Biology subjects

Andrews, B.

Publications and source records attributed to Andrews, B..

15 recordsLinked to original sources

bioRxiv2026

Limit-pushing overexpression reveals constraints on protein abundance

Proteins are often classified as toxic or non-toxic without measuring the abundance reached, leaving constraints on tolerable protein abundance unresolved. We established a limit-pushing approach in Saccharomyces cerevisiae combining strong inducible expression with gTOW-mediated high-copy selection to counteract copy-number compensation while measuring protein abundance and growth. Nearly all of approximately 80 chromosome I proteins severely inhibited growth or reduced viability at sufficiently high abundance. We established IE50, the expression level associated with a 50% reduction in growth rate, to quantify their widely varying overexpression tolerance. IE50 was positively associated with predicted structural order and cytoplasmic localization propensity and negatively associated with sulphur content. Single-cell imaging linked higher tolerance to proteins remaining cytoplasmic without becoming aggregation-positive and revealed abundance-dependent changes in localization and organelle morphology. At extreme abundance, Fun12, Nup60, and Pex22 generated distinct large-scale intracellular states through specific sequence regions. These findings establish overexpression toxicity as a quantitative property linked to protein characteristics and reveal both constraints on tolerable abundance and sequence-dependent capacities for intracellular organization.

systems biology↗

bioRxiv2026

The Phenotypic Landscape of a Circadian Clock

Circadian clocks produce near-24-hour oscillations through biochemical feedback loops. To study their architecture, we developed a deep sequencing assay that measures the phenotypes of thousands of mutant clocks in parallel. We reveal a landscape where oscillator properties are factorized: mutations change period without decreasing amplitude and while maintaining a balanced waveform. Mutations that either shorten or lengthen period localize to specific protein-protein interaction surfaces, while a particularly sensitive region near the KaiC interdomain linker can cause extreme effects. After entrainment, high amplitude mutant oscillators form a tunable low-dimensional manifold in the period-phase plane, suggesting that most period mutations leave the coupling to the environment unchanged. In contrast, mutations that reduce amplitude are concentrated in a specific long period phenotype. This correlation structure may support the evolvability of this dynamical molecular system and is a powerful constraint on underlying mechanism.

biophysics↗

bioRxiv2026

BCAR: A fast and general barcode-sequence mapper for correcting sequencing errors

MotivationDNA barcodes are commonly used as a tool to distinguish genuine mutations from sequencing errors in sequencing-based assays. In the presence of indel errors, utilizing barcodes requires accurate alignment of the raw reads to distinguish genuine indels from indel errors. Existing strategies to do this generally rely on aligners built for homology comparison and do not fully utilize quality scores. We reasoned that developing an aligner purpose-built for error correction could yield higher quality barcode-sequence maps. ResultsHere, we present BCAR, a fast barcode-sequence mapper for correcting sequencing errors. BCAR considers all of the evidence for each base call at each position both during alignment and during final consensus generation. BCAR creates high-accuracy barcode-sequence maps from simulated reads across a broad range of error rates and read lengths, outperforming existing methods. We apply BCAR to two experimental datasets, where it generates high-quality barcode-sequence maps. Availability and implementationBCAR source code, documentation and test data are available from: https://github.com/dry-brews/BCAR

bioinformatics↗

bioRxiv2025

Inhibition of METTL3 by STC-15 induces RNA misprocessing that results in dsRNA formation and activates innate immunity

