A multistudy analysis reveals that evoked pain intensity representation is distributed across brain systems.

Information is coded in the brain at multiple anatomical scales: locally, distributed across regions and networks, and globally. For pain, the scale of representation has not been formally tested, and quantitative comparisons of pain representations across regions and networks are lacking. In this multistudy analysis of 376 participants across 11 studies, we compared multivariate predictive models to investigate the spatial scale and location of evoked heat pain intensity representation. We compared models based on (a) a single most pain-predictive region or resting-state network; (b) pain-associated cortical-subcortical systems developed from prior literature ("multisystem models"); and (c) a model spanning the full brain. We estimated model accuracy using leave-one-study-out cross-validation (CV; 7 studies) and subsequently validated in 4 independent holdout studies. All spatial scales conveyed information about pain intensity, but distributed, multisystem models predicted pain 20% more accurately than any individual region or network and were more generalizable to multimodal pain (thermal, visceral, and mechanical) and specific to pain. Full brain models showed no predictive advantage over multisystem models. These findings show that multiple cortical and subcortical systems are needed to decode pain intensity, especially heat pain, and that representation of pain experience may not be circumscribed by any elementary region or canonical network. Finally, the learner generalization methods we employ provide a blueprint for evaluating the spatial scale of information in other domains.

A Generalizable Multivariate Brain Pattern for Interpersonal Guilt.

Feeling guilty when we have wronged another is a crucial aspect of prosociality, but its neurobiological bases are elusive. Although multivariate patterns of brain activity show promise for developing brain measures linked to specific emotions, it is less clear whether brain activity can be trained to detect more complex social emotional states such as guilt. Here, we identified a distributed guilt-related brain signature (GRBS) across two independent neuroimaging datasets that used interpersonal interactions to evoke guilt. This signature discriminated conditions associated with interpersonal guilt from closely matched control conditions in a cross-validated training sample (N = 24; Chinese population) and in an independent test sample (N = 19; Swiss population). However, it did not respond to observed or experienced pain, or recalled guilt. Moreover, the GRBS only exhibited weak spatial similarity with other brain signatures of social-affective processes, further indicating the specificity of the brain state it represents. These findings provide a step toward developing biological markers of social emotions, which could serve as important tools to investigate guilt-related brain processes in both healthy and clinical populations.

Multiple Brain Networks Mediating Stimulus-Pain Relationships in Humans.

The brain transforms nociceptive input into a complex pain experience comprised of sensory, affective, motivational, and cognitive components. However, it is still unclear how pain arises from nociceptive input and which brain networks coordinate to generate pain experiences. We introduce a new high-dimensional mediation analysis technique to estimate distributed, network-level patterns that formally mediate the relationship between stimulus intensity and pain. We applied the model to a large-scale analysis of functional magnetic resonance imaging data (N = 284), focusing on brain mediators of the relationship between noxious stimulus intensity and trial-to-trial variation in pain reports. We identify mediators in both traditional nociceptive pathways and in prefrontal, midbrain, striatal, and default-mode regions unrelated to nociception in standard analyses. The whole-brain mediators are specific for pain versus aversive sounds and are organized into five functional networks. Brain mediators predicted pain ratings better than previous brain measures, including the neurologic pain signature (Wager et al. 2013). Our results provide a broader view of the networks underlying pain experience, as well as novel brain targets for interventions.

Pain-Evoked Reorganization in Functional Brain Networks.

Recent studies indicate that a significant reorganization of cerebral networks may occur in patients with chronic pain, but how immediate pain experience influences the organization of large-scale functional networks is not yet well characterized. To investigate this question, we used functional magnetic resonance imaging in 106 participants experiencing both noxious and innocuous heat. Painful stimulation caused network-level reorganization of cerebral connectivity that differed substantially from organization during innocuous stimulation and standard resting-state networks. Noxious stimuli increased somatosensory network connectivity with (a) frontoparietal networks involved in context representation, (b) "ventral attention network" regions involved in motivated action selection, and (c) basal ganglia and brainstem regions. This resulted in reduced "small-worldness," modularity (fewer networks), and global network efficiency and in the emergence of an integrated "pain supersystem" (PS) whose activity predicted individual differences in pain sensitivity across 5 participant cohorts. Network hubs were reorganized ("hub disruption") so that more hubs were localized in PS, and there was a shift from "connector" hubs linking disparate networks to "provincial" hubs connecting regions within PS. Our findings suggest that pain reorganizes the network structure of large-scale brain systems. These changes may prioritize responses to painful events and provide nociceptive systems privileged access to central control of cognition and action during pain.

