Biology subjects

Patil, K. R.

Publications and source records attributed to Patil, K. R..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Emergent metabolic interactions in resistance to Clostridioides difficile invasion

Commensal gut bacteria are key contributors to the resilience against pathogen invasion. This is exemplified by the success of fecal microbiota transplantation in treating recurrent Clostridioides difficile infection. Yet, characteristics of communities that can confer colonization resistance and the underlying mechanisms remain largely unknown. Here we use a synthetic community of 14 commensal gut bacteria to uncover inter-species interactions and metabolic pathways underpinning the emergent resilience against C. difficile invasion. We challenged this synthetic community as well as fecal-matter-derived communities with antibiotic treatment and C. difficile in a continuous flow bioreactor. Using generalized Lotka-Volterra and genome-scale metabolic modelling, we identified interactions between Escherichia coli and Bacteroides/Phocaeicola sp. as key to the pathogens suppression. Metabolomics analysis further revealed that fructooligosaccharide metabolism, vitamin B3 biosynthesis, and competition for Stickland metabolism precursors contribute to suppression. Analysis of metagenomics data from patient cohorts and clinical trials attested the in vivo relevance of the identified metabolic pathways and the ratio between Bacteroides and Escherichia in successful colonization resistance. The latter was found to be a much stronger discriminator than commonly used alpha diversity metrics. Our study uncovers emergent microbial interactions in pathogen resistance with implications for rational design of bacteriotherapies.

systems biology↗

bioRxiv2024

Emergent survival and extinction of species within gut bacterial communities

Synthetic communities can help uncover metabolic forces shaping microbial ecosystems. Yet, in case of the gut microbiota, culturing in undefined media has prevented detection of metabolic dependencies. Here we show, using chemically defined media, how species survival is jointly determined by supplied resources and community metabolism. We used 63 representative gut bacterial strains and varied inoculum compositions to assemble stable communities in 14 defined media. Over 95% of the species showed markedly improved or diminished performance relative to monoculture in at least one condition, including 153 cases (21%) of emergent survival, i.e., species incapable of surviving on their own but thriving in a community, and 252 (35%) community-driven extinctions. Through single species additions and exclusions, metabolomic analysis, and ecological modelling, we demonstrate how inter-species dependencies - especially in poor media - are mediated by biotic nutrient supply. Our results highlight communal metabolic dividend as a key biotic force promoting emergent survival and diversity.

microbiology↗

bioRxiv2023

Commentary on Pang et al. (2023) Nature

Pang et al. (2023) observe that the geometric eigenmodes, derived from the shape of the cortical surface, are better at reconstructing patterns of both spontaneous and stimulus-evoked activity, when contrasted with three alternative connectome-based models including structural connectome derived eigenmodes. Based on this observation they propose that geometric eigenmodes offer a good model for explaining brain function, noting that "wave dynamics offer a more accurate and parsimonious mechanistic account of macroscale, spontaneous cortical dynamics captured by fMRI". They then question the prevailing view that brain activity is "localized to focal, spatially isolated clusters" and it is driven by "intricate patterns of anatomical connections". While the observation that geometric properties fit brain activity well is intriguing, we argue that accepting geometric eigenmodes as a model for brain function risks the logical fallacy of "affirming the consequent". A representation that effectively describes the underlying geometry is inherently adept at fitting patterns within that geometric space; it does not necessarily shed light on mechanisms of the brains functional attributes. To this end, we provide two lines of empirical results: (a) Basic parcel-based representations, which capture localized structures, can reconstruct activity patterns as well. (b) Geometric eigenmodes demonstrate a high flexibility when fit to a range of manipulated patterns, which evokes the danger of overfitting. Based on those results, theoretical considerations, and previous data we argue that more consideration is needed regarding "parsimony, robustness and generality of geometric eigenmodes as a basis set for brain function". While we recognize the potential role of the brains geometry in influencing its dynamics, assertions regarding its efficacy should be weighed against the performance of simpler models, an inherent risk of overfitting and anatomical evidence. Pang et al.1 put forth harmonic modes derived from the brains geometry as a previously underrecognized model to explain brain-wide dynamics. Their reconstruction framework relies on multiple linear regression to fit brain patterns using a basis set and then calculating Pearsons correlation between original data and fitted data, both parcellated using an atlas with 180 parcels in each hemisphere2. In addition to the overarching challenge of accepting any model as an actual reflection of the real world3, we here provide empirical results and theoretical arguments that highlight the need for further consideration regarding geometric eigenmodes as a model of macroscale brain activity.

