Biology subjects

Twizere, J.-C.

Publications and source records attributed to Twizere, J.-C..

At least 19 recordsLinked to original sources

bioRxiv2026

Conserved metabolic vulnerabilities across pathogenic coronaviruses nominate host-directed therapeutic targets

Pathogenic coronaviruses profoundly rewire host cell metabolism to support viral replication, yet whether these metabolic alterations expose shared and actionable vulnerabilities remains unclear. By integrating transcriptomic profiles from cells infected with SARS-CoV, SARS-CoV-2, and MERS-CoV with genome-scale metabolic models, we identify conserved and virus-specific metabolic perturbations affecting mitochondrial transport, nucleotide biosynthesis, fatty acid metabolism, and redox balance. Despite distinct transcriptional responses, all three viruses converge on a limited set of metabolic reactions whose flux ranges deviate strongly from healthy states. Using a network-based predictive framework, we systematically identify gene-pair perturbations that restore perturbed reaction fluxes toward non-infected metabolic states. Predicted rescue mechanisms reveal shared metabolic dependencies across coronaviruses, as well as time-dependent virus-specific vulnerabilities, and nominate druggable host targets. Notably, several top predictions align with independent experimental and clinical evidence, including metabolic interventions shown to reduce viral replication or disease severity in COVID-19 patients. Together, our results define conserved metabolic rescue pathways in coronavirus infection and provide a general strategy for identifying host-directed therapeutic opportunities from transcriptomic data. HighlightsO_LICoronaviruses converge on shared metabolic vulnerabilities in host cells C_LIO_LINiTRO predicts gene pairs that rescue viral-induced metabolic states C_LIO_LIMitochondrial transport emerges as a key pan-coronavirus target C_LIO_LITop predictions validated by clinical trials and in vitro evidence C_LI eTOC BlurbDohai et al. develop NiTRO, a network-based algorithm that integrates coronavirus-induced transcriptomic changes with genome-scale metabolic models to identify gene-pair perturbations capable of rescuing infected metabolic states. The approach reveals shared and virus-specific druggable metabolic vulnerabilities, with top predictions corroborated by clinical evidence.

systems biology↗

bioRxiv2026

Dynamic engagement of dual-role regulators by the Sin3 complex

Gene expression is governed by dynamic switches between repressive and activating transcriptional states1,2. Among the molecules mediating these transitions, chromatin readers and transcription factors play pivotal roles3,4. However, how they assemble with regulatory machineries to enable crosstalk between gene repression and activation remains unknown. Here, we use an integrative structural dynamics approach - combining cryo-EM, crosslinking mass spectrometry, fragment-resolved protein interactome mapping and crystallography - to show how the dual-role chromatin reader Cti6 and transcription factors Ash1 and Ume6 engage the Sin3 deacetylase complex, a major regulatory hub in eukaryotes5. We find that Cti6 competes with Ash1 to drive its dynamic recruitment to a shared peripheral module, while Ume6 engages the Sin3 scaffold through a defined, minimal interface. Using high-throughput mutational scanning, we reveal deleterious and gain-of-function mutations in Sin3, identifying evolutionarily conserved residues essential for anchoring transcription factors. Together, these results provide structural and functional insights into how dual-role regulators engage the central Sin3 complex, revealing subtle assembly principles that may facilitate crosstalk between gene repression and activation. They also establish an integrative multidisciplinary framework to dissect the dynamics of macromolecular assemblies across biological systems.

molecular biology↗

bioRxiv2025

Multi-omics decipher the molecular mechanisms driving high-lipid production in an artificially-evolved Chlamydomonas mutant

