Biology subjects

Jacobson, J.

Publications and source records attributed to Jacobson, J..

9 recordsLinked to original sources

bioRxiv2026

Archival data storage using DNA proximity graphs

DNA offers a promising medium for long-term archival data storage, but the high cost of synthesizing data-specific sequences remains a major barrier to its adoption. Here we introduce SPAN (Synthesis-free Proximity-graph Archiving in Nucleic acids), a synthesis-independent storage architecture in which digital information is written as a physical pattern and encoded in the spatial relationships of randomly barcoded DNA molecules. Barcodes are deposited onto a patterned surface, and an enzymatic reaction records pairs of neighboring molecules. Sequencing these pairs produces a proximity graph from which the original information can be reconstructed. We demonstrate this principle experimentally by writing a 136-bit pattern using photolithography and correctly recovering 114 bits. Simulations further establish the physical parameters governing reconstruction accuracy. Our cost analysis shows that the demonstrated system already has lower estimated writing costs than existing DNA synthesis-based methods, and projects a further five-order-of-magnitude reduction using established optical writing technologies. Although SPAN requires more expensive readout, this trade-off makes it a promising candidate for deep archival storage, where information is written once and rarely, if ever, retrieved.

synthetic biology↗

bioRxiv2026

Integrated ex vivo screening and transcriptomic profiling to prioritize drug combinations for rare cancers

Discovering effective drug combinations requires testing many dose combinations across a diverse panel of tumor models. This approach is limited in rare cancers by a scarcity of cell lines and representative high-throughput models that would make exhaustive screening feasible and predictive. Patient-derived xenograft (PDX) models are genomically representative but too low-throughput for this purpose. Culturing PDX cells ex vivo in three-dimensional (3D) matrices offers a genomically representative and clinically relevant platform for preclinical drug testing, capturing the microenvironmental cues that shape in vivo drug response while requiring only limited tissue per assay. Toward this end, we designed and validated an experimental-computational framework, "ex vivo assessment of combination therapies" (EXACT), to enable drug combination discovery in rare tumors. Using PDX models of malignant peripheral nerve sheath tumors (MPNST), we built a platform to culture PDX cells ex vivo over multiple days, monitoring drug sensitivity and measuring transcriptomic responses to treatment. Computational analysis of these transcriptomic responses then identifies which compensatory pathway creates a unique vulnerability to a second drug. EXACT thus offers a biologically informed, scalable approach for prioritizing drug combinations in rare tumors, nominating drugs alongside biological rationale. Using this methodology, we identified a MEK inhibitor plus HDAC inhibitor combination with enhanced activity in vitro and in vivo, forming the basis of an active clinical trial. This platform could be adapted for real-time use with primary patient specimens, enabling personalized therapeutic discovery. SignificanceEXACT integrates PDX-derived 3D drug screening with biologically informed computational analysis to identify and explain effective combinations, providing a scalable strategy for therapeutic discovery in rare cancers such as MPNST.

cancer biology↗

bioRxiv2025

Designing proteins with reduced T-cell epitopes through policy optimization

AO_SCPLOWBSTRACTC_SCPLOWDeep generative models for protein structure and sequence are increasingly used to design proteins with therapeutic and industrial applications, but the clinical success of designed therapeutics ultimately depends on their compatibility with the human immune system. Immune response is triggered by a cascade of molecular events, including proteasomal cleavage, peptide elution, and binding to the highly polymorphic Major Histocompatibility Complex (MHC) Class I molecules, yet prior approaches have generally modeled these processes in isolation or restricted attention to a limited set of alleles. In this work, we develop predictors for cleavage, elution, and binding affinity in the MHC Class I pathway, incorporating evidential deep learning to provide unified uncertainty estimates. We first perform supervised fine-tuning of a protein language model on human proteins, and then align the model through group relative policy optimization (GRPO) to reduce MHC Class I epitopes under a curriculum learning framework, in which the curriculum progressively increases the number of masked predicted epitopes and the number of alleles considered during training. This strategy enables the generation of protein candidates that are optimized for immune compatibility across diverse MHC Class I alleles while accounting for predictive uncertainty in modeling the immune response.

bioengineering↗

bioRxiv2025

Snekmer Learn/Apply: A kmer-based vector similarity approach to proteinclassification suitable for metagenomic datasets

Advances in whole genome sequencing have led to a rapid and ongoing increase in the amount of sequence data available, but 40-50% of known genes have no functional annotation and only 25-30% have specific functional annotations. Current functional annotation approaches typically rely on computationally expensive pairwise or multiple sequence alignments, preventing rapid development of models for novel protein functions and sometimes limiting methods to one ontology. Representation of sequence in short segments (kmers) has been used in many applications for nucleotide sequence, and more recently has been applied to protein sequence as well. We previously developed Snekmer, a tool which uses kmer patterns to develop alignment-free individual protein family models. Other approaches, such as MMSeqs2 and DIAMOND, use protein kmers as a fast filter to reduce search space for subsequent sequence alignment. Here, we describe a novel addition to the Snekmer tool which builds kmer libraries for protein families and uses those libraries to map functional annotations to new sequences. We first demonstrate that our method accurately applies TIGRFAMs annotations to protein fragments and to a low-sequence identity benchmark dataset, and further use it to annotate a set of drought stress associated soil and rhizosphere metagenome sequences with higher sensitivity towards several important protein function classes than that shown by HMMs. We have incorporated this workflow into Snekmer.

