Biology subjects

Varshney, L. R.

Publications and source records attributed to Varshney, L. R..

5 recordsLinked to original sources

bioRxiv2026

Comparative connectomics of parasite esophagus suggests evolutionary simplification of a nervous system

Several prominent examples suggest that the evolution of parasitism is accompanied by nervous system simplification. However, it is unclear if this is a generalizable rule and whether parasite- associated simplification occurs at the level of synaptic connectivity. The nematode esophagus is a specialized neuromuscular feeding organ that varies with diet and lifestyle. Plant-parasitic nematodes are major agricultural pests that feed through a protrusible stylet and release of extensive glandular effectors; however, the neuronal mechanisms controlling parasite feeding are unclear. Here, we used serial-section electron microscopy to reconstruct the esophageal connectome of the infective second-stage juvenile of the soybean cyst nematode, Heterodera glycines, and compared it with those of the free-living species Caenorhabditis elegans and Pristionchus pacificus. Similar to these species, H. glycines has 20 esophageal neurons with relatively conserved cell body positions. Despite this conservation, the H. glycines chemical synaptic network is highly reduced in output to musculature. A unique ensheathment of neurons by a gland cell facilitates novel synaptic connectivity in H. glycines. The H. glycines esophageal network is strongly biased toward monadic synapses and shows a greater proportion of neuron- neuron and neuron-gland connections. Consistent with a reduction in motor output, network analysis indicates that the H. glycines esophageal network is smaller and less clustered than free-living species. Using centrality analysis and synthetic ablation, we predict that control of multiple feeding modules in H. glycines depends on distinct neurons compared to free-living species. These findings show how parasitism reshapes a feeding circuit and identify candidate species-specific circuits for parasite control. Significance statementAnimal nervous systems are tuned to the behaviors they support. Nematodes occupy diverse ecological niches, and their feeding organ, the esophagus, is specialized to their diet. Understanding the esophageal nervous system provides evolutionary insights into parasitism as well as pathways for future control targets. We present the esophageal connectome of the soybean cyst nematode Heterodera glycines, which causes devastating damage to soybean production worldwide. By comparing H. glycines and free-living species, which diverged over 350 million years ago, we identify evolutionarily conserved features of the feeding circuit and highlight parasite-specific circuits as candidate targets for control.

neuroscience↗

bioRxiv2023

Human Intelligence and the Connectome are Driven by Structural Brain Network Control

Research in network neuroscience demonstrates that human intelligence is shaped by the structural brain connectome, which enables a globally coordinated and dynamic architecture for general intelligence. Building on this perspective, the network neuroscience theory proposes that intelligence arises from system-wide network dynamics and the capacity to flexibly transition between network states. According to this view, network flexibility is made possible by network controllers that move the system into specific network states, enabling solutions to familiar problems by accessing nearby, easy-to-reach network states and adapting to novel situations by engaging distant, difficult-to-reach network states. Although this framework predicts that general intelligence depends on network controllability, the specific cortical regions that serve as network controllers and the nature of their control operations remain to be established. We therefore conducted a comprehensive investigation of the relationship between regional measures of network controllability and general intelligence within a sample of 275 healthy young adults using structural and diffusion-weighted MRI data. Our findings revealed significant associations between intelligence and network controllers located within the frontal, temporal and parietal cortex. Furthermore, we discovered that these controllers collectively enable access to both easy- and difficult-to-reach network states, aligning with the predictions made by the network neuroscience framework. Additionally, our research demonstrated that the identified network controllers are primarily localized within the left hemisphere and do not reside within regions or connections that possess the highest capacity for structural control in general. This discovery suggests that the identified regions may facilitate specialized control operations and motivates further exploration of the network topology and dynamics underlying intelligence in the human brain. SummaryThis study examines the relationship between regional measures of network controllability and general intelligence within a sample of 275 healthy young adults using structural and diffusion-weighted MRI data. We report that individual differences in general intelligence are associated average and modal controllability in specific left-hemisphere cortical regions, and further show that controller regions associated with intelligence are distinct from regions with the highest, centrality, controllability, or communication. These findings reveal a significant structural role for individual regions in controlling the trajectory of the connectome, advancing our understanding of the nature and mechanisms of network controllability in general intelligence.

neuroscience↗

bioRxiv2020

BERTology Meets Biology: Interpreting Attention in Protein Language Models

Transformer architectures have proven to learn useful representations for protein classification and generation tasks. However, these representations present challenges in interpretability. Through the lens of attention, we analyze the inner workings of the Transformer and explore how the model discerns structural and functional properties of proteins. We show that attention (1) captures the folding structure of proteins, connecting amino acids that are far apart in the underlying sequence, but spatially close in the three-dimensional structure, (2) targets binding sites, a key functional component of proteins, and (3) focuses on progressively more complex biophysical properties with increasing layer depth. We also present a three-dimensional visualization of the interaction between attention and protein structure. Our findings align with known biological processes and provide a tool to aid discovery in protein engineering and synthetic biology. The code for visualization and analysis is available at https://github.com/salesforce/provis.

bioinformatics↗

bioRxiv2019

Optimal recovery of missing values for non-negative matrix factorization

We extend the approximation-theoretic technique of optimal recovery to the setting of imputing missing values in clustered data, specifically for non-negative matrix factorization (NMF), and develop an implementable algorithm. Under certain geometric conditions, we prove tight upper bounds on NMF relative error, which is the first bound of this type for missing values. We also give probabilistic bounds for the same geometric assumptions. Experiments on image data and biological data show that this theoretically-grounded technique performs as well as or better than other imputation techniques that account for local structure.

bioinformatics↗

bioRxiv2019

Registration for Image-Based Transcriptomics: Parametric Signal Features and Multivariate Information Measures

Image-based transcriptomics involves determining spatial patterns in gene expression across cells and tissues. Image registration is a necessary component of data analysis pipelines that study gene expression levels across different cells and intracellular structures. We consider images from multiplexed single molecule fluorescent in situ hybridization (smFISH) and multiplexed in situ sequencing (ISS) datasets from the Human Cell Atlas project and demonstrate a novel approach to groupwise image registration using a parametric representation of images based on finite rate of innovation sampling, together with practical optimization of empirical multivariate information measures.

bioinformatics↗