The RNA methyltransferase METTL3 is responsible for the generation of m6A, the most abundant modification mark on mRNA and long non-coding RNA. Accumulating evidence suggests numerous roles of METTL3 in cancer initiation and progression and highlights the potential for targeting this enzyme in oncology. STC-15 is a potent and selective METTL3 inhibitor and the first RNA modifying enzyme inhibitor to enter human clinical development. It is structurally related to the previously published tool inhibitors STM2457 and STM3675. We previously identified the induction of a cancer cell-intrinsic interferon response following pharmacological inhibition of METTL3, leading to activation of T-cell-mediated anti-tumour response. Here, we profiled m6A levels at nucleotide resolution using GLORI and characterised RNA changes following METTL3 inhibition with STC-15 or STM3675. Following loss of m6A, we uncovered aberrant mRNA transcripts arising from intron retention (IR) and transcriptional run-on (RO) events downstream of m6A-enriched exons in human cancer cells and in tumour samples in vivo. We found that these IR and RO events produce double-stranded RNA and are bound by the cytoplasmic dsRNA sensor MDA5. Using preclinical in vitro and in vivo models, we characterised in detail the anti-tumour immune responses induced by STC-15. Our study reveals how METTL3 inhibition leads to dsRNA accumulation, which triggers a type I interferon response and induces anti-tumour immunity. Together, these findings provide a mechanistic rationale for STC-15 as a novel anti-cancer drug both as monotherapy and in combination with anti-PD1 checkpoint inhibitors.

cancer biology↗

bioRxiv2025

Causal Modeling Reveals Cell-Cell Communication Dynamics in the Tumor Microenvironment During Anti-PD-1 Therapy in Breast Cancer Patients

BackgroundImmune checkpoint blockade (ICB) targeting PD-1/PD-L1 plays a crucial role in breast cancer treatment. Despite clinical success, how ICB reshapes the tumor microenvironment (TME) to enhance anti-tumor activity remains unclear. Anti-PD-1 therapy alters TME cells beyond PD-1+ T cells, with extensive cell-cell communication (CCC) playing a key role. Understanding the dynamic CCC changes upon anti-PD-1 treatment can illuminate ICB mechanisms of action and TME dynamics. MethodsWe analyzed single-cell RNA-seq data from 31 breast cancer patients before and after anti-PD-1 (pembrolizumab) treatment (Bassez et al., 2021). We identified differentially expressed genes (DEGs) induced by treatment in major cell types. We then applied an instrumental variable approach to uncover causal relationships between T-cell and non-T-cell DEGs. We further mapped ligand-receptor interactions mediating signal transduction between cells and constructed a CCC network from T to non-T cells. ResultsAnti-PD-1 therapy induced widespread transcriptional changes across multiple cell populations. Key pathways modulated in T cells included NF-{kappa}B, interferon-{gamma}, and interleukin signaling. CD4+ and CD8+ exhausted T cells engaged in distinct ligand-receptor interactions with tumor-associated macrophages (TAMs) and other types of cells, reshaping the TME. Our results indicated CD4+ exhausted T cells activated M1-like TAMs via TNF-TNFRSF1A and TNFSF14-LTBR, while CD8+ exhausted T cells engaged M1-like TAMs through ICAM1-ITGAL/ITGB2 and CCL8-CCR2, promoting anti-tumor immunity. Conversely, immunosuppressive interactions were also observed, such as TNF-TNFRSF1B (TNFR2) and TNFSF14-TNFRSF14 (HVEM) from CD4 T cells, as well as CSF1-CSF1R and RPS19-C5AR1 from CD8 T cells, which likely promote M2-like tumor-associated macrophage (TAM) polarization and contribute to pro-tumor immune regulation and resistance to therapy. Notably, key receptors in the causal CCC networks, such as C5AR1, TNFR2, and CSF1R, emerged as potential targets to enhance anti-PD-1 efficacy. ConclusionsThese findings elucidate TME remodeling during anti-PD-1 therapy and underscore the pivotal role of CCC in treatment response. Our study identifies critical communication networks that may be biomarkers for immunotherapy responsiveness and highlights novel therapeutic targets, including C5AR1 and HVEM. Furthermore, our application of causal inference methodologies provides a robust framework for dissecting CCC mechanisms in immunotherapy.

