Biology subjects

Law, J.

Publications and source records attributed to Law, J..

8 recordsLinked to original sources

bioRxiv2025

AI-assisted Drug Re-purposing for Human Liver Fibrosis

Liver fibrosis is a severe disease with few treatment options due to the poor quality of the available animal and in vitro models. To address this, we investigated whether a hypothesis generating multi-agent AI system (AI co-scientist) could assist in re-purposing drugs for treatment of liver fibrosis and direct their experimental characterization. A multi-parameter image analysis workflow, which enabled anti-fibrotic efficacy and drug toxicity to be serially assessed in multi-lineage human hepatic organoids grown in microwells (i.e., microHOs), was used to assess the effects of 14 drugs. Remarkably, two of the three AI co-scientist-recommended drugs that targeted epigenomic modifiers exhibited significant anti-fibrotic activity. Analysis of the anti-fibrotic effects of five drugs indicated that two inhibited TGF{beta}-induced intracellular signaling and three drugs altered TGF{beta}-induced mesenchymal cell differentiation. Since all five of the anti-fibrotic drugs reduced TGF{beta}-induced chromatin structural changes, epigenomic changes play an important role in the pathogenesis of liver fibrosis. One AI co-scientist recommended drug is an FDA-approved anti-cancer treatment (Vorinostat) that reduced TGF{beta}-induced chromatin structural changes by 91% and promoted liver parenchymal cell regeneration in microHOs. Hence, the use of AI co-scientist and this microHO platform identified a potential new generation of liver fibrosis treatments that also promote liver regeneration.

bioinformatics↗

bioRxiv2025

Transcription factors instruct DNA methylation patterns in plant reproductive tissues

DNA methylation is maintained by forming self-reinforcing connections with other repressive chromatin modifications, resulting in stably silenced genes and transposons. However, these mechanisms fail to explain how new methylation patterns are generated. In Arabidopsis, CLASSY3 (CLSY3) targets the RNA-directed DNA methylation (RdDM) machinery to different loci in reproductive tissues, generating distinct epigenomes via unknown mechanism(s). Here, we discovered that several different REPRODUCTIVE MERISTEM (REM) transcription factors are required for methylation at CLSY3 targets specific to male or female reproductive tissues. We designate these factors as REM INSTRUCT METHYLATION (RIMs) and demonstrate that disruption of their DNA binding domains, or the motifs they recognize, blocks RdDM. These findings not only reveal RIMs as the first sex-specific RdDM proteins but also establish a critical role for genetic information in targeting DNA methylation. This novel mode of targeting expands our understanding of how methylation is regulated to include inputs from both genetic and epigenetic information.

plant biology↗

bioRxiv2023

Breakage fusion bridge cycles drive high oncogene copy number, but not intratumoral genetic heterogeneity or rapid cancer genome change

Oncogene amplification is a major driver of cancer pathogenesis. Breakage fusion bridge (BFB) cycles, like extrachromosomal DNA (ecDNA), can lead to high copy numbers of oncogenes, but their impact on intratumoral heterogeneity, treatment response, and patient survival are not well understood due to difficulty in detecting them by DNA sequencing. We describe a novel algorithm that detects and reconstructs BFB amplifications using optical genome maps (OGMs), called OM2BFB. OM2BFB showed high precision (>93%) and recall (92%) in detecting BFB amplifications in cancer cell lines, PDX models and primary tumors. OM-based comparisons demonstrated that short-read BFB detection using our AmpliconSuite (AS) toolkit also achieved high precision, albeit with reduced sensitivity. We detected 371 BFB events using whole genome sequences from 2,557 primary tumors and cancer lines. BFB amplifications were preferentially found in cervical, head and neck, lung, and esophageal cancers, but rarely in brain cancers. BFB amplified genes show lower variance of gene expression, with fewer options for regulatory rewiring relative to ecDNA amplified genes. BFB positive (BFB (+)) tumors showed reduced heterogeneity of amplicon structures, and delayed onset of resistance, relative to ecDNA(+) tumors. EcDNA and BFB amplifications represent contrasting mechanisms to increase the copy numbers of oncogene with markedly different characteristics that suggest different routes for intervention.

cancer biology↗

bioRxiv2023

The influence of marker number and sequencing depth on the ability to identify mismatch repair deficient tumours.

1.Analysis of somatic mutation patterns is widely used to infer exposure to exogenous and endogenous mutagenic influences. This raises the question of the amount of sequence data required to detect factors of interest. A common use of mutation pattern analysis is the identification of increased microsatellite instability to uncover mismatch repair (MMR) defects in tumours and normal tissues. Here we explore the effects of sequencing depth and the number of loci analysed on the ability to detect MMR deficiency using artificial neural networks and publicly available amplicon sequencing data from colorectal tumours on 24 short quasi monomorphic microsatellites (up to 12 bp in length, PMID 31471937) split in a training (99 samples) and a test set (95 samples). We show that, at a sequencing depth of 200, pairs mononucleotide repeats can achieve discrimination between MMR proficient and deficient colorectal tumours similar to that obtained with the full 24 marker panel, with accuracies above 97% and ROC AUCs in excess of 99% in the test set. Our results indicate that for short monomorphic microsatellites considering the length distribution of the different alleles at each locus, representing these distributions as two-dimensional structures and including convolutional layers in the network can facilitate discrimination between MMR deficient and proficient tumour material. They also indicate that, despite the limitations imposed by amplification, sequencing accuracy and the limited divergence time between the sequences from one locus, high depth sequencing can be used to identify MMR deficiency from a limited number of loci. However, they also suggest that, for a fixed total number of reads per sample, increasing sensitivity by increasing the number of targets is more efficient than by increasing per target sequencing depth. These results are of interest for screening large numbers of samples and for assessing the impact MMR deficiency in different areas of the genome.