neuroscience↗

bioRxiv2023

Sex classification from functional brain connectivity: Generalization to multiple datasets

Machine learning (ML) approaches are increasingly being applied to neuroimaging data. Studies in neuroscience typically have to rely on a limited set of training data which may impair the generalizability of ML models. However, it is still unclear which kind of training sample is best suited to optimize generalization performance. In the present study, we systematically investigated the generalization performance of sex classification models trained on the parcelwise connectivity profile of either single samples or a compound sample containing data from four different datasets. Generalization performance was quantified in terms of mean across-sample classification accuracy and spatial consistency of accurately classifying parcels. Our results indicate that generalization performance of pwCs trained on single dataset samples is dependent on the specific test samples. Certain datasets seem to "match" in the sense that classifiers trained on a sample from one dataset achieved a high accuracy when tested on the respected other one and vice versa. The pwC trained on the compound sample demonstrated overall highest generalization performance for all test samples, including one derived from a dataset not included in building the training samples. Thus, our results indicate that a big and heterogenous training sample comprising data of multiple datasets is best suited to achieve generalizable results.

neuroscience↗

bioRxiv2023

Gut microbiome remains stable following COVID-19 vaccination in healthy and immuno-compromised individuals

The bidirectional interaction between the immune system and the gut microbiota is a key contributor to various host physiological functions. Immune-associated diseases such as cancer and autoimmunity, as well as the efficacy of immunomodulatory therapies, have been linked to microbiome variation. While COVID-19 infection has been shown to cause microbial dysbiosis, it remains understudied whether the inflammatory response associated with vaccination also impacts the microbiota. Here, we investigate the temporal impact of COVID-19 vaccination on the gut microbiome in healthy and immuno-compromised individuals; the latter included patients with primary immunodeficiency and cancer patients on immunomodulating therapies. We find that the gut microbiome remained remarkably stable post-vaccination irrespective of diverse immune status, vaccine response, and microbial composition spanned by the cohort. The stability is evident at all evaluated levels including diversity, phylum, species, and functional capacity. Our results indicate the resilience of the gut microbiome to host immune changes triggered by COVID-19 vaccination and suggest minimal, if any, impact on microbiome-mediated processes. These findings encourage vaccine acceptance, particularly when contrasted with the significant microbiome shifts observed during COVID-19 infection.

microbiology↗

bioRxiv2023

Predicting Executive Functioning from Brain Networks: Modality Specificity and Age Effects

Healthy aging is associated with structural and functional network changes in the brain, which have been linked to deterioration in executive functioning (EF), while their neural implementation at the individual level remains unclear. As the biomarker potential of individual resting-state functional connectivity (RSFC) patterns has been questioned, we investigated to what degree individual EF abilities can be predicted from gray-matter volume (GMV), regional homogeneity, fractional amplitude of low-frequency fluctuations (fALFF), and RSFC within EF-related, perceptuo-motor, and whole-brain networks in young and old adults. We examined whether differences in out-of-sample prediction accuracy were modality-specific and depended on age or task-demand levels. Both uni- and multivariate analysis frameworks revealed overall low prediction accuracies and moderate to weak brain-behavior associations (R2 < .07, r < .28), further challenging the idea of finding meaningful markers for individual EF performance with the metrics used. Regional GMV, well linked to overall atrophy, carried the strongest information about individual EF differences in older adults, whereas fALFF, measuring functional variability, did so for younger adults. Our study calls for future research analyzing more global properties of the brain, different task-states and applying adaptive behavioral testing to result in sensitive predictors for young and older adults, respectively.

neuroscience↗

bioRxiv2023

Emergence of community behaviors in the gut microbiota upon drug treatment

Pharmaceuticals can directly inhibit the growth of gut bacteria, but the degree such interactions manifest in complex community settings is an open question. Here we compared the effects of 30 drugs on a 32-species synthetic community with their effects on each community member in isolation. While most individual drug-species interactions remained the same in the community context, communal behaviors emerged in 26% of all tested cases. Cross-protection, during which drug-sensitive species became protected in community, was 6-times more frequent than cross-sensitization, the converse phenomenon. Cross-protection decreased and cross-sensitization increased at higher drug concentrations, suggesting that the resilience of microbial communities can collapse when perturbations get stronger. By metabolically profiling drug-treated communities, we showed that both drug biotransformation and bioaccumulation contribute mechanistically to communal protection. As a proof-of-principle, we molecularly dissected a prominent case: species expressing specific nitroreductases degraded niclosamide, thereby protecting both themselves and sensitive community members.

