Biology subjects

Raj, A.

Publications and source records attributed to Raj, A..

At least 73 records · Page 4Linked to original sources

bioRxiv2022

A visual atlas of genes tissue-specific pathological roles

Dysregulation of a genes function, either due to mutations or impairments in regulatory networks, often triggers pathological states in the affected tissue. Comprehensive mapping of these apparent gene-pathology relationships is an ever daunting task, primarily due to genetic pleiotropy and lack of suitable computational approaches. With the advent of high throughput genomics platforms and community scale initiatives such as the Human Cell Landscape (HCL) project [1], researchers have been able to create gene expression portraits of healthy tissues resolved at the level of single cells. However, a similar wealth of knowledge is currently not at our finger-tip when it comes to diseases. This is because the genetic manifestation of a disease is often quite heterogeneous and is confounded by several clinical and demographic covariates. To circumvent this, we mined ~18 million PubMed abstracts published till May 2019 and selected ~6.1 million of them that describe the pathological role of genes in different diseases. Further, we employed a word embedding technique from the domain of Natural Language Processing (NLP) to learn vector representation of entities such as genes, diseases, tissues, etc., in a way such that their relationship is preserved in a vector space. Notably, Pathomap, by the virtue of its underpinning theory, also learns transitive relationships. Pathomap provided a vector representation of words indicating a possible association between DNMT3A/BCOR with CYLD cutaneous syndrome (CCS). The first manuscript reporting this finding was not part of our training data. Key pointsO_LIWe mined ~18 million PubMed abstracts to extract latent knowledge pertaining to tissue specific pathological roles of genes. C_LIO_LIWe found well-defined gene modules implicated in disease pathogenesis in anatomically proximal tissues. C_LIO_LIWe demonstrated an ahead of time discovery of the association between DNMT3A/BCOR with CYLD cutaneous syndrome (CCS), as a knowledge synthesis use-case. C_LI

bioinformatics↗

bioRxiv2021

Multi-taskLearning and Ensemble Approach to Predict Cognitive Scores for Patients with Alzheimer's Disease

During its chronic degenerative course, Alzheimers Disease severely harms the patients cognitive abilities. Assessment of current and future cognition is an integral component of a diagnosis of dementia, and therefore an important clinical and scientific goal. Unfortunately, subjective, time-consuming and operator-sensitive clinical surveys or neuropyschiatric batteries remain the only viable methods of assessing cognition. Given that MRI is the most prevalent, cost-effective, and clinically important imaging modality, it may be considered a suitable predictor of cognition. Yet, it has hitherto proved very challenging to predict one from the other. We propose that an image-based Deep Learning model can be custom-built to achieve this goal. We designed a novel multi-task UNet model to predict the subjects current and future cognition (via ADAS-Cog scores), taking as input baseline T1-weighted MRI and demographic risk factors. The key innovation in the model is that it seeks to solve two adjacent but relevant tasks: image segmentation into tissue types; and prediction of cognition. The first task gives a high-accuracy brain segmentation, comparable to other cutting edge methods. The features trained from the segmentation task are used in the cognition task. This combination is far superior to stand-alone single-shot cognition models. We achieved excellent accuracy in both baseline and time-series forecast of ADAS-Cog scores. Through further feature map analysis made on the receptive fields, we managed to impart much-needed model interpretability, critical for real-world clinical practice. This study constitutes the best-reported performance of any comparable approach, and opens the door towards machine-based tracking of AD progression.

neuroscience↗

bioRxiv2021

Pre-determined diversity in resistant fates emerges from homogenous cells after anti-cancer drug treatment

Even amongst genetically identical cancer cells, therapy resistance often only emerges from a very small subset of those cells. Much effort has gone into uncovering the molecular differences in rare individual cells in the initial population that may allow certain cells to become therapy resistant; however, comparatively little is known about variability in the resistant outcomes themselves. Here, we develop and apply FateMap, a framework that combines DNA barcoding with single-cell RNA sequencing to reveal the fates of hundreds of thousands of clones exposed to anti-cancer therapies. We show that resistant clones emerging from single-cell-derived cancer cells adopt molecularly, morphologically, and functionally distinct fate types. These different resistant types are largely predetermined by molecular differences between cells before addition of drug and not by extrinsic cell-specific microenvironmental factors. Changes in dose and kind of drug can, however, switch the resistant fate type of an initial cell, even resulting in the generation and elimination of certain fate types. Diversity in resistant fates was observed across several single-cell-derived cancer cell lines and types treated with a variety of drugs. Cell fate diversity as a result of variability in intrinsic cell states may be a generic feature of response to external cues.

