Biology subjects

Srinivasan, K.

Publications and source records attributed to Srinivasan, K..

At least 19 recordsLinked to original sources

bioRxiv2026

Distinct activation mechanisms underlie ligand efficacy at a GPCR

Ligands that bind to the same G protein-coupled receptor (GPCR) can produce widely different levels of signaling. These differences in efficacy are central to pharmacology, yet how a receptor sequence determines signaling level remains incompletely understood. Here we develop a deep mutational pharmacology approach to measure how each residue in the -opioid receptor (OR) shapes responses to 15 opioids, yielding ~400,000 measurements of variant effects on signaling and expression, including full concentration-response series for three agonists. Mutational effects on potency are strongest near the orthosteric pocket, whereas effects on efficacy extend through the transmembrane core and intracellular face. Ligands share many functional determinants, but sensitivity to mutation differs with efficacy. As efficacy decreases, the residues required for signaling shift from the binding pocket towards the intracellular face; partial agonists therefore depend on residues that are dispensable for full agonists. Mutations can broadly increase receptor responses, allowing antagonists such as naloxone to produce signaling comparable to a full agonist and increasing the efficacy of partial agonist ligands. Combined mutations can also reorder the relative efficacies of ligands, showing that sequence changes reshape pharmacological differences rather than simply increasing or decreasing all responses together. These findings support a model in which ligands of different efficacy activate the receptor through distinct allosteric mechanisms which are determined by residues distributed across the receptor. Mapping these contributions across diverse ligands with deep mutational pharmacology provides a way to understand how one receptor converts molecular recognition into a range of signaling outputs.

biophysics↗

bioRxiv2026

Programming Targeted Immune Cell Migration Using Orthogonal Chemokine-Receptor Pairs

Chemokines orchestrate the migration of immune cells throughout the body, but harnessing chemokine signaling to engineer targeted cell trafficking is challenging because of their overlapping interactions and broad expression patterns. Here we used structure-guided protein design to engineer orthogonal chemokine-receptor pairs with minimal crosstalk to native systems. T cells engineered with an ortho-receptor selectively infiltrated tissues producing the ortho-chemokine, in vitro and in vivo. By engineering T cells that both sense and secrete the ortho-chemokine, we generated collective swarming behavior in which pioneer cells attract follower cells via an ortho-chemokine gradient self-organized by positive feedback. By providing orthogonal genetic control over both the generation and sensing of chemokine gradients, this system enables the construction of autonomous spatial targeting programs in engineered cells.

synthetic biology↗

bioRxiv2026

A Closed-Loop Robot Scientist for Autonomous Biological Discovery

Biological systems respond to a wide range of physical inputs and an overarching goal of biological research is understanding how these signaling events coordinate cellular, tissue, and organism-level outcomes. Yet the vast combinatorial space of physical and chemical interventions that influence these processes remains largely unexplored. Thus, a search process is required that efficiently learns how inputs affect a biological target via a series of automatically generated interventions, and a robot scientist that can conduct them. To this end we here introduce the Multimodal Organismal Modulation Robot (MOMbot), a robot scientist that integrates four physical intervention modalities - chemical delivery, electrical field application, mechanical vibration, and thermal modulation -within a single hardware platform. MOMbots multimodal hardware is paired with high-resolution imaging and an online active learning algorithm that autonomously generates, executes, and refines interventions based on accumulated data. We validate the system across biological materials spanning three orders of magnitude in scale, including Xenopus laevis embryos, motile mucociliary organoids, and disaggregated ectodermal stem cells, to show the utility of this approach across diverse biological disciplines. Using these biological models, we demonstrate precise thermal control of embryonic developmental rate, automated chemical perturbation with behavioral readouts, programmable electrical field stimulation, and vibrational assembly of functional mucociliary organoids from loose cells. We also describe the active learning algorithm that autonomously concentrates interventions near regions of maximal outcome uncertainty, yielding sample-efficient discovery of an electrical duration-response relationship for motile organoids. These results establish MOMbot as a scalable robot/AI scientist team for biological discovery, enabling autonomous, multimodal physical experimentation and AI-driven exploration of complex biological systems.

developmental biology↗

bioRxiv2026

Dynamic inositol pyrophosphate synthesis is a targetable therapeutic opportunity in ovarian cancer.

