Biology subjects

Srinivasan, R.

Publications and source records attributed to Srinivasan, R..

8 recordsLinked to original sources

bioRxiv2018

Old fibroblasts secrete inflammatory cytokines that drive variability in reprogramming efficiency and may affect wound healing between old individuals

Age-associated chronic inflammation (inflammaging) has emerged as a central hallmark of aging1-3, but its impact on specific cells is still largely unknown. Fibroblasts are present in all tissues and contribute to wound healing4-6. They are also the cell type that is mostly used for induced pluripotent stem cell (iPSC) reprogramming7 - a process that has implications for regenerative medicine and rejuvenation strategies8-17. Here we show that primary fibroblasts from old mice secrete inflammatory cytokines and that there is an increased variability in reprogramming efficiency between fibroblast cultures from old individuals. Individual-to-individual variability is emerging as a key feature of old age18-21, which could reflect distinct aging trajectories, but the underlying causes remain unknown. To identify drivers of this variability, we perform a multi-omic assessment of young and old fibroblast cultures with different reprogramming efficiency. This approach, coupled with single cell transcriptomics, reveals that old fibroblast cultures are heterogeneous and show a greater proportion of activated fibroblasts that secrete inflammatory cytokines, which correlates with reprogramming efficiency. We experimentally validate that activated fibroblasts express inflammatory cytokines in vivo and that their presence is linked to enhanced reprogramming efficiency in culture. Conditioned-media swapping experiments show that extrinsic factors secreted by activated fibroblasts are more critical than intrinsic factors for the individual-to-individual variability in reprogramming efficiency, and we identify TNF as a key inflammatory cytokine underlying this variability. Interestingly, old mice also exhibit variability in wound healing efficiency in vivo and old wounds show an increased subpopulation of activated fibroblasts with a unique TNF signature. Our study shows that a switch in fibroblast composition, and the ratio of inflammatory cytokines they secrete, drives variability in reprogramming in vitro and may influence wound healing in vivo. These findings could help identify personalized strategies to improve iPSC generation and wound healing in older individuals.

cell biology↗

bioRxiv2018

Network Structure During Encoding Predicts Working Memory Performance

Working memory operates through networks that integrate distributed modular brain activity. We characterize the structure of networks in different electroencephalographic frequency bands while individuals perform a working memory task. The objective was to identify network properties that support working memory function during the encoding, maintenance, and retrieval of memory. In each EEG frequency band, we estimated a complex-valued Gaussian graphical model to characterize the structure of brain networks using measures from graph theory. Critically, the structural characteristics of brain networks that facilitate performance are all established during encoding, suggesting that they reflect the effect of attention on the quality of the representation in working memory. Segregation of networks in the alpha and beta bands during encoding increased with accuracy. In the theta band, greater integration of functional clusters involving the temporal lobe with other cortical areas predicted faster response time, starting in the encoding interval and persisting throughout the task, indicating that functional clustering facilitates rapid memory manipulation.

neuroscience↗

bioRxiv2018

Multi-Scale Neural Sources of EEG: Genuine, Equivalent, and Representative

A biophysical framework needed to interpret electrophysiological data recorded at multiple spatial scales of brain tissue is developed. Micro current sources at membrane surfaces produce local field potentials (LFP), electrocorticography (ECoG), and electroencephalography (EEG). We categorize multi-scale sources as genuine, equivalent, or representative. Genuine sources occur at the micro scale of cell surfaces. Equivalent sources provide identical experimental outcomes over a range of scales and applications. In contrast, each representative source distribution is just one of many possible source distributions that yield similar experimental outcomes. Macro sources (\"dipoles\") may be defined at the macrocolumn (mm) scale and depend on several features of the micro sources--magnitudes, micro synchrony within columns, and distribution through the cortical depths. These micro source properties are determined by brain dynamics and the columnar structure of cortical tissue. The number of representative sources underlying EEG data depends on the spatial scale of neural tissue under study. EEG inverse solutions (e.g. dipole localization) and high resolution estimates (e.g. Laplacian, dura imaging) have both strengths and limitations that depend on experimental conditions. The proposed theoretical framework informs studies of EEG source localization, source characterization, and low passes filtering. It also facilitates interpretations of brain dynamics and cognition, including measures of synchrony, functional connections between cortical locations, and other aspects of brain complexity.

neuroscience↗

bioRxiv2018

Timing of readiness potentials reflect a decision-making process in the human brain

Decision-making in two-alternative forced choice tasks has several underlying components including stimulus encoding, perceptual categorization, response selection, and response execution. Sequential sampling models of decision-making are based on an evidence accumulation process to a decision boundary. Animal and human studies have focused on perceptual categorization and provide evidence linking brain signals in parietal cortex to the evidence accumulation process. In this exploratory study, we use a task where the dominant contribution to response time is response selection and model the response time data with the drift-diffusion model. EEG measurement during the task show that the Readiness Potential (RP) recorded over motor areas has timing consistent with the evidence accumulation process. The duration of the RP predicts decision-making time, the duration of evidence accumulation, suggesting that the RP partly reflects an evidence accumulation process for response selection in the motor system. Thus, evidence accumulation may be a neural implementation of decision-making processes in both perceptual and motor systems. The contributions of perceptual categorization and response selection to evidence accumulation processes in decision-making tasks can be potentially evaluated by examining the timing of perceptual and motor EEG signals.

neuroscience↗

bioRxiv2018

Visual encoding signals around 200 milliseconds track onset of evidence accumulation during human decision making