bioinformatics↗

bioRxiv2025

Protein language models accelerate the discovery of Plastic-Degrading Enzymes

Plastic pollution presents a critical environmental challenge, necessitating innovative and sustainable solutions. In this context, biodegradation using microorganisms and enzymes offers an environmentally friendly alternative. This work introduces an AI-driven frame-work that integrates machine learning (ML) and generative models to accelerate the discovery and design of plastic-degrading enzymes. By leveraging pre-trained protein language models and curated datasets, we developed seven ML-based binary classification models to identify enzymes targeting specific plastic substrates, achieving an average accuracy of 89%. The framework was applied to over 6,000 enzyme sequences from the RemeDB to classify enzymes targeting diverse plastics, including PET, PLA, and Nylon. Besides, generative learning strategies combined with trained classification models in this work were applied for de novo generation of PET-degrading enzymes. Structural bioinformatics validated potential candidates through in-silico analysis, highlighting differences in physicochemical properties between generated and experimentally validated enzymes. Moreover, generated sequences exhibited lower molecular weights and higher aliphatic indices, features that may enhance interactions with hydrophobic plastic substrates. These findings highlight the utility of AI-based approaches in enzyme discovery, providing a scalable and efficient tool for addressing plastic pollution. Future work will focus on experimental validation of promising candidates and further refinement of generative strategies to optimize enzymatic performance.

bioengineering↗

bioRxiv2024

TSC22D, WNK and NRBP gene families exhibit functional buffering and evolved with Metazoa for macromolecular crowd sensing

The ability to sense and respond to osmotic fluctuations is critical for the maintenance of cellular integrity. Myriad redundancies have evolved across all facets of osmosensing in metazoans, including among water and ion transporters, regulators of cellular morphology, and macromolecular crowding sensors, hampering efforts to gain a clear understanding of how cells respond to rapid water loss. In this study, we harness the power of gene co-essentiality analysis and genome-scale CRISPR-Cas9 screening to identify an unappreciated relationship between TSC22D2, WNK1 and NRBP1 in regulating cell volume homeostasis. Each of these genes have paralogs and are functionally buffered for macromolecular crowd sensing and cell volume control. Within seconds of hyperosmotic stress, TSC22D, WNK and NRBP family members physically associate into cytoplasmic biocondensates, a process that is dependent on intrinsically disordered regions (IDRs). A close examination of these protein families across metazoans reveals that TSC22D genes evolved alongside a domain in NRBPs that specifically binds to TSC22D proteins, which we have termed NbrT (NRBP binding region with TSC22D), and this co-evolution is concomitant with rapid IDR length expansion in WNK family kinases. Our study identifies functions for unrecognized components of the cell volume sensing machinery and reveals that TSC22D, WNK and NRBP genes evolved as cytoplasmic crowding sensors in metazoans to co-regulate rapid cell volume changes in response to osmolarity.

cell biology↗

bioRxiv2023

Expanded diversity of hemoplasmas in rare and undersampled Neotropical bats

Hemotropic mycoplasmas are emerging as a model system for studying bacterial pathogens in bats, but taxonomic coverage of sampled host species remains biased. We leveraged a long-term field study in Belize to uncover novel hemoplasma diversity in bats by analyzing 80 samples from 19 species, most of which are infrequently encountered. PCR targeting the partial 16S rRNA gene found 41% of bats positive for hemoplasmas. Phylogenetic analyses found two novel host shifts of hemoplasmas, four entirely new hemoplasma genotypes, and the first hemoplasma detections in four bat species. One of these novel hemoplasmas (from Neoeptesicus furinalis) shared 97.6% identity in the partial 16S rRNA gene to a human hemoplasma (Candidatus Mycoplasma haemohominis). Additional analysis of the partial 23S rRNA gene allowed us to also designate two novel hemoplasma species, in Myotis elegans and Phyllostomus discolor, with the proposed names Candidatus Mycoplasma haematomyotis sp. nov. and Candidatus Mycoplasma haematophyllostomi sp. nov., respectively. Our analyses show that additional hemoplasma diversity in bats can be uncovered by targeting rare or undersampled host species.

