Biology subjects

Srinivasan, A.

Publications and source records attributed to Srinivasan, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Human-calibrated automated intelligibility evaluation of speech neuroprostheses

Objective: Brain-to-voice neuroprostheses can synthesize speech from the neural activity of people who have lost the ability to speak, enabling more natural and expressive communication. Assessing the intelligibility of synthesized speech, however, remains challenging because there is no ground-truth acoustic signal. The field instead relies on human transcription, which is costly and difficult to scale or compare across studies. Automatic speech recognition (ASR) offers a potential alternative, but state-of-the-art ASR models are optimized for transcription accuracy on typical fluent speech and may not reflect how humans perceive atypical synthesized speech. Approach: We collected human transcriptions for ~14.5 hours of synthesized speech from intracortical brain-to-voice decoders for one participant (yielding ~72 hours of transcribed speech). We used the human-transcribed synthesized speech to fine-tune Whisper-large-v3 with low-rank adaptation (LoRA) so that its word error rate (WER) approximates human WER. We then evaluated the model on held-out sentences. Main results: Fine-tuning increased the Pearson correlation between model WER and human WER from 0.62 to 0.76. The fine-tuned model's WER closely tracked human WER across evaluation sets, including speech synthesized by unseen brain-to-voice decoders. Close agreement with human WER was achieved with only 1.5 hours of synthesized speech. Significance: These results demonstrate that human-calibrated ASR can provide a scalable measure of brain-to-voice intelligibility that reflects human transcription more closely than zero-shot ASR does.

neuroscience↗

bioRxiv2026

Adaptive laboratory evolution rewires Pseudomonas putida for resource-efficient acetate assimilation

Acetate is an attractive renewable two-carbon substrate for microbial biotechnology, but its toxicity limits growth and carbon-use efficiency at process-relevant concentrations. Here, we used adaptive laboratory evolution to improve acetate tolerance in a genome-reduced strain of Pseudomonas putida and combined whole-genome sequencing, reverse engineering, transcriptomics, proteomics, and 13C-acetate fluxomics to resolve the underlying adaptation mechanisms. Evolution under increasing acetate concentrations selected recurrent mutations in gacA and fabB, which encode a global response regulator and a fatty acid biosynthesis enzyme, respectively. Reverse engineering of these mutations recovered most of the evolved phenotype, including shorter lag phase and substantially higher biomass yield from acetate. Multi-omic analyses showed repression of type VI secretion systems, carbohydrate storage functions, fatty acid metabolism, and oxidative stress-associated proteins, indicating resource reallocation away from costly stress and non-essential programs. Fluxomics further revealed reduced EDEMP cycling and increased glyoxylate shunt flux, consistent with improved acetate-carbon retention in biomass. These results establish acetate tolerance in P. putida as a resource-efficiency phenotype and identify gacA and fabB as actionable targets for acetate-based bioproduction.

synthetic biology↗

bioRxiv2026

A generalizable speech neuroprosthesis

Intracortical brain-computer interfaces (BCIs) can restore communication to people with vocal tract paralysis by decoding cortical activity during attempted speech into text. State-of-the-art systems pairing neural-to-phoneme decoders with phoneme-to-word language models have achieved word error rates (WERs) as low as 1%, but only after collecting thousands of sentences of training data. Shortening the data collection process would facilitate scaling this new technology by reducing the time from device implant to high-accuracy communication. Here we introduce a transformer-based decoder model trained jointly across six intracortical speech BCI participants. For every participant -- regardless of sex, disease etiology, or attempted speaking strategy -- a multi-user model decoded speech more accurately (over 50% lower relative WER on average) than models trained on individual users data. Notably, the multi-user model could be finetuned on fewer than 200 sentences from a held-out user to achieve a WER below 7%. These results reveal how to pool intracortical data across people to yield more accurate, generalizable, and rapidly-deployable decoding models.