Enhancing lipid accumulation in microalgae is critical for commercial viability but often compromises growth. We previously identified an artificially evolved Chlamydomonas reinhardtii mutant (H5) that retains wild-type growth (CC-503) while producing significantly more lipids. Here, we present multi-omic analyses that elucidate the molecular basis of this phenotype. Whole-genome sequencing revealed over 3,000 mutations in H5, including 45 in protein-coding genes (e.g., phosphofructokinase, acyl-carrier protein, glycerol kinase). Six corresponding CLiP insertion mutants also showed elevated lipid content. Transcriptomics revealed upregulation of key genes for glycolysis, nutrient uptake, and proliferation (e.g., pyruvate carboxylase, carbonic anhydrase) under nutrient-replete conditions. Metabolomics identified a striking increase in malonate, a metabolite that supports fatty acid synthesis and cell proliferation. Epigenomic profiling showed hypomethylation in triacylglycerol (TAG) biosynthesis genes and hypermethylation in energy balance regulators. Together, these data suggest that accelerated glycolysis and streamlined metabolism drive lipid accumulation in H5 without compromising growth. Our findings provide a blueprint for engineering high-lipid microalgal strains for industrial applications. HIGHLIGHTSO_LIHigh-lipid Chlamydomonas mutant (H5) exhibits cancer-like metabolism: pseudo-hypoxia and nutrient deprivation response C_LIO_LIMulti-omics reveals 45 high-impact mutations synergistically enhance lipid production in H5 C_LIO_LISix CLiP mutants of H5-disrupted genes showed significantly increased lipid content C_LIO_LIMalonate levels increased 10-fold in H5, indicating altered mitochondrial function C_LIO_LIH5 upregulates glycolytic genes while maintaining wild-type growth rates C_LIO_LITranscriptomes from H5 and CC-503 converge after nitrogen deprivation despite replete-state differences C_LIO_LIH5 shows altered lipid composition with increased TAG diversity, decreased DAGs C_LIO_LIEpigenomic profiling reveals 14,720 differentially methylated transcribed regions in H5 C_LI

genomics↗

bioRxiv2024

A near telomere-to-telomere phased reference assembly for the male mountain gorilla

The endangered mountain gorilla, Gorilla beringei beringei, faces numerous threats to its survival, highlighting the urgent need for genomic resources to aid conservation efforts. Here, we present a near telomere-to-telomere, haplotype-phased reference genome assembly for a male mountain gorilla generated using PacBio HiFi (26.77x ave. coverage) and Oxford Nanopore Technologies (52.87x ave. coverage) data. The resulting non-scaffolded assembly exhibits exceptional contiguity, with contig N50 of [~]95 Mbp for the combined pseudohaplotype (3,540,458,497 bp), 56.5 Mbp (3.1 Gbp) and 51.0 Mbp (3.2 Gbp) for each haplotype, an average QV of 65.15 (error rate = 3.1 x 10-7), and a BUSCO score of 98.4%. These represent substantial improvements over most other available primate genomes. This first high-quality reference genome of the mountain gorilla provides an invaluable resource for future studies on gorilla evolution, adaptation, and conservation, ultimately contributing to the long-term survival of this iconic species.

genomics↗

bioRxiv2024

SARS-CoV2 infection triggers reactive astrocyte states and inflammatory conditions in long-term Human Cortical Organoids

SARS-CoV2, severe acute respiratory syndrome coronavirus 2, is frequently associated with neurological manifestations. Despite the presence of mild to severe CNS-related symptoms in a cohort of patients, there is no consensus whether the virus can infect directly brain tissue or if the symptoms in patients are a consequence of peripheral infectivity of the virus. Here, we use long-term human stem cell-derived cortical organoids to assess SARS-CoV2 infectivity of brain cells and unravel the cell-type tropism and its downstream pathological effects. Our results show consistent and reproducible low levels of SARS-CoV2 infection of astrocytes, deep projection neurons, upper callosal neurons and inhibitory neurons in 6 months human cortical organoids. Interestingly, astrocytes showed the highest infection rate among all infected cell populations that led to increased presence of reactive states. Further, transcriptomic analysis revealed overall changes in expression of genes related to cell metabolism, astrocyte activation and, inflammation and further, upregulation of cell survival pathways. Thus, local and minor infectivity of SARS-CoV2 in the brain may induce widespread adverse effects and may lead to resilience of dysregulated neurons and astrocytes within an inflammatory environment.

neuroscience↗

bioRxiv2024

A comprehensive two-hybrid analysis to explore the L. pneumophila effector-effector interactome

Legionella pneumophila uses over 300 translocated effector proteins to rewire host cells during infection and create a replicative niche for intracellular growth. To date, several studies have identified L. pneumophila effectors that indirectly and directly regulate the activity of other effectors, providing an additional layer of regulatory complexity. Amongst these are "metaeffectors" - a special class of effectors that regulate the activity of other effectors once inside the host. A defining feature of metaeffectors is direct, physical interaction with a target effector. Metaeffector identification to date has depended on phenotypes in heterologous systems and experimental serendipity. Using a multiplexed, recombinant-barcode-based yeast two-hybrid technology we screened for protein-protein interactions amongst all L. pneumophila effectors and several components of the Dot/Icm type IV secretion system (>167,000 protein combinations). Of the 52 protein interactions identified by this approach, 44 are novel protein interactions, including ten novel effector-effector interactions (doubling the number of known effector-effector interactions).