Mental health literacy in a diverse sample of undergraduate students: demographic, psychological, and academic correlates.

BACKGROUND: Investigating variables associated with mental health literacy in the college-age population takes us one step closer to providing intervention for this vulnerable group, where growing rates of psychological disorders are a serious public concern. This study adds to the existing literature by incorporating, within a single model, multi-faceted variables (demographic, psychological, and academic) that contribute to mental health literacy in demographically and ethnically diverse college students. METHODS: Participants were undergraduate students enrolled at nine different colleges that are part of a large, urban, public university system. A total of 1213 respondents (62.0% female, 73.3% non-white) completed an in-person assessment of mental health literacy and answered questions about demographics, college experience, and mental health experience. Data were analyzed to identify which variables best discriminated between high, mid-level, and low performers on this assessment. RESULTS: Discriminant correspondence analysis revealed that the difference between high and low performers (accounting for 90.27% of the total variance) was driven by participants who had taken at least one course related to clinical psychology and who typically majored in psychology and applied health science fields. These participants were more likely to report being white, female, between the ages of 28-32, and in the fourth year or later of their undergraduate program. In addition, high performers were more likely to have been diagnosed and/or treated for a psychological disorder, have more experience with psychological disorders through personal, family, or peer history, and have families who are open to discussing mental health issues. CONCLUSION: The main contributor to variation in mental health literacy scores was having taken a clinical psychology course, followed by majoring in psychology. Importantly, our findings identified not only the high performers, but also the low performers, for whom an increase in knowledge and awareness of mental health is crucial to overall psychological well-being. These results have important implications for the design of educational interventions aimed at improving mental health literacy at the college level, especially for students who otherwise would not have been exposed to this information from coursework or their major.

Improving Access and Quality of Palliative Care in Kerala: A Cross-sectional Study of Providers in Routine Practice.

BACKGROUND: Expanding access and improving quality of palliative care in low-middle income countries is a pressing priority. OBJECTIVE: The objective of the study was to describe structure and processes of care across government (GO) and non-GO (NGO) palliative care providers (PCPs) in Kerala, India. DESIGN: This was a cross-sectional telephone survey. SETTING/SUBJECTS: This study consisted of 200 randomly selected PCPs across Kerala. The survey explored organizational structure, patient assessment, opioid availability, and explored elements considered essential to palliative care delivery in the Indian context. RESULTS: One hundred GO and 100 NGO PCPs agreed to participate (100% response rate). The annual median number of new patients was 183 and 159 in GO and NGO PCPs, respectively. The median number of annual home visits in GO PCPs was 1398 and in NGO PCPs was 979. Median number of outpatient visits in GO PCPs was 600 and in NGO PCPs was 520. Only 24% (47/200) of the PCPs had full-time physicians with training in palliative care; 60% of GO PCPs had no full time physician with training in palliative care. Patient-reported pain was routinely documented by 21% (21/100) and 65% (65/100) of the GO and NGO sites, respectively (P < 0.001). None of the GO and only 36% (36/100) of NGO PCPs were able to prescribe oral morphine. Psychosocial and spiritual issues were documented routinely in only 4% (4/100) and 48% (47/99) of the GO and NGO PCPs, respectively (P < 0.001). CONCLUSIONS: Caution is needed against potential challenges to quality care in any new initiative. Kerala has achieved remarkable success in the coverage of palliative care through hundreds of locally active NGOs through GO machinery involving every primary health center. Yet, there are gaps such as absence of full-time physicians. This study highlights the need for periodic review to identify gaps in care.

A Sensitive and Specific Neural Signature for Picture-Induced Negative Affect.

Neuroimaging has identified many correlates of emotion but has not yet yielded brain representations predictive of the intensity of emotional experiences in individuals. We used machine learning to identify a sensitive and specific signature of emotional responses to aversive images. This signature predicted the intensity of negative emotion in individual participants in cross validation (n =121) and test (n = 61) samples (high-low emotion = 93.5% accuracy). It was unresponsive to physical pain (emotion-pain = 92% discriminative accuracy), demonstrating that it is not a representation of generalized arousal or salience. The signature was comprised of mesoscale patterns spanning multiple cortical and subcortical systems, with no single system necessary or sufficient for predicting experience. Furthermore, it was not reducible to activity in traditional "emotion-related" regions (e.g., amygdala, insula) or resting-state networks (e.g., "salience," "default mode"). Overall, this work identifies differentiable neural components of negative emotion and pain, providing a basis for new, brain-based taxonomies of affective processes.