We previously reported that the phosphate exporter XPR1 is required to prevent toxic phosphate accumulation in ovarian cancer cells. To guide therapeutic development, we sought to systematically compare potential strategies to inhibit XPR1: directly targeting the phosphate efflux channel, targeting its partner protein KIDINS220, or inhibiting the synthesis of inositol pyrophosphates (PP-InsPs), metabolites which activate XPR1. We evaluated functional domains in XPR1 and KIDINS220 using mutational scanning and found that loss of function mutations in XPR1 clustered in distinct regions throughout the protein, with the most deleterious mutations in the PP-InsP-binding domain. In contrast, loss of function mutations in KIDINS220 were infrequent and altered the localization of XPR1, consistent with a scaffolding role for KIDINS220. These data highlight the functional relevance of PP-InsPs, which we confirmed by inhibiting their synthesis using IP6K inhibitors. We demonstrate that IP6K inhibition phenocopies XPR1 inhibition across hundreds of cancer cell lines, with the mechanism of sensitivity solely due to inhibition of cellular phosphate efflux. Finally, we show that IP6K inhibitors decrease tumor burden in xenograft models of ovarian cancer, but that the rapid resynthesis of PP-InsPs requires high exposures to achieve efficacy. This study comprehensively evaluates the XPR1-dependent phosphate efflux network and reinforces the concept of directly targeting XPR1 as a precision medicine strategy to benefit patients with ovarian cancer.

cancer biology↗

bioRxiv2026

Structural investigation suggests Elongation Factor-G1 is the bona fide canonical translation factor for ribosome recycling in mycobacteria

Bacterial elongation factor G (EF-G) facilitates mRNA and tRNA translocation during peptide elongation as well as promotes splitting of ribosomal subunits during ribosome recycling in coordination with ribosome recycling factor (RRF). Two homologs of EF-G (EF-G1 and EF-G2) have been identified in some bacterial species, including mycobacteria and in mitochondria. We have previously reported that mycobacterial EF-G2 functions as a stress-specific factor. Here, we demonstrate that EF-G1 functions as the canonical elongation factor responsible for ribosome recycling in mycobacteria. In addition, our structural analyses explain why mycobacterial EF-G2 lacks recycling activity, in contrast to its mitochondrial counterpart. We report cryo-EM structures of the M. smegmatis 70S ribosome and 50S subunit in complex with RRF and/or EF-G1 at 3-4 [A] resolution, elucidating the molecular architecture of intermediate structures and mechanistic basis of ribosome recycling. Notably, GTP hydrolysis on EF-G1 is not required for ribosomal disassembly in mycobacteria, unlike in E. coli.

biochemistry↗

bioRxiv2026

Toward a Random Background for Ligand Optimization

Ligand optimization is central to drug discovery as hundreds of analogs might be designed and synthesized between an initial hit and a therapeutic candidate. The efficiency of this process is unclear, at least partly because there is no random background for optimization against which to compare. Such a random background might emerge from synthetically accessible but otherwise systematic random small substitutions across starting ligands, measuring likelihood of achieving a substantial improvement in affinity/potency or other property by any single perturbation. Recent literature and ligand-affinity/potency databases suggest that perhaps 10% of analogs with minor modifications improve upon a parents potency substantially (by [≥]10-fold), but this number is clouded by reporting bias, intentional improvement, and inter-group reproducibility. To begin to establish a background expectation for ligand optimization, we comprehensively and systematically modified 18 lead molecules across six targets with single atom changes; 257 compounds were synthesized. Unexpectedly, 11.2% of these random small perturbation analogs improved potency by [≥]10-fold over their parents. Conversely, these more potent analogs typically had worse in vitro pharmacokinetics (e.g. reduced metabolic stability, lower plasma free fraction). While it was possible to find analogs where the potency increase compensated for inferior exposure and half-life, resulting in more potent compounds in vivo, overall a frustrated landscape for ligand optimization is revealed. This study begins to establish a background expectation for ligand potency optimization and offers a simple strategy to do so. It also begins to quantify the challenges confronting the field in moving beyond in vitro potency.

pharmacology and toxicology↗

bioRxiv2026

Epipelagic to mesopelagic variability of acoustic backscatter in the California Current