Encoding of a sensory stimulus is believed to be the first step in perceptual decision making. Previous research has shown that electrical signals recorded from the human brain track evidence accumulation during perceptual decision making (Gold and Shadlen, 2007; OConnell et al., 2012; Philiastides et al., 2014). In this study we directly tested the hypothesis that the latency of the N200 recorded by EEG (a negative peak occurring between 150 and 275 ms after stimulus presentation in human participants) reflects the visual encoding time (VET) required for completion of figure-ground segregation before evidence accumulation. We show that N200 latencies vary across individuals, are modulated by external visual noise, and increase response time by x milliseconds when they increase by x milliseconds, reflecting a linear regression slope of 1. Simulations of cognitive decision-making theory show that variation in human response times not related to evidence accumulation (including VET) are tracked by the fastest response times. A relationship with a slope of 1 between N200 latencies and VET was found by fitting a linear model between trial-averaged N200 latencies and the 10th percentiles of response times. A slope of 1 was also found between single-trial N200 latencies and response times. Fitting a novel neuro-cognitive model of decision-making also yielded a slope of 1 between N200 latency and non-decision time in multiple visual noise conditions, indicating that N200 latencies track the completion of visual encoding and the onset of evidence accumulation. The N200 waveforms were localized to the cortical surface at distributed temporal and extrastriate locations, consistent with a distributed network engaged in figure-ground segregation of the target stimulus.\n\nSignificance StatementEncoding of a sensory stimulus is believed to be the first step in perceptual decision making. In this study, we report evidence that visual evoked potentials (EPs) around 200 ms after stimulus presentation track the time of visual figure-ground segregation before the onset of evidence accumulation during decision making. These EP latencies vary across individuals, are modulated by external visual noise, and increase response time by x milliseconds when they increase by x milliseconds. Hierarchical Bayesian model-fitting was also used to relate these EPs to a specific cognitive parameter that tracks time related to visual encoding in a decision-making model of response time. This work adds to the growing literature that suggests that EEG signals can track the component cognitive processes of decision making.

neuroscience↗

bioRxiv2018

Methionine coordinates a hierarchically organized anabolic program through GCN4

Methionine availability during overall amino acid limitation metabolically reprograms cells to support proliferation, the underlying basis for which remains unclear. Here, we construct the organization of this methionine mediated anabolic program, using yeast. Combining comparative transcriptome analysis, biochemical and metabolic flux based approaches, we discover that methionine rewires overall metabolic outputs by increasing the activity of three key regulatory nodes. These are: the pentose phosphate pathway coupled with reductive biosynthesis, and overall transamination capacity, including the synthesis of glutamate/glutamine. These provides the cofactors or substrates that enhance subsequent rate-limiting reactions in the synthesis of costly amino acids, and nucleotides, which are also induced in a methionine dependent manner. This thereby results in a biochemical cascade establishing an overall anabolic program. For this methionine mediated anabolic program leading to proliferation, cells co-opt a \"starvation stress response\" regulator, Gcn4p. Collectively, our data suggest a hierarchical metabolic framework explaining how methionine mediates an anabolic switch.

systems biology↗

bioRxiv2017

FOXS1 is a Master Regulator of Pathological Epithelial to Mesenchymal Transition in Human Epithelia

Epithelial to mesenchymal transition (EMT) is a biological process involved in normal tissue morphogenesis and also in disease pathology. It causes dramatic alterations in cell morphology, migration, proliferation, and phenotype. We captured the global transcriptional and epigenetic programs elicited when polarized, cobblestone human retinal pigment epithelial (RPE) cells were stimulated to undergo EMT, a process associated with several retinal pathologies. The reorganization of chromatin landscapes occurred preferentially at distal enhancers, rather than promoter regions, accompanied by 3136 significantly changing genes. Of the 95 significantly changing transcription factors, FOXS1 was most upregulated. Loss and gain of function experiments demonstrated that FOXS1 is upstream of canonical EMT regulators, and can stimulate EMT in RPE and other epithelia. Inhibition of p38, stimulated by combined action of the EMT-inducing factors, TGF{beta}1 and TNF, dampened FOXS1 expression. An increase of FOXS1 in several cancers indicates it has a role in several EMT-involved pathologies.

cell biology↗

bioRxiv2017

Sequence Alignment On Directed Graphs

Genomic variations in a reference collection are naturally represented as genome variation graphs. Such graphs encode common subsequences as vertices and the variations are captured using additional vertices and directed edges. The resulting graphs are directed graphs possibly with cycles. Existing algorithms for aligning sequences on such graphs make use of partial order alignment (POA) techniques that work on directed acyclic graphs (DAG). For this, acyclic extensions of the input graphs are first constructed through expensive loop unrolling steps (DAGification). Also, such graph extensions could have considerable blow up in their size and in the worst case the blow up factor is proportional to the input sequence length. We provide a novel alignment algorithm V-ALIGN that aligns the input sequence directly on the input graph while avoiding such expensive DAGification steps. V-ALIGN is based on a novel dynamic programming formulation that allows gapped alignment directly on the input graph. It supports affine and linear gaps. We also propose refinements to V-ALIGN for better performance in practice. In this, the time to fill the DP table has linear dependence on the sizes of the sequence, the graph and its feedback vertex set. We perform experiments to compare against the POA based alignment. For aligning short sequences, standard approaches restrict the expensive gapped alignment to small filtered subgraphs having high similarity to the input sequence. In such cases, the performance of V-ALIGN for gapped alignment on the filtered subgraph depends on the subgraph sizes.

genomics↗