Biology subjects

Pandi, A.

Publications and source records attributed to Pandi, A..

11 recordsLinked to original sources

bioRxiv2026

Serotype-independent inhibition of S. pneumoniae by SMiTE, a commensal-derived bacteriocin

Streptococcus pneumoniae remains a leading cause of disease despite widespread vaccination, highlighting the need for serotype-independent strategies. We recently identified commensal streptococci that produce bacteriocins with anti-pneumococcal activity. Here, we evaluate these bacteriocins as candidates for pneumococcal control. Using cell-free protein synthesis, we screened 58 bacteriocins, the majority of which absent in available pneumococcal genomes, and identified SMiTE as the most potent. Purified SMiTE disrupted pneumococcal membrane integrity, as shown by confocal, transmission, and scanning electron microscopy, induced ATP leakage, and triggered transcriptional responses consistent with envelope stress and metabolic remodeling. In a mouse nasopharyngeal colonization model, intranasal SMiTE treatments reduced pneumococcal loads by 65-fold with no significant weight loss. SMiTE inhibited a broad range of serotypes, with strongest activity against serotype 3, which is poorly controlled by current vaccines, while sparing most oral and upper respiratory tract commensals. Repeated sub-inhibitory exposure did not select resistant mutants in line with the membrane-targeting mechanism. These findings establish SMiTE as a commensal-derived strategy for serotype-independent, microbiota-sparing pneumococcal decolonization.

microbiology↗

bioRxiv2025

Generalizable prediction of liquid-liquid phase separation from protein sequence

Liquid-liquid phase separation (LLPS) is emerging as a fundamental process supporting multiple facets of biological systems. This phenomenon enables the dynamic compartmentalization of biomolecules contributing to a wide range of cellular functions, though in many instances its precise role and evolution remain unclear. Protein phase separation naturally occurs within cells and is prevalent across all species. Despite a recent surge in protein LLPS discovery, current predictive models lack generalizability and fail to identify the full spectrum of phase-separating proteins. To address this shortcoming, we developed Phaseek, a hybrid model integrating contextual sequence encoding with statistical graph representations to score LLPS propensity of amino acid sequences. Phaseek accurately identifies phase-separating proteins across diverse biological contexts, predicting key functional regions and the effects of point mutations. Proteome-wide predictions for 18 species highlight important physicochemical features. Gene Ontology enrichments recapitulate known processes (e.g., nucleic acid binding, nuclear localization, chromatin organization) and suggest novel areas of investigation. Phylogenetic analysis of orthologs further suggests that LLPS is evolutionarily conserved beyond sequence similarity. In addition, we used Phaseek to design de novo phase-separating peptides and achieved a 70% success rate in vivo. Provided with a user-friendly implementation, Phaseek serves as a multipurpose LLPS predictor for advancing both fundamental and applied LLPS research.

molecular biology↗

bioRxiv2024

qByte: Open-source isothermal fluorimeter for democratizing analysis of nucleic acids, proteins and cells

Access to affordable and reliable scientific instrumentation remains a significant barrier to the democratization of healthcare and scientific research. In the field of biotechnology, in particular, the complexity, cost, and infrastructure requirements of many instruments continue to limit their accessibility, especially in resource-limited environments. Despite the recent increase in the development of open-source tools, driven by advances in digital fabrication and electronic prototyping, few of these projects have reached large-scale implementation or validation in real-world settings. Here, we present qByte, an open-source, 8-tube isothermal fluorimeter designed to overcome these barriers by offering a cost-effective ($60) yet production-ready solution. qByte leverages standard digital manufacturing and Printed Circuit Board (PCB) assembly techniques and is designed to be portable, making it ideal for both laboratory and field use. The device has been benchmarked against commercial real-time thermocyclers and spectrophotometers, showing comparable results across four key applications: nucleic acid amplification and detection, analysis of protein activity and stability, genetic construct characterization, and bacterial viability tests. Further testing including the on-site diagnosis of human parasites in Kumasi, Ghana, and field work in Patagonia, Chile, validated qBytes reliability in real-world conditions. Taken together, our results proved qByte as flexible and reliable equipment for a variety of biological tests and applications, while its affordability and open-source design simplify further development and allow adaptation to the needs of future users.