Mid-Trophic Level (MTL) organisms--including krill, forage fish, and mesopelagic fish-- are abundant in the California Current System (CCS) and play an essential role in transferring energy and biomass from primary producers to top predators. However, their spatiotemporal distribution and variability remain poorly understood, particularly with respect to vertical structure across epipelagic and mesopelagic habitats and coastal-offshore gradients. This lack of understanding emerges from both the complexity of MTL interactions with a heterogeneous environment and the challenges associated with sampling these organisms at high spatial and temporal resolution. To address this gap, we analyze 11 years of fisheries acoustic observations in the CCS (2006-2016) to characterize the spatiotemporal dynamics of MTLs as inferred from acoustic backscatter. Acoustic observations at 38 and 120 kHz, collected during day and night across depth strata from 15 to 495 m, reveal consistent cross-shore, seasonal, and latitudinal patterns in the backscatter of acoustically defined zooplankton, epipelagic fish, and mesopelagic fish communities. These patterns include: (1) weaker cross-shore gradients in mesopelagic relative to epipelagic communities; (2) a temporal succession among communities associated with seasonal upwelling; and (3) a multimodal latitudinal distribution with distinct coastal backscatter peaks. We further investigate relationships between acoustic backscatter and co-located environmental variables from in situ, remote sensing, and reanalysis products to elucidate plausible mechanisms underlying MTL dynamics. HighlightsO_LIFisheries acoustics resolve variability in mid-trophic communities C_LIO_LIEleven years of backscatter reveal consistent patterns in the California Current C_LIO_LIEpipelagic backscatter declines faster from the coast to offshore than mesopelagic C_LIO_LISeasonal changes in community composition are linked to upwelling dynamics C_LIO_LIBackscatter exhibits multimodal latitudinal distributions with distinct peaks C_LI

ecology↗

bioRxiv2025

Niche dimensionality drives microbial community structure

Niche dimensionality links environmental complexity to ecosystem structure. Although niche theory is often invoked in investigations of microbiomes, most models assume very high-dimensional coexistence, effectively sidestepping the role of dimensionality. However, direct estimates of niche dimensionality of microbiome have been lacking. Here we use joint species distribution modeling (JSDM) to infer niche dimensionality from relative abundance data alone. In paired 16S rRNA-metabolomics datasets, inferred dimensionality closely tracked the complexity of the metabolic environment, validating our abundance-only approach. Across nearly 200 human gut microbiome studies, lower dimensionality coincided with greater inter-species competition (metabolic niche overlap) and reduced biodiversity, consistent with the macroecological niche dimensionality hypothesis. Consumer/resource simulations reproduced these empirical relationships when both species-intrinsic metabolic tradeoffs and species-extrinsic environmental tradeoffs were imposed, with the latter dominating. Dimensionality was reduced in stressed or diet-simplified microbiomes and correlated broadly with ecological stress markers; negatively with prevalence of generalists and positively with microbial load. Together, these results establish niche dimensionality as a measurable and previously overlooked driver of microbial community structure.

ecology↗

bioRxiv2024

Mycobacterial methionine aminopeptidase type 1c moonlights as an anti-association factor on the 30S ribosomal subunit

Methionine aminopeptidase (MetAP), an essential metalloprotease, binds the 70S ribosome near the exit site of peptide tunnel and cleaves the N-terminal methionine from the nascent polypeptide chains. While E. coli expresses a single subclass of type1 MetAP, mycobacteria possesses two subclasses, namely, MetAP1a and MetAP1c, differentiated by the presence of a 40 amino acid long N-terminal tether in the later. Here we reveal a yet unknown moonlighting role of MetAP1c in mycobacteria. We observe stationary phase-specific enhancement of expression and a unique 30S subunit affinity of MetAP1c. A 5[A] cryo-EM map of Mycobacterium smegmatis (Msm) MetAP1c-30S subunit complex manifests for the first time the binding of MetAP1c at the inter-subunit side in the vicinity of proteins S13 and S19. MetAP1c binding and resulting conformational changes make the 30S subunit unfit for association with the 50S subunit, thereby conferring an anti-association property on MetAP1c. We further constructed two N-terminal mutants to establish the pivotal role of the N-terminal extension of MetAP1c in its moonlighting function.

