Using machine learning to predict sudden gains in intensive treatment for PTSD.

Sudden gains have been found in PTSD treatment across samples and treatment modality. Sudden gains have consistently predicted better treatment response, illustrating clear clinical implications, though attempts to identify predictors of sudden gains have produced inconsistent findings. To date, sudden gains have not been examined in intensive PTSD treatment programs (ITPs). This study explored the occurrence of sudden gains in a 3-week and 2-week ITP (n = 465 and n = 235), evaluated the effect of sudden gains on post-treatment and follow-up PTSD severity while controlling for overall change, and used three machine learning algorithms to assess our ability to predict sudden gains. We found 31% and 19% of our respective samples experienced a sudden gain during the ITP. In both ITPs, sudden gain status predicted greater PTSD symptom improvement at post-treatment (t2 W=-8.57, t3 W=-14.86, p < .001) and at 3-month follow-up (t2 W=-3.82, t3 W=-5.32, p < .001). However, the effect for follow-up was no longer significant after controlling for total symptom reduction across the ITP (t2 W=-1.59, t3 W=-0.32, p > .05). Our ability to predict sudden gains was poor (AUC <.7) across all three machine learning algorithms. These findings demonstrate that sudden gains can be detected in intensive treatment for PTSD, though their implications for treatment outcomes may be limited. Moreover, despite the use of three machine-learning methods across two fairly large clinical samples, we were still unable to identify variables that accurately predict whether an individual will experience a sudden gain during treatment. Implications for clinical application of these findings and for future studies are discussed.

Detection of High-Risk Paraneoplastic Antibodies against TRIM9 and TRIM67 Proteins.

OBJECTIVE: Co-occurring anti-tripartite motif-containing protein 9 and 67 autoantibodies (TRIM9/67-IgG) have been reported in only a very few cases of paraneoplastic cerebellar syndrome. The value of these biomarkers and the most sensitive methods of TRIM9/67-IgG detection are not known. METHODS: We performed a retrospective, multicenter study to evaluate the cerebrospinal fluid and serum of candidate TRIM9/67-IgG cases by tissue-based immunofluorescence, peptide phage display immunoprecipitation sequencing, overexpression cell-based assay (CBA), and immunoblot. Cases in which TRIM9/67-IgG was detected by at least 2 assays were considered TRIM9/67-IgG positive. RESULTS: Among these cases (n = 13), CBA was the most sensitive (100%) and revealed that all cases had TRIM9 and TRIM67 autoantibodies. Of TRIM9/67-IgG cases with available clinical history, a subacute cerebellar syndrome was the most common presentation (n = 7/10), followed by encephalitis (n = 3/10). Of these 10 patients, 70% had comorbid cancer (7/10), 85% of whom (n = 6/7) had confirmed metastatic disease. All evaluable cancer biopsies expressed TRIM9 protein (n = 5/5), whose expression was elevated in the cancerous regions of the tissue in 4 of 5 cases. INTERPRETATION: TRIM9/67-IgG is a rare but likely high-risk paraneoplastic biomarker for which CBA appears to be the most sensitive diagnostic assay. ANN NEUROL 2023;94:1086-1101.

Who will respond to intensive PTSD treatment? A machine learning approach to predicting response prior to starting treatment.

Despite the established effectiveness of evidence-based PTSD treatments, not everyone responds the same. Specifically, some individuals respond early while others respond minimally throughout treatment. Our ability to predict these trajectories at baseline has been limited. Predicting which individuals will respond to a certain type of treatment can significantly reduce short- and long-term costs and increase the ability to preemptively match individuals with treatments to which they are most likely to respond. In the present study, we examined whether veterans' responses to a 3-week Cognitive Processing Therapy-based intensive PTSD treatment program could be accurately predicted prior to the first session. Using a sample of 432 veterans, and a wide range of demographic and clinical data collected during intake, we assessed six machine learning and statistical methods and their ability to predict fast and minimal responders prior to treatment initiation. For fast response classification, gradient boosted models (GBM) had the highest AUC-PR (0.466). For minimal response classification, elastic net (EN) had the highest mean CV AUC-PR (0.628). Using the best performing classifiers, we were able to predict both fast and minimal responders prior to starting treatment with relatively high AUC-ROC of 0.765 (GBM) and 0.826 (EN), respectively. These results may inform treatment modifications, although the accuracy may not be sufficient for clinicians to base inclusion/exclusion decisions entirely on the classifiers. Future research should evaluate whether these classifiers can be expanded to predict to which treatment type(s) an individual is most likely to respond based on various clinical, circumstantial, and biological features.