bioengineering↗

bioRxiv2024

CodonTransformer: a multispecies codon optimizer using context-aware neural networks

The genetic code is degenerate allowing a multitude of possible DNA sequences to encode the same protein. This degeneracy impacts the efficiency of heterologous protein production due to the codon usage preferences of each organism. The process of tailoring organism-specific synonymous codons, known as codon optimization, must respect local sequence patterns that go beyond global codon preferences. As a result, the search space faces a combinatorial explosion that makes exhaustive exploration impossible. Nevertheless, throughout the diverse life on Earth, natural selection has already optimized the sequences, thereby providing a rich source of data allowing machine learning algorithms to explore the underlying rules. Here, we introduce CodonTransformer, a multispecies deep learning model trained on over 1 million DNA-protein pairs from 164 organisms spanning all kingdoms of life. The model demonstrates context-awareness thanks to the attention mechanism and bidirectionality of the Transformers we used, and to a novel sequence representation that combines organism, amino acid, and codon encodings. CodonTransformer generates host-specific DNA sequences with natural-like codon distribution profiles and with negative cis-regulatory elements. This work introduces a novel strategy of Shared Token Representation and Encoding with Aligned Multi-masking (STREAM) and provides a state-of-the-art codon optimization framework with a customizable open-access model and a user-friendly interface.

synthetic biology↗

bioRxiv2024

Phage-mediated intercellular CRISPRi for biocomputation in bacterial consortia

Coordinated actions of cells in microbial communities and multicellular organisms enable them to perform complex tasks otherwise difficult for single cells. This has inspired biological engineers to build cellular consortia for larger circuits with improved functionalities, while implementing communication systems for coordination among cells. Here, we investigate the signalling dynamics of a phage-mediated synthetic DNA messaging system, and couple it with CRISPR interference to build distributed circuits that perform logic gate operations in multicellular bacterial consortia. We find that growth phases of both sender and receiver cells, as well as resource competition between them, shape communication outcomes. Leveraging the easy programmability of DNA messages, we build 8 orthogonal signals and demonstrate that intercellular CRISPRi (i-CRISPRi) regulates gene expression across cells. Finally, we multiplex the i-CRISPRi system to implement several multicellular logic gates that involve up to 7 cells and take up to 3 inputs simultaneously, with single- and dual-rail encoding: NOT, YES, AND, and AND-AND-NOT. The communication system developed here lays the groundwork for implementing complex biological circuits in engineered bacterial communities, using phage signals for communication.

synthetic biology↗

bioRxiv2022

Cell-free biosynthesis combined with deep learning accelerates de novo-development of antimicrobial peptides

Bioactive peptides are key molecules in health and medicine. Deep learning holds a big promise for the discovery and design of bioactive peptides. Yet, suitable experimental approaches are required to validate candidates in high throughput and at low cost. Here, we established a cell- free protein synthesis (CFPS) pipeline for the rapid and inexpensive production of antimicrobial peptides (AMPs) directly from DNA templates. To validate our platform, we used deep learning to design thousands of AMPs de novo. Using computational methods, we prioritized 500 candidates that we produced and screened with our CFPS pipeline. We identified 30 functional AMPs, which we characterized further through molecular dynamics simulations, antimicrobial activity and toxicity. Notably, six de novo-AMPs feature broad-spectrum activity against multidrug-resistant pathogens and do not develop bacterial resistance. Our work demonstrates the potential of CFPS for production and testing of bioactive peptides within less than 24 hours and <10$ per screen.

synthetic biology↗

bioRxiv2022

A synthetic communication system uncovers self-jamming of bacteriophage transmission