microbiology↗

bioRxiv2024

Widespread variation in molecular interactions and regulatory properties among transcription factor isoforms

Most human Transcription factors (TFs) genes encode multiple protein isoforms differing in DNA binding domains, effector domains, or other protein regions. The global extent to which this results in functional differences between isoforms remains unknown. Here, we systematically compared 693 isoforms of 246 TF genes, assessing DNA binding, protein binding, transcriptional activation, subcellular localization, and condensate formation. Relative to reference isoforms, two-thirds of alternative TF isoforms exhibit differences in one or more molecular activities, which often could not be predicted from sequence. We observed two primary categories of alternative TF isoforms: "rewirers" and "negative regulators", both of which were associated with differentiation and cancer. Our results support a model wherein the relative expression levels of, and interactions involving, TF isoforms add an understudied layer of complexity to gene regulatory networks, demonstrating the importance of isoform-aware characterization of TF functions and providing a rich resource for further studies.

systems biology↗

bioRxiv2023

Nanobodies against SARS-CoV-2 non-structural protein Nsp9 inhibit viral replication by targeting innate immunity

Nanobodies are emerging as critical tools for drug design. Several have been recently created to serve as inhibitors of SARS-Cov-2 entry in the host cell by targeting surface-exposed Spike protein. However, due to the high frequency of mutations that affect Spike, these nanobodies may not target it to their full potential and as a consequence, inhibition of viral entry may not be efficient. Here we have established a pipeline that instead targets highly conserved viral proteins that are made only after viral entry into the host cell when the SARS-Cov-2 RNA-based genome is translated. As proof of principle, we designed nanobodies against the SARS-CoV-2 non-structural protein Nsp9, required for viral genome replication. To find out if this strategy efficiently blocks viral replication, one of these anti-Nsp9 nanobodies, 2NSP23, previously characterized using immunoassays and NMR spectroscopy for epitope mapping, was encapsulated into lipid nanoparticles (LNP) as mRNA. We show that this nanobody, hereby referred to as LNP-mRNA- 2NSP23, is internalized and translated in HEK293 cells. We next infected HEK293-ACE2 cells with multiple SARS-CoV-2 variants and subjected them to LNP-mRNA-2NSP23 treatment. Analysis of total RNA isolated from infected cells treated or untreated with LNP-mRNA-2NSP23 using qPCR and RNA deep sequencing shows that the LNP-mRNA-2NSP23 nanobody protects HEK293-ACE2 cells and suppresses replication of several SARS-CoV-2 variants. These observations indicate that following translation, the nanobody 2NSP23 inhibits viral replication by targeting Nsp9 in living cells. We speculate that LNP-mRNA-2NSP23 may be translated into an innovative technology to generate novel antiviral drugs highly efficient across coronaviruses.

cell biology↗

bioRxiv2023

A gut meta-interactome map reveals modulation of human immunity by microbiome effectors

The molecular mechanisms by which the gut microbiome influences human health remain largely unknown. Pseudomonadota is the third most abundant phylum in normal gut microbiomes. Several pathogens in this phylum can inject so-called virulence effector proteins into host cells. We report the identification of intact type 3 secretion systems (T3SS) in 5 - 20% of commensal Pseudomonadota in normal human gut microbiomes. To understand their functions, we experimentally generated a high-quality protein-protein meta-interactome map consisting of 1,263 interactions between 289 bacterial effectors and 430 human proteins. Effector targets are enriched for metabolic and immune functions and for genetic variation of microbiome-influenced traits including autoimmune diseases. We demonstrate that effectors modulate NF-{kappa}B signaling, cytokine secretion, and adhesion molecule expression. Finally, effectors are enriched in metagenomes of Crohns disease, but not ulcerative colitis patients pointing toward complex contributions to the etiology of inflammatory bowel diseases. Our results suggest that effector-host protein interactions are an important regulatory layer by which the microbiome impacts human health.

microbiology↗

bioRxiv2023

AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor

Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays and AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.