bioengineering↗

bioRxiv2026

Brain2voice 2.0: High-performance voice synthesis brain-computer interface

Brain-computer interfaces (BCIs) offer a promising solution to speech loss due to neurological injury by decoding intended speech directly from brain activity. While recent BCIs have restored high-accuracy text-based communication, they fail to provide instantaneous voice output essential for the natural flow of conversation. Brain-to-voice BCIs address this gap by decoding voice directly from neural signals. However, even the state-of-the-art (SOTA) BCI-synthesized voice is not yet intelligible enough for real-world adoption. We introduce brain2voice 2.0, a new multimodal Transformer-based BCI decoder architecture capable of synthesizing highly intelligible voice from intracortical neural signals in real-time. Brain2voice 2.0 is trained on continuous and custom-tokenized acoustic targets and phoneme targets, leveraging their complementary speech information. We use self-supervised and adversarial training objectives that enhance acoustic feature quality and improve synthesis intelligibility. At each 10 ms timestep, the model causally outputs continuous and tokenized acoustic features for real-time voice synthesis as well as time-aligned phoneme predictions (raw phoneme error rate: 7%, comparable to the latest brain-to-text models). We evaluated this new approach on our prior intracortical brain-to-voice benchmark dataset (Wairagkar et al. 2025). Naive human listeners transcribed brain2voice 2.0 synthesized voice with a word error rate of 5.24%--an 8x improvement in intelligibility over previous SOTA results (43.75%). Brain2voice 2.0 demonstrates that highly intelligible real-time voice synthesis from neural signals is achievable, for the first time crossing the intelligibility threshold necessary for clinically viable brain-to-voice BCIs for people with paralysis.

neuroscience↗

bioRxiv2026

Functional Genomics Reveals TNT Bioremediation Strategies in Pantoea sp. MT58 and Pseudomonas putida KT2440

2,4,6-Trinitrotoluene (TNT) is a recalcitrant and pervasive environmental pollutant. Although different environmental microbes have demonstrated their ability to degrade or transform TNT, the underlying genetic basis and cellular machinery remain unclear. In this study, we investigated bacterial strategies in response to TNT exposure in Pantoea sp. MT58 and P. putida KT2440 using proteomics and random barcode transposon-site sequencing (RB-TnSeq). Pantoea sp. MT58 was found to utilize TNT as a sole nitrogen source, whereas P. putida KT2440 exhibited only stress tolerance without assimilation. Pantoea sp. MT58 encodes multiple putative nitroreductases that were upregulated, yet deletion of these genes did not affect growth on TNT, revealing pathway redundancy. Furthermore, fitness profiling provided no evidence for genes involved in the canonical Meisenheimer-complex pathway associated with nitrite release. Instead, the data are most consistent with a sequential nitro-group reduction route in which nitrogen is ultimately recovered as ammonium, with nitrogen routed through the GS-GOGAT pathway with purine and urea pools as the candidate buffering architecture for TNT mineralization. Conversely, P. putida KT2440 relied on Ttg/RND efflux pumps and toluene tolerance proteins for survival without nitrogen assimilation from TNT. This work distinguishes routes for productive nitrogen assimilation from those involved in nitroaromatic tolerance, expanding the mechanistic understanding of anthropogenic compound metabolism to inform future bioremediation efforts.

microbiology↗

bioRxiv2026

Memory erasure by dopamine-gated retrospective learning

Erasing outdated memories is crucial for adaptive behavior. Yet once a cue-outcome association is learned, repeated cue exposure without outcome suppresses conditioned behavior without erasing the underlying memory. This allows rapid behavioral recovery when outcomes are reintroduced. Here, we confirm this limitation for standard "prospective extinction" protocols that present cues without the associated outcome, but show that true memory erasure is achieved by inverting the paradigm: presenting outcomes without associated cues, i.e., "retrospective extinction". We demonstrate that orbitofrontal cortex activity at outcome is necessary for the rapid behavioral recovery following prospective extinction, and that mesolimbic dopamine activity at outcome is necessary for retrospective extinction. These findings reconceptualize extinction mechanisms and suggest complementary strategies to mitigate relapse and erase maladaptive memories.

