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Kowalczyk, A.

Publications and source records attributed to Kowalczyk, A..

At least 19 recordsLinked to original sources

Cortico-cerebellar beta-band dynamics predict flexible motor timing

Flexible motor control requires that movements adapt to changing temporal contexts. Here, we test whether flexible timing is driven by context-dependent encoding across cortico-cerebellar circuits and how neural dynamics within these circuits enable accurate performance. To overcome the signal-to-noise limitations of conventional electro- and magnetoencephalography, we recorded whole-head neural dynamics using optically pumped magnetometer arrays (OPM-MEG). Participants learned a context-dependent task, executing manual button presses at time intervals 800 ms (T1) and 1,600 ms (T2), respectively, to avoid a periocular air puff, which was associated with an implicit conditioned eyeblink. We found that the motor cortex contralateral to the hand and bilateral cerebellar lobule VI dynamically encoded these intervals through beta-band (13-30 Hz) event-related desynchronisation (ERD) that precisely scaled with T1 and T2. The cerebellar trial-by-trial latency of the beta-band ERD predicted the timing of explicit manual actions. Finally, partial directed coherence revealed that baseline bidirectional beta-band coupling across the network transiently weakened from the ipsilateral cerebellum to the contralateral motor cortex during finger movement execution. Our findings show that cortico-cerebellar coupling functions as a gating mechanism and suggest that cerebellar circuits modulate cortical motor activity for flexible, accurate motor timing.

neuroscience↗

Fine-mapping candidate neuropsychiatric regulatory variants using cell type-aware comparative genomics

AbstractMeasures of nucleotide sequence conservation across species are useful for identifying functional genomic loci, but can fail when regulatory function is maintained, often in a cell type-specific manner, even when sequence is not. We introduce CTACIT, the Cell Type-Aware Conservation Inference Toolkit, to identify trait-associated regulatory variants. CTACIT integrates sequence conservation scores with cell type-specific open chromatin data collected from a few mammalian species to impute function for hundreds more. Applying CTACIT to neuropsychiatric trait loci identifies higher heritability enrichment and more fine-mapped variants than nucleotide conservation and human chromatin data alone. Our in vivo reporter assays validate predictions for enhancers with risk variants near the DRD2 schizophrenia risk locus. By integrating genome conservation and multi-species open chromatin data, CTACIT prioritizes variants within regions of conserved regulatory function for in vivo characterization and addresses a major challenge in translating disease associations to mechanistic understanding. One-Sentence SummaryRegulatory functions conserved across mammals underlying caudate cell type evolution reveal functions of human risk variants.

genomics↗

RERconverge Update: Runtime Reduction and Analysis Function Overhaul

MotivationConvergent evolution, or the independent acquisition of similar phenotypes in distinct lineages, provides a powerful framework for investigating genomic changes associated with a phenotype. This paper details an update to RERconverge, a powerful R package that tests for associations between gene relative evolutionary rates (RERs) and convergent phenotypes to infer genomic regions associated with traits or selective pressures. We introduce new customizable analysis choices and scalable and efficient algorithms that can process larger genomic datasets, a critical improvement as genomic data become available for more species. ResultsModifications to core functions in the RERconverge pipeline resulted in an immense speedup (by a factor of up to 28.6). The function that tests for associations between phenotypes and RERs has been expanded to include two new analytical methods for outlier control; we also provide here a summary of the statistical tests users can perform, along with their use cases. Availability and implementationThe code and walkthrough vignettes for the package are available at https://github.com/nclark-lab/RERconverge. ContactNathan L. Clark nclark@pitt.edu; Maria Chikina mchikina@pitt.edu

genomics↗

Decoding with multivariate pattern analysis is superior for optically pumped magnetometer-based magnetoencephalography compared to superconducting quantum interference device-based systems