systems biology↗

bioRxiv2023

Identification of small molecule antivirals against HTLV-1 by targeting the hDLG1-Tax-1 protein-protein interaction

Human T-cell leukemia virus type-1 (HTLV-1) is the first pathogenic retrovirus discovered in human. Although HTLV-1-induced diseases are well characterized and linked to the encoded Tax-1 oncoprotein, there is currently no strategy to target Tax-1 functions with small molecules. Here, we analysed the binding of Tax-1 to the human homolog of the drosophila discs large tumor suppressor (hDLG1/SAP97), a multi-domain scaffolding protein involved in Tax-1-transformation ability. We have solved the structures of the PDZ binding motif (PBM) of Tax-1 in complex with the PDZ1 and PDZ2 domains of hDLG1 and assessed the binding of 10 million molecules by virtual screening. Among the 19 experimentally confirmed compounds, one systematically inhibited the Tax-1-hDLG1 interaction in different biophysical and cellular assays, as well as HTLV-1 cell-to-cell transmission in a T-cell model. Thus, our work demonstrates that interactions involving Tax-1 PDZ-domains are amenable to small-molecule inhibition, which provides a framework for the design of targeted therapies for HTLV-1-induced diseases.

biochemistry↗

bioRxiv2021

Assessment of community efforts to advance computational prediction of protein-protein interactions

Comprehensive insights from the human protein-protein interaction (PPI) network, known as the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of new PPIs. Many such approaches have been proposed. However, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 24 representative network-based methods to predict PPIs across five different interactomes, including a synthetic interactome generated by the duplication-mutation-complementation model, and the interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. We selected the top-seven methods through a computational validation on the human interactome. We next experimentally validated their top-500 predicted PPIs (in total 3,276 predicted PPIs) using the yeast two-hybrid assay, finding 1,177 new human PPIs (involving 633 proteins). Our results indicate that task-tailored similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods. Through experimental validation, we confirmed that the top-ranking methods show promising performance externally. For example, from the top 500 PPIs predicted by an advanced similarity-base method [MPS(B&T)], 430 were successfully tested by Y2H with 376 testing positive, yielding a precision of 87.4%. These results establish advanced similarity-based methods as powerful tools for the prediction of human PPIs.

systems biology↗

bioRxiv2021

A quantitative mapping approach to identify direct interactions within complexomes

Complementary methods are required to fully characterize all protein complexes, or the complexome, of a cell. Affinity purification coupled to mass-spectrometry (AP-MS) can identify the composition of complexes at proteome-scale. However, information on direct contacts between subunits is often lacking. In contrast, solving the 3D structure of protein complexes can provide this information, but structural biology techniques are not yet scalable for systematic, proteome-wide efforts. Here, we optimally combine two orthogonal high-throughput binary interaction assays, LuTHy and N2H, and demonstrate that their quantitative readouts can be used to differentiate direct interactions from indirect associations within multiprotein complexes. We also show that LuTHy allows accurate distance measurements between proteins in live cells and apply these findings to study the impact of the polyglutamine expansion mutation on the structurally unresolved N-terminal domain of Huntingtin. Thus, we present a new framework based on quantitative interaction assays to complement structural biology and AP-MS techniques, which should help to provide first-approximation contact maps of multiprotein complexes at proteome-scale. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=132 SRC="FIGDIR/small/457734v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1cc7cf1org.highwire.dtl.DTLVardef@1eed9borg.highwire.dtl.DTLVardef@8197b2org.highwire.dtl.DTLVardef@e5cb8_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2021

Interactome and structural basis for targeting the human T-cell leukemia virus Tax oncoprotein

Human T-cell leukemia virus type-1 (HTLV-1) is the causative agent of adult T-cell leukemia (ATL). Although ATL is a well-characterized T-cell neoplasm, linked to intermittent expression of the viral Tax-1 protein, there is currently no strategy to target Tax-1 functions using small molecules. Here, we report a comprehensive interaction map between Tax-1 and human PDZ domain-containing proteins (hPDZome). We show that Tax-1 interacts with more than one-third of hPDZome components, including proteins involved in cell cycle, cell-cell junctions, cytoskeleton organization, and membrane complex assembly. Using nuclear magnetic resonance (NMR) spectroscopy, we have determined the structural basis of the interaction between the C-terminal PDZ binding motif (PBM) of Tax-1, and the PDZ domains of syntenin-1, an evolutionary conserved hub that controls exosome trafficking. Finally, we have used confocal imaging, molecular modelling, NMR and mammalian cell-based assays to demonstrate that the Tax-1/syntenin-1 interaction is amenable to small-molecule inhibition. Altogether, our study highlights the biological significance of Tax-PDZ interactome and its interplay with exosome formation. It shows a direct link between extracellular vesicles and HTLV-1 transmission, providing a novel framework for the design of targeted therapies for HTLV-1-induced diseases.