neuroscience↗

bioRxiv2025

Distinct 5' and 3' Coverage Biases Shape Transcriptome Interpretation in Nanopore Direct RNA versus PCR-cDNA Sequencing

Long-read RNA sequencing enables isoform-resolved transcriptomics, but library preparation introduces systematic biases that shape biological interpretation. We benchmarked Oxford Nanopores two protocols--PCR-cDNA and direct RNA--using SKMM2 myeloma cells stimulated with interleukin-6 (IL-6) and ERCC synthetic spike-ins. Direct RNA produced longer, higher-quality reads and more high-confidence isoforms, but showed pronounced 5' coverage loss. PCR-cDNA yielded shorter fragments with 3' underrepresentation, detecting more low-abundance transcripts at reduced confidence. Protocol-specific biases had major consequences: differential expression analysis revealed limited overlap in IL-6-responsive genes, and pathway enrichment was broader in direct RNA. At the isoform level, differential transcript usage was almost entirely protocol-specific, with case studies (e.g. RPL22L1, GRB2, RNF220) illustrating concordance and divergence. ERCC controls confirmed these biases as technical rather than biological. Together, our results show that while both methods provide accurate gene-level quantification, transcript-level conclusions depend critically on protocol choice, highlighting the need for careful selection in long-read transcriptomics.

cell biology↗

bioRxiv2025

Translation Inhibition by Rocaglamide A Enhances Susceptibility of Yeasts to Caspofungin

Fks1 catalyzes the synthesis of {beta}-1,3-glucans, a major structural component of the fungal cell wall, and is the primary target of the antifungal drug caspofungin. Mutations in Fks1 confer caspofungin resistance by disrupting its interaction with the drug, thereby reducing inhibition of Fks1 enzymatic activity. Previous studies demonstrated that translation of the FKS1 mRNA was highly dependent on the translation initiation helicases eIF4A and Ded1 (1). Therefore, we investigated whether treatment of Saccharomyces cerevisiae cells with the eIF4A inhibitor Rocaglamide A (RocA) or mutation of Ded1 affects translation of FKS1 and susceptibility to caspofungin. Using WT and temperature-sensitive ded1-ts strains, we demonstrated that RocA enhanced caspofungin-mediated growth inhibition and translation repression. Sensitivity to both drugs was further enhanced in ded1-ts strains, suggesting specifically targeting Ded1 in fungi could be an effective mechanism to prevent caspofungin resistance. We extended the analysis to Candida glabrata, a related fungal pathogen, and found similar results. Importantly, combining RocA with caspofungin was fungicidal in both species, suggesting the combination could decrease development of caspofungin resistance in pathogenic yeasts. Together these findings highlight the potential of targeting translation initiation helicases for effective combination antifungal treatments.

molecular biology↗

bioRxiv2025

Utility of recombinant envelope domain III as a diagnostic antigen for the specific detection of Kyasanur Forest Disease

Kyasanur Forest Disease (KFD), commonly known as "monkey fever," is a highly neglected tropical disease caused by the Kyasanur Forest Disease Virus (KFDV). KFD is endemic to Western Ghats of Karnataka, India, with seasonal outbreaks during December to June every year. As there is no standard treatment regime, KFD can be fatal with a mortality rate of 2-10%. Currently, KFD is detected through a non-specific IgM-ELISA followed by RT-PCR, which often delays diagnosis, leading to increased disease severity and even death. To address this, we focused on developing a specific antigen-based KFD detection. The KFDV Envelope Domain III (EDIII) and Non-Structural 1 (NS1) proteins were chosen as detection markers, cloned, and expressed using pET28a(+) vector in BL-21 (Rosetta) E. coli and purified. These proteins were used to raise polyclonal antibodies in rabbits and the antibody titre was found to be 1:256,000 and 1:512,000 against rEDIII and rNS1 proteins, respectively. Importantly, these polyclonal antibodies showed no cross-reactivity against corresponding dengue virus EDIII and NS1 proteins. Using polyclonal antibodies against rEDIII, we developed sandwich ELISA for the specific detection of KFD, which has demonstrated high specificity and sensitivity. Further, anti-rEDIII polyclonal antibodies also detected full-length KFDV-E protein expressed in mammalian cells, confirming the antibody specificity for the native viral antigen.