systems biology↗

bioRxiv2021

Stability and dynamics of a spectral graph model of brain oscillations

We explore the stability and dynamic properties of a hierarchical, linearized, and analytic spectral graph model for neural oscillations that integrates the structuring wiring of the brain. Previously we have shown that this model can accurately capture the frequency spectra and the spatial patterns of the alpha and beta frequency bands obtained from magnetoencephalography recordings without regionally varying parameters. Here, we show that this macroscopic model based on long-range excitatory connections exhibits dynamic oscillations with a frequency in the alpha band even without any oscillations implemented at the mesoscopic level. We show that depending on the parameters, the model can exhibit combinations of damped oscillations, limit cycles, or unstable oscillations. We determined bounds on model parameters that ensure stability of the oscillations simulated by the model. Finally, we estimated time-varying model parameters to capture the temporal fluctuations in magnetoencephalography activity. We show that a dynamic spectral graph modeling framework with a parsimonious set of biophysically interpretable model parameters can thereby be employed to capture oscillatory fluctuations observed in electrophysiological data in various brain states and diseases.

neuroscience↗

bioRxiv2021

Allelic correlation is a marker of tradeoffs between barriers to transmission of expression variability and signal responsiveness in genetic networks

Accurately functioning genetic networks should be responsive to signals but prevent transmission of stochastic bursts of expression. Existing data in mammalian cells suggests that such transcriptional "noise" is transmitted by some genes and not others, suggesting that noise transmission is tunable, perhaps at the expense of other signal processing capabilities.However, systematic claims about noise transmission in genetic networks have been limited by the inability to directly measure noise transmission. Here we build a mathematical framework capable of modeling allelic correlation and noise transmission. We find that allelic correlation and noise transmission correspond across a broad range of model parameters and network architectures. We further find that limiting noise transmission comes with the trade-off of being unresponsive to signals, and that within the parameter regimes that are responsive to signals, there is a further trade-off between response time and basal noise transmission. Using a published allele specific single cell RNA-sequencing dataset, we found that genes with high allelic odds ratios are enriched for cell-type specific functions, and that within multiple signaling pathways, factors which are upstream in the pathway have higher allelic odds ratios than downstream factors. Overall, our findings suggest that some degree of noise transmission is required to be responsive to signals, but that minimization of noise transmission can be accomplished by trading-off for a slower response time.

systems biology↗

bioRxiv2021

CTCF blocks anti-sense transcription initiation at divergent gene promoters

Transcription at most promoters is divergent, initiating at closely spaced oppositely oriented core promoters to produce sense transcripts along with often unstable upstream antisense (uasTrx). How antisense transcription is regulated and to what extent it is coordinated with sense transcription is largely unknown. Here by combining acute degradation of the multi-functional transcription factor CTCF and nascent transcription measurements, we find that CTCF specifically suppresses antisense but not sense transcription at hundreds of divergent promoters, the great majority of which bear proximal CTCF binding sites. Genome editing, chromatin conformation studies, and high-resolution transcript mapping revealed that precisely positioned CTCF directly suppresses the initiation of uasTrx, in a manner independent of its chromatin architectural function. Primary transcript RNA FISH revealed co-bursting of sense and anti-sense transcripts is disfavored, suggesting CTCF-regulated competition for transcription initiation. In sum, CTCF shapes the transcriptional landscape in part by suppressing upstream antisense transcription.

