Biology subjects

Ramakrishnan, N.

Publications and source records attributed to Ramakrishnan, N..

12 recordsLinked to original sources

bioRxiv2026

K2P Channels Regulate Presynaptic Organisation through a Membrane Potential-Independent Mechanism

Neuronal ion channels have well-established effects on synaptic plasticity, in many cases by influencing pathways that depend on membrane excitability. Here we find that a C. elegans two-pore domain potassium (K2P) channel, TWK-40, regulates presynaptic organisation through a membrane potential-independent mechanism. Instead, this mechanism depends on TWK-40's effects on intracellular potassium levels. Loss-of-function mutations in TWK-40 lead to excessive presynaptic protein accumulation, while gain-of-function mutations lead to depleted presynaptic components and cause synaptic transmission deficits. These abnormalities are phenocopied by transporter mutations that mimic TWK-40's effects on intracellular potassium concentration, but not by sodium channel mutations that mimic its effects on membrane excitability. This indicates that cytoplasmic potassium promotes presynaptic assembly. This process depends on the PYK-1 pyruvate kinase, a potassium-sensitive enzyme, and three transcription factors. These findings establish a new pathway linking neuronal potassium homeostasis to the control of presynaptic organisation and synaptic function.

molecular biology↗

bioRxiv2026

An Explainable Machine Learning Approach to study the positional significance of histone post-translational modifications in gene regulation

Epigenetic mechanisms regulate gene-expression by altering the structure of the chromatin without modifying the underlying DNA sequence. Histone post-translational modifications (PTMs) are critical epigenetic signals that influence transcriptional activity, promoting or repressing gene-expression.Understanding the impact of individual PTMs and the combinatorial effects is essential to deciphering gene regulatory mechanisms.In this study,we analyzed the ChIP-seq data for 26 PTMs in yeast, examining the PTM intensities gene-wise from positions-3 to 8 in each gene.Using XGBoost classifiers, we predicted gene transcription rates and identified key histone modifications and nucleosomal positions that are critical in gene-expression using explainability measures (such as SHAP). Our study provides a comprehensive insight into the histone modifications, their positions and their combinations that are most critical in gene regulation in yeast.The proposed explainable Machine Learning models can be easily extended to other model organisms to provide meaningful insights into gene regulation by epigenetic mechanisms.

bioinformatics↗

bioRxiv2025

Multivariate Mutual Information based Feature Selection for Predicting Histone Post-Translational Modifications in Epigenetic Datasets

Mutual information (MI) has been traditionally employed in many areas including biology to identify the non-linear relationships between features. This technique is particularly useful in the biological context to identify features such as genes, histone post-translational modifications (PTMs), transcriptional factors etc. In this work, instead of considering the conventional pairwise MI between PTM features, we evaluate multivariate mutual information (MMI) between PTM triplets, to identify a set of outlier features. This enables us to form a small subset of PTMs that serve as principal features for the prediction of the values of any histone PTM across the epigenome. We also compare the principal MMI features with those from the traditional feature selection techniques such as PCA and Orthogonal Matching Pursuit. We predict all the remaining histone PTM intensities using XGBoost based regression on the selected features. The accuracy of this technique is demonstrated on the ChIP-seq datasets from the yeast and the human epigenomes.The results indicate that the proposed MMI based feature selection technique can serve as a useful method across various biological datasets.

bioinformatics↗

bioRxiv2024

Neural Networks model biological evolution of faithful epigenetic inheritance

The layer of histone Post-Translational Modification (PTM) patterns, present above the DNA strand, forms an important epigenetic marker sequence which regulates gene expression. The specific pattern of histone PTMs in the region of chromatin housing the gene is critical for turning on/off the expression of the corresponding gene. During DNA replication in mitotic cells, the available evidence suggests that the histone PTMs from the mother chromatid are transferred uniformly at random among the two daughter chromatids. Parental epigenetic memory as well as interactions among multiple PTMs at the same histone facilitates the reconstruction of the PTM sequence at the daughter chromatids. We show that this biological marvel aided by the epigenetic memory has evolutionary analogs in the sense that it can be learnt by an appropriate extended neural network. We show through simulations that high fidelity reconstruction of the mother chromatins patterns for certain PTMs can be achieved by our network. This model can be enhanced to include several more interacting histone PTMs, elucidating the role of each. The proposed neural network can possibly be used in a multitude of biological applications related to gene expression regulation.

bioengineering↗

bioRxiv2024

Antagonistic histone post-translational modifications improve the fidelity of epigenetic inheritance - a Bayesian perspective