O-GlcNAcase Inhibitor ASN90 is a Multimodal Drug Candidate for Tau and α-Synuclein Proteinopathies.

Neurodegenerative proteinopathies are characterized by the intracellular formation of insoluble and toxic protein aggregates in the brain that are closely linked to disease progression. In Alzheimer's disease and in rare tauopathies, aggregation of the microtubule-associated tau protein leads to the formation of neurofibrillary tangles (NFT). In Parkinson's disease (PD) and other α-synucleinopathies, intracellular Lewy bodies containing aggregates of α-synuclein constitute the pathological hallmark. Inhibition of the glycoside hydrolase O-GlcNAcase (OGA) prevents the removal of O-linked N-acetyl-d-glucosamine (O-GlcNAc) moieties from intracellular proteins and has emerged as an attractive therapeutic approach to prevent the formation of tau pathology. Like tau, α-synuclein is known to be modified with O-GlcNAc moieties and in vitro these have been shown to prevent its aggregation and toxicity. Here, we report the preclinical discovery and development of a novel small molecule OGA inhibitor, ASN90. Consistent with the substantial exposure of the drug and demonstrating target engagement in the brain, the clinical OGA inhibitor ASN90 promoted the O-GlcNAcylation of tau and α-synuclein in brains of transgenic mice after daily oral dosing. Across human tauopathy mouse models, oral administration of ASN90 prevented the development of tau pathology (NFT formation), functional deficits in motor behavior and breathing, and increased survival. In addition, ASN90 slowed the progression of motor impairment and reduced astrogliosis in a frequently utilized α-synuclein-dependent preclinical rodent model of PD. These findings provide a strong rationale for the development of OGA inhibitors as disease-modifying agents in both tauopathies and α-synucleinopathies. Since tau and α-synuclein pathologies frequently co-exist in neurodegenerative diseases, OGA inhibitors represent unique, multimodal drug candidates for further clinical development.

Protein prediction for trait mapping in diverse populations.

Genetically regulated gene expression has helped elucidate the biological mechanisms underlying complex traits. Improved high-throughput technology allows similar interrogation of the genetically regulated proteome for understanding complex trait mechanisms. Here, we used the Trans-omics for Precision Medicine (TOPMed) Multi-omics pilot study, which comprises data from Multi-Ethnic Study of Atherosclerosis (MESA), to optimize genetic predictors of the plasma proteome for genetically regulated proteome-wide association studies (PWAS) in diverse populations. We built predictive models for protein abundances using data collected in TOPMed MESA, for which we have measured 1,305 proteins by a SOMAscan assay. We compared predictive models built via elastic net regression to models integrating posterior inclusion probabilities estimated by fine-mapping SNPs prior to elastic net. In order to investigate the transferability of predictive models across ancestries, we built protein prediction models in all four of the TOPMed MESA populations, African American (n = 183), Chinese (n = 71), European (n = 416), and Hispanic/Latino (n = 301), as well as in all populations combined. As expected, fine-mapping produced more significant protein prediction models, especially in African ancestries populations, potentially increasing opportunity for discovery. When we tested our TOPMed MESA models in the independent European INTERVAL study, fine-mapping improved cross-ancestries prediction for some proteins. Using GWAS summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study, which comprises ∼50,000 Hispanic/Latinos, African Americans, Asians, Native Hawaiians, and Native Americans, we applied S-PrediXcan to perform PWAS for 28 complex traits. The most protein-trait associations were discovered, colocalized, and replicated in large independent GWAS using proteome prediction model training populations with similar ancestries to PAGE. At current training population sample sizes, performance between baseline and fine-mapped protein prediction models in PWAS was similar, highlighting the utility of elastic net. Our predictive models in diverse populations are publicly available for use in proteome mapping methods at https://doi.org/10.5281/zenodo.4837327.