BackgroundMultivariate pattern analysis (MVPA) has become an increasingly important method for decoding distributed brain activity from neural electrophysiological recordings by leveraging both temporal and spatial features. These multivariate approaches have proven important for both cognitive neuroscience and brain-computer interfaces. MVPA might benefit from magnetoencephalography (MEG) systems based on optically pumped magnetometers (OPMs), as these sensors can be placed closer to the scalp, providing higher spatial resolution compared to conventional MEG systems that rely on superconducting quantum interference devices (SQUIDs). As OPM-based MEG systems become available at more institutions, it is essential to experimentally compare their performance with traditional SQUID-based systems using MVPA. MethodsWe adapted a visual object-word paradigm from a previous study, originally implemented on a TRIUX MEGIN SQUID system, to the FieldLine HEDscan OPM system. Participants were recruited and we recorded their ingoing brain activity while did the same task in both systems. Visual stimuli of different objects were presented alternately in two modalities: pictures and the corresponding written words. For each modality, MVPA was used to classify the objects from OPM and SQUID magnetometers data respectively. To further investigate the advantages of OPM, we evaluated the effect classification accuracy of two spatial factors by controlling the number of sensors included and the spatial frequency content of the sensor data. ResultsWe found higher time-resolved decoding accuracy for the OPM compared to the SQUID data. Moreover, OPMs show higher classification performance compared to SQUIDs when controlling for the same number of sensors; consistently, the OPM system required fewer sensors to reach the performance limit of the SQUID system. Our analysis considering the spatial frequency content of the signal revealed that decoding accuracy plateaued for the SQUID system at lower spatial frequencies while the performance of the OPM system continued to improve when higher-order spatial components were included. ConclusionOur OPM-MEG system outperformed the SQUID-MEG system on MVPA on decoding of visual processing. This advantage of OPM is driven by its higher spatial resolution, resulting from the sensors being positioned closer to the head and thus able to capture higher spatial frequency components of the brain signal. OPM may facilitate cognitive neuroscience research as well as brain-computer interfaces by providing higher sensitivity when employing paradigms using multi-variate data analysis.

neuroscience↗

Genes Underlying Adaptive Physiological Shifts Among Hibernating Mammals

Hibernation has evolved several times in mammals to overcome harsh winter climates and food scarcity. During hibernation, animals undergo extreme shifts in metabolic rate, heart rate, respiration, and body temperature. These changes reduce energy consumption and allow animals to survive solely on their fat reserves. Understanding the mechanisms for these extreme shifts has long been recognized as a model for translational medicine as hibernators do not exhibit the same adverse effects of extended immobility that non-hibernating mammals suffer. Though work on individual species has illuminated important mechanisms of these functional changes, the genomic basis of this phenotype remains largely unknown, and few studies have drawn on comparative work to elucidate commonalities across diverse hibernating mammals. Synthesizing both single species and comparative approaches, we use metabolomic data from active and denning black bears (Ursus americanus) to guide bioinformatic analyses of genes using tests of selection and evolutionary rate convergence across independent lineages of hibernating mammals. We identify several genes with significant signatures of selection and evolutionary rate convergence in hibernators that represent both previously known and novel genetic mechanisms of the hibernation phenotype. These data provide novel insights into the genetic basis of this adaptation and serve to direct clinical research in hibernation-based therapies.

evolutionary biology↗

DERIVATION OF ELEPHANT INDUCED PLURIPOTENT STEM CELLS

The crisis of biodiversity loss in the anthropogenic era requires new tools for studying non-model organisms. Elephants, for example, are both an endangered species and excellent models studying complex phenotypes like size, social behavior, and longevity, but they remain severely understudied. Here we report the first derivation of elephant (Elephas maximus) induced pluripotent stem cells (emiPSCs) achieved via a two-step process of chemical-media induction and colony selection, followed by overexpression of elephant transcription factors OCT4, SOX2, KLF4, MYC {+/-} NANOG and LIN28A, and modulation of the TP53 pathway. Since the seminal discovery of reprogramming by Shinya Yamanaka, iPSCs from many species including the functionally extinct northern white rhinocerous have been reported, but emiPSCs have remained elusive. While for multiple species the reprogramming protocol was adopted with little changes compared to model organisms like mouse and human, our emiPSC protocol requires a longer timeline and inhibition of TP53 expansion genes that are hypothesized to confer unique cancer resistance in elephants. iPSCs unlock tremendous potential to explore cell fate determination, cell and tissue development, cell therapies, drug screening, disease modeling, cancer development, gametogenesis and beyond to further our understanding of this iconic megafauna. This study opens new frontiers in advanced non-model organism cellular models for genetic rescue and conservation.