biochemistry↗

bioRxiv2024

Transfer learning predicts species-specific drug interactions in emerging pathogens

Machine learning (ML) algorithms are necessary to efficiently identify potent drug combinations within a large candidate space to combat drug resistance. However, existing ML approaches cannot be applied to emerging and under-studied pathogens with limited training data. To address this, we developed a transfer learning and crowdsourcing framework (TACTIC) to train ML models on data from multiple bacteria. TACTIC was built using 2,965 drug interactions from 12 bacterial strains and outperformed traditional ML models in predicting drug interaction outcomes for species that lack training data. Top TACTIC model features revealed genetic and metabolic factors that influence cross- species and species-specific drug interaction outcomes. Upon analyzing [~]600,000 predicted drug interactions across 9 metabolic environments and 18 bacterial strains, we identified a small set of drug interactions that are selectively synergistic against Gram- negative (e.g., A. baumannii) and non-tuberculous mycobacteria (NTM) pathogens. We experimentally validated synergistic drug combinations containing clarithromycin, ampicillin, and mecillinam against M. abscessus, an emerging pathogen with growing levels of antibiotic resistance. Lastly, we leveraged TACTIC to propose selectively synergistic drug combinations to treat bacterial eye infections (endophthalmitis).

systems biology↗

bioRxiv2024

Revealing non-trivial information structures in aneural biological tissues via functional connectivity

A central challenge in the progression of a variety of open questions in biology, such as morphogenesis, wound healing, and development, is learning from empirical data how information is integrated to support tissue-level function and behavior. Information-theoretic approaches provide a quantitative framework for extracting patterns from data, but so far have been predominantly applied to neuronal systems at the tissue-level. Here, we demonstrate how time series of Ca2+ dynamics can be used to identify the structure and information dynamics of other biological tissues. To this end, we expressed the calcium reporter GCaMP6s in an organoid system of explanted amphibian epidermis derived from the African clawed frog Xenopus laevis, and imaged calcium activity pre- and post- a puncture injury, for six replicate organoids. We constructed functional connectivity networks by computing mutual information between cells from time series derived using medical imaging techniques to track intracellular Ca2+. We analyzed network properties including degree distribution, spatial embedding, and modular structure. We find organoid networks exhibit more connectivity than null models, with high degree hubs and mesoscale community structure with spatial clustering. Utilizing functional connectivity networks, we show the tissue retains non-random features after injury, displays long range correlations and structure, and non-trivial clustering that is not necessarily spatially dependent. Our results suggest increased integration after injury, possible cellular coordination in response to injury, and some type of generative structure of the anatomy. While we study Ca2+ in Xenopus epidermal cells, our computational approach and analyses highlight how methods developed to analyze functional connectivity in neuronal tissues can be generalized to any tissue and fluorescent signal type. Our framework therefore provides a bridge between neuroscience and more basal modes of information processing. Author summaryA central challenge in understanding several diverse processes in biology, including morphogenesis, wound healing, and development, is learning from empirical data how information is integrated to support tissue-level function and behavior. Significant progress in understanding information integration has occurred in neuroscience via the use of observable live calcium reporters throughout neural tissues. However, these same techniques have seen limited use in the non-neural tissues of multicellular organisms despite similarities in tissue communication. Here we utilize methods designed for neural tissues and modify them to work on any tissue type, demonstrating how non-neural tissues also contain non-random and potentially meaningful structures to be gleaned from information theoretic approaches. In the case of epidermal tissue derived from developing amphibians, we find non-trivial informational structure over greater spatial and temporal scales than those found in neural tissue. This hints at how more exploration into information structures within these tissue types could provide a deeper understanding into information processing within living systems beyond the nervous system.

systems biology↗

bioRxiv2024

The recovery of parabolic avalanches in spatially subsampled neuronal networks at criticality