Clonally expanded B cells in multiple sclerosis bind EBV EBNA1 and GlialCAM.

Multiple sclerosis (MS) is a heterogenous autoimmune disease in which autoreactive lymphocytes attack the myelin sheath of the central nervous system. B lymphocytes in the cerebrospinal fluid (CSF) of patients with MS contribute to inflammation and secrete oligoclonal immunoglobulins1,2. Epstein-Barr virus (EBV) infection has been epidemiologically linked to MS, but its pathological role remains unclear3. Here we demonstrate high-affinity molecular mimicry between the EBV transcription factor EBV nuclear antigen 1 (EBNA1) and the central nervous system protein glial cell adhesion molecule (GlialCAM) and provide structural and in vivo functional evidence for its relevance. A cross-reactive CSF-derived antibody was initially identified by single-cell sequencing of the paired-chain B cell repertoire of MS blood and CSF, followed by protein microarray-based testing of recombinantly expressed CSF-derived antibodies against MS-associated viruses. Sequence analysis, affinity measurements and the crystal structure of the EBNA1-peptide epitope in complex with the autoreactive Fab fragment enabled tracking of the development of the naive EBNA1-restricted antibody to a mature EBNA1-GlialCAM cross-reactive antibody. Molecular mimicry is facilitated by a post-translational modification of GlialCAM. EBNA1 immunization exacerbates disease in a mouse model of MS, and anti-EBNA1 and anti-GlialCAM antibodies are prevalent in patients with MS. Our results provide a mechanistic link for the association between MS and EBV and could guide the development of new MS therapies.

Dual ankyrinG and subpial autoantibodies in a man with well-controlled HIV infection with steroid-responsive meningoencephalitis: A case report.

Neuroinvasive infection is the most common cause of meningoencephalitis in people living with human immunodeficiency virus (HIV), but autoimmune etiologies have been reported. We present the case of a 51-year-old man living with HIV infection with steroid-responsive meningoencephalitis whose comprehensive pathogen testing was non-diagnostic. Subsequent tissue-based immunofluorescence with acute-phase cerebrospinal fluid revealed anti-neural antibodies localizing to the axon initial segment (AIS), the node of Ranvier (NoR), and the subpial space. Phage display immunoprecipitation sequencing identified ankyrinG (AnkG) as the leading candidate autoantigen. A synthetic blocking peptide encoding the PhIP-Seq-identified AnkG epitope neutralized CSF IgG binding to the AIS and NoR, thereby confirming a monoepitopic AnkG antibody response. However, subpial immunostaining persisted, indicating the presence of additional autoantibodies. Review of archival tissue-based staining identified candidate AnkG autoantibodies in a 60-year-old woman with metastatic ovarian cancer and seizures that were subsequently validated by cell-based assay. AnkG antibodies were not detected by tissue-based assay and/or PhIP-Seq in control CSF (N = 39), HIV CSF (N = 79), or other suspected and confirmed neuroinflammatory CSF cases (N = 1,236). Therefore, AnkG autoantibodies in CSF are rare but extend the catalog of AIS and NoR autoantibodies associated with neurological autoimmunity.

Distinctive waves of innate immune response in the retina in experimental autoimmune encephalomyelitis.