cell biology↗

RERconverge Expansion: Using Relative Evolutionary Rates to Study Complex Categorical Trait Evolution

Comparative genomics approaches seek to associate evolutionary genetic changes with the evolution of phenotypes across a phylogeny. Many of these methods, including our evolutionary rates based method, RERconverge, lack the capability of analyzing non-ordinal, multicategorical traits. To address this limitation, we introduce an expansion to RERconverge that associates shifts in evolutionary rates with the convergent evolution of multi-categorical traits. The categorical RERconverge expansion includes methods for performing categorical ancestral state reconstruction, statistical tests for associating relative evolutionary rates with categorical variables, and a new method for performing phylogenetic permulations on multi-categorical traits. In addition to demonstrating our new method on a three-category diet phenotype, we compare its performance to naive pairwise binary RERconverge analyses and two existing methods for comparative genomic analyses of categorical traits: phylogenetic simulations and a phylogenetic signal based method. We also present a diagnostic analysis of the new permulations approach demonstrating how the method scales with the number of species and the number of categories included in the analysis. Our results show that our new categorical method outperforms phylogenetic simulations at identifying genes and enriched pathways significantly associated with the diet phenotype and that the new ancestral reconstruction drives an improvement in our ability to capture diet-related enriched pathways. Our categorical permulations were able to account for non-uniform null distributions and correct for non-independence in gene rank during pathway enrichment analysis. The categorical expansion to RERconverge will provide a strong foundation for applying the comparative method to categorical traits on larger data sets with more species and more complex trait evolution.

bioinformatics↗

Investigating task-free functional connectivity patterns in newborns using functional near-infrared spectroscopy

SignificanceResting-state networks (RSN), particularly the sensorimotor network, begin to develop in the third trimester of pregnancy and mature extensively by term age. The integrity and structure of these networks have been linked to neurological health outcomes in neonates, highlighting the significance of monitoring RSN development. To this end, functional near-infrared spectroscopy (fNIRS) has emerged as a neuroimaging technique that utilizes near-infrared light to indirectly measure neural activity by detecting changes in oxygenated (HbO) and deoxygenated (HbR) hemoglobin concentrations. Compared to other imaging methods, fNIRS is non-invasive and allows for naturalistic monitoring of neural activity at the bedside, particularly in awake infants. AimUse fNIRS to expand on previous findings regarding the development of functional networks in awake neonates. ApproachfNIRS was acquired in 41 term-born neonates (17 females, gestational age range=36+0 to 42+1 weeks) within the first 48 hours after birth. ResultsGroup level analysis of functional connectivity showed strong positive connectivity in most channel-pairs over the sensorimotor network, especially the left hemisphere (q < 0.05). Next, we examined the relationship between functional connectivity, gestational age and postnatal age, while controlling for sex and subject effects. Both gestational and postnatal age were found to be positively associated with an increase in functional connectivity in the sensorimotor RSN, especially in channels covering the posterior portion. ConclusionsOur findings emphasize the importance of considering developmental changes in functional networks in awake infants. Moreover, our study demonstrates the potential of fNIRS as a valuable tool for studying neural activity in naturalistic settings in neonates.

neuroscience↗

Evolutionary constraint and innovation across hundreds of placental mammals

Evolutionary constraint and acceleration are powerful, cell-type agnostic measures of functional importance. Previous studies in mammals were limited by species number and reliance on human-referenced alignments. We explore the evolution of placental mammals, including humans, through reference-free whole-genome alignment of 240 species and protein-coding alignments for 428 species. We estimate 10.7% of the human genome is evolutionarily constrained. We resolve constraint to single nucleotides, pinpointing functional positions, and refine and expand by over seven-fold the catalog of ultraconserved elements. Overall, 48.5% of constrained bases are as yet unannotated, suggesting yet-to-be-discovered functional importance. Using species-level phenotypes and an updated phylogeny, we associate coding and regulatory variation with olfaction and hibernation. Focusing on biodiversity conservation, we identify genomic metrics that predict species at risk of extinction.