bioinformatics↗

bioRxiv2023

GTP signaling links metabolism, DNA repair, and responses to genotoxic stress

How cell metabolism regulates DNA repair is incompletely understood. Here, we define a GTP-mediated signaling cascade that links metabolism to DNA repair and has significant therapeutic implications. GTP, but not other nucleotides, regulates the activity of Rac1, a G protein, that promotes the dephosphorylation of serine 323 on Abl-interactor 1 (Abi-1) by protein phosphatase 5 (PP5). Dephosphorylated Abi-1, a protein previously not known to activate DNA repair, promotes non-homologous end joining. In patients and mouse models of glioblastoma, Rac1 and dephosphorylated Abi-1 mediate DNA repair and resistance to standard of care genotoxic treatments. The GTP-Rac1-PP5-Abi-1 signaling axis is not limited to brain cancer, as GTP supplementation promotes DNA repair and Abi-1-S323 dephosphorylation in non-malignant cells and protects mouse tissues from genotoxic insult. This unexpected ability of GTP to regulate DNA repair independently of deoxynucleotide pools has important implications for normal physiology and cancer treatment.

cancer biology↗

bioRxiv2022

A serine-folate metabolic unit controls resistance and tolerance of infection

Immune activation drives metabolic change in most animals. Immune-induced metabolic change is most conspicuous as a driver of pathology in serious or prolonged infection, but it is normally expected to be important to support immune function and recovery. Many of the signalling mechanisms linking immune detection with metabolic regulation, and their specific consequences, are unknown. Here, we show that Drosophila melanogaster respond to many bacterial infections by altering expression of genes of the folate cycle and associated enzymes of amino acid metabolism. The net result of these changes is increased flow of carbon from glycolysis into serine and glycine synthesis and a shift of folate cycle activity from the cytosol into the mitochondrion. Immune-induced transcriptional induction of astray and Nmdmc, the two most-induced of these enzymes, depends on Dif and foxo. Loss of astray or Nmdmc results in infection-specific immune defects. Our work thus shows a key mechanism that connects immune-induced changes in metabolic signalling with the serine-folate metabolic unit to result in changed immune function.

immunology↗

bioRxiv2020

Synergism and antagonism of bacterial-viral co-infection in the upper respiratory tract

Streptococcus pneumoniae (the pneumococcus) is a leading cause of pneumonia in children under five years old. Co-infection by pneumococci and respiratory viruses enhances disease severity. Little is known about pneumococcal co-infections with Respiratory Syncytial Virus (RSV). Here, we developed a novel infant mouse model of co-infection using Pneumonia Virus of Mice (PVM), a murine analogue of RSV, to examine the dynamics of co-infection in the upper respiratory tract, an anatomical niche that is essential for host-to-host transmission and progression to disease. Coinfection increased damage to the nasal tissue and increased production of the chemokine CCL3. Pneumococcal nasopharyngeal density and shedding in nasal secretions were increased by co-infection. In contrast, co-infection reduced PVM loads in the nasopharynx, an effect that was independent of pneumococcal strain and the order of infection. We showed this antagonistic effect was abrogated using a pneumococcal mutant deficient in capsule production and incapable of nasopharyngeal carriage. The pneumococcal-mediated reduction in PVM loads was caused by accelerated viral clearance from the nasopharynx. Although these synergistic and antagonistic effects occurred with both wild-type pneumococcal strains used in this study, the magnitude of the effects was strain dependent. Lastly, we showed that pneumococci can also antagonize influenza virus. Taken together, our study has uncovered multiple novel facets of bacterial-viral co-infection. Our findings have important public health implications, including for bacterial and viral vaccination strategies in young children.

microbiology↗

bioRxiv2020

The microbiome affects liver sphingolipids and plasma fatty acids in a murine model of the Western diet based on soybean oil

Studies in mice using germfree animals as controls for microbial colonization have shown that the gut microbiome mediates diet-induced obesity. Such studies use diets rich in saturated fat, however, Western diets in the USA are enriched in soybean oil, composed of unsaturated fatty acids (FAs), either linoleic or oleic acid. Here we addressed whether the microbiome is a variable in fat metabolism in mice on a soybean oil diet. We used conventionally-raised, low-germ, and germfree mice fed for 10 weeks diets either high (HF) or low (LF) in high-linoleic-acid soybean oil as the sole source of fat. All mice, including germfree, gained relative fat weight and consumed more calories on the HF versus LF soybean oil diet. Plasma fatty acid levels were generally dependent on diet, with microbial colonization status affecting iso-C18:0, C20:3n-6, C14:0, and C15:0 levels. Colonization status, but not diet, impacted levels of liver sphingolipids including ceramides, sphingomyelins, and sphinganine. Our results confirm that absorbed fatty acids are mainly a reflection of the diet, and show that microbial colonization influences liver sphingolipid pools.

systems biology↗

bioRxiv2017

Resilience mechanisms of small intestinal lactobacilli to the toxicity of soybean oil fatty acids

Over the past century, soybean oil (SBO) consumption in the United States increased dramatically. The main SBO fatty acid, linoleic acid (18:2), inhibits in vitro the growth of lactobacilli, beneficial members of the small intestinal microbiota. Human-associated lactobacilli have declined in prevalence in Western microbiomes, but how dietary changes may have impacted their ecology is unclear. Here, we compared the in vitro and in vivo effects of 18:2 on Lactobacillus reuteri and L. johnsonii. Directed evolution in vitro in both species led to strong 18:2 resistance with mutations in genes for lipid biosynthesis, acid stress, and the cell membrane or wall. Small-intestinal Lactobacillus populations in mice were unaffected by chronic and acute 18:2 exposure, yet harbored both 18:2- sensitive and resistant strains. This work shows that extant small intestinal lactobacilli are protected from toxic dietary components via the gut environment as well as their own capacity to evolve resistance.

microbiology↗