molecular biology↗

bioRxiv2021

Spectral graph theory of brain oscillations -- revisited and improved

Mathematical modeling of the relationship between the functional activity and the structural wiring of the brain has largely been undertaken using non-linear and biophysically detailed mathematical models with regionally varying parameters. While this approach provides us a rich repertoire of multistable dynamics that can be displayed by the brain, it is computationally demanding. Moreover, although neuronal dynamics at the microscopic level are nonlinear and chaotic, it is unclear if such detailed nonlinear models are required to capture the emergent meso- (regional population ensemble) and macroscale (whole brain) behavior, which is largely deterministic and reproducible across individuals. Indeed, recent modeling effort based on spectral graph theory has shown that an analytical model without regionally varying parameters can capture the empirical magnetoencephalography frequency spectra and the spatial patterns of the alpha and beta frequency bands accurately. In this work, we demonstrate an improved hierarchical, linearized, and analytic spectral graph theorybased model that can capture the frequency spectra obtained from magnetoencephalography recordings of resting healthy subjects. We reformulated the spectral graph theory model in line with classical neural mass models, therefore providing more biologically interpretable parameters, especially at the local scale. We demonstrated that this model performs better than the original model when comparing the spectral correlation of modeled frequency spectra and that obtained from the magnetoencephalography recordings. This model also performs equally well in predicting the spatial patterns of the empirical alpha and beta frequency bands. HighlightsO_LIWe show an improved hierarchical, linearized, and analytic spectral graph theory-based model that can capture the frequency spectra obtained from magnetoencephalography recordings C_LIO_LIThis model also accurately captures the spatial patterns of the empirical alpha and beta frequency bands, requiring only 5-10 graph eigenmodes to do so C_LI

neuroscience↗

bioRxiv2021

Cell type determination for cardiac differentiation occurs soon after seeding of human induced pluripotent stem cells

Cardiac directed differentiation of human induced pluripotent stem cells consistently produces a mixed population of cardiomyocytes and non-cardiac cell types even when using very well-characterized protocols. We wondered whether differentiated cell types might result from intrinsic differences in hiPS cells prior to the onset of differentiation. By associating individual differentiated cells that share a common hiPS cell precursor, we were able to test whether expression variability in differentiated cells was pre-determined from the hiPS cell state. Although within a single experiment, differentiated cells that share an hiPS cell progenitor were more transcriptionally similar to each other than to other cells in the differentiated population, when the same hiPS cells were differentiated in parallel, we did not observe high transcriptional similarity across differentiations. Additionally, we found that substantial cell death occurred during differentiation in a manner that suggested that all cells were equally likely to survive or die, suggesting that there was no intrinsic selection bias for cells descended from particular hiPS cell progenitors. These results led us to wonder about how cells grow out spatially during the directed differentiation process. Labeling cells by their expression of a few canonical cell type marker genes, we showed that cells expressing the same marker tended to occur in patches observable by visual inspection, suggesting that cell type determination across multiple cell types, once initiated, is maintained in a cell-autonomous manner for multiple divisions. Altogether, our results show that while there is substantial heterogeneity in the initial hiPS cell population, that heterogeneity is not responsible for heterogeneous outcomes, and that the window during which cell type specification occurs is likely to begin shortly after the seeding of hiPS cells for differentiation.

genomics↗

bioRxiv2021

Automated, high-dimensional evaluation of physiological aging and resilience in outbred mice

Behavior and physiology are essential readouts in many studies but have not benefited from the high-dimensional data revolution that has transformed molecular and cellular phenotyping. To address this, we developed an approach that combines commercially available automated phenotyping hardware with a systems biology analysis pipeline to generate a high-dimensional readout of mouse behavior/physiology, as well as intuitive and health-relevant summary statistics (resilience and biological age). We used this platform to longitudinally evaluate aging in hundreds of outbred mice across an age range from 6 months to 3.4 years. In contrast to the assumption that aging can only be measured at the limits of animal ability via challenge-based tasks, we observed widespread physiological and behavioral aging starting in early life. Using network connectivity analysis, we found that organism-level resilience exhibited an accelerating decline with age that was distinct from the trajectory of individual phenotypes. We developed a method, Combined Aging and Survival Prediction of Aging Rate (CASPAR), for jointly predicting chronological age and survival time and showed that the resulting model is able to predict both variables simultaneously, a behavior that is not captured by separate age and mortality prediction models. This study provides a uniquely high-resolution view of physiological aging in mice and demonstrates that systems-level analysis of physiology provides insights not captured by individual phenotypes. The approach described here allows aging, and other processes that affect behavior and physiology, to be studied with sophistication and rigor.

