologic research on environmental risks and genome-wide studies that have uncovered tens or hundreds of susceptibility loci for each disease. We hypothesize that the microbiome is the missing link. Genetic studies have shown that overexpression of alpha-synuclein, a key pathological protein in Parkinson's disease (PD), can cause familial PD and variants at alpha-synuclein locus confer risk of idiopathic PD. Recently, dysbiosis of gut microbiome in PD was identified: altered abundances of three microbial clusters were found, one of which was composed of opportunistic pathogens. Using two large datasets, we found evidence that the overabundance of opportunistic pathogens in PD gut is influenced by the host genotype at the alpha-synuclein locus, and that the variants responsible modulate alpha-synuclein expression. Results put forth testable hypotheses on the role of gut microbiome in the pathogenesis of PD, the incomplete penetrance of PD susceptibility genes, and potential triggers of pathology in the gut.

Large-scale association analyses identify host factors influencing human gut microbiome composition.

To study the effect of host genetics on gut microbiome composition, the MiBioGen consortium curated and analyzed genome-wide genotypes and 16S fecal microbiome data from 18,340 individuals (24 cohorts). Microbial composition showed high variability across cohorts: only 9 of 410 genera were detected in more than 95% of samples. A genome-wide association study of host genetic variation regarding microbial taxa identified 31 loci affecting the microbiome at a genome-wide significant (P < 5 × 10-8) threshold. One locus, the lactase (LCT) gene locus, reached study-wide significance (genome-wide association study signal: P = 1.28 × 10-20), and it showed an age-dependent association with Bifidobacterium abundance. Other associations were suggestive (1.95 × 10-10 < P < 5 × 10-8) but enriched for taxa showing high heritability and for genes expressed in the intestine and brain. A phenome-wide association study and Mendelian randomization identified enrichment of microbiome trait loci in the metabolic, nutrition and environment domains and suggested the microbiome might have causal effects in ulcerative colitis and rheumatoid arthritis.

Innate and adaptive immune responses in Parkinson's disease.

Parkinson's disease (PD) has classically been defined as a movement disorder, in which motor symptoms are explained by the aggregation of alpha-synuclein (α-syn) and subsequent death of dopaminergic neurons of the substantia nigra pars compacta (SNpc). More recently, the multisystem effects of the disease have been investigated, with the immune system being implicated in a number of these processes in the brain, the blood, and the gut. In this review, we highlight the dysfunctional immune system found in both human PD and animal models of the disease, and discuss how genetic risk factors and risk modifiers are associated with pro-inflammatory immune responses. Finally, we emphasize evidence that the immune response drives the pathogenesis and progression of PD, and discuss key questions that remain to be investigated in order to identify immunomodulatory therapies in PD.

Characterizing dysbiosis of gut microbiome in PD: evidence for overabundance of opportunistic pathogens.

In Parkinson's disease (PD), gastrointestinal features are common and often precede the motor signs. Braak and colleagues proposed that PD may start in the gut, triggered by a pathogen, and spread to the brain. Numerous studies have examined the gut microbiome in PD; all found it to be altered, but found inconsistent results on associated microorganisms. Studies to date have been small (N = 20 to 306) and are difficult to compare or combine due to varied methodology. We conducted a microbiome-wide association study (MWAS) with two large datasets for internal replication (N = 333 and 507). We used uniform methodology when possible, interrogated confounders, and applied two statistical tests for concordance, followed by correlation network analysis to infer interactions. Fifteen genera were associated with PD at a microbiome-wide significance level, in both datasets, with both methods, with or without covariate adjustment. The associations were not independent, rather they represented three clusters of co-occurring microorganisms. Cluster 1 was composed of opportunistic pathogens and all were elevated in PD. Cluster 2 was short-chain fatty acid (SCFA)-producing bacteria and all were reduced in PD. Cluster 3 was carbohydrate-metabolizing probiotics and were elevated in PD. Depletion of anti-inflammatory SCFA-producing bacteria and elevated levels of probiotics are confirmatory. Overabundance of opportunistic pathogens is an original finding and their identity provides a lead to experimentally test their role in PD.

Plasticity-related gene 3 (LPPR1) and age at diagnosis of Parkinson disease.

OBJECTIVE: To identify modifiers of age at diagnosis of Parkinson disease (PD). METHODS: Genome-wide association study (GWAS) included 1,950 individuals with PD from the NeuroGenetics Research Consortium (NGRC) study. Replication was conducted in the Parkinson's, Genes and Environment study, including 209 prevalent (PAGEP) and 517 incident (PAGEI) PD cases. Cox regression was used to test association with age at diagnosis. Individuals without neurologic disease were used to rule out confounding. Gene-level analysis and functional annotation were conducted using Functional Mapping and Annotation of GWAS platform (FUMA). RESULTS: The GWAS revealed 2 linked but seemingly independent association signals that mapped to LPPR1 on chromosome 9. LPPR1 was significant in gene-based analysis (p = 1E-8). The top signal (rs17763929, hazard ratio [HR] = 1.88, p = 5E-8) replicated in PAGEP (HR = 1.87, p = 0.01) but not in PAGEI. The second signal (rs73656147) was robust with no evidence of heterogeneity (HR = 1.95, p = 3E-6 in NGRC; HR = 2.14, p = 1E-3 in PAGEP + PAGEI, and HR = 2.00, p = 9E-9 in meta-analysis of NGRC + PAGEP + PAGEI). The associations were with age at diagnosis, not confounded by age in patients or in the general population. The PD-associated regions included variants with Combined Annotation Dependent Depletion (CADD) scores = 10-19 (top 1%-10% most deleterious mutations in the genome), a missense with predicted destabilizing effect on LPPR1, an expression quantitative trait locus (eQTL) for GRIN3A (false discovery rate [FDR] = 4E-4), and variants that overlap with enhancers in LPPR1 and interact with promoters of LPPR1 and 9 other brain-expressed genes (Hi-C FDR < 1E-6). CONCLUSIONS: Through association with age at diagnosis, we uncovered LPPR1 as a modifier gene for PD. LPPR1 expression promotes neuronal regeneration after injury in animal models. Present data provide a strong foundation for mechanistic studies to test LPPR1 as a driver of response to damage and a therapeutic target for enhancing neuroregeneration and slowing disease progression.