Histone Post-Translational Modifications (PTMs) are important epigenetic marks regulating gene expression. The specific pattern of histone PTMs present over the gene is critical for turning on/off the expression of the correspond ing gene. During DNA replication in mitotic cells, the histone PTMs are dislodged from the mother chromatid, ahead of the replication fork, and distributed uniformly at random among the daughter chromatids. Building on our previous work which modelled the inheritance of a single PTM, the current work considers the effect of an additional antagonistic PTM. We model the joint PTM sequence by an appropriate Markov model and the DNA replication fork as a noisy communication channel. The optimal Bayesian sequence estimator is then employed at each of the daughter chromatids to reconstruct the mother PTM pattern. A high-fidelity reconstruction, aided by the enzyme machinery, is shown to be possible in the presence of epigenetic memory. The structural properties derived for the optimal estimator are then verified through simulations, which show the improvement in fidelity of inheritance in the presence of antagonism. This is further validated through recent experimental data.

bioengineering↗

bioRxiv2023

Differential Regulation of Excitatory vs Inhibitory Synaptic Release by Complexin/CPX-1 and CAPS/UNC-31 at the C. elegans Neuromuscular Junctions

The excitation and inhibition (E/I) balance at neuromuscular junctions plays a crucial role in coordinating animal motor behavior. Prominent synaptic vesicle secretory regulatory proteins, specifically complexin and CAPS (Calcium-dependent Activator Protein for Secretion), have not garnered sufficient attention for E/I balance regulation. Here, we investigate the roles of complexin/CPX-1 and CAPS/UNC-31 in excitatory vs inhibitory synapses of C. elegans neuromuscular junctions. In our study, cpx-1 null mutants displayed remarkable reduction evoked release in both excitatory and inhibitory synapses. Intriguingly, these mutants exhibited an enhanced level of spontaneous release, particularly within the context of excitatory synapse. This enhancement aligns with its "clamp" role that preventing SV from fusing with the presynaptic membrane. Additionally, a clamping-specific knockin mutant cpx-1({Delta}12), which displayed no alterations in evoked release at either type of synapse, also revealed a biased substantial increase in excitatory spontaneous release. In contrast, unc-31 null mutation, with normal spontaneous release, led to a more pronounced decreased evoked release in the excitatory synapse independent of dense-core vesicle regulation. Intriguingly, we found that the enhanced excitatory spontaneous release observed in cpx-1 mutants was abolished by unc-31 in a Ca2+-dependent manner, implying that UNC-31 is essential for maintaining a high level of spontaneous fusion events. Collectively, our findings unveil a distinct regulatory pattern governing excitatory and inhibitory synaptic release, orchestrated by the interplay of CPX-1 and UNC-31. Notably, we uncover an unforeseen role of UNC-31 in influencing CPX-1s "clamp" function, further adding complexity to the neural dynamics, which may underlie complex behavioral phenotypes observed in C. elegans.

neuroscience↗

bioRxiv2021

The humanized nanobody RBD-1-2G tolerates the spike N501Y mutation to neutralize SARS-CoV-2

Neutralizing antibodies targeting the SARS-CoV-2 spike protein have shown a great preventative/therapeutic potential. Here, we report a rapid and efficient strategy for the development and design of SARS-CoV-2 neutralizing humanized nanobody constructs with sub-nanomolar affinities and nanomolar potencies. CryoEM-based structural analysis of the nanobodies in complex with spike revealed two distinct binding modes. The most potent nanobody, RBD-1-2G(NCATS-BL8125), tolerates the N501Y RBD mutation and remains capable of neutralizing the B.1.1.7 (Alpha) variant. Molecular dynamics simulations provide a structural basis for understanding the neutralization process of nanobodies exclusively focused on the spike-ACE2 interface with and without the N501Y mutation on RBD. A primary human airway air-lung interface (ALI) ex vivo model showed that RBD-1-2G-Fc antibody treatment was effective at reducing viral burden following WA1 and B.1.1.7 SARS-CoV-2 infections. Therefore, this presented strategy will serve as a tool to mitigate the threat of emerging SARS-CoV-2 variants.

synthetic biology↗

bioRxiv2021

High fidelity epigenetic inheritance: Information theoretic model predicts k-threshold filling of histone modifications post replication