systems biology↗

bioRxiv2021

High activity and high functional connectivity are mutually exclusive in resting state zebrafish and human brains

Active neurons impact cell types with which they are functionally connected. Both activity and functional connectivity are heterogeneous across the brain, but the nature of their relationship is not known. Here we employ brain-wide calcium imaging at cellular resolution in larval zebrafish to record spontaneous activity of >12,000 neurons in the forebrain. By classifying their activity and functional connectivity into three levels (high, medium, low), we find that highly active neurons have low functional connections and highly connected neurons are of low activity. Intriguingly, deploying the same analytical methods on functional magnetic resonance imaging (fMRI) data from the resting state human brain, we uncover a similar relationship between activity and functional connectivity, that is, regions of high activity are non-overlapping with those of high connectivity. These findings reveal a previously unknown and evolutionarily conserved brain organizational principle that have implications for understanding disease states and designing artificial neuronal networks.

neuroscience↗

bioRxiv2021

Intermittent fasting and caloric restriction interact with genetics to shape physiological health in mice

Dietary interventions can dramatically affect physiological health and organismal lifespan. The degree to which organismal health is improved depends upon genotype and the severity of dietary intervention, but neither the effects of these factors, nor their interaction, have been quantified in an outbred population. Moreover, it is not well understood what physiological changes occur shortly after dietary change and how these may affect the health of early adulthood population. In this article, we investigated the effect of six month exposure of either caloric restriction or intermittent fasting on a broad range of physiological traits in 960 one year old Diversity Outbred mice. We found caloric restriction and intermittent fasting affected distinct aspects of physiology and neither the magnitude nor the direction (beneficial or detrimental) of effects were concordant with the severity of the intervention. In addition to the effects of diet, genetic variation significantly affected 31 of 36 traits (heritabilties ranged from 0.04-0.65). We observed significant covariation between many traits that was due to both diet and genetics and quantified these effects with phenotypic and genetic correlations. We genetically mapped 16 diet-independent and 2 diet-dependent significant quantitative trait loci, both of which were associated with cardiac physiology. Collectively, these results demonstrate the degree to which diet and genetics interact to shape the physiological health of early adult-hood mice following six months of dietary intervention.

genetics↗

bioRxiv2021

Time-varying Dynamic Network Model For Dynamic Resting State Functional Connectivity in fMRI and MEG imaging

Dynamic resting state functional connectivity (RSFC) characterizes fluctuations that occurs over time in functional brain networks. Existing methods to extract dynamic RSFCs, such as sliding-window and clustering methods, have various limitations due to their inherent non-adaptive nature and high-dimensionality including an inability to reconstruct brain signals, insufficiency of data for reliable estimation, insensitivity to rapid changes in dynamics, and a lack of generalizability across multimodal functional imaging datasets. To overcome these deficiencies, we develop a novel and unifying time-varying dynamic network (TVDN) framework for examining dynamic resting state functional connectivity. TVDN includes a generative model that describes the relation between low-dimensional dynamic RSFC and the brain signals, and an inference algorithm that automatically and adaptively learns to detect dynamic state transitions in data and a low-dimensional manifold of dynamic RSFC. TVDN is generalizable to handle multimodal functional neuroimaging data (fMRI and MEG/EEG). The resulting estimated low-dimensional dynamic RSFCs manifold directly links to the frequency content of brain signals. Hence we can evaluate TVDN performance by examining whether learnt features can reconstruct observed brain signals. We conduct comprehensive simulations to evaluate TVDN under hypothetical settings. We then demonstrate the application of TVDN with real fMRI and MEG data, and compare the results with existing benchmarks. Results demonstrate that TVDN is able to correctly capture the dynamics of brain activity and more robustly detect brain state switching both in resting state fMRI and MEG data.