Neurodegeneration mediates neurological disability in inflammatory demyelinating diseases of the CNS. The role of innate immune cells in mediating this damage has remained controversial with evidence for destructive and protective effects. This has complicated efforts to develop treatment. The time sequence and dynamic evolution of the opposing functions are especially unclear. Given limits of in vivo monitoring in human diseases such as multiple sclerosis (MS), animal models are warranted to investigate the association and timing of innate immune activation with neurodegeneration. Using noninvasive in vivo retinal imaging of experimental autoimmune encephalitis (EAE) in CX3CR1GFP/+-knock-in mice followed by transcriptional profiling, we are able to show 2 distinct waves separated by a marked reduction in the number of innate immune cells and change in cell morphology. The first wave is characterized by an inflammatory phagocytic phenotype preceding the onset of EAE, whereas the second wave is characterized by a regulatory, antiinflammatory phenotype during the chronic stage. Additionally, the magnitude of the first wave is associated with neuronal loss. Two transcripts identified - growth arrest-specific protein 6 (GAS6) and suppressor of cytokine signaling 3 (SOCS3) - might be promising targets for enhancing protective effects of microglia in the chronic phase after initial injury.

Transcriptome prediction performance across machine learning models and diverse ancestries.

Transcriptome prediction methods such as PrediXcan and FUSION have become popular in complex trait mapping. Most transcriptome prediction models have been trained in European populations using methods that make parametric linear assumptions like the elastic net (EN). To potentially further optimize imputation performance of gene expression across global populations, we built transcriptome prediction models using both linear and non-linear machine learning (ML) algorithms and evaluated their performance in comparison to EN. We trained models using genotype and blood monocyte transcriptome data from the Multi-Ethnic Study of Atherosclerosis (MESA) comprising individuals of African, Hispanic, and European ancestries and tested them using genotype and whole-blood transcriptome data from the Modeling the Epidemiology Transition Study (METS) comprising individuals of African ancestries. We show that the prediction performance is highest when the training and the testing population share similar ancestries regardless of the prediction algorithm used. While EN generally outperformed random forest (RF), support vector regression (SVR), and K nearest neighbor (KNN), we found that RF outperformed EN for some genes, particularly between disparate ancestries, suggesting potential robustness and reduced variability of RF imputation performance across global populations. When applied to a high-density lipoprotein (HDL) phenotype, we show including RF prediction models in PrediXcan revealed potential gene associations missed by EN models. Therefore, by integrating other ML modeling into PrediXcan and diversifying our training populations to include more global ancestries, we may uncover new genes associated with complex traits.

Gut microbiota-specific IgA+ B cells traffic to the CNS in active multiple sclerosis.

Changes in gut microbiota composition and a diverse role of B cells have recently been implicated in multiple sclerosis (MS), a central nervous system (CNS) autoimmune disease. Immunoglobulin A (IgA) is a key regulator at the mucosal interface. However, whether gut microbiota shape IgA responses and what role IgA+ cells have in neuroinflammation are unknown. Here, we identify IgA-bound taxa in MS and show that IgA-producing cells specific for MS-associated taxa traffic to the inflamed CNS, resulting in a strong, compartmentalized IgA enrichment in active MS and other neuroinflammatory diseases. Unlike previously characterized polyreactive anti-commensal IgA responses, CNS IgA cross-reacts with surface structures on specific bacterial strains but not with brain tissue. These findings establish gut microbiota-specific IgA+ cells as a systemic mediator in MS and suggest a critical role of mucosal B cells during active neuroinflammation with broad implications for IgA as an informative biomarker and IgA-producing cells as an immune subset to harness for therapeutic interventions.

Comparing local ancestry inference models in populations of two- and three-way admixture.