Isolation and Characterization of Microcystin-Degrading Bacteria from Lake Erie.

Heterotrophic bacteria are suggested as the major agents that degrade microcystins (MCs), a major cyanotoxins, in natural environments. However, little is known of the taxonomic and functional diversity of MC-degrading bacteria in Lake Erie of the Laurentian Great Lakes, the largest freshwater system on earth. This study obtained six bacterial pure isolates from Lake Erie with an ability to use MCs as the sole carbon and energy sources. MC degradation rates of the isolates were impacted by temperature and pH. The key gene for MC degradation (mlrA) were failed to be PCR amplified from for all 6 MC degraders, indicating they may possess a novel MC degradation pathway. In addition for potentials used in MC bioremediation, two isolates maybe can offer extra benefits as biofertilizers.

Group-regularized individual prediction: theory and application to pain.

Multivariate pattern analysis (MVPA) has become an important tool for identifying brain representations of psychological processes and clinical outcomes using fMRI and related methods. Such methods can be used to predict or 'decode' psychological states in individual subjects. Single-subject MVPA approaches, however, are limited by the amount and quality of individual-subject data. In spite of higher spatial resolution, predictive accuracy from single-subject data often does not exceed what can be accomplished using coarser, group-level maps, because single-subject patterns are trained on limited amounts of often-noisy data. Here, we present a method that combines population-level priors, in the form of biomarker patterns developed on prior samples, with single-subject MVPA maps to improve single-subject prediction. Theoretical results and simulations motivate a weighting based on the relative variances of biomarker-based prediction-based on population-level predictive maps from prior groups-and individual-subject, cross-validated prediction. Empirical results predicting pain using brain activity on a trial-by-trial basis (single-trial prediction) across 6 studies (N=180 participants) confirm the theoretical predictions. Regularization based on a population-level biomarker-in this case, the Neurologic Pain Signature (NPS)-improved single-subject prediction accuracy compared with idiographic maps based on the individuals' data alone. The regularization scheme that we propose, which we term group-regularized individual prediction (GRIP), can be applied broadly to within-person MVPA-based prediction. We also show how GRIP can be used to evaluate data quality and provide benchmarks for the appropriateness of population-level maps like the NPS for a given individual or study.

The impact of T1 versus EPI spatial normalization templates for fMRI data analyses.

Spatial normalization of brains to a standardized space is a widely used approach for group studies in functional magnetic resonance imaging (fMRI) data. Commonly used template-based approaches are complicated by signal dropout and distortions in echo planar imaging (EPI) data. The most widely used software packages implement two common template-based strategies: (1) affine transformation of the EPI data to an EPI template followed by nonlinear registration to an EPI template (EPInorm) and (2) affine transformation of the EPI data to the anatomic image for a given subject, followed by nonlinear registration of the anatomic data to an anatomic template, which produces a transformation that is applied to the EPI data (T1norm). EPI distortion correction can be used to adjust for geometric distortion of EPI relative to the T1 images. However, in practice, this EPI distortion correction step is often skipped. We compare these template-based strategies empirically in four large datasets. We find that the EPInorm approach consistently shows reduced variability across subjects, especially in the case when distortion correction is not applied. EPInorm also shows lower estimates for coregistration distances among subjects (i.e., within-dataset similarity is higher). Finally, the EPInorm approach shows higher T values in a task-based dataset. Thus, the EPInorm approach appears to amplify the power of the sample compared to the T1norm approach when not using distortion correction (i.e., the EPInorm boosts the effective sample size by 12-25%). In sum, these results argue for the use of EPInorm over the T1norm when no distortion correction is used. Hum Brain Mapp 38:5331-5342, 2017. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Cluster-extent based thresholding in fMRI analyses: pitfalls and recommendations.