microbiology↗

bioRxiv2023

Network and State Specificity in Connectivity-Based Predictions of Individual Behavior

Predicting individual behavior from brain functional connectivity (FC) patterns can contribute to our understanding of human brain functioning. This may apply in particular if predictions are based on features derived from circumscribed, a priori defined functional networks, which improves interpretability. Furthermore, some evidence suggests that task-based FC data may yield more successful predictions of behavior than resting-state FC data. Here, we comprehensively examined to what extent the correspondence of functional network priors and task states with behavioral target domains influences the predictability of individual performance in cognitive, social, and affective tasks. To this end, we used data from the Human Connectome Project for large-scale out-of-sample predictions of individual abilities in working memory (WM), theory-of-mind cognition (SOCIAL), and emotion processing (EMO) from FC of corresponding and non-corresponding states (WM/SOCIAL/EMO/resting-state) and networks (WM/SOCIAL/EMO/whole-brain connectome). Using root mean squared error and coefficient of determination to evaluate model fit revealed that predictive performance was rather poor overall. Predictions from whole-brain FC were slightly better than those from FC in task-specific networks, and a slight benefit of predictions based on FC from task versus resting state was observed for performance in the WM domain. Beyond that, we did not find any significant effects of a correspondence of network, task state, and performance domains. Together, these results suggest that multivariate FC patterns during both task and resting states contain rather little information on individual performance levels, calling for a reconsideration of how the brain mediates individual differences in mental abilities. HighlightsO_LIBetter prediction of behavior from task vs. resting-state FC only in a cognitive domain C_LIO_LILittle evidence for specificity of state, network, or task similarity C_LIO_LIPredicting complex behavior based on FC remains a significant challenge C_LIO_LIWe extend research on brain-based behavior prediction beyond the cognitive domain C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=98 SRC="FIGDIR/small/540387v2_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@74f08borg.highwire.dtl.DTLVardef@15c7375org.highwire.dtl.DTLVardef@a029e6org.highwire.dtl.DTLVardef@11ed815_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2023

standard-GEM: standardization of open-source genome-scale metabolic models

The field of metabolic modelling at the genomescale continues to grow with more models being created and curated. This comes with an increasing demand for adopting common principles regarding transparency and versioning, in addition to standardisation efforts regarding file formats, annotation and testing. Here, we present a standardised template for git-based and GitHub-hosted genome-scale metabolic models (GEMs) supporting both new models and curated ones, following FAIR principles (findability, accessibility, interoperability, and reusability), and incorporating bestpractices. standard-GEM facilitates the reuse of GEMs across web services and platforms in the metabolic modelling field and enables automatic validation of GEMs. The use of this template for new models, and its adoption for existing ones, paves the way for increasing model quality, openness, and accessibility with minimal effort. Availabilitystandard-GEM is available from github.com/MetabolicAtlas/standard-GEM under the conditions of the CC BY 4.0 licence along with additional supporting material.

systems biology↗

bioRxiv2023

The Burden of Reliability: How Measurement Noise Limits Brain-Behaviour Predictions

Major efforts in human neuroimaging strive to understand individual differences and find biomarkers for clinical applications by predicting behavioural phenotypes from brain imaging data. An essential prerequisite for identifying generalizable and replicable brain-behaviour prediction models is sufficient measurement reliability. However, the selection of prediction targets is predominantly guided by scientific interest or data availability rather than reliability considerations. Here we demonstrate the impact of low phenotypic reliability on out-of-sample prediction performance. Using simulated and empirical data from the Human Connectome Projects, we found that reliability levels common across many phenotypes can markedly limit the ability to link brain and behaviour. Next, using 5000 subjects from the UK Biobank, we show that only highly reliable data can fully benefit from increasing sample sizes from hundreds to thousands of participants. Overall, our findings highlight the importance of measurement reliability for identifying brain-behaviour associations from individual differences.