Local ancestry estimation infers the regional ancestral origin of chromosomal segments in admixed populations using reference populations and a variety of statistical models. Integrating local ancestry into complex trait genetics has the potential to increase detection of genetic associations and improve genetic prediction models in understudied admixed populations, including African Americans and Hispanics. Five methods for local ancestry estimation that have been used in human complex trait genetics are LAMP-LD (2012), RFMix (2013), ELAI (2014), Loter (2018), and MOSAIC (2019). As users rather than developers, we sought to perform direct comparisons of accuracy, runtime, memory usage, and usability of these software tools to determine which is best for incorporation into association study pipelines. We find that in the majority of cases RFMix has the highest median accuracy with the ranking of the remaining software dependent on the ancestral architecture of the population tested. Additionally, we estimate the O(n) of both memory and runtime for each software and find that for both time and memory most software increase linearly with respect to sample size. The only exception is RFMix, which increases quadratically with respect to runtime and linearly with respect to memory. Effective local ancestry estimation tools are necessary to increase diversity and prevent population disparities in human genetics studies. RFMix performs the best across methods, however, depending on application, other methods perform just as well with the benefit of shorter runtimes. Scripts used to format data, run software, and estimate accuracy can be found at https://github.com/WheelerLab/LAI_benchmarking.

Multi-ethnic transcriptome-wide association study of prostate cancer.

The genetic risk for prostate cancer has been governed by a few rare variants with high penetrance and over 150 commonly occurring variants with lower impact on risk; however, most of these variants have been identified in studies containing exclusively European individuals. People of non-European ancestries make up less than 15% of prostate cancer GWAS subjects. Across the globe, incidence of prostate cancer varies with population due to environmental and genetic factors. The discrepancy between disease incidence and representation in genetics highlights the need for more studies of the genetic risk for prostate cancer across diverse populations. To better understand the genetic risk for prostate cancer across diverse populations, we performed PrediXcan and GWAS in a case-control study of 4,769 self-identified African American (2,463 cases and 2,306 controls), 2,199 Japanese American (1,106 cases and 1,093 controls), and 2,147 Latin American (1,081 cases and 1,066 controls) individuals from the Multiethnic Genome-wide Scan of Prostate Cancer. We used prediction models from 46 tissues in GTEx version 8 and five models from monocyte transcriptomes in the Multi-Ethnic Study of Atherosclerosis. Across the three populations, we predicted 19 gene-tissue pairs, including five unique genes, to be significantly (lfsr < 0.05) associated with prostate cancer. One of these genes, NKX3-1, replicated in a larger European study. At the SNP level, 110 SNPs met genome-wide significance in the African American study while 123 SNPs met significance in the Japanese American study. Fine mapping revealed three significant independent loci in the African American study and two significant independent loci in the Japanese American study. These identified loci confirm findings from previous GWAS of prostate cancer in diverse populations while PrediXcan-identified genes suggest potential new directions for prostate cancer research in populations across the globe.

A pathogenic and clonally expanded B cell transcriptome in active multiple sclerosis.

Central nervous system B cells have several potential roles in multiple sclerosis (MS): secretors of proinflammatory cytokines and chemokines, presenters of autoantigens to T cells, producers of pathogenic antibodies, and reservoirs for viruses that trigger demyelination. To interrogate these roles, single-cell RNA sequencing (scRNA-Seq) was performed on paired cerebrospinal fluid (CSF) and blood from subjects with relapsing-remitting MS (RRMS; n = 12), other neurologic diseases (ONDs; n = 1), and healthy controls (HCs; n = 3). Single-cell immunoglobulin sequencing (scIg-Seq) was performed on a subset of these subjects and additional RRMS (n = 4), clinically isolated syndrome (n = 2), and OND (n = 2) subjects. Further, paired CSF and blood B cell subsets (RRMS; n = 7) were isolated using fluorescence activated cell sorting for bulk RNA sequencing (RNA-Seq). Independent analyses across technologies demonstrated that nuclear factor kappa B (NF-κB) and cholesterol biosynthesis pathways were activated, and specific cytokine and chemokine receptors were up-regulated in CSF memory B cells. Further, SMAD/TGF-ß1 signaling was down-regulated in CSF plasmablasts/plasma cells. Clonally expanded, somatically hypermutated IgM+ and IgG1+ CSF B cells were associated with inflammation, blood-brain barrier breakdown, and intrathecal Ig synthesis. While we identified memory B cells and plasmablast/plasma cells with highly similar Ig heavy-chain sequences across MS subjects, similarities were also identified with ONDs and HCs. No viral transcripts, including from Epstein-Barr virus, were detected. Our findings support the hypothesis that in MS, CSF B cells are driven to an inflammatory and clonally expanded memory and plasmablast/plasma cell phenotype.

