Biology subjects

Shridhar, S.

Publications and source records attributed to Shridhar, S..

6 recordsLinked to original sources

bioRxiv2026

Punctuated Evolution of Endomembrane Compartments in Proto-Eukaryotes

Eukaryotic cells are defined by their endomembranes: compartments such as the endoplasmic reticulum (ER), Golgi and endosomes, exchanging cargo via vesicles. The evolutionary origins of endomembrane compartments remain unclear. Here we construct molecular-evolutionary trajectories for the stepwise addition of compartments after the emergence of the proto-ER in an ancestral eukaryote. We represent compartments and vesicles as nodes and edges of a directed graph. Vesicle budding and fusion regulators such as coats and SNAREs control cargo flows and determine compartment compositions. We computationally sample billions of possible graphs, and enumerate how duplication, deletion and mutation of regulators drive graph transitions. We find that evolutionary trajectories display punctuated shifts in compartment composition and number, interspersed with thousands of neutral mutations. The first added compartment inherits functions from the proto-ER or plasma membrane, or gains novel functions. Our results show how, given a billion years, simple molecular steps can generate complex endomembrane systems. SO_SCPLOWIGNIFICANCEC_SCPLOW SO_SCPLOWTATEMENTC_SCPLOWEukaryotic cells contain a system of endomembrane compartments that sort, process and deliver molecules to precise cellular destinations. This endomembrane system is a defining feature of all complex life, yet its evolutionary origins remain obscure. How did a proto-eukaryote with a single ancestral endomembrane compartment evolve into a cell with a Golgi, endosomes, lysosomes and other compartments characteristic of modern eukaryotes? We model this process from first principles, connecting the duplication, deletion and mutation of molecular regulators to compartment gain or loss. We find a punctuated pattern of endomembrane elaboration: a long phase of neutral exploration, driven by the mutation of duplicate gene copies, precedes the emergence of new compartments and functions.

evolutionary biology↗

bioRxiv2024

Explicitly Acquired Interrelations Among Mental Schema Reduces Cognitive Load and Facilitates Emergence of Novel Responses in Mice and Artificial Neural Networks.

Mammalian brain has evolved to infer from past experiences and elicit context relevant novel behavioural responses hitherto unexpressed by the animal. However, little is known about how prior knowledge influences the emergence of such responses. Remarkably, the brain not only arrives at these responses through logical inferences based on previous leanings, but also acquire new related information, without causing catastrophic interference. Mental schemas have often been proposed as the framework for this phenomenon. In this study, using mice as a model animal, we show that schematic networks not only enhance the cognitive load handling capacity (CLHC) and prevent catastrophic interference, but also facilitate the generation of novel, contextually relevant responses. Interestingly, when the animals were trained in a paradigm that did not invoke the pre-formed mental schema, we observed neither an enhancement to CLHC nor a generation of novel context relevant responses. Based on the principles of mental schemas discovered in our animal experiments, we developed a biologically plausible artificial neural network (ANN) that avoids catastrophic interference and captures the learning properties observed in our experiments. The custom architecture of this ANN enables it to generate responses similar to those of animals in novel scenarios. Significance StatementLittle is known about the role of mental schemas in preventing memory interference--a process in which overlapping or similar memories hinder the acquisition and retention of related information. In this study, we demonstrate that mental schemas enhance cognitive load handling capacity and improve the ability to solve novel but related problems. Using mice as a model, we show that the mere existence of a mental schema is not enough for improved cognitive load handling; instead, the relationship between existing and new information must be explicitly established during the learning process. Based on these findings, we developed a minimalistic artificial neural network (ANN) that effectively mimics this behaviour. These insights pave the way for developing more efficient learning and teaching strategies.

neuroscience↗

bioRxiv2024

Optimizing real-time phase detection in diverse rhythmic biological signals for phase-specificneuromodulation

AbstractClosed-loop, phase-specific neurostimulation is a powerful method to modulate ongoing brain activity for clinical and research applications. Phase-specific stimulation relies on estimating the phase of an ongoing oscillation in real time and issuing a control command at a target phase. Phase detection algorithms based on Fast Fourier transform (FFT) are widely used due to their computational efficiency and robustness. However, it is unclear how algorithm performance depends on the spectral properties of the input signal and how algorithm parameters can be optimized. We used offline simulation to evaluate the performance of three algorithms (endpoint-corrected Hilbert Transform, Hilbert Transform and phase mapping) on three rhythmic biological signals with distinct spectral properties (rodent hippocampal theta potential, human EEG alpha and human essential tremor). First, we found that algorithm performance was more strongly influenced by signal amplitude and frequency variation compared with signal to noise ratio. Second, our simulations showed that the size of the data window for phase estimation was critical for the performance of FFT-based algorithms, where the optimal data window corresponds to the period of the oscillation. We validated this prediction with real time phase detection of hippocampal theta oscillations in freely behaving rats performing spatial navigation. Our findings define the relationship between signal properties and algorithm performance and provide a convenient method for optimizing FFT-based phase detection algorithms.