Understanding how delivery and exchange of genetic information by bacteriophages shapes bacterial populations is important for designing applications for phage therapy, biocontrol, and microbiome engineering. Here, we present a synthetic intercellular communication system that repurposes phage M13 for genetic exchange between Escherichia coli cells and build mathematical models of the communication behaviour. Our models, based on Chemical Reaction Networks, capture the growth burden, cell density, and growth phase dependence of phage secretion and infection kinetics and predict the stochasticity characterising phage-bacterial interactions at low numbers. In co-cultures of phage sender and receiver cells, resource sharing and selection pressure determine the choice of horizontal versus vertical phage transmission. Surprisingly, we discover that a phage-encoded immunity factor confers extracellular protection to uninfected bacteria, reducing infection rates by 70%. In a simulated gut environment, this novel "self-jamming" mechanism enables the phage to farm uninfected bacteria for future infections, increasing the overall success of both M13 and E. coli. The synthetic system developed here lays the groundwork for implementing population level controls in engineered bacterial communities, using phage signals for communication.

synthetic biology↗

bioRxiv2021

A versatile active learning workflow for optimization of genetic and metabolic networks

The study, engineering and application of biological networks require practical and efficient approaches. Current optimization efforts of these systems are often limited by wet lab labor and cost, as well as the lack of convenient, easily adoptable computational tools. Aimed at democratization and standardization, we describe METIS, a modular and versatile active machine learning workflow with a simple online interface for the optimization of biological target functions with minimal experimental datasets. We demonstrate our workflow for various applications, from simple to complex gene circuits and metabolic networks, including several cell-free transcription and translation systems, a LacI-based multi-level controller and a 27-variable synthetic CO2-fixation cycle (CETCH cycle). Using METIS, we could improve above systems between one and two orders of magnitude compared to their original setup with minimal experimental efforts. For the CETCH cycle, we explored the combinatorial space of [~]1025 conditions with only 1,000 experiments to yield the most efficient CO2-fixation cascade described to date. Beyond optimization, our workflow also quantifies the relative importance of individual factors to the performance of a system. This allows to identify so far unknown interactions and bottlenecks in complex systems, which paves the way for their hypothesis-driven improvement, which we demonstrate for the LacI multi-level controller that we were able to improve by 34-fold after having identified resource competition as limiting factor. Overall, our workflow opens the way for convenient optimization and prototyping of genetic and metabolic networks with customizable adjustments according to user experience, experimental setup, and laboratory facilities.

synthetic biology↗

bioRxiv2019

Large scale active-learning-guided exploration to maximize cell-free production

Lysate-based cell-free systems have become a major platform to study gene expression but batch-to-batch variation makes protein production difficult to predict. Here we describe an active learning approach to explore a combinatorial space of ~4,000,000 cell-free compositions, maximizing protein production and identifying critical parameters involved in cell-free productivity. We also provide a one-step-method to achieve high quality predictions for protein production using minimal experimental effort regardless of the lysate quality.

synthetic biology↗

bioRxiv2019

Metabolic Perceptrons for Neural Computing in Biological Systems

AbstractSynthetic biological circuits are promising tools for developing sophisticated systems for medical, industrial, and environmental applications. So far, circuit implementations commonly rely on gene expression regulation for information processing using digital logic. Here, we present a new approach for biological computation through metabolic circuits designed by computer-aided tools, implemented in both whole-cell and cell-free systems. We first combine metabolic transducers to build an analog adder, a device that sums up the concentrations of multiple input metabolites. Next, we build a weighted adder where the contributions of the different metabolites to the sum can be adjusted. Using a computational model trained on experimental data, we finally implement two four-input \"perceptrons\" for desired binary classification of metabolite combinations by applying model-predicted weights to the metabolic perceptron. The perceptron-mediated neural computing introduced here lays the groundwork for more advanced metabolic circuits for rapid and scalable multiplex sensing.

synthetic biology↗

bioRxiv2018

Plug-and-Play Metabolic Transducers Expand the Chemical Detection Space of Cell-Free Biosensors

Cell-free transcription-translation systems have great potential for biosensing, yet the range of detectable chemicals is limited. Here we provide a framework to expand the range of molecules detectable by cell-free biosensors by combining synthetic metabolic cascades with transcription factor-based networks. These hybrid cell-free biosensors are highly-sensitive and have a fast response and high-dynamic range. This work provides a foundation to engineer modular cell-free biosensors tailored for many applications.

synthetic biology↗