Genomic and serologic characterization of enterovirus A71 brainstem encephalitis.

OBJECTIVE: In 2016, Catalonia experienced a pediatric brainstem encephalitis outbreak caused by enterovirus A71 (EV-A71). Conventional testing identified EV in the periphery but rarely in CSF. Metagenomic next-generation sequencing (mNGS) and CSF pan-viral serology (VirScan) were deployed to enhance viral detection and characterization. METHODS: RNA was extracted from the CSF (n = 20), plasma (n = 9), stool (n = 15), and nasopharyngeal samples (n = 16) from 10 children with brainstem encephalitis and 10 children with meningitis or encephalitis. Pathogens were identified using mNGS. Available CSF from cases (n = 12) and pediatric other neurologic disease controls (n = 54) were analyzed with VirScan with a subset (n = 9 and n = 50) validated by ELISA. RESULTS: mNGS detected EV in all samples positive by quantitative reverse transcription polymerase chain reaction (qRT-PCR) (n = 25). In qRT-PCR-negative samples (n = 35), mNGS found virus in 23% (n = 8, 3 CSF samples). Overall, mNGS enhanced EV detection from 42% (25/60) to 57% (33/60) (p-value = 0.013). VirScan and ELISA increased detection to 92% (11/12) compared with 46% (4/12) for CSF mNGS and qRT-PCR (p-value = 0.023). Phylogenetic analysis confirmed the EV-A71 strain clustered with a neurovirulent German EV-A71. A single amino acid substitution (S241P) in the EVA71 VP1 protein was exclusive to the CNS in one subject. CONCLUSION: mNGS with VirScan significantly increased the CNS detection of EVs relative to qRT-PCR, and the latter generated an antigenic profile of the acute EV-A71 immune response. Genomic analysis confirmed the close relation of the outbreak EV-A71 and neuroinvasive German EV-A71. A S241P substitution in VP1 was found exclusively in the CSF.

The Physical-Virtual Patient Simulator: A Physical Human Form With Virtual Appearance and Behavior.

INTRODUCTION: We introduce a new type of patient simulator referred to as the Physical-Virtual Patient Simulator (PVPS). The PVPS combines the tangible characteristics of a human-shaped physical form with the flexibility and richness of a virtual patient. The PVPS can exhibit a range of multisensory cues, including visual cues (eg, capillary refill, facial expressions, appearance changes), auditory cues (eg, verbal responses, heart sounds), and tactile cues (eg, localized temperature, pulse). METHODS: We describe the implementation of the technology, technical testing with healthcare experts, and an institutional review board-approved pilot experiment involving 22 nurse practitioner students interacting with a simulated child in 2 scenarios: sepsis and child abuse. The nurse practitioners were asked qualitative questions about ease of use and the cues they noticed. RESULTS: Participants found it easy to interact with the PVPS and had mixed but encouraging responses regarding realism. In the sepsis scenario, participants reported the following cues leading to their diagnoses: temperature, voice, mottled skin, attitude and facial expressions, breathing and cough, vitals and oxygen saturation, and appearance of the mouth and tongue. For the child abuse scenario, they reported the skin appearance on the arms and abdomen, perceived attitude, facial expressions, and inconsistent stories. CONCLUSIONS: We are encouraged by the initial results and user feedback regarding the perceived realism of visual (eg, mottling), audio (eg, breathing sounds), and tactile (eg, temperature) cues displayed by the PVPS, and ease of interaction with the simulator.

Minimal breast milk transfer of rituximab, a monoclonal antibody used in neurological conditions.