microbiology↗

bioRxiv2021

A late-stage assembly checkpoint of the human mitochondrial ribosome large subunit

The epitranscriptome plays a key regulatory role in cellular processes in health and disease, including ribosome biogenesis. Here, analysis of the human mitochondrial transcriptome shows that 2-O-methylation is limited to residues of the mitoribosomal large subunit (mtLSU) 16S mt-rRNA, modified by MRM1, MRM2, and MRM3. Ablation of MRM2 leads to a severe impairment of the oxidative phosphorylation system, caused by defective mitochondrial translation and accumulation of mtLSU assembly intermediates. Structures of these particles (2.58 [A]) present disordered RNA domains, partial occupancy of bL36m and bound MALSU1:L0R8F8:mtACP anti-association module. Additionally, we present five mtLSU assembly states with different intersubunit interface configurations. Complementation studies demonstrate that the methyltransferase activity of MRM2 is dispensable for mitoribosome biogenesis. The Drosophila melanogaster orthologue, DmMRM2, is an essential gene, with its knock-down leading to developmental arrest. This work identifies a key late-stage quality control step during mtLSU assembly, ultimately contributing to the maintenance of mitochondrial homeostasis.

molecular biology↗

bioRxiv2020

Balance between promiscuity and specificity in phage {lambda} host range

As hosts acquire resistance to viruses, viruses must overcome that resistance to re-establish infectivity, or go extinct. Despite the significant hurdles associated with adapting to a resistant host, viruses are evolutionarily successful and maintain stable coevolutionary relationships with their hosts. To investigate the factors underlying how pathogens adapt to their hosts, we performed a deep mutational scan of the region of the λ tail fiber tip protein that mediates contact with the λ host, E. coli. Phages harboring amino acid substitutions were subjected to selection for infectivity on wild type E. coli, revealing a highly restrictive fitness landscape, in which most substitutions completely abrogate function. By comparing this lack of mutational tolerance to evolutionary diversity, we highlight a set of mutationally intolerant and diverse positions associated with host range expansion. Imposing selection for infectivity on three λ-resistant hosts, each harboring a different missense mutation in the λ receptor, reveals hundreds of adaptive variants in λ. We distinguish λ variants that confer promiscuity, a general ability to overcome host resistance, from those that drive host-specific infectivity. Both processes may be important in driving adaptation to a novel host.Competing Interest StatementThe authors have declared no competing interest.View Full Text

molecular biology↗

bioRxiv2018

A computational platform for high-throughput analysis of RNA sequences and modifications by mass spectrometry

The field of epitranscriptomics is growing in importance, with chemical modification of RNA being associated with a wide variety of biological phenomena. A pivotal challenge in this area is the identification of modified RNA residues within their sequence contexts. Next-generation sequencing approaches are generally unable to capture modifications, although workarounds for some epigenetic marks exist. Mass spectrometry (MS) offers a comprehensive solution by using analogous approaches to shotgun proteomics. However, software support for the analysis of RNA MS data is inadequate at present and does not allow high-throughput processing. In particular, existing software solutions lack the raw performance and statistical grounding to efficiently handle the large variety of modifications present on RNA. We present a free and open-source database search engine for RNA MS data, called NucleicAcidSearchEngine (NASE), that addresses these shortcomings. We demonstrate the capability of NASE to reliably identify a wide range of modified RNA sequences in three original datasets of varying complexity. In a human tRNA sample, we characterize over 20 different modification types simultaneously and find many cases of incomplete modification.

bioinformatics↗

bioRxiv2018

Multidimensional dynamics of the proteome in the degenerative and ageing mammalian brain