bioengineering↗

bioRxiv2021

Emergence of directional bias in tau deposition from axonal transport dynamics

Defects in axonal transport may partly underpin the differences between the observed pathophysiology of Alzheimers disease (AD) and that of other non-amyloidogenic tauopathies. Particularly, pathological tau variants may have molecular properties that dysregulate motor proteins responsible for the anterograde-directed transport of tau in a disease-specific fashion. Here we develop the first computational model of tau-modified axonal transport that produces directional biases in the spread of tau pathology. We simulated the spatiotemporal profiles of soluble and insoluble tau species in a multicompartment, two-neuron system using biologically plausible parameters and time scales. Changes in the balance of tau transport feedback parameters can elicit anterograde and retrograde biases in the distributions of soluble and insoluble tau between compartments in the system. Aggregation and fragmentation parameters can also perturb this balance, suggesting a complex interplay between these distinct molecular processes. Critically, we show that the model faithfully recreates the characteristic network spread biases in both AD-like and non-AD-like mouse tauopathy models. Tau transport feedback may therefore help link microscopic differences in tau conformational states and the resulting variety in clinical presentations. Author SummaryThe misfolding and spread of the axonal protein tau is a hallmark of the pathology of many neurodegenerative disorders, including Alzheimers disease and frontotemporal lobar dementia. How tau misfolding causes disorders with distinct neuropathology and clinical presentations is the subject of ongoing research. Although current evidence suggests that the specific conformations tau adopts affect where it spreads throughout the brain, a mechanistic explanation has remained elusive. Here we propose that the conformer-specific dysregulation of axonal transport can lead to directionally biased spread, and we employ a mathematical model to explore how tau spreads between neurons in the context of this transport feedback mechanism. We find that conformation-specific feedback is flexible enough to explain bias in either direction and thoroughly explore how this bias emerges as a function of the models key parameters. Further, the model reproduces the temporal evolution of directionality observed in two classes of in vivo tauopathy models, demonstrating that transport feedback is sufficient to explain differential tau spread as a function of conformation.

neuroscience↗

bioRxiv2021

Modelling seeding and neuroanatomic spread of pathology in amyotrophic lateral sclerosis

The neurodegenerative disorder amyotrophic lateral sclerosis (ALS) is characterized by the progressive loss of upper and lower motor neurons, with pathological involvement of cerebral motor and, additionally, extra-motor areas, in a clinicopathological spectrum with frontotemporal dementia (FTD). A key unresolved question is whether the distribution of pathology in ALS is driven by molecular factors such as regional gene expression, by differential network vulnerability, or is a combination of both. A system of histopathological staging of ALS based on the regional burden of TDP-43 pathology observed in post mortem brains has been supported to some extent by analysis of distribution of in vivo structural MRI changes. In this paper, computational modelling using a Network Diffusion Model (NDM) was used to investigate whether a process of focal pathological seeding followed by structural network-based spread recapitulated post mortem histopathological staging and, secondly, whether this had any relationship to the pattern of expression of a panel of genes implicated in ALS across the healthy brain. Regionally parcellated T1-weighted MRI data from ALS patients (baseline n=79) was studied in relation to a healthy control structural connectome and a database of associated regional cerebral gene expression. The NDM provided strong support for a structural network-based basis for regional pathological spread in ALS, but no simple relationship to the spatial distribution of ALS-related genes in the healthy brain. Intriguingly, the critical seed regions for spread within the model were not within the primary motor cortex but basal ganglia, thalamus and insula, where NDM recapitulated aspects of the post mortem histopathological staging system. Within the ALS-FTD clinicopathological spectrum, non-primary motor structures may be among the earliest sites of cerebral pathology.

neuroscience↗

bioRxiv2021

Systematically quantifying morphological features reveals constraints on organoid phenotypes

Organoids recapitulate complex 3D organ structures and represent a unique opportunity to probe the principles of self-organization. While we can alter an organoids morphology by manipulating the culture conditions, the morphology of an organoid often resembles that of its original organ, suggesting that organoid morphologies are governed by a set of tissue-specific constraints. Here, we establish a framework to identify constraints on an organoids morphological features by quantifying them from microscopy images of organoids exposed to a range of perturbations. We apply this framework to Madin-Darby Canine Kidney cysts and show that they obey a number of constraints taking the form of scaling relationships or caps on certain parameters. For example, we found that the number, but not size, of cells increases with increasing cyst size. We also find that these constraints vary with cyst age and can be altered by varying the culture conditions. We observed similar sets of constraints in intestinal organoids. This quantitative framework for identifying constraints on organoid morphologies may inform future efforts to engineer organoids.