Scaling relationships are key in characterizing complex systems at criticality. In the brain, they are evident in neuronal avalanches--scale-invariant cascades of neuronal activity quantified by power laws. Avalanches manifest at the cellular level as cascades of neuronal groups that fire action potentials simultaneously. Such spatiotemporal synchronization is vital to theories on brain function yet avalanche synchronization is often underestimated when only a fraction of neurons is observed. Here, we investigate biases from fractional sampling within a balanced network of excitatory and inhibitory neurons with all-to-all connectivity and critical branching process dynamics. We focus on how mean avalanche size scales with avalanche duration. For parabolic avalanches, this scaling is quadratic, quantified by the scaling exponent,{chi} = 2, reflecting rapid spatial expansion of simultaneous neuronal firing over short durations. However, in networks sampled fractionally,{chi} is significantly lower. We demonstrate that applying temporal coarse-graining and increasing a minimum threshold for coincident firing restores{chi} = 2, even when as few as 0.1% of neurons are sampled. This correction crucially depends on the network being critical and fails for near sub- and supercritical branching dynamics. Using cellular 2-photon imaging, our approach robustly identifies{chi} = 2 over a wide parameter regime in ongoing neuronal activity from frontal cortex of awake mice. In contrast, the common crackling noise approach fails to determine{chi} under similar sampling conditions at criticality. Our findings overcome scaling bias from fractional sampling and demonstrate rapid, spatiotemporal synchronization of neuronal assemblies consistent with scale-invariant, parabolic avalanches at criticality.

neuroscience↗

bioRxiv2023

Stimulus representations in visual cortex shaped by spatial attention and microsaccades

SUMMARY/ABSTRACTMicrosaccades (MSs) are commonly associated with spatially directed attention, but how they affect visual processing is still not clear. We studied MSs in a task in which the animal was randomly cued to attend to a target stimulus and ignore distractors, and it was rewarded for detecting a color change in the target. We found that the enhancement of firing rates normally found with attention to a cued stimulus was delayed until the first MS directed towards that stimulus. Once that MS occurred, attention to the target was engaged and there were persistent effects of attention on firing rates for the remainder of the trial. These effects were found in the superficial and deep layers of V4 as well as the lateral pulvinar and IT cortex. Although the tuning curves of V4 cells do not change depending on the locus of spatial attention, we found pronounced effects of MS direction on stimulus representations that persisted for the length of the trial in V4. In intervals following a MS towards the target in the RF, stimulus decoding from population activity was substantially better than in intervals following a MS away from the target. Likewise, turning curves of cells were substantially sharper following a MS towards the target in the RF. This sharpening appeared to result from both a "refreshing" of the initial transient sensory response to stimulus onset, and a magnification of the effects of attention in this condition. MSs to the target also enhanced the neuronal response to the behaviorally relevant target color change and led to faster reaction times. These results thus reveal a major link between spatial attention, object processing and its coordination with eye movements.

neuroscience↗

bioRxiv2022

Parabolic avalanche scaling in the synchronization of cortical cell assemblies

Neurons in cortex synchronize their spiking in response to local and distant inputs. These synchronized assemblies are fundamental to cortex function, yet basic dynamical aspects about their size and duration are largely unknown. Using 2-photon imaging of neurons in superficial cortex of awake mice, we show that synchronized assemblies organize as scale-invariant avalanches that quadratically grow with duration. This quadratic expansion was found only for correlated neurons and required temporal coarse graining to compensate for spatial subsampling when network dynamics are critical, as demonstrated in simulations. The corresponding time course of an inverted parabola with exponent of {chi} = 2 described avalanches of up to 5 s duration and maximized temporal complexity in the ongoing activity of prefrontal and somatosensory cortex and in visual responses of primary visual cortex. Our results identify a scale-invariant order in the synchronization of highly diverse cortical cell assemblies in the form of parabolic avalanches. Significance StatementThe synchronization of cell assemblies is fundamental to many brain theories. Here we show such synchronization to grow according to an inverted parabola that maximizes temporal complexity. This quadratic scaling found for cell assemblies of highly diverse size and duration is in line with prediction for neuronal avalanches and the cortex being in a critical state.

neuroscience↗

bioRxiv2020

Probing the Plasticity in the Active Site of Protein N-terminal Methyltransferase 1 Using Bisubstrate Analogs