genomics↗

Application of rapid invisible frequency tagging for brain computer interfaces

BackgroundBrain-computer interfaces (BCI) based on steady-state visual evoked potentials (SSVEPs/SSVEFs) are among the most commonly used BCI systems. They require participants to covertly attend to visual objects flickering at specified frequencies. The attended location is decoded in real-time by analysing the power of neuronal responses at the flicker frequency. New methodWe implemented a novel rapid invisible frequency-tagging technique, utilizing a state-of-the-art projector with refresh rates of up to 1440 Hz. We flickered the luminance of visual objects at 56 and 60 Hz, which was invisible to participants but produced strong neuronal responses measurable with magnetoencephalography (MEG). The direction of covert attention, decoded from frequency-tagging responses, was used to control a real-time BCI PONG game. ResultsOur results show that seven out of eight participants were able to play the pong game controlled by the frequency-tagging signal, with average accuracies exceeding 60%. Importantly, participants were able to modulate the power of the frequency-tagging response within a 1-second interval, while only seven occipital sensors were required to reliably decode the neuronal response. Comparison with existing methodsIn contrast to existing SSVEP-based BCI systems, rapid frequency-tagging does not produce a visible flicker. This extends the time-period participants can use it without fatigue, by avoiding distracting visual input. Furthermore, higher frequencies increase the temporal resolution of decoding, resulting in higher communication rates. ConclusionUsing rapid invisible frequency-tagging opens new avenues for fundamental research and practical applications. In combination with novel optically pumped magnetometers (OPMs), it could facilitate the development of high-speed and mobile next-generation BCI systems.

neuroscience↗

A cautionary tale on proper use of branch-site models to detect convergent positive selection

Comparative genomics has become a powerful tool to elucidate genotype-phenotype relationships, particularly through the study of convergently acquired phenotypes. By identifying genes under positive selection specifically on branches with the convergent phenotype we can potentially link genes to that phenotype. Such gene scans are often done using branch-site codon models. However, we have observed a recent troubling trend of misinterpretation of branch-site models in which phylogeny-wide positive selection is not distinguished from positive selection specific to convergent branches. Here, we use simulations and real data to demonstrate how failing to discern between these two cases leads to false inferences of positive selection associated with a convergent trait. We then present a " drop-out" test solution to distinguish the two cases and thereby truly capture positive selection events associated with convergent phenotypes, thus allowing for further insights into both genetic and phenotypic evolution.

evolutionary biology↗

GATA4 regionalizes intestinal metabolism and barrier function to prevent immunopathology

Different regions of the gastrointestinal tract have distinct digestive and absorptive functions, which may be locally disrupted by infection or autoimmune disease. Yet, the mechanisms underlying intestinal regionalization and its dysregulation in disease are not well understood. Here, we used mouse models, transcriptomics, and immune profiling to show that regional epithelial expression of the transcription factor GATA4 prevented adherent bacterial colonization and inflammation in the proximal small intestine by regulating retinol metabolism and luminal IgA. Loss of epithelial GATA4 expression increased mortality in mice infected with Citrobacter rodentium. In active celiac patients with villous atrophy, low GATA4 expression was associated with metabolic alterations, mucosal Actinobacillus, and increased IL-17 immunity. This study reveals broad impacts of GATA4-regulated intestinal regionalization and highlights an elaborate interdependence of intestinal metabolism, immunity, and microbiota in homeostasis and disease. One-sentence summaryEpithelial GATA4 regulates intestinal regionalization of bacterial colonization, metabolic pathways, and tissue immunity.

immunology↗

Evolutionary rate covariation identifies SLC30A9 (ZnT9) as a mitochondrial zinc transporter.