Beyond the genetic code, there is another layer of information encoded as chemical modifications on histone proteins positioned along the DNA. Maintaining these modifications is crucial for survival and identity of cells. How the information encoded in the histone marks gets inherited, given that only half the parental nucleosomes are transferred to each daughter chromatin, is a puzzle. Mapping DNA replication and reconstruction of modifications to equivalent problems in communication of information, we ask how well enzymes can recover the parental modifications, if they were ideal computing machines. Studying a parameter regime where realistic enzymes can function, our analysis predicts that, pragmatically, enzymes may implement a threshold - k filling algorithm which fills unmodified regions of length at most k. This algorithm, motivated from communication theory, is derived from the maximum a posteriori probability (MAP) decoding which identifies the most probable modification sequence based on available observations. Simulations using our method produce modification patterns similar to what has been observed in recent experiments. We also show that our results can be naturally extended to explain inheritance of spatially distinct antagonistic modifications.

biophysics↗

bioRxiv2020

Uncovering biomarkers during therapeutic neuromodulation with PARRM: Period-based Artifact Reconstruction and Removal Method

Advances in device development have enabled concurrent stimulation and recording at adjacent locations in the central nervous system. However, stimulation artifacts obscure the sensed underlying neural activity. Here, we developed a novel method, termed Period-based Artifact Reconstruction and Removal Method (PARRM), to remove stimulation artifacts from neural recordings by leveraging the exact period of stimulation to construct and subtract a high-fidelity template of the artifact. Benchtop saline experiments, computational simulations, five unique in vivo paradigms across animal and human studies, and an obscured movement biomarker were used for validation. Performance was found to exceed that of state-of-the-art filters in recovering complex signals without introducing contamination. PARRM has several advantages: it is 1) superior in signal recovery; 2) easily adaptable to several neurostimulation paradigms; and 3) low-complexity for future on-device implementation. Real-time artifact removal via PARRM will enable unbiased exploration and detection of neural biomarkers to enhance efficacy of closed-loop therapies. SummaryOnline, real-time artifact removal via PARRM will enable unbiased exploration of neural biomarkers previously obscured by stimulation artifact.

neuroscience↗

bioRxiv2020

SWITCHES: Searchable Web Interface for Topologies of CHEmical Switches.

Bistable biochemical switches are key motifs in cellular state decisions and long-term storage of cellular memory. There are a few known biological switches that have been well characterized, however these examples are insufficient for systematic surveys of properties of these important systems. Here we present a resource of all possible bistable biochemical reaction networks with up to 6 reactions between 3 molecules, and 3 reactions between 4 molecules. Over 35,000 reaction topologies were constructed by identifying unique combinations of reactions between a fixed number of molecules. Then, these topologies were populated with rates within a biologically realistic range. The Searchable Web Interface for Topologies of CHEmical Switches (SWITCHES, https://switches.ncbs.res.in) provides a bistability and parameter analysis of over 7 million models from this systematic survey of chemical reaction space. This database will be useful for theoreticians interested in analyzing stability in chemical systems and also experimentalists for creating robust synthetic biological switches. Availability and ImplementationFreely available on the web at https://switches.ncbs.res.in. Website implemented in PHP, MariaDB, Graphviz, and Apache, with all major browsers supported. Supplementary InformationNot applicable.

bioinformatics↗

bioRxiv2020

Cell cycle checkpoints cooperate to suppress DNA and RNA associated molecular pattern recognition and anti-tumor immune responses

The DNA dependent pattern recognition receptor, cGAS mediates communication between genotoxic stress and the immune system. Mitotic chromosome missegregation is an established stimulator of cGAS activity, however, it is unclear if progression through mitosis is required for cancer cell intrinsic activation of immune mediated anti-tumor responses. Moreover, it is unknown if disruption of cell cycle checkpoints can restore responses in cancer cells that are recalcitrant to DNA damage induced inflammation. Here we demonstrate that prolonged cell cycle arrest at the G2-mitosis boundary from either CDK1 inhibition or excessive DNA damage prevents inflammatory stimulated gene expression and immune mediated destruction of distal tumors. Remarkably, DNA damage induced inflammatory signaling is restored in a cGAS-and RIG-I-dependent manner upon concomitant disruption of p53 and the G2 checkpoint. These findings link aberrant cell progression and p53 loss to an expanded spectrum of damage associated molecular pattern recognition and have implications for the design of rational approaches to augment antitumor immune responses.

cell biology↗

bioRxiv2020

ProtTox: Toxin identification from Protein Sequences

Toxin classification of protein sequences is a challenging task with real world applications in healthcare and synthetic biology. Due to an ever expanding database of proteins and the inordinate cost of manual annotation, automated machine learning based approaches are crucial. Approaches need to overcome challenges of homology, multi-functionality, and structural diversity among proteins in this task. We propose a novel deep learning based method ProtTox, that aims to address some of the shortcomings of previous approaches in classifying proteins as toxins or not. Our method achieves a performance of 0.812 F1-score which is about 5% higher than the closest performing baseline.

bioinformatics↗