neuroscience↗

bioRxiv2023

A systematic comparison of VBM pipelines and their application to age prediction

Voxel-based morphometry (VBM) analysis is commonly used for localized quantification of gray matter volume (GMV). Several alternatives exist to implement a VBM pipeline. However, how these alternatives compare and their utility in applications, such as the estimation of aging effects, remain largely unclear. This leaves researchers wondering which VBM pipeline they should use for their project. In this study, we took a user-centric perspective and systematically compared five VBM pipelines, together with registration to either a general or a study-specific template, utilizing three large datasets (n>500 each). Considering the known effect of aging on GMV, we first compared the pipelines in their ability of individual-level age prediction and found markedly varied results. To examine whether these results arise from systematic differences between the pipelines, we classified them based on their GMVs, resulting in near-perfect accuracy. To gain deeper insights, we examined the impact of different VBM steps using the region-wise similarity between pipelines. The results revealed marked differences, largely driven by segmentation and registration steps. We observed large variability in subject-identification accuracies, highlighting the interpipeline differences in individual-level quantification of GMV. As a biologically meaningful criterion we correlated regional GMV with age. The results were in line with the age-prediction analysis, and two pipelines, CAT and the combination of fMRIPrep for tissue characterization with FSL for registration, reflected age information better.

neuroscience↗

bioRxiv2022

Brain-age prediction: a systematic comparison of machine learning workflows

The difference between age predicted using anatomical brain scans and chronological age, i.e., the brain-age delta, provides a proxy for atypical aging. Various data representations and machine learning (ML) algorithms have been used for brain-age estimation. However, how these choices compare on performance criteria important for real-world applications, such as; (1) within-site accuracy, (2) cross-site generalization, (3) test-retest reliability, and (4) longitudinal consistency, remains uncharacterized. We evaluated 128 workflows consisting of 16 feature representations derived from gray matter (GM) images and eight ML algorithms with diverse inductive biases. Using four large neuroimaging databases covering the adult lifespan (total N = 2953, 18-88 years), we followed a systematic model selection procedure by sequentially applying stringent criteria. The 128 workflows showed a within-site mean absolute error (MAE) between 4.73-8.38 years, from which 32 broadly sampled workflows showed a cross-site MAE between 5.23-8.98 years. The test-retest reliability and longitudinal consistency of the top 10 workflows were comparable. The choice of feature representation and the ML algorithm both affected the performance. Specifically, voxel-wise feature spaces (smoothed and resampled), with and without principal components analysis, with non-linear and kernel-based ML algorithms performed well. Strikingly, the correlation of brain-age delta with behavioral measures disagreed between within-site and cross-site predictions. Application of the best-performing workflow on the ADNI sample showed a significantly higher brain-age delta in Alzheimers and mild cognitive impairment patients. However, in the presence of age bias, the delta estimates in the diseased population varied depending on the sample used for bias correction. Taken together, brain-age shows promise, but further evaluation and improvements are needed for its real-world application. HighlightsO_LIThere is an effect of both feature space and ML algorithm on prediction error. C_LIO_LIVoxel-wise features performed better than parcel-wise features. C_LIO_LIGPR, KRR and RVR algorithms performed well. C_LIO_LIThe within-site and cross-site delta-behavior correlations disagree. C_LIO_LIHigher brain-age delta inference in AD depends on data used for bias correction. C_LI

neuroscience↗

bioRxiv2022

Intermediately Synchronised Brain States optimise trade-off between Subject Identifiability and Predictive Capacity

Functional connectivity (FC) refers to the statistical dependencies between activity of distinct brain areas. To study temporal fluctuations in FC within the duration of a functional magnetic resonance imaging (fMRI) scanning session, researchers have proposed the computation of an edge time series (ETS) and their derivatives. Evidence suggests that FC is driven by a few time points of high-amplitude co-fluctuation (HACF) in the ETS, which may also contribute disproportionately to interindividual differences. However, it remains unclear to what degree different time points actually contribute to brain-behaviour associations. Here, we systematically evaluate this question by assessing the predictive utility of FC estimates at different levels of co-fluctuation using machine learning (ML) approaches. We demonstrate that time points of lower and intermediate co-fluctuation levels provide overall highest subject specificity as well as highest predictive capacity of individual-level phenotypes.

neuroscience↗

bioRxiv2022

The Uniqueness of Human Vulnerability to Brain Aging in Great Ape Evolution

Aging is associated with robust decline of the brains gray matter. This spatially specific, morphological change in humans has recently been found in chimpanzees. Direct comparison of age-related brain deterioration between these great ape species can provide a unique evolutionary perspective on human brain aging. Here, we present a data-driven, cross-species comparative framework to explore the relationship between gray matter atrophy with age and cross-species cerebral expansion in chimpanzees and humans. In humans, we found a positive relationship between cerebral aging and cortical expansion, whereas, in chimpanzees no such relationship was found. The greater aging and expansion effects in higher-order cognitive regions like the orbito-frontal cortex were observed to be unique to humans. This resembles the last in, first out hypothesis for neurodevelopment and may represent a biological cost for recent evolutionary developments of human faculties.