Recent advances in genome sequencing have led to the identification of new ion and metabolite transporters, many of which have not been characterized. Due to the variety of subcellular localizations, cargo and transport mechanisms, such characterization is a daunting task, and predictive approaches focused on the functional context of transporters are very much needed. Here we present a case for identifying a transporter localization using evolutionary rate covariation (ERC), a computational approach based on pairwise correlations of amino acid sequence evolutionary rates across the mammalian phylogeny. As a case study, we find that poorly characterized transporter SLC30A9 (ZnT9) uniquely and prominently coevolves with several components of the mitochondrial oxidative phosphorylation chain, suggesting mitochondrial localization. We confirmed this computational finding experimentally using recombinant human SLC30A9. SLC30A9 loss caused zinc mishandling in the mitochondria, suggesting that under normal conditions it acts as a zinc exporter. We therefore propose that ERC can be used to predict the functional context of novel transporters and other poorly characterized proteins.

evolutionary biology↗

Complementary evolution of coding and noncoding sequence underlies mammalian hairlessness

Body hair is a defining mammalian characteristic, but several mammals, such as whales, naked mole-rats, and humans, have notably less hair than others. To find the genetic basis of reduced hair quantity, we used our evolutionary-rates-based method, RERconverge, to identify coding and noncoding sequences that evolve at significantly different rates in so-called hairless mammals compared to hairy mammals. Using RERconverge, we performed an unbiased, genome-wide scan over 62 mammal species using 19,149 genes and 343,598 conserved noncoding regions to find genetic elements that evolve at significantly different rates in hairless mammals compared to hairy mammals. We show that these rate shifts resulted from relaxation of evolutionary constraint on hair-related sequences in hairless species. In addition to detecting known and potential novel hair-related genes, we also discovered hundreds of putative hair-related regulatory elements. Computational investigation revealed that genes and their associated noncoding regions show different evolutionary patterns and influence different aspects of hair growth and development. Many genes under accelerated evolution are associated with the structure of the hair shaft itself, while evolutionary rate shifts in noncoding regions also included the dermal papilla and matrix regions of the hair follicle that contribute to hair growth and cycling. Genes that were top-ranked for coding sequence acceleration included known hair and skin genes KRT2, KRT35, PKP1, and PTPRM that surprisingly showed no signals of evolutionary rate shifts in nearby noncoding regions. Conversely, accelerated noncoding regions are most strongly enriched near regulatory hair-related genes and microRNAs, such as mir205, ELF3, and FOXC1, that themselves do not show rate shifts in their protein-coding sequences. Such dichotomy highlights the interplay between the evolution of protein sequence and regulatory sequence to contribute to the emergence of a convergent phenotype.

evolutionary biology↗

Phylogenetic Permulations: a statistically rigorous approach to measure confidence in associations between phenotypes and genetic elements in a phylogenetic context

The wealth of high-quality genomes for numerous species has motivated many investigations into the genetic underpinnings of phenotypes. Comparative genomics methods approach this task by identifying convergent shifts at the genetic level that are associated with traits evolving convergently across independent lineages. However, these methods have complex statistical behaviors that are influenced by non-trivial and oftentimes unknown confounding factors. Consequently, using standard statistical analyses in interpreting the outputs of these methods leads to potentially inaccurate conclusions. Here, we introduce phylogenetic permulations, a novel statistical strategy that combines phylogenetic simulations and permutations to calculate accurate, unbiased p-values from phylogenetic methods. Permulations construct the null expectation for p-values from a given phylogenetic method by empirically generating null phenotypes. Subsequently, empirical p-values that capture the true statistical confidence given the correlation structure in the data are directly calculated based on the empirical null expectation. We examine the performance of permulation methods by analyzing both binary and continuous phenotypes, including marine, subterranean, and long-lived large-bodied mammal phenotypes. Our results reveal that permulations improve the statistical power of phylogenetic analyses and correctly calibrate statements of confidence in rejecting complex null distributions while maintaining or improving the enrichment of known functions related to the phenotype. We also find that permulations refine pathway enrichment analyses by correcting for non-independence in gene ranks. Our results demonstrate that permulations are a powerful tool for improving statistical confidence in the conclusions of phylogenetic analysis when the parametric null is unknown.

evolutionary biology↗

Predictive and Aetiological Potential of Polygenic Strata with Extreme and Moderate Disease Risks Predictions: Case Study of Coeliac Disease GWAS