The interactions of a series of bisubstrate analogs with protein N-terminal methyltransferase 1 (NTMT1) were examined to probe the molecular properties of the NTMT1 active site through biochemical characterization and structural studies. Our results indicate that a 2-C to 4-C atom linker enables its respective bisubstrate analog to occupy both substrate and cofactor binding sites of NTMT1, but the bisubstrate analog with a 5-C atom linker only interacts with the substrate binding site and functions as a substrate. Furthermore, the 4-C atom linker is the optimal and produces the most potent inhibitor (Ki, app = 130 {+/-} 40 pM) for NTMT1 to date, displaying over 100,000-fold selectivity over other methyltransferases and 3,000-fold even to its homolog NTMT2. This study reveals the molecular basis for the plasticity of the NTMT1 active site. Additionally, our study outlines a general guidance on the development of bisubstrate inhibitors for any methyltransferases.

biochemistry↗

bioRxiv2019

Mechanisms of tissue-specific genetic regulation revealed by latent factors across eQTLs

BackgroundGenetic regulation of gene expression, revealed by expression quantitative trait loci (eQTLs), varies across tissues in complex patterns ranging from highly tissue-specific effects to effects shared across many or all tissues. Improved characterization of these patterns may allow us to better understand the biological mechanisms that underlie tissue-specific gene regulation and disease etiology.\n\nResultsWe develop a constrained matrix factorization model to learn patterns of tissue sharing and tissue specificity of eQTLs across 49 human tissues from the Genotype-Tissue Expression (GTEx) project. The learned factors include patterns reflecting tissues with known biological similarity or shared cell types, in addition to a dense factor representing a universal genetic effect across all tissues. To explore the regulatory mechanisms that generate tissue-specific patterns of expression, we evaluate chromatin state enrichment and identify specific transcription factors with binding sites enriched for eQTLs from each factor.\n\nConclusionsOur results demonstrate that matrix factorization can be applied to learn the tissue specificity pattern of eQTLs and that it exhibits better biological interpretability than heuristic methods. We present a framework to characterize the tissue specificity of eQTLs, and we identify examples of tissue-specific eQTLs that may be driven by tissue-specific transcription factor (TF) binding, with relevance to interpretation of disease association.

genomics↗

bioRxiv2019

Alzheimer’s patient brain myeloid cells exhibit enhanced aging and unique transcriptional activation

Gene expression changes in brain microglia from mouse models of Alzheimers disease (AD) are highly characterized and reflect specific myeloid cell activation states that could modulate AD risk or progression. While some groups have produced valuable expression profiles for human brain cells1-4, the cellular clarity with which we now view transcriptional responses in mouse AD models has not yet been realized for human AD tissues due to limited availability of fresh tissue samples and technological hurdles of recovering transcriptomic data with cell-type resolution from frozen samples. We developed a novel method for isolating multiple cell types from frozen post-mortem specimens of superior frontal gyrus for RNA-Seq and identified 66 genes differentially expressed between AD and control subjects in the myeloid cell compartment. Myeloid cells sorted from fusiform gyrus of the same subjects showed similar changes, and whole tissue RNA analyses further corroborated our findings. The changes we observed did not resemble the \"damage-associated microglia\" (DAM) profile described in mouse AD models5, or other known activation states from other disease models. Instead, roughly half of the changes were consistent with an \"enhanced human aging\" phenotype, whereas the other half, including the AD risk gene APOE, were altered in AD myeloid cells but not differentially expressed with age. We refer to this novel profile in human Alzheimers microglia/myeloid cells as the HAM signature. These results, which can be browsed at research-pub.gene.com/BrainMyeloidLandscape/reviewVersion, highlight considerable differences between myeloid activation in mouse models and human disease, and provide a genome-wide picture of brain myeloid activation in human AD.

neuroscience↗

bioRxiv2018

Prediction of Algal Blooms in the Great Lakes through a Convolution Neural Network of Remote Sensing Data

Harmful algal blooms (HABs) are the proliferation of algae due to eutrophication and have severe repercussions to the ecological balance in many water bodies, due to the toxins the algae produce. Additionally, the identification and prediction of these HABs has been a challenge in the scientific community due to the interactions between both biological and physical processes that cause the HABs. Here, we used remote sensing data to bypass these issues; remote sensing data provides significant information about the coverage of chlorophyll which can be used to locate HABs. Using this indicator of HABs, we trained a Convolution Neural Network (CNN) to identify nine types of algal blooms, using 25 epochs of 900 images, which can predict algal bloom shapes with an 80 percent accuracy. This approach of HAB identification can easily be applied to other aquatic ecosystems where remote sensing data is present.

ecology↗