neuroscience↗

bioRxiv2021

Dominant role of adult neurogenesis-induced structural heterogeneities in driving plasticity heterogeneity in dentate gyrus granule cells

Neurons and synapses manifest pronounced variability in the amount of plasticity induced by identical activity patterns. The mechanisms underlying such plasticity heterogeneity, implicated in context-specific resource allocation during encoding, have remained unexplored. Here, we employed a systematic, unbiased, and physiologically constrained search to identify the mechanisms behind plasticity heterogeneity in dentate gyrus granule cells. We found that each of intrinsic, synaptic, and structural heterogeneities independently yielded heterogeneous plasticity profiles obtained with two different induction protocols. However, prior predictions about strong relationships between neuronal intrinsic excitability and plasticity emerged only when adult-neurogenesis-induced structural heterogeneities were accounted for. Strikingly, despite the concomitant expression of heterogeneities in structural, synaptic, and intrinsic neuronal properties, similar plasticity profiles were attainable through synergistic interactions among these heterogeneities. Importantly, consequent to strong relationships with intrinsic excitability measurements, we found that synaptic plasticity in the physiological range was achieved in immature cells despite their electrophysiologically-observed weak synaptic strengths. Together, our analyses unveil the dominance of neurogenesis-induced structural heterogeneities in driving plasticity heterogeneity in granule cells. Broadly, these analyses emphasize that the mechanistic origins of and the implications for plasticity heterogeneities need quantitative characterization across brain regions, particularly focusing on context-specific encoding of learned behavior.

neuroscience↗

bioRxiv2021

Implicitly Learned Higher Order Associations Differentiates Recent and Remote Retrieval of Temporal Order Memory

Memory of an ordered sequence of distinct events requires encoding the temporal order as well as the intervals that separates these events. In this study, using order place association task where the animal learns to associate the location of the food pellet to the order of entry into the event arena, we probe the nature of temporal order memory in mice. In our task, individual trials, become distinct events, as the animal is trained to form unique association between entry order and a correct location. The inter-trial intervals (> 30 mins) are chosen deliberately to minimise the working memory contributions. We develop this paradigm initially using 4 order place associates and later extend it to 5 paired associates. Our results show that animals not only acquire these explicit (entry order to place) associations but also higher order associations that can only be inferred implicitly from the temporal order of these events. As an indicator of such higher order learning during the probe trail the mice exhibit predominantly prospective errors that declines proportionally with temporal distance. On the other hand, prior to acquiring the sequence the retrospective errors are dominant. Additionally, we also tested the nature of such acquisitions when temporal order CS is presented along with flavour as a compound stimulus comprising of order and flavour both simultaneously being paired with location. Results from these experiments indicate that the animal learns both order-place and flavour-place associations. Comparing with pure order place training, we find that the additional flavour in compound training did not interfere with the ability of the animals to acquire the order place associations. When tested remotely, pure order place associations could be retrieved only after a reminder training. Further higher order associations representing the temporal relationship between the events is markedly absent in the remote time.

animal behavior and cognition↗

bioRxiv2017

Insights into regeneration from the genome, transcriptome and metagenome analysis of Eisenia fetida

Earthworms show a wide spectrum of regenerative potential with certain species like Eisenia fetida capable of regenerating more than two-thirds of their body while other closely related species, such as Paranais litoralis seem to have lost this ability. Earthworms belong to the phylum annelida, in which the genomes of the marine oligochaete Capitella telata, and the freshwater leech Helobdella robusta have been sequenced and studied. The terrestrial annelids, in spite of their ecological relevance and unique biochemical repertoire, are represented by a single rough genome draft of Eisenia fetida (North American isolate), which suggested that extensive duplications have led to a large number of HOX genes in this annelid. Herein, we report the draft genome sequence of Eisenia fetida (Indian isolate), a terrestrial redworm widely used for vermicomposting assembled using short reads and mate-pair reads. An in-depth analysis of the miRNome of the worm, showed that many miRNA gene families have also undergone extensive duplications. Genes for several important proteins such as sialidases and neurotrophins were identified by RNA sequencing of tissue samples. We also used de novo assembled RNA-Seq data to identify genes that are differentially expressed during regeneration, both in the newly regenerating cells and in the adjacent tissue. Sox4, a master regulator of TGF-beta induced epithelial-mesenchymal transition was induced in the newly regenerated tissue. The regeneration of the ventral nerve cord was also accompanied by the induction of nerve growth factor and neurofilament genes. The metagenome of the worm, characterized using 16S rRNA sequencing, revealed the identity of several bacterial species that reside in the nephridia of the worm. Comparison of the bodywall and cocoon metagenomes showed exclusion of hereditary symbionts in the regenerated tissue. In summary, we present extensive genome, transcriptome and metagenome data to establish the transcriptome and metagenome dynamics during regeneration.

genomics↗