We show by using example of coeliac disease (CD) that a genomic risk assessment could significantly improve efficiency of disease diagnosing. It can detect novel highly deleterious rare variants (penetrance 100%, frequency [~]1:6,700) as well as common protective variants (penetrance 0.03%, frequency [~]1:3). However, the major translational gains with potential for multi-billion-dollar cost savings in Australia or USA alone, could be in assessing patients in cohorts with moderately elevated CD risk (3% -10%) exhibiting clinical symptoms or with family history of CD. The gains result from judicious re-direction of expensive confirmatory testing towards [~]30% of the cohort with the highest likelihood of the condition ([~]90% of cohort CD cases), while avoiding costs, inconvenience and risk of side-complications for the remaining majority of [~]70%.\n\nWe build our estimates using concrete results of CD Genome Wide Association Studies (GWAS) already in the public domain1-4. The largest of five Genomic Risk Score (GRS) models1 considered here deploys 228 directly genotyped Simple Nucleotide Polymorphisms (SNPs), while the simplest2 uses only 6 SNPs. Thus, a DNA profile supporting all these models can be easily accommodated on any commodity, Direct-to-Consumer5 (DTC), saliva-based genotyping platform. Once generated, such a generic profile of over 600,000 SNPs could assist medical practitioners in diagnosing this as well as thousands of other diseases on demand, virtually genotyping cost free.

genomics↗

Cancer control is a key functionality underlying evolution of extended lifespan in mammals

The biological origin of life expectancy remains a fundamental and unanswered scientific question with important ramifications for human health, especially as the bulk of burden of human healthcare shifts from infectious to age-related diseases. The striking variability in life-span among animals occupying similar ecological niches1 and the numerous mutations that have been shown to increase lifespan in model organisms2-5 point to a considerable genetic contribution. Using mammalian comparative genomics, we correlate lifespan phenotypes with relative evolutionary rates, a measure of evolutionary selective pressure6. Our analysis demonstrates that many genes and pathways are under increased evolutionary constraint in both Long-Lived Large-bodied mammals (3L) and mammals Exceptionally Long-Lived given their size (ELL), suggesting that these genes and pathways contribute to the maintenance of both traits. For 3L species, we find strong evolutionary constraint on multiple pathways involved in controlling carcinogenesis, including cell cycle, apoptosis, and immune pathways. These findings provide additional perspective on the well-known Petos Paradox that large animals with large numbers of cells do not get cancer at higher rates than smaller animals with fewer cells7. For the ELL phenotype, our analysis strongly implicates pathways involved in DNA repair, further supporting the importance of DNA repair processes in aging8-12. Moreover, these correlations with lifespan phenotypes are consistent across the entire mammalian phylogeny, suggesting that additional constraint on these pathways is a universal requirement for long lifespan.

evolutionary biology↗

Improved HLA-based prediction of coeliac disease identifies two novel HLA risk modifiers, DQ6.2 and DQ7.3

PurposeHuman Leukocyte Antigen (HLA) testing is useful in the clinical work-up of coeliac disease (CD), with high negative but low positive predictive value. We construct a genomic risk score (GRS) using HLA risk loci to improve CD prediction and guide exclusion criteria.\n\nMethodsImputed HLA genotypes for five European CD case-control GWAS (n>15,000) were used to construct and validate an HLA based risk models (HDQ15). Conditioning on this score, we identified novel HLA interactions which modified CD risk, and integrated these novel alleles into a new risk score (HDQ17).\n\nResultsA GRS from HLA risk allele genotypes yields performance equivalent to a state-of-the-art GRS (GRS228) using 228 single nucleotide polymorphisms (SNPs) and significantly improves upon all previous HLA based risk models. Conditioning on this model, we find two novel associations, HLA-DQ6.2 and HLA-DQ7.3, that interact significantly with HLA-DQ2.5 (p = 2.51 x 10-9, 1.99 x 10-7 for DQ6.2 and DQ7.3 respectively). These epistatic interactions yield the best performing risk score (HDQ17) which retains performance when implemented using 6 tag SNPs. Using the HDQ17 model, the positive predictive value of CD testing in high risk populations increases from 17.5% to 27.1% while maintaining a negative predictive value above 99%.\n\nConclusionOur proposed HLA-based GRS achieves state-of-the-art risk prediction, helps elucidate further risk factors and improves HLA typing exclusionary criteria, which may reduce the number of patients requiring unnecessary endoscopies.

genetics↗