Cluster-extent based thresholding is currently the most popular method for multiple comparisons correction of statistical maps in neuroimaging studies, due to its high sensitivity to weak and diffuse signals. However, cluster-extent based thresholding provides low spatial specificity; researchers can only infer that there is signal somewhere within a significant cluster and cannot make inferences about the statistical significance of specific locations within the cluster. This poses a particular problem when one uses a liberal cluster-defining primary threshold (i.e., higher p-values), which often produces large clusters spanning multiple anatomical regions. In such cases, it is impossible to reliably infer which anatomical regions show true effects. From a survey of 814 functional magnetic resonance imaging (fMRI) studies published in 2010 and 2011, we show that the use of liberal primary thresholds (e.g., p<.01) is endemic, and that the largest determinant of the primary threshold level is the default option in the software used. We illustrate the problems with liberal primary thresholds using an fMRI dataset from our laboratory (N=33), and present simulations demonstrating the detrimental effects of liberal primary thresholds on false positives, localization, and interpretation of fMRI findings. To avoid these pitfalls, we recommend several analysis and reporting procedures, including 1) setting primary p<.001 as a default lower limit; 2) using more stringent primary thresholds or voxel-wise correction methods for highly powered studies; and 3) adopting reporting practices that make the level of spatial precision transparent to readers. We also suggest alternative and supplementary analysis methods.

Long-term changes in thickness, live/dead bacterial ratio, and mineral content in biofilm on ceramic and stainless steel orthodontic attachments.

PURPOSE: Fixed orthodontic appliances induce biofilm deposition, which harbors a microbial population harmful to the periodontal health of the individual. The present study evaluated the changes in thickness, live/dead bacterial ratio, and mineral content in dental biofilm over 6 months in patients with either stainless steel or ceramic orthodontic attachments. METHODS: Eighty patients who require fixed orthodontic appliance treatment with first premolar extraction for correcting their malocclusion were selected and bonded with either stainless steel or ceramic orthodontic attachments on the buccal side. The attached buttons were retrieved at different periods-1 week, 1 month, 3 months, and 6 months. They were stained and visualized through confocal microscopy to detect biofilm thickness and the ratio of live/dead bacteria. X­ray diffraction was used to identify the presence of calcium and phosphorous. RESULTS: Ceramic attachments showed a greater increase in biofilm thickness in comparison to stainless steel attachments except in the initial 1­week evaluation. A higher live/dead bacterial ratio was observed in stainless steel attachments than in their ceramic counterparts at all four evaluation periods. Both stainless steel and ceramic surfaces exhibited the presence of mineral deposition (calcium and phosphorous) at all periods. CONCLUSIONS: More biofilm adhesion was observed over ceramic surfaces than over stainless steel orthodontic attachments. Stainless steel attachments exhibited biofilm with a higher live/dead bacterial ratio than their ceramic counterparts at all evaluation periods. The presence of calcium and phosphorous in the adhered biofilm, pointing toward its calcification process, was identified.

Impact of chlorine or peracetic acid on inactivation of Salmonella, Escherichia coli, and Listeria monocytogenes in agricultural water.

Preharvest agricultural water has been recognized as one of the routes of contamination for foodborne pathogens during fruit and vegetable production. Several strategies have been proposed to reduce the risk of pathogens, including preharvest water chemigation, but literature is lacking with regards to microbiological inactivation of common bacterial foodborne pathogens associated with fresh produce contamination, Salmonella enterica, Shiga-toxigenic Escherichia coli (STEC), and Listeria monocytogenes, in surface irrigation water after exposure to chlorine and peracetic acid (PAA). Surface water supplied by a local irrigation district was collected over the summer of 2019. Water was autoclaved, divided into 100 mL samples, and inoculated with a cocktail of five Salmonella, STEC, or Listeria monocytogenes strains or a single strain non-pathogenic E. coli. Samples were then treated with 3, 5, or 7 ppm of free chlorine or PAA, and surviving populations were evaluated using a time-kill assay. A first-order kinetic model was used to fit the inactivation data and obtain the D-values. A secondary model was used to explain the changes due to the type of water, treatment, and microorganism. At a concentration of 3 ppm, the observed and predicted D-values of free chlorine treatments were higher than that of PAA treatments for ground and surface water. Results indicated that PAA was more effective inactivating bacteria than sodium hypochlorite at concentrations of 3 and 5 ppm for both water sources (surface and ground). However, at 7 ppm, the effectiveness of PAA and sodium hypochlorite showed no statistically significant difference for both surface and groundwater. Findings will provide information regarding efficacy of chemical sanitizers like chlorine and PAA for inactivation of Salmonella, Listeria, and STEC in surface water from which treatments can be derived. Ultimately benefitting growers in the selection of an appropriate method for in-field treatment of irrigation water if deemed necessary.

Individual variability in brain representations of pain.

