Biology subjects

Isaev, K.

Publications and source records attributed to Isaev, K..

11 recordsLinked to original sources

bioRxiv2026

Bioplastic Production from simulated 100% in situ Mars resources

A sustained human presence on Mars requires local production of bulk materials, particularly polymers. Prior approaches to space biomanufacturing rely on human waste streams, Earth-sourced consumables, or complex chemical infrastructure, limiting their ability to scale. Here we demonstrate production of polyhydroxyalkanoate (PHA) bioplastic from simulated 100% martian resources: regolith-derived soluble nutrients, acetate electrochemically fixed from martian atmosphere, and water. We screened 16 candidate organisms for growth in a chemically defined Mars medium and identified Cupriavidus necator H16 and Pseudomonas putida KT2440 as promising chassis organisms. Adaptive laboratory evolution totaling more than 1012 cumulative cell divisions improved both species' tolerance to high concentrations of acetate and leached regolith. The top C. necator evolved isolate, referred to as sPL.001, produced more than three-fold higher PHA titer under simulated Mars conditions compared to its parent strain. These results establish a path to polymer production on Mars where consumable mass is derived entirely from local resources, decoupling bulk material production from Earth supply chains.

bioengineering↗

bioRxiv2026

Defined Mars Media (DMM), a chemically defined simulant of the soluble macro- and micro- nutrients in Mars regolith for use in biological research

Mars harsh yet workable surface conditions, such as manageable temperatures, availability of solar energy, and in situ resources like water ice, carbon dioxide, and mineral-rich regolith, make it a compelling target for supporting life beyond Earth. However, existing experiments testing chemical habitability in Mars conditions generally rely on leachates of physical regolith simulants, which vary in composition across simulant types, leaching conditions, and production batches. We introduce a defined Mars media (DMM) that accurately simulates the biologically relevant nutrients (nitrogen, phosphorus, and sulfur) and stressors (perchlorates, heavy metals) in Martian regolith when it is leached in water at neutral pH. We formulated DMM by combining direct rover and lander measurements from Mars with laboratory measurements of regolith simulant leachates. We validate DMM from a 1x to 20x concentrate, equivalent to 40 g/L to 800 g/L of leached regolith. Using DMM with acetate as a Mars atmosphere-derived carbon source, we grew eight bacteria, demonstrating that organisms can source all essential nutrients from Martian resources. We also demonstrate that microbial growth in DMM is robust to uncertainties in Martian regolith composition: sensitivity experiments can identify limiting trace element nutrients and toxins in DMM, and show that bacterial growth is maintained across at least an order of magnitude variation in their concentrations. This is the first defined Mars regolith media recipe containing both macro- and micro-nutrients, and designed specifically for biological experimentation. By shifting from variable leachate-based approaches to a defined aqueous analog, we enable controlled hypothesis testing of microbial survival, growth, and function in a Martian chemical environment. DMM will enable further research on astrobiology, biological in situ resource utilization, large-scale soil remediation, and terraforming. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=121 SRC="FIGDIR/small/719001v2_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@37408forg.highwire.dtl.DTLVardef@16fabfforg.highwire.dtl.DTLVardef@84fa43org.highwire.dtl.DTLVardef@4a7c5e_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2026

BioBloom, a method for barcoded saturation mutagenesis of an entire bacterial genome

Saturation mutagenesis is a powerful tool for understanding and engineering the function of biological systems. and has been applied successfully to characterize the mutational landscape of individual proteins and genetic loci. However. it has not been applied at the whole-genome scale due to the challenges of both creating and quantifying a saturating set of mutations. Here we introduce BioBloom. a retron-based method for barcoded saturation mutagenesis at the scale of a whole bacterial genome. We constructed a barcoded BioBloom library with >99% projected sampling of saturating single - nucleotide polymorphism (SNP) mutations of the E. coli genome, and applied it to identify beneficial mutations under salt and antibiotic selection. Relative to other techniques like CRISPR-enabled mutagenesis or Adaptive Laboratory Evolution, BioBloom excels at identifying diverse causal SNPs quickly and at smaller working volumes. A barcoded, saturating mutation library is also a shared resource, and we are releasing the updated BioBloom-Ec.-2.0 library to the scientific community for broader adoption and application. Together. BioBloom makes barcoded saturation mutagenesis accessible at whole-genome scale, creating new opportunities for large-scale data collection and bacterial engineering.

microbiology↗

bioRxiv2025

Aging-associated alternative splicing programs conserved between human and mouse tissues

