Biology subjects

Melo, H.

Publications and source records attributed to Melo, H..

8 recordsLinked to original sources

bioRxiv2023

A semiconductor 96-microplate platform for real-time impedance-based high-throughput screening

Profiling compounds and genetic perturbations via high-content imaging has become increasingly popular for drug discovery, but the technique is limited to endpoint images of fixed cells. In contrast, electronic-based devices offer label-free, functional information of live cells, yet current approaches suffer from low-spatial resolution or single-well throughput. Here, we report a semiconductor 96-microplate platform designed for high-resolution real-time impedance "imaging" at scale. Each well features 4,096 electrodes at 25 {micro}m spatial resolution while a miniaturized data interface allows 8x parallel plate operation (768 total wells) within each incubator for enhanced throughputs. New electric field-based, multi-frequency measurement techniques capture >20 parameter images including tissue barrier, cell-surface attachment, cell flatness, and motility every 15 min throughout experiments. Using these real-time readouts, we characterized 16 cell types, ranging from primary epithelial to suspension, and quantified heterogeneity in mixed epithelial and mesenchymal co-cultures. A proof-of-concept screen of 904 diverse compounds using 13 semiconductor microplates demonstrates the platforms capability for mechanism of action (MOA) profiling with 25 distinct responses identified. The scalability of the semiconductor platform combined with the translatability of the high dimensional live-cell functional parameters expands high-throughput MOA profiling and phenotypic drug discovery applications.

bioengineering↗

bioRxiv2022

Genome-scale mapping of DNA damage suppressors identifies GNB1L as essential for ATM and ATR biogenesis

To maintain genome integrity, cells must avoid DNA damage by ensuring the accurate duplication of the genome and by having efficient repair and signaling systems that counteract the genome-destabilizing potential of DNA lesions. To uncover genes and pathways that suppress DNA damage in human cells, we undertook genome-scale CRISPR/Cas9 screens that monitored the levels of DNA damage in the absence or presence of DNA replication stress. We identified 160 genes in RKO cells whose mutation caused high levels of DNA damage in the absence of exogenous genotoxic treatment. This list was highly enriched in essential genes, highlighting the importance of genomic integrity for cellular fitness. Furthermore, the majority of these 160 genes are involved in a limited set of biological processes related to DNA replication and repair, nucleotide biosynthesis, RNA metabolism and iron sulfur cluster biogenesis, suggesting that genome integrity may be insulated from a wide range of cellular processes. Among the many genes identified and validated in this study, we discovered that GNB1L, a schizophrenia/autism-susceptibility gene implicated in 22q11.2 syndrome, protects cells from replication catastrophe promoted by mild DNA replication stress. We show that GNB1L is involved in the biogenesis of ATR and related phosphatidylinositol 3-kinase-related kinases (PIKKs) through its interaction with the TTT co-chaperone complex. These results implicate PIKK biogenesis as a potential root cause for the neuropsychiatric phenotypes associated with 22q11.2 syndrome. The phenotypic mapping of genes that suppress DNA damage in human cells therefore provides a powerful approach to probe genome maintenance mechanisms.

molecular biology↗

bioRxiv2021

XENet: Using a new graph convolution to accelerate the timeline for protein design on quantum computers

Graph representations are traditionally used to represent protein structures in sequence design protocols where the folding pattern is known. This infrequently extends to machine learning projects: existing graph convolution algorithms have shortcomings when representing protein environments. One reason for this is the lack of emphasis on edge attributes during massage-passing operations. Another reason is the traditionally shallow nature of graph neural network architectures. Here we introduce an improved message-passing operation that is better equipped to model local kinematics problems such as protein design. Our approach, XENet, pays special attention to both incoming and outgoing edge attributes. We compare XENet against existing graph convolutions in an attempt to decrease rotamer sample counts in Rosettas rotamer substitution protocol. This use case is motivating because it allows larger protein design problems to fit onto near-term quantum computers. XENet outperformed competing models while also displaying a greater tolerance for deeper architectures. We found that XENet was able to decrease rotamer counts by 40% without loss in quality. This decreased the problem size of our use case by more than a factor of 3. Additionally, XENet displayed an ability to handle deeper architectures than competing convolutions. Author summaryGraphs data structures are ubiquitous in the field of protein design and are at the core of the recent advances in artificial intelligence brought forth by graph neural networks (GNNs). GNNs have led to some impressive results in modeling protein interactions, but are not as common as other tensor representations. Most GNN architectures tend to put little to no emphasis on the information stored on edges; however, protein modeling tools often use edges to represent vital geometric relationships about residue pair interactions. In this paper, we show that a more advanced processing of edge attributes can lead to considerable benefits when modeling chemical data. We introduce XENet, a new member of the GNN family that is shown to have improved ability to model protein residue environments based on chemical and geometric data. We use XENet to intelligently simplify the optimization problem that is solved when designing proteins. This task is important to us and others because it allows larger proteins to be designed on near-term quantum computers. We show that XENet is able to train on our protein modeling data better than existing methods, successfully resulting in a dramatic decrease in protein design sample space with no loss in quality.