Characterizing cerebral contributions to individual variability in pain processing is crucial for personalized pain medicine, but has yet to be done. In the present study, we address this problem by identifying brain regions with high versus low interindividual variability in their relationship with pain. We trained idiographic pain-predictive models with 13 single-trial functional MRI datasets (n = 404, discovery set) and quantified voxel-level importance for individualized pain prediction. With 21 regions identified as important pain predictors, we examined the interindividual variability of local pain-predictive weights in these regions. Higher-order transmodal regions, such as ventromedial and ventrolateral prefrontal cortices, showed larger individual variability, whereas unimodal regions, such as somatomotor cortices, showed more stable pain representations across individuals. We replicated this result in an independent dataset (n = 124). Overall, our study identifies cerebral sources of individual differences in pain processing, providing potential targets for personalized assessment and treatment of pain.

Partial Least Squares (PLS) methods for neuroimaging: a tutorial and review.

Partial Least Squares (PLS) methods are particularly suited to the analysis of relationships between measures of brain activity and of behavior or experimental design. In neuroimaging, PLS refers to two related methods: (1) symmetric PLS or Partial Least Squares Correlation (PLSC), and (2) asymmetric PLS or Partial Least Squares Regression (PLSR). The most popular (by far) version of PLS for neuroimaging is PLSC. It exists in several varieties based on the type of data that are related to brain activity: behavior PLSC analyzes the relationship between brain activity and behavioral data, task PLSC analyzes how brain activity relates to pre-defined categories or experimental design, seed PLSC analyzes the pattern of connectivity between brain regions, and multi-block or multi-table PLSC integrates one or more of these varieties in a common analysis. PLSR, in contrast to PLSC, is a predictive technique which, typically, predicts behavior (or design) from brain activity. For both PLS methods, statistical inferences are implemented using cross-validation techniques to identify significant patterns of voxel activation. This paper presents both PLS methods and illustrates them with small numerical examples and typical applications in neuroimaging.

Horizontal Transfer of miR-643 from Cisplatin-Resistant Cells Confers Chemoresistance to Recipient Drug-Sensitive Cells by Targeting APOL6.

Acquisition of resistance to cisplatin is a major impediment to the success of cisplatin-based combination therapies for cancer. Recent studies indicate that exosomal miRNAs derived from drug-resistant tumour cells can confer resistance properties to recipient cells by a horizontal transfer mechanism. Although the role of horizontal transfer of a few miRNAs has been described, little is known about the concerted action of horizontal transfer of miRNAs in conferring cisplatin resistance. The present study was designed to identify the role of miR-643, which is one of the most significantly increased miRNA in exosomes released from cisplatin-resistant Heptocarcinoma cells, in altering the cisplatin resistance properties of recipient cells. Drug-sensitivity assays involving miR-643 revealed that ectopic expression of miR-643 can desensitise the cells towards cisplatin. Furthermore, we identified APOL6 as a major target of miR-643. Further mechanistic studies showed that miR-643 can modulate APOL6 mRNA and protein levels, leading to a reversal of APOL6-mediated apoptosis. Altogether, our results suggest an APOL6-dependent mechanism for miR-643 mediated cisplatin resistance upon the horizontal transfer across cell types.

Development, item analysis, and initial reliability and validity of three forms of a multiple-choice mental health literacy assessment for college students (MHLA-c).

Despite increasing rates of mental health disorders among college students, there are a limited number of validated mental health literacy measures that can be quickly administered and scored in this population. We developed a 54-item multiple-choice measure, consisting of three forms with 18 items on each form. Our items focus on knowledge of more than 20 mental health disorders including their etiology, risk factors, diagnoses, symptoms, treatment, course, and outcome, as well as the application of this knowledge to real world situations. Data were collected on three independent samples of undergraduate students enrolled at an urban public university system in the northeast United States: pilot (n=292), test refinement (n=1,272), and validation (n=683). Basic demographics for the combined test refinement and validation samples were: age=22 ± 4.9 years; 62.2% female; 71.7% non-White. We report on the development of the Mental Health Literacy Assessment-college (MHLA-c) and provide support for its reliability and validity. We also provide descriptive statistics, stratified by gender, college major, and personal experience with a mental health issue to enable its use in diverse settings. The MHLA-c may be useful in measuring knowledge of mental health disorders and related topics among college students. Moreover, the availability of parallel forms will facilitate its use within educational or interventional studies that employ pre-post testing designs.

The Neural Correlates of Cued Reward Omission.