microbiology↗

bioRxiv2025

Encoding of speech modes and loudness in ventral precentral gyrus

The ability to vary the mode and loudness of speech is an important part of the expressive range of human vocal communication. However, the encoding of these behaviors in the ventral precentral gyrus (vPCG) has not been studied at the resolution of neuronal firing rates. We investigated this in two participants who had intracortical microelectrode arrays implanted in their vPCG as part of a speech neuroprosthesis clinical trial. Neuronal firing rates modulated strongly in vPCG as a function of attempted mimed, whispered, normal or loud speech. At the neural ensemble level, mode/loudness and phonemic content were encoded in distinct neural subspaces. Attempted mode/loudness could be decoded from vPCG with an accuracy of 94% and 89% for two participants respectively, and corresponding neural preparatory activity could be detected hundreds of milliseconds before speech onset. We then developed a closed-loop loudness decoder that achieved 94% online accuracy in modulating a brain-to-text speech neuroprosthesis output based on attempted loudness. These findings demonstrate the feasibility of decoding mode and loudness from vPCG, paving the way for speech neuroprostheses capable of synthesizing more expressive speech.

neuroscience↗

bioRxiv2025

Novel origin of replication for environmentally isolated Pantoea strain enables expression of heterologous proteins, pathways and products

Plasmids isolated or characterized from environmental samples serve as a resource that can be used to develop genetic tools for characterizing recently isolated or less-studied microbes. In this report, we leveraged sequences from a previously characterized groundwater plasmidome and developed a screen to identify novel plasmid origins. Putative origin sequences were used to construct a barcoded plasmid library, which contained both known and newly predicted origins. This library was tested against a panel of representative bacterial strains and led to the identification of 3 novel origins that putatively replicate in gram-negative bacteria not previously associated with these origin sequences. We empirically validated one of the newly identified origins, 6911, to be functional in both the model bacterial strain, Escherichia coli BW25113, as well as in Pantoea sp. MT58, a fast growing and metal tolerant, environmentally important bacterium from the widespread Pantoea genus. We confirmed that a plasmid bearing origin 6911 as the sole origin could replicate and had a copy number of 9 ({+/-} 2) in Pantoea sp. MT58. We successfully used a plasmid based on the new origin to express the reporter protein GFP, and two non-native metabolite pathways for the natural product, indigoidine and the terpenoid compound, isoprenol. By pairing functional novel origins of replication to non-model organisms this pipeline can expand the tool kit for genetic manipulations of both model and less-studied bacteria. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=56 SRC="FIGDIR/small/653210v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@45689corg.highwire.dtl.DTLVardef@d6daeeorg.highwire.dtl.DTLVardef@1bf4d06org.highwire.dtl.DTLVardef@59ccfd_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOAbstract figure.C_FLOATNO We developed a host-origin pair identification pipeline by constructing a plasmid library containing both literature-sourced and computationally predicted origins of replication. By conjugating this library into non-model microbes, we identified functional origins in diverse bacteria, and could leverage these findings to develop genetically tractable hosts for microbial engineering. C_FIG

microbiology↗

bioRxiv2025

Single-cell-guided identification of logic-gated antigen combinations for designing effective and safe CAR therapy