bioinformatics↗

bioRxiv2021

CCNE1 amplification is synthetic-lethal with PKMYT1 kinase inhibition

Amplification of the gene encoding cyclin E (CCNE1) is an oncogenic driver in several malignancies and is associated with chemoresistance and poor prognosis. To uncover therapeutic targets for CCNE1-amplified tumors, we undertook genome-scale CRISPR/Cas9-based synthetic lethality screens in cellular models of CCNE1 amplification. Here, we report that increasing CCNE1 dosage engenders a vulnerability to the inhibition of the PKMYT1 kinase, a negative regulator of CDK1. To inhibit PKMYT1, we developed RP-6306, an orally bioavailable and selective inhibitor that shows single-agent activity and durable tumor regressions when combined with gemcitabine in models of CCNE1-amplification. RP-6306 treatment causes unscheduled activation of CDK1 selectively in CCNE1 overexpressing-cells, promoting early mitosis in cells undergoing DNA synthesis. CCNE1 overexpression disrupts CDK1 homeostasis at least in part through an early activation of the FOXM1/MYBL2/MuvB-dependent mitotic transcriptional program. We conclude that PKMYT1 inhibition is a promising therapeutic strategy for CCNE1-amplified cancers.

cancer biology↗

bioRxiv2021

CIP2A is a prime synthetic-lethal target for BRCA-mutated cancers

BRCA1/2-mutated cancer cells must adapt to the genome instability caused by their deficiency in homologous recombination. Identifying and targeting these adaptive mechanisms may provide new therapeutic strategies. Here we present the results of genome-scale CRISPR/Cas9-based synthetic lethality screens in isogenic pairs of BRCA1- and BRCA2-deficient cells that identified the gene encoding CIP2A as essential in a wide range of BRCA1- and BRCA2-mutated cells. Unlike PARP inhibition, CIP2A-deficiency does not cause accumulation of replication-associated DNA lesions that require homologous recombination for their repair. CIP2A is cytoplasmic in interphase but, in mitosis, accumulates at DNA lesions as part of a complex with TOPBP1, a multifunctional genome stability factor. In BRCA-deficient cells, the CIP2A-TOPBP1 complex prevents lethal mis-segregation of acentric chromosomes that arises from impaired DNA synthesis. Finally, physical disruption of the CIP2A-TOPBP1 complex is highly deleterious in BRCA-deficient cells and tumors, indicating that targeting this mitotic chromosome stability process represents an attractive synthetic-lethal therapeutic strategy for BRCA1- and BRCA2-mutated cancers.

cancer biology↗

bioRxiv2019

A genetic map of the response to DNA damage in human cells

The response to DNA damage is critical for cellular homeostasis, tumor suppression, immunity and gametogenesis. In order to provide an unbiased and global view of the DNA damage response in human cells, we undertook 28 CRISPR/Cas9 screens against 25 genotoxic agents in the retinal pigment epithelium-1 (RPE1) cell line. These screens identified 840 genes whose loss causes either sensitivity or resistance to DNA damaging agents. Mining this dataset, we uncovered that ERCC6L2, which is mutated in a bone-marrow failure syndrome, codes for a canonical non-homologous end-joining pathway factor; that the RNA polymerase II component ELOF1 modulates the response to transcription-blocking agents and that the cytotoxicity of the G-quadruplex ligand pyridostatin involves trapping topoisomerase II on DNA. This map of the DNA damage response provides a rich resource to study this fundamental cellular system and has implications for the development and use of genotoxic agents in cancer therapy.

cancer biology↗

bioRxiv2019

Designing Peptides on a Quantum Computer

Although a wide variety of quantum computers are currently being developed, actual computational results have been largely restricted to contrived, artificial tasks. Finding ways to apply quantum computers to useful, real-world computational tasks remains an active research area. Here we describe our mapping of the protein design problem to the D-Wave quantum annealer. We present a system whereby Rosetta, a state-of-the-art protein design software suite, interfaces with the D-Wave quantum processing unit to find amino acid side chain identities and conformations to stabilize a fixed protein backbone. Our approach, which we call the QPacker, uses a large side-chain rotamer library and the full Rosetta energy function, and in no way reduces the design task to a simpler format. We demonstrate that quantum annealer-based design can be applied to complex real-world design tasks, producing designed molecules comparable to those produced by widely adopted classical design approaches. We also show through large-scale classical folding simulations that the results produced on the quantum annealer can inform wet-lab experiments. For design tasks that scale exponentially on classical computers, the QPacker achieves nearly constant runtime performance over the range of problem sizes that could be tested. We anticipate better than classical performance scaling as quantum computers mature.

bioengineering↗

bioRxiv2018

TRPV1 regulates opioid analgesia during inflammation

Acute inflammation in humans or mice enhances the analgesic properties of opioids. However, the inflammatory transducers that prime opioid receptor signaling in nociceptors are unknown. We found that TRPV1-/- mice are insensitive to peripheral opioid analgesia in an inflammatory pain model. We report that TRPV1 channel activation drives a MAPK signaling pathway accompanied by the shuttling of {beta}-arrestin2 to the nucleus. This shuttling in turn prevents: {beta}-arrestin2-receptor recruitment, subsequent internalization of agonist-bound mu opioid receptor (MOR), and suppression of DAMGO-induced inhibition of N-type calcium current observed upon desensitization. Consequently, inflammation-induced activation of TRPV1 preserves opioid analgesic potency in a mouse model of opioid receptor desensitization. Overall, our work reveals a TRPV1-mediated signaling mechanism, involving {beta}-arrestin2 nuclear translocation, that underlies the peripheral opioid control of inflammatory pain. Our data single out TRPV1 channels as modulators of opioid analgesia.

neuroscience↗