OBJECTIVE: To determine the transfer of rituximab, an anti-CD20 monoclonal antibody widely used for neurologic conditions, into mature breast milk. METHODS: Breast milk samples were collected from 9 women with MS who received rituximab 500 or 1,000 mg intravenous once or twice while breastfeeding from November 2017 to April 2019. Serial breast milk samples were collected before infusion and at 8 hours, 24 hours, 7 days, and 18-21 days after rituximab infusion in 4 patients. Five additional patients provided 1-2 samples at various times after rituximab infusion. RESULTS: The median average rituximab concentration in mature breast milk was low at 0.063 µg/mL (range 0.046-0.097) in the 4 patients with serial breast milk collection, with an estimated median absolute infant dose of 0.0094 mg/kg/d and a relative infant dose (RID) of 0.08% (range 0.06%-0.10%). Most patients had a maximum concentration at 1-7 days after infusion. The maximum concentration occurred in a woman with a single breast milk sample and was 0.29 µg/mL at 11 days postinfusion, which corresponds with an estimated RID of 0.33%. Rituximab concentration in milk was virtually undetectable by 90 days postinfusion. CONCLUSIONS: We determined minimal transfer of rituximab into mature breast milk. The RID for rituximab was less than 0.4% and well below theoretically acceptable levels of less than 10%. Low oral bioavailability would probably also limit the absorption of rituximab by the newborn. In women with serious autoimmune neurologic conditions, monoclonal antibody therapy may afford an acceptable benefit to risk ratio, supporting both maternal treatment and breastfeeding.

Pan-viral serology implicates enteroviruses in acute flaccid myelitis.

Since 2012, the United States of America has experienced a biennial spike in pediatric acute flaccid myelitis (AFM)1-6. Epidemiologic evidence suggests non-polio enteroviruses (EVs) are a potential etiology, yet EV RNA is rarely detected in cerebrospinal fluid (CSF)2. CSF from children with AFM (n = 42) and other pediatric neurologic disease controls (n = 58) were investigated for intrathecal antiviral antibodies, using a phage display library expressing 481,966 overlapping peptides derived from all known vertebrate and arboviruses (VirScan). Metagenomic next-generation sequencing (mNGS) of AFM CSF RNA (n = 20 cases) was also performed, both unbiased sequencing and with targeted enrichment for EVs. Using VirScan, the viral family significantly enriched by the CSF of AFM cases relative to controls was Picornaviridae, with the most enriched Picornaviridae peptides belonging to the genus Enterovirus (n = 29/42 cases versus 4/58 controls). EV VP1 ELISA confirmed this finding (n = 22/26 cases versus 7/50 controls). mNGS did not detect additional EV RNA. Despite rare detection of EV RNA, pan-viral serology frequently identified high levels of CSF EV-specific antibodies in AFM compared with controls, providing further evidence for a causal role of non-polio EVs in AFM.

Pseudomonas Diversity Within Urban Freshwaters.

Freshwater lakes are home to bacterial communities with 1000s of interdependent species. Numerous high-throughput 16S rRNA gene sequence surveys have provided insight into the microbial taxa found within these waters. Prior surveys of Lake Michigan waters have identified bacterial species common to freshwater lakes as well as species likely introduced from the urban environment. We cultured bacterial isolates from samples taken from the Chicago nearshore waters of Lake Michigan in an effort to look more closely at the genetic diversity of species found there within. The most abundant genus detected was Pseudomonas, whose presence in freshwaters is often attributed to storm water or runoff. Whole genome sequencing was conducted for 15 Lake Michigan Pseudomonas strains, representative of eight species and three isolates that could not be resolved with named species. These genomes were examined specifically for genes encoding functionality which may be advantageous in their urban environment. Antibiotic resistance, amidst other known virulence factors and defense mechanisms, were identified in the genome annotations and verified in the lab. We also tested the Lake Michigan Pseudomonas strains for siderophore production and resistance to the heavy metals mercury and copper. As the study presented here shows, a variety of pseudomonads have inhabited the urban coastal waters of Lake Michigan.