Chimeric antigen receptor (CAR) T-cell therapy has revolutionized the treatment of hematological malignancies. However, its application in solid tumors remains limited because single targets are unlikely to suffice due to tumor antigen heterogeneity and off-tumor toxicities. To overcome these obstacles, we developed LogiCAR designer, a computational approach that utilizes single-cell transcriptomics data from patient tumors to systematically identify the cancer-specific antigen circuits with logic gates ("AND," "OR," and "NOT") that target the majority of cancer cells in a tumor while sparing normal cells and tissues as much as possible. LogiCAR designer efficiently scales to higher-order antigen combinations involving up to five genes. Applied to a large-scale dataset encompassing approximately 2 million cells (including > 620k tumor cells) from 342 clinical patient samples across all major breast cancer subtypes, LogiCAR designer identified antigen circuits with enhanced tumor-targeting efficacy and improved safety profiles compared to both previously reported circuits and single-target therapies in clinical trials. However, even these optimized shared circuits still proved insufficient for some patients. We hence systematically studied LogiCAR designers ability to identify highly effective CAR circuits that are individualized to each patient. Remarkably, such personalized CAR circuits provide estimated tumor-targeting efficacy tantamount to complete response in 76% of patients and partial response for all patients. Taken together, this analysis is the first systematic quantification of the efficacy and safety of all possible CAR circuits, showing that: (a) the quality of existing solutions leaves much to be desired; (b) the ability of shared circuits optimized across many patients is moderate, and finally, (c) individually tailored circuits offer significantly higher tumor-targeting efficacies for patients. LogiCAR designer offers a rigorous, data-driven way to facilitate the rational design of safe and effective CAR-based immunotherapies for cancer. Statement of Significance Development of a computational approach that efficiently identifies logic-gated CAR target combinations, called circuits, from single-cell transcriptomics, addressing a critical unmet clinical need. Application to the largest ensemble of breast cancer datasets to date, comprising [~]2 million cells (> 620k tumor cells) from 17 clinical cohorts, to identify CAR circuits predicted to be effective. Comprehensive safety profiling of candidate circuits spanning major tissues at both RNA and protein levels. Logic-gated CAR circuits generated by our pipeline address tumor heterogeneity and achieve efficacy and safety scores that surpass clinical trial and previously computationally identified circuits. Individualized rational CAR design offers a transformative approach to deliver precision-engineered CAR therapies with unprecedented efficacy.

bioinformatics↗

bioRxiv2025

Evolution guided tolerance engineering of Pseudomonas putida KT2440 for production of the sustainable aviation fuel precursor isoprenol

Isoprenol (3-methyl-3-buten-1-ol) is a sustainable aviation fuel precursor and commodity chemical which can be microbially synthesized from renewable carbon streams. Its production has been demonstrated in Pseudomonas putida KT2440 but its titers, rates, and yields have yet to reach commercially viable levels, potentially due to its inherent toxicity. We hypothesized that utilization of Tolerization Adaptive Laboratory Evolution (TALE) would generate P. putida hosts more tolerant to isoprenol and suitable for enhanced production phenotypes. Here, we performed a comprehensive TALE campaign using three strains, the wild-type and two strains lacking subsets of known isoprenol catabolism and transport functions in quadruplicate independently evolved lineages. Several evolved clones from each starting strain displayed robust growth (up to 0.2 h-1) at 8 g/L of isoprenol, where starting strains could not grow. Whole genome resequencing of the 12 independent strain lineages identified convergent mutations. Reverse engineering four commonly mutated regions individually (gnuR, ttgB-PP_1394, PP_3024-PP_5558, PP_1695) each resulted in a partial recovery of the tolerance phenotypes observed in the evolved strains. Additionally, a proteomics-guided deletion of the master motility regulator, fleQ, in an evolved clone alleviated the tolerance vs. production trade-off, restoring isoprenol titers and consumption to levels observed in the starting strains. Collectively, this work demonstrated that an integrated strategy of laboratory evolution and rational engineering was effective to develop robust biofuel production hosts with minimized product toxicity.

microbiology↗

bioRxiv2025

Identifying a logical specification and a program for an LLM-based generator of lead molecules

Our interest is in the generation of "lead" molecules in early-stage drug design. Leads are small molecules (ligands) that can bind to a part of pre-specified target and also satisfy multiple physico-chemical constraints. We propose using techniques developed in Inductive Logic Programming (ILP) to identify a logical specification of feasible molecules; and then using this specification to construct a program that uses a large language model (LLM) to generate new molecules. We ensure the program constructed is correct, in the sense that every molecule generated by the program is feasible according the specification. Our focus is on contributing to on-going drug-discovery research on novel inhibitors for Dopamine {beta}-hydroxylase (DBH), an enzyme that plays a pivotal role in several diseases related to the brain and the heart. We find molecules comparable in affinity to the latest generation drugs currently in clinical trials, and chemical assessment of synthesisablity of the molecules generated. For completeness, we also provide results obtained on the classic benchmark datasets used in recent work reported in [1].