genetics↗

bioRxiv2023

Periplasmic stress contributes to a tradeoff between protein secretion and cell growth in E. Coli Nissile

Maximizing protein secretion is an important target in the design of engineered living systems. In this paper, we characterize a tradeoff between cell growth and per cell protein secretion in the curli biofilm secretion system of E Coli Nissile 1917. Initial characterization using 24-hour continuous growth and protein production monitoring confirms decreased growth rates at high induction leading to a local maximum in total protein production at intermediate induction. Propidium iodide staining at the endpoint indicates that cellular death is a dominant cause of growth reduction. Assaying variants with combinatorial constructs of inner and outer membrane secretion tags, we find that diminished growth at high production is specific to secretory variants associated with accumulation of protein containing the outer membrane transport tag in the periplasmic space. RNA sequencing experiments indicate upregulation of known periplasmic stress response genes in the highly secreting variant, further implicating periplasmic stress in the growth-secretion tradeoff. Overall, these results motivate additional strategies for optimizing total protein production and longevity of secretory engineered living systems.

synthetic biology↗

bioRxiv2022

Subcellular distribution of PP1 isoforms in holoenzyme complexes

Unlike its counterpart Ser/Thr kinases, the predominant Ser/Thr protein phosphatase 1 (PP1) is a promiscuous enzyme that gains its subcellular localization and substrate specificity from a large panel of regulatory proteins with which it associates in predominantly dimeric complexes. Inhibition of specific PP1-mediated dephosphorylation events relies on targeting the regulatory rather than the catalytic subunit, which in turn relies on a comprehensive understanding of the holoenzyme complexes that underlie its distribution throughout the cell. Proteomic, bioinformatic and biochemical screens have assembled lists of putative regulatory proteins, which have been studied to varying degrees. We took a non-biased approach to link steady-state localization to complexes, using a combination of fluorescence imaging, cellular fractionation and quantitative affinity purification/mass spectrometry (AP/MS) to map interactomes for PP1[a]/{beta}/{gamma} in 3 human cell lines. Comparing the distribution of each isoform between the pool of identified regulatory subunits highlighted key signaling pathways and identified c20orf27 as a novel PP1 regulatory protein. Steady-state association of a large fraction of PP1 with the evolutionarily conserved SDS22 was demonstrated, as was redistribution at the entry to mitosis. This is consistent with recent work suggesting that SDS22 acts as a PP1 sink from which it can be recruited as needed. Moving forward, this approach can be used to assess the redistribution of PP1 during other cellular processes or in response to perturbations or disease states, facilitating identification of the relevant complexes and the design of strategies to target them therapeutically.

cell biology↗

bioRxiv2020

Data integration through heterogeneous ensembles for protein function prediction

MotivationIntegrating multimodal data represents an effective approach to predicting biomedical characteristics, such as protein functions and disease outcomes. However, existing data integration approaches do not sufficiently address the heterogeneous semantics of multimodal data. In particular, early and intermediate approaches that rely on a uniform integrated representation reinforce the consensus among the modalities, but may lose exclusive local information. The alternative late integration approach that can address this challenge has not been systematically studied for biomedical problems. ResultsWe propose Ensemble Integration (EI) as a novel systematic implementation of the late integration approach. EI infers local predictive models from the individual data modalities using appropriate algorithms, and uses effective heterogeneous ensemble algorithms to integrate these local models into a global predictive model. We also propose a novel interpretation method for EI models. We tested EI on the problems of predicting protein function from multimodal STRING data, and mortality due to COVID-19 from multimodal data in electronic health records. We found that EI accomplished its goal of producing significantly more accurate predictions than each individual modality. It also performed better than several established early integration methods for each of these problems. The interpretation of a representative EI model for COVID-19 mortality prediction identified several disease-relevant features, such as laboratory test (blood urea nitrogen (BUN) and calcium) and vital sign measurements (minimum oxygen saturation) and demographics (age). These results demonstrated the effectiveness of the EI framework for biomedical data integration and predictive modeling. AvailabilityCode and data are available at https://github.com/GauravPandeyLab/ensemble_integration. Contactgaurav.pandey@mssm.edu

bioinformatics↗

bioRxiv2019

Integrating Protein Localization with Automated Signaling Pathway Reconstruction

Understanding cellular responses via signal transduction is a core focus in systems biology. Tools to automatically reconstruct signaling pathways from protein-protein interactions (PPIs) can help biologists generate testable hypotheses about signaling. However, automatic reconstruction of signaling pathways suffers from many interactions with the same confidence score leading to many equally good candidates. Further, some reconstructions are biologically misleading due to ignoring protein localization information. We propose LocPL, a method to improve the automatic reconstruction of signaling pathways from PPIs by incorporating information about protein localization in the reconstructions. The method relies on a dynamic program to ensure that the proteins in a reconstruction are localized in cellular compartments that are consistent with signal transduction from the membrane to the nucleus. LocPL and existing reconstruction algorithms are applied to two PPI networks and assessed using both global and local definitions of accuracy. LocPL produces more accurate and biologically meaningful reconstructions on a versatile set of signaling pathways. LocPL is a powerful tool to automatically reconstruct signaling pathways from PPIs that leverages cellular localization information about proteins. The underlying dynamic program and signaling model are flexible enough to study cellular signaling under different settings of signaling flow across the cellular compartments.

bioinformatics↗