Compared to our understanding of positive prediction error signals occurring due to unexpected reward outcomes, less is known about the neural circuitry in humans that drives negative prediction errors during omission of expected rewards. While classical learning theories such as Rescorla-Wagner or temporal difference learning suggest that both types of prediction errors result from a simple subtraction, there has been recent evidence suggesting that different brain regions provide input to dopamine neurons which contributes to specific components of this prediction error computation. Here, we focus on the brain regions responding to negative prediction error signals, which has been well-established in animal studies to involve a distinct pathway through the lateral habenula. We examine the activity of this pathway in humans, using a conditioned inhibition paradigm with high-resolution functional MRI. First, participants learned to associate a sensory stimulus with reward delivery. Then, reward delivery was omitted whenever this stimulus was presented simultaneously with a different sensory stimulus, the conditioned inhibitor (CI). Both reward presentation and the reward-predictive cue activated midbrain dopamine regions, insula and orbitofrontal cortex. While we found significant activity at an uncorrected threshold for the CI in the habenula, consistent with our predictions, it did not survive correction for multiple comparisons and awaits further replication. Additionally, the pallidum and putamen regions of the basal ganglia showed modulations of activity for the inhibitor that did not survive the corrected threshold.

Characterization of microcystin-induced apoptosis in HepG2 hepatoma cells.

Microcystins (MCs) are a class of hepatotoxins that are commonly produced by freshwater cyanobacteria. MCs harm liver cells through inhibiting protein phosphatases 1 and 2A (PP1 and PP2A) and can produce dualistic effects, i.e., cell death and uncontrolled cellular proliferation. The induction of programmed cell death, i.e., apoptosis, in MC treated hepatic cells has been described previously; however, its exact pathway remains unclear. To address this, HepG2 human hepatoma cells were exposed to MC-LR, the most prevalent isomer of MCs, and morphological and physiological responses were examined. Microscopy and Alamar Blue assay showed that HepG2 cells responded to MC-LR treatment with apoptosis characteristics, such as clumping and shrinking of cells and detachment from the monolayer culture surface. A fluorescent caspase activation assay further revealed activation of all tested apoptosis-dependent caspases (i.e., caspase-3/7, 8 and 9) after 24 h of MC-LR treatment. Furthermore, caspase-8 was found being activated 4 h after MC-LR treatment, earlier than observed activation of caspase-9 (8 h after MC-LR treatment). These data demonstrated that MC-LR can induce apoptosis of HepG2 cells through both extrinsic and intrinsic pathways and that the extrinsic pathway may be activated before the intrinsic pathway. This indicates that extrinsic pathway is more sensitive than intrinsic pathway in MC induced apoptosis. This knowledge contributes to a better understanding of MC hepatotoxicity and can be further used for developing treatments for MC exposed hepatic cells.

Taxonomic and Genotypical Heterogeneity of Microcystin degrading Bacterioplankton in Western Lake Erie.

Microcystins (MCs) are among the predominant cyanotoxins that are primarily degraded by heterotrophic bacteria in various freshwater environments, including Lake Erie, a Laurentian Great Lake. However, despite the prevalence of MCs in Lake Erie basins, our knowledge about the taxonomic diversity of local MC-degrading bacteria is largely limited. The current study obtained thirty-four MC-degrading bacterial pure isolates from Lake Erie surface water and characterized their taxonomical and phenotypic identities as well as their MC-degradation rates under different pH, temperature, availability of organic substrates and with other MC-degrading isolates. Obtained MC-degrading isolates included both Gram-positive (18 isolates of Actinobacteria and Firmicutes) and Gram-negative bacteria (16 isolates of Gamma-proteobacteria); and 7 of these isolates were motile, and 13 had the capacity to form biofilms. In general, MC-degradation rates of the isolates were impacted by temperature and pH but insensitive to the presence of cyanobacterial exudates. At the optimal temperature (30-35°C) and pH (7-8), individual isolates degraded MC-LR, the most abundant MC isomer, at an average of 0.20 µg/mL/hr. With additions of cyanobacterial exudates, only Pseudomonas sp. LEw-2029, a non-motile biofilm maker, showed increased MC degradation (0.25 µg/mL/hr). Five out of nine tested dual culture mixtures showed rises in MC degradation rates than their corresponding monocultures; the highest rate reached 0.40 µg/mL/hr for the pair LEw-(1132 + 2029). PCR amplification of mlrA genes yielded negative results for all isolates; subsequent enzyme assay-Mass Spectrum analysis identified no product associated with the mlr gene-based MC degradation pathway. Collectively, our results demonstrated that a diversity of indigenous Lake Erie bacteria can degrade MCs via a novel mlr-independent pathway. Obtained MC degraders, especially Pseudomonas sp. LEw-2029, may serve as candidates for the development of biological filters to remove cyanotoxins in water treatment systems.