bioinformatics↗

bioRxiv2025

A field-focused systems approach reveals mRNA covalent modifications linked to sorghum growth and development under drought stress

RNA Covalent Modifications (RCMs) are post-transcriptional chemical alterations that influence RNA stability and translation efficiency, thus play critical roles in eukaryotic growth and development. However, their role in regulating plant performance under abiotic stress remain largely unexplored. Here, we integrated multi-omics data in six Sorghum bicolor accessions under water-limiting conditions in the field to explore the relationship between RCMs and drought response. Within a stress and photosynthesis-associated gene co-expression module, we identified SbDUS2, a member of family of enzymes, conserved across eukaryotes, which catalyzes the reduction of uracil to dihydrouridine (DHU) on RNA molecules. DHU-modified transcripts in this module were enriched for photosynthetic functions and showed strong correlation with photosynthetic traits. To elucidate the function of this RCM, we characterized loss of function dus2 mutants in the genetic model, Arabidopsis thaliana. Under control conditions, these DHU-deficient mutants exhibited impaired germination and delayed development. Furthermore, when exposed to heat or water-limiting conditions, these mutants showed significantly reduced net CO2 assimilation and survival. Using multiple transcriptome-wide RNA stability assays, we demonstrated that transcripts associated with lower DHU level in a dus2 background generally exhibited increased stability compared to Col-0 controls. Particularly, lack of DUS2 led to the hyperstability of photosynthesis-related transcripts, impeding their turnover and likely preventing proper photosynthetic acclimation during stress. We propose a model based on these data where DHU acts as a critical post-transcriptional regulator marking mRNAs for rapid turnover under stress, highlighting an overlooked regulatory layer contributing to plant resilience.

plant biology↗

bioRxiv2024

Spatiotemporal distribution of the glycoprotein pherophorin II reveals stochastic geometry of the growing ECM of Volvox carteri

The evolution of multicellularity involved the transformation of a simple cell wall of unicellular ancestors into a complex, multifunctional extracellular matrix (ECM). A suitable model organism to study the formation and expansion of an ECM during ontogenesis is the multicellular green alga Volvox carteri, which, along with the related volvocine algae, produces a complex, self-organized ECM composed of multiple substructures. These self-assembled ECMs primarily consist of hydroxyproline-rich glycoproteins, a major component of which is pherophorins. To investigate the geometry of the growing ECM, we fused the yfp gene with the gene for pherophorin II (PhII) in V. carteri. Confocal microscopy reveals PhII:YFP localization at key structures within the ECM, including the boundaries of compartments surrounding each somatic cell and the outer surface of the organism. Image analysis during the life cycle allows the stochastic geometry of those growing compartments to be quantified. We find that their areas and aspect ratios exhibit robust gamma distributions and exhibit a transition from a tight polygonal to a looser acircular packing geometry with stable eccentricity over time, evoking parallels and distinctions with the behavior of hydrated foams. These results provide a quantitative benchmark for addressing a general, open question in biology: How do cells produce structures external to themselves in a robust and accurate manner?

developmental biology↗

bioRxiv2024

AI-assisted imaging screening reveals mechano-molecular tissue organizers and network of signaling hubs.