Alternative splicing is a key gene regulatory process that diversifies the proteome and controls gene dosage. Previous studies have detected aging-associated splicing changes across various tissues. However, their use of bulk RNA-seq obfuscates the impacted cell-types and may confound cell-type proportion changes with cell-intrinsic ones. We present a framework that first assembles and maps alternative splicing events in appropriate single cell RNA-seq data (scRNA-seq), then applies LeafletFA, a probabilistic model that discovers coordinated splicing programs (SPs) without requiring prior knowledge of cell types or clinical information, such as age. Applying this framework to over 200,000 cells from mouse and human Smart-seq2 multi-tissue atlases, we discovered global and cell type specific aging-associated SPs. In mice, integrating SPs with gene expression significantly improved age prediction in 46 of 76 tissue-cell types tested. We identified the RNA-binding protein Snrnp70 as a key upstream mediator driving the shift from youthful to aged SPs. To test evolutionary conservation of these patterns, we employed transfer learning to map the murine splicing dictionary onto human transcriptomes. This revealed a conserved "youth" program (SP4) that is maintained in quiescent endothelial tissues but lost in high-turnover organs. We identified a conserved, age-dependent alternative 5 splice site usage in the splicing factor SRSF5 as a molecular marker of this program in both species. Our work establishes alternative splicing as a coordinated, evolutionarily conserved dimension of cellular aging and provides LeafletFA as an open-source tool for single-cell splicing analysis.

systems biology↗

bioRxiv2025

Robust Integration of Sparse Single-Cell Alternative Splicing and Gene Expression Data with SpliceVI

Alternative splicing (AS) and gene expression (GE) are tightly related regulatory processes, critical for defining cell types and states, yet are rarely modeled together in single-cell analyses. This hinders a comprehensive understanding of cellular identity. We address this by introducing SpliceVI, adapted from MultiVI (Multi-modal Variational Inference) to specifically handle AS. Applied to a large multisample mouse Smart-seq2 dataset (n = 142, 315 cells/nuclei), SpliceVI jointly learns from both AS and GE using a partial variational autoencoder that effectively handles the sparsity and missingness of splicing data. We show that SpliceVIs joint embeddings are more expressive and informative of biological correlates like age than a GE-only approach (scVI). SpliceVI also uncovers splicingbased differences between neuronal subclusters. This approach reveals the distinct yet synergistic relationship between AS and GE in shaping cellular diversity in mouse.

systems biology↗

bioRxiv2024

Orthrus: Towards Evolutionary and Functional RNA Foundation Models

In the face of rapidly accumulating genomic data, our ability to accurately predict key mature RNA properties that underlie transcript function and regulation remains limited. Pre-trained genomic foundation models offer an avenue to adapt learned RNA representations to biological prediction tasks. However, existing genomic foundation models are trained using strategies borrowed from textual domains that do not leverage biological domain knowledge. Here, we introduce Orthrus, a Mamba-based mature RNA foundation model pre-trained using a novel self-supervised contrastive learning objective with biological augmentations. Orthrus is trained by maximizing embedding similarity between curated pairs of RNA transcripts, where pairs are formed from splice isoforms of 10 model organisms and transcripts from orthologous genes in 400+ mammalian species from the Zoonomia Project. This training objective results in a latent representation that clusters RNA sequences with functional and evolutionary similarities. We find that the generalized mature RNA isoform representations learned by Orthrus significantly outperform genomic foundation models on mRNA property prediction tasks, and requires only a fraction of fine-tuning data to do so. Finally, we show that Orthrus is capable of capturing divergent biological function of individual transcript isoforms.

genomics↗

bioRxiv2023

Investigating RNA splicing as a source of cellular diversity using a binomial mixture model

Alternative splicing (AS) contributes significantly to RNA and protein variability yet its role in defining cellular diversity is not fully understood. While Smart-seq2 offers enhanced coverage across transcripts compared to 10X single cell RNA-sequencing (scRNA-seq), current computational methods often miss the full complexity of AS. Most approaches for single cell based differential splicing analysis focus on simple AS events such as exon skipping, and rely on predefined cell type labels or low-dimensional gene expression representations. This limits their ability to detect more complex AS events and makes them dependent on prior knowledge of cell classifications. Here, we present Leaflet, a splice junction centric approach inspired by Leafcutter, our tool for quantifying RNA splicing variation with bulk RNA-seq. Leaflet is a probabilistic mixture model designed to infer AS-driven cell states without the need for cell type labels. We detail Leaflets generative model, inference methodology, and its efficiency in detecting differentially spliced junctions. By applying Leaflet to the Tabula Muris brain cell dataset, we highlight cell-state specific splicing patterns, offering a deeper insight into cellular diversity beyond that captured by gene expression alone.

systems biology↗

bioRxiv2023

Cas13d-mediated isoform-specific RNA knockdown with a unified computational and experimental toolbox