neuroscience↗

bioRxiv2022

Accurate sex prediction of cisgender and transgender individuals without brain size bias

Brain size differs substantially between human males and females. This difference in total intracranial volume (TIV) can cause bias when employing machine-learning approaches for the investigation of sex differences in brain morphology. TIV-biased models will likely not capture actual qualitative sex differences in brain organization but rather learn to classify an individuals sex based on brain size differences, thus leading to spurious and misleading conclusions, for example when comparing brain morphology between cisgender- and transgender individuals. Here, TIV bias in sex classification models applied to cis- and transgender individuals was systematically investigated by controlling for brain size either through featurewise confound removal or by matching training samples for TIV. Our results provide evidence that non-TIV-biased models can classify the sex of both cis- and transgender individuals with high accuracy, highlighting the importance of appropriate modelling to avoid bias in automated decision making. TeaserAccurate non-biased structural sex classification in cis- and transgender individuals by matching training samples for TIV

neuroscience↗

bioRxiv2022

Whole-brain dynamical modeling for classification of Parkinson's disease

Simulated whole-brain connectomes demonstrate an enhanced inter-individual variability depending on data processing and modeling approach. By considering the human brain connectome as an individualized attribute, we investigate how empirical and simulated whole-brain connectome-derived features can be utilized to classify patients with Parkinsons disease against healthy controls in light of varying data processing and model validation. To this end, we applied simulated blood oxygenation level-dependent signals derived by a whole-brain dynamical model simulating electrical signals of neuronal populations to reveal differences between patients and controls. In addition to the widely used model validation via fitting the dynamical model to empirical neuroimaging data, we invented a model validation against behavioral data, such as subject classes, which we refer to as behavioral model fitting and show that it can be beneficial for Parkinsonian patient classification. Furthermore, the results of machine-learning reported in this study also demonstrated that performance of the patient classification can be improved when the empirical data are complemented by the simulation results. We also showed that temporal filtering of blood oxygenation level-dependent signals influences the prediction results, where the filtering in the low-frequency band is advisable for Parkinsonian patient classification. In addition, composing the feature space of empirical and simulated data from multiple brain parcellation schemes provided complementary features that improve prediction performance. Based on our findings, we suggest including the simulation results with empirical data is effective for inter-individual research and its clinical application.

neuroscience↗

bioRxiv2022

A topography-based predictive framework for naturalistic viewing fMRI

Recent work has shown great interest in understanding individual differences in complex brain function under naturalistic viewing (NV) conditions. However, methods specifically designed for achieving this goal remain limited. Here, we propose a novel approach, called TOpography-based Predictive Framework (TOPF), to investigate individual differences in evoked brain activity on NV fMRI data. Specifically, TOPF identifies individual-specific evoked activity topographies in a data- driven manner and examines their behavioural relevance using a machine learning predictive framework. Our results show that these topographies successfully predict individual phenotypes across cognition, emotion and personality on unseen subjects, and the identified predictive brain regions are neurobiologically interpretable. Further, the prediction accuracy exceeds that of the commonly-used functional connectivity-based features. Conceptually, we highlight the importance of examining multivariate evoked activity patterns for studying brain-behaviour relationships. In summary, we provide a powerful tool for understanding individual differences and brain-behaviour relationships on NV fMRI data.

neuroscience↗

bioRxiv2022

Highly parallelized laboratory evolution of wine yeasts for enhanced metabolic phenotypes

Adaptive Laboratory Evolution (ALE) of microbes can improve the efficiency of sustainable industrial processes important to the global economy, but chance and genetic background effects often lead to suboptimal outcomes. Here we report an ALE platform to circumvent these flaws through parallelized clonal evolution at an unprecedented scale. Using this platform, we clonally evolved 104 yeast populations in parallel from many strains for eight desired wine production traits. Expansions of both ALE replicates and lineage numbers broadened the evolutionary search spectrum and increased the chances of evolving improved wine yeasts unencumbered by unwanted side effects. ALE gains often coincided with distinct aneuploidies and the emergence of semi-predictable side effects that were characteristic of each selection niche. Many high performing ALE strains retained their desired traits upon transfer to industrial conditions and produced high quality wine. Overall, our ALE platform brings evolutionary engineering into the realm of high throughput science and opens opportunities for rapidly optimizing microbes for use in many industrial sectors which otherwise could take many years to accomplish.

bioengineering↗