Cadherin-mediated adhesions are crucial mechanical and signaling hubs that connect cells within a tissue and probe the mechanics of the surrounding environment. They constitute a physical link between the actin cytoskeleton of neighboring cells, providing the mechanical coordination needed for morphogenetic processes, tissue homeostasis, collective migration, and regeneration. Disruptions in adhesion mechanisms are closely linked to the breakdown of epithelial structure and the emergence of disease-related traits characteristic of cancer progression. The cadhesome network comprises over 170 structural and regulatory proteins involved in cadherin-mediated adhesion. While this network is essential for coordinating tissue responses to mechanical stress, its complexity has historically limited our understanding of how individual components contribute to force transmission and tissue homeostasis. Recent technological advances offer tools to investigate large molecular networks in cellular function and pathology (functional omics). Leveraging these advances, we developed an experimental and analytical platform combining high-throughput gene silencing, imaging, and artificial intelligence (AI) to systematically profile each role of each protein in tissue formation, mechanical stability, and response to induced tension. Using EpH4 cells as an epithelial tissue model, we performed systematic silencing in triplicate, capturing a range of tissue phenotypes under baseline and tension-inducing conditions. Machine learning methods were used to analyze complex imaging data, quantify tissue ruptures, characterize junctional organization, and measure tension states of the tissue. By incorporating machine learning algorithms, we automated image feature extraction, clustering, and classification, enabling an unprecedented quantitative evaluation of tissue mechanics at scale. Our machine learning models allowed us to identify significant patterns, including protein-specific responses to tension and their roles in tissue-level mechanical integrity. Finally, we constructed a protein interaction network detailing the roles of each protein, their physical interactions, and known links to cancer. The network analysis revealed three prominent mechanotransductive and signaling subnetworks centered around E-cadherin, EGFR, and RAC1. Our study provides a foundational framework for investigating mechanosensing proteins and it offers a scalable blueprint for discovering potential therapeutic targets in diseases like cancer, where tissue mechanics play a crucial role. TeaserAI-aided screening identifies key regulators of epithelial tissue mechanics, uncovering potential therapeutic targets in cancer.

cell biology↗

bioRxiv2024

Alternate routes to acetate tolerance lead to varied isoprenol production from mixed carbon sources in Pseudomonas putida

Lignocellulose is a renewable resource for the production of a diverse array of platform chemicals, including the biofuel isoprenol. While this carbon stream provides a rich source of sugars, other organic compounds, such as acetate, can be used by microbial hosts. Here we examined growth and isoprenol production in a Pseudomonas putida strain pre-tolerized ("PT") background where its native isoprenol catabolism pathway is deleted, using glucose and acetate as carbon source. We found that PT displays impaired growth in minimal medium containing acetate, and often fails to grow in glucose-acetate medium. Using a mutant recovery-based approach, we generated tolerized strains that overcame these limitations, achieving fast growth and isoprenol production in the mixed carbon feed. Changes in the glucose and acetate assimilation routes, including an upregulation in PP_0154 (SpcC, succinyl-CoA:acetate CoA-transferase) and differential expression of the gluconate assimilation pathways, were key for higher isoprenol titers in the tolerized strains, while a different set of mechanisms were likely enabling tolerance phenotypes in media containing acetate. Among these, a coproporphyrinogen-III oxidase (HemN) was upregulated across all tolerized strains, and in one isolate required for acetate tolerance. Utilizing a defined glucose and acetate mixture ratio reflective of lignocellulosic feedstocks for isoprenol production in P. putida allowed us to obtain insights into the dynamics and challenges unique to dual carbon source utilization that are obscured when studied separately. Together, this enabled development of a P. putida bioconversion chassis able to use a more complex carbon stream to produce isoprenol. ImportanceAcetate is a relatively abundant component of many lignocellulosic carbon streams and has the potential of being used together with sugars, especially in microbes with versatile catabolism such as P. putida. However, the use of mixed carbon streams necessitates additional optimization. Further, the use of P. putida for the production of the biofuel target, isoprenol, requires the use of engineered strains that have additional growth and production constraints when cultivated in acetate and glucose mixtures. In this study we generate acetate tolerant P. putida strains that overcome these challenges and examine their ability to produce isoprenol. We show that acetate tolerance and isoprenol production, though independent phenotypes can both be optimized in a given P. putida strain. Using proteomics and whole genome sequencing we examine the molecular basis of both phenotypes and show that tolerance to acetate can occur via alternate routes and result in different impacts on isoprenol production.

microbiology↗