Alternative splicing is an essential mechanism for diversifying proteins, in which mature RNA isoforms produce proteins with potentially distinct functions. Two major challenges in characterizing the cellular function of isoforms are the lack of experimental methods to specifically and efficiently modulate isoform expression and computational tools for complex experimental design. To address these gaps, we developed and methodically tested a strategy which pairs the RNA-targeting CRISPR/Cas13d system with guide RNAs that span exon-exon junctions in the mature RNA. We performed a high-throughput essentiality screen, quantitative RT-PCR assays, and PacBio long read sequencing to affirm our ability to specifically target and robustly knockdown individual RNA isoforms. In parallel, we provide computational tools for experimental design and screen analysis. Considering all possible splice junctions annotated in GENCODE for multi-isoform genes and our gRNA efficacy predictions, we estimate that our junction-centric strategy can uniquely target up to 89% of human RNA isoforms, including 50,066 protein-coding and 11,415 lncRNA isoforms. Importantly, this specificity spans all splicing and transcriptional events, including exon skipping and inclusion, alternative 5 and 3 splice sites, and alternative starts and ends.

genomics↗

bioRxiv2019

Comprehensive analysis of lncRNAs reveals candidate prognostic biomarkers in multiple cancer types

Long non-coding RNAs (lncRNAs) are increasingly recognized as functional units in cancer pathways and powerful molecular biomarkers, however most lncRNAs remain uncharacterized. Here we performed a systematic discovery of prognostic lncRNAs in 9,326 patient tumors of 29 types using a proportional-hazards elastic net machine-learning framework. lncRNAs showed highly tissue-specific transcript abundance patterns. We identified 179 prognostic lncRNAs whose abundance correlated with patient risk and improved the performance of common clinical variables and molecular tumor subtypes. Pathway analysis revealed a large diversity of the high-risk tumors stratified by lncRNAs and suggested their functional associations. In lower-grade gliomas, discrete activation of HOXA10-AS indicated poor patient prognosis, neurodevelopmental pathway activation and a transcriptomic similarity to glioblastomas. HOXA10-AS knockdown in patient-derived glioblastoma cells caused decreased cell proliferation and deregulation of glioma driver genes and proliferation pathways. Our study underlines the pan-cancer potential of the non-coding transcriptome for developing molecular biomarkers and innovative therapeutic strategies.

cancer biology↗

bioRxiv2017

Discovery and characterization of coding and non-coding driver mutations in more than 2,500 whole cancer genomes

Discovery of cancer drivers has traditionally focused on the identification of protein-coding genes. Here we present a comprehensive analysis of putative cancer driver mutations in both protein-coding and non-coding genomic regions across >2,500 whole cancer genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We developed a statistically rigorous strategy for combining significance levels from multiple driver discovery methods and demonstrate that the integrated results overcome limitations of individual methods. We combined this strategy with careful filtering and applied it to protein-coding genes, promoters, untranslated regions (UTRs), distal enhancers and non-coding RNAs. These analyses redefine the landscape of non-coding driver mutations in cancer genomes, confirming a few previously reported elements and raising doubts about others, while identifying novel candidate elements across 27 cancer types. Novel recurrent events were found in the promoters or 5UTRs of TP53, RFTN1, RNF34, and MTG2, in the 3UTRs of NFKBIZ and TOB1, and in the non-coding RNA RMRP. We provide evidence that the previously reported non-coding RNAs NEAT1 and MALAT1 may be subject to a localized mutational process. Perhaps the most striking finding is the relative paucity of point mutations driving cancer in non-coding genes and regulatory elements. Though we have limited power to discover infrequent non-coding drivers in individual cohorts, combined analysis of promoters of known cancer genes show little excess of mutations beyond TERT.

genomics↗

bioRxiv2017

Candidate cancer driver mutations in super-enhancers and long-range chromatin interaction networks

A comprehensive catalogue of the mutations that drive tumorigenesis and progression is essential to understanding tumor biology and developing therapies. Protein-coding driver mutations have been well-characterized by large exome-sequencing studies, however many tumors have no mutations in protein-coding driver genes. Non-coding mutations are thought to explain many of these cases, however few non-coding drivers besides TERT promoter are known. To fill this gap, we analyzed 150,000 cis-regulatory regions in 1,844 whole cancer genomes from the ICGC-TCGA PCAWG project. Using our new method, ActiveDriverWGS, we found 41 frequently mutated regulatory elements (FMREs) enriched in non-coding SNVs and indels (FDR<0.05) characterized by aging-associated mutation signatures and frequent structural variants. Most FMREs are distal from genes, reported here for the first time and also recovered by additional driver discovery methods. FMREs were enriched in super-enhancers, H3K27ac enhancer marks of primary tumors and long-range chromatin interactions, suggesting that the mutations drive cancer by distally controlling gene expression through threedimensional genome organization. In support of this hypothesis, the chromatin interaction network of FMREs and target genes revealed associations of mutations and differential gene expression of known and novel cancer genes (e.g., CNNB1IP1, RCC1), activation of immune response pathways and altered enhancer marks. Thus distal genomic regions may include additional, infrequently mutated drivers that act on target genes via chromatin loops. Our study is an important step towards finding such regulatory regions and deciphering the somatic mutation landscape of the non-coding genome.

cancer biology↗