biochemistry↗

bioRxiv2021

Binary Interactome Models of Inner- Versus Outer-Complexome Organization

Hundreds of different protein complexes that perform important functions across all cellular processes, collectively comprising the "complexome" of an organism, have been identified1. However, less is known about the fraction of the interactome that exists outside the complexome, in the "outer-complexome". To investigate features of "inner"- versus outer-complexome organisation in yeast, we generated a high-quality atlas of binary protein-protein interactions (PPIs), combining three previous maps2-4 and a new reference all-by-all binary interactome map. A greater proportion of interactions in our map are in the outer-complexome, in comparison to those found by affinity purification followed by mass spectrometry5-7 or in literature curated datasets8-11. In addition, recent advances in deep learning predictions of PPI structures12 mirror the existing experimentally resolved structures in being largely focused on the inner complexome and missing most interactions in the outer-complexome. Our new PPI network suggests that the outer-complexome contains considerably more PPIs than the inner-complexome, and integration with functional similarity networks13-15 reveals that interactions in the inner-complexome are highly detectable and correspond to pairs of proteins with high functional similarity, while proteins connected by more transient, harder-to-detect interactions in the outer-complexome, exhibit higher functional heterogeneity.

systems biology↗

bioRxiv2021

A map of direct SARS-CoV-2 protein interactions implicates specific human host processes

Key steps in viral propagation, immune suppression, and pathology are mediated by direct, binary, physical interactions between viral and host proteins. To understand the biology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, we generated an unbiased systematic map of binary interactions between viral and host proteins, complementing previous co-complex association maps by conveying more direct mechanistic understanding and potentially enabling targeted disruption of direct interactions. To this end, we deployed two parallel strategies, identifying 205 virus-host and 27 intraviral binary interactions amongst 171 host and 19 viral proteins, and confirming high quality of these interactions via a calibrated orthogonal assay. Host proteins interacting with SARS-CoV-2 proteins are enriched in various cellular processes, including immune signaling and inflammation, protein ubiquitination, and membrane trafficking. Specific subnetworks provide new hypotheses related to viral modulation of host protein homeostasis and T-cell regulation. The binary virus-host protein interactions we identified can now be prioritized as targets for therapeutic intervention. More generally, we provide a resource of systematic maps describing which SARS-CoV-2 and human proteins interact directly.

systems biology↗

bioRxiv2021

Host PDZ-containing proteins targeted by SARS-Cov-2

Small linear motif targeting protein interacting domains called PDZ have been identified at the C-terminus of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteins E, 3a, and N. Using a high-throughput approach of affinity-profiling against the full human PDZome, we identified sixteen human PDZ binders of SARS-CoV-2 proteins E, 3A and N showing significant interactions with dissociation constants values ranging from 3 M to 82 M. Six of them (TJP1, PTPN13, HTRA1, PARD3, MLLT4, LNX2) are also recognized by SARS-CoV while three (NHERF1, MAST2, RADIL) are specific to SARS-CoV-2 E protein. Most of these SARS-CoV-2 protein partners are involved in cellular junctions/polarity and could be also linked to evasion mechanisms of the immune responses during viral infection. Seven of the PDZ-containing proteins among binders of the SARS-CoV-2 proteins E, 3a or N affect significantly viral replication under knock-down gene expression in infected cells. This PDZ profiling identifying human proteins potentially targeted by SARS-CoV-2 can help to understand the multifactorial severity of COVID19 and to conceive effective anti-coronaviral agents for therapeutic purposes.

biochemistry↗

bioRxiv2021

The viral oncoproteins Tax and HBZ reprogram the cellular mRNA splicing landscape

While viral infections are known to hijack the transcription and translation of the host cell, the extent to which encoded viral proteins coordinate these perturbations remains unclear. Here we demonstrate that the oncoviral proteins Tax and HBZ interact with specific components of the spliceosome machinery, including the U2 auxiliary factor large subunit (U2AF2), and