The amount of any given protein in the brain is determined by the rates of its synthesis and destruction, which are regulated by different cellular mechanisms. Here, we combine metabolic labelling in live mice with global proteomic profiling to simultaneously quantify both the flux and amount of proteins in mouse models of neurodegeneration. In multiple models, protein turnover increases were associated with increasing pathology. This method distinguishes changes in protein expression mediated by synthesis from those mediated by degradation. In the AppNL-F knockin mouse model of Alzheimers disease increased turnover resulted from imbalances in both synthesis and degradation, converging on proteins associated with synaptic vesicle recycling (Dnm1, Cltc, Rims1) and mitochondria (Fis1, Ndufv1). In contrast to disease models, ageing in wildtype mice caused a widespread decrease in protein recycling associated with a decrease in autophagic flux. This simple multidimensional approach enables the comprehensive mapping of proteome dynamics and identifies affected proteins in mouse models of disease and other live animal test settings.

systems biology↗

bioRxiv2017

Evaluation and Design of Genome-wide CRISPR/Cas9 Knockout Screens

The adaptation of CRISPR/Cas9 technology to mammalian cell lines is transforming the study of human functional genomics. Pooled libraries of CRISPR guide RNAs (gRNAs), targeting human protein-coding genes and encoded in viral vectors, have been used to systematically create gene knockouts in a variety of human cancer and immortalized cell lines, in an effort to identify whether these knockouts cause cellular fitness defects. Previous work has shown that CRISPR screens are more sensitive and specific than pooled library shRNA screens in similar assays, but currently there exists significant variability across CRISPR library designs and experimental protocols. In this study, we re-analyze 17 genome-scale knockout screens in human cell lines from three research groups using three different genome-scale gRNA libraries, using the Bayesian Analysis of Gene Essentiality (BAGEL) algorithm to identify essential genes, to refine and expand our previously defined set of human core essential genes, from 360 to 684 genes. We use this expanded set of reference Core Essential Genes (CEG2), plus empirical data from six CRISPR knockout screens, to guide the design of a sequence-optimized gRNA library, the Toronto KnockOut version 3.0 (TKOv3) library. We demonstrate the high effectiveness of the library relative to reference sets of essential and nonessential genes as well as other screens using similar approaches. The optimized TKOv3 library, combined with the CEG2 reference set, provide an efficient, highly optimized platform for performing and assessing gene knockout screens in human cell lines.

systems biology↗

bioRxiv2017

Systematic identification of pleiotropic genes from genetic interactions

Modular structures in biological networks are ubiquitous and well-described, yet this organization does not capture the complexity of genes individually influencing many modules. Pleiotropy, the phenomenon of a single genetic locus with multiple phenotypic effects, has previously been measured according to many definitions, which typically count phenotypes associated with genes. We take the perspective that, because genes work in complex and interconnected modules, pleiotropy can be treated as a network-derived characteristic. Here, we use the complete network of yeast genetic interactions (GI) to measure pleiotropy of nearly 2700 essential and nonessential genes. Our method uses frequent item set mining to discover GI modules, annotates them with high-level processes, and uses entropy to measure the functional spread of each genes set of containing modules. We classify genes whose modules indicate broad functional influence as having high pleiotropy, while genes with focused functional influence have low pleiotropy. These pleiotropy classes differed in a number of ways: high-pleiotropy genes have comparatively higher expression variance, higher protein abundance, more domains, and higher copy number, while low pleiotropy genes are more likely to be in protein complexes and have many curated phenotypes. We discuss the implications of these results regarding the nature and evolution of pleiotropy.

genomics↗

bioRxiv2017

Functional Annotation of Chemical Libraries across Diverse Biological Processes

Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells to a compound, revealing chemical-genetic interactions that can elucidate a compounds mode of action. We developed a highly parallel and unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized, diagnostic mutant collection that is predictive all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode sequencing protocol, enabling assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened 7 different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action.

systems biology↗