systems biology↗

bioRxiv2020

The role of directionality in determining spatiotemporal tau pathology differs between AD-Like and Non-AD-Like mouse models

IntroductionCurrent research indicates divergent spatiotemporal tauopathy progression between conditions and implicates transsynaptic connectome-based spread as a main mechanism. We examine tauopathy and connectome interactions to investigate why different spatiotemporal patterns of pathology arise. MethodsWe test whether divergent spatiotemporal tau pathology patterns from 15 mouse-model datasets can be explained by a directional bias in tau transmission along fiber tracts via a mathematical model called Directed Network Transmission (DNT). ResultsAmyloid-comorbid tauopathic mouse models meant to mimic AD demonstrate spatiotemporal tauopathy patterns consistent with retrograde direction spread biases. Non-amyloid-comorbid mice demonstrate no consistent spread biases. Further, canonically early tau pathology regions in AD are implicated as having earliest pathology in a simulation with random tauopathy seeding locations with retrograde biased spread. DiscussionThese results implicate directional biases in tau pathology spread along fiber tracts as a strong candidate explanation for divergent spatiotemporal tau progression between conditions.

neuroscience↗

bioRxiv2020

Age and diet shape the genetic architecture of body weight in Diversity Outbred mice

AO_SCPLOWBSTRACTC_SCPLOWUnderstanding how genetic variation shapes an age-dependent complex trait relies on accurate quantification of both the additive genetic effects and genotype-environment interaction effects in an age-dependent manner. We used a generalization of the linear mixed model to quantify diet-dependent genetic contributions to body weight and growth rate measured from early development through adulthood of 960 Diversity Outbred female mice subjected to five dietary interventions. We observed that heritability of body weight remained substantially high (h2 {approx} 0.8) throughout adulthood under the 40% calorie restriction diet, while heritability, although still appreciably high, declined with age under all other dietary regimes. We identified 14 loci significantly associated with body weight in an age-dependent manner and 19 loci that contribute to body weight in an age- and diet-dependent manner. We found the effect of body weight alleles to be dynamic with respect to genomic background, age, and diet, identifying the scope of pleiotropy and several instances of allelic heterogeneity. In many cases, we fine-mapped these loci to narrow genomic intervals containing a few genes and impute putative functional variants from the genome sequence of the DO founders. Of the loci associated with body weight in a diet-dependent manner, many have been previously linked to neurological function and behavior in mice or humans. These results enable us to more fully understand the dynamics of the genetic architecture of body weight with age and in response to different dietary interventions, and to predict the effectiveness of dietary intervention on overall health in distinct genetic backgrounds.

genetics↗

bioRxiv2020

Emergence of Canonical Functional Networks from Complex-valued Laplacian of Structural Connectome

AO_SCPLOWBSTRACTC_SCPLOWHow do functional brain networks emerge from the underlying wiring of the brain? We examine how resting-state functional activation patterns emerge from the underlying connectivity and length of white matter fibers that constitute its "structural connectome". By introducing realistic signal transmission delays along fiber projections, we obtain a complex-valued graph Laplacian matrix that depends on two parameters: coupling strength and oscillation frequency. This complex Laplacian admits a complex-valued eigen-basis in the frequency domain that is highly tunable and capable of reproducing the spatial patterns of canonical functional networks without requiring any detailed neural activity modeling. Specific canonical functional networks can be predicted using linear superposition of small subsets of complex eigenmodes. Using a novel parameter inference procedure we show that the complex Laplacian outperforms the real-valued Laplacian in predicting functional networks. The complex Laplacian eigenmodes therefore constitute a tunable yet parsimonious substrate on which a rich repertoire of realistic functional patterns can emerge. Although brain activity is governed by highly complex nonlinear processes and dense connections, our work suggests that simple extensions of linear models to the complex domain effectively approximate rich macroscopic spatial patterns observable on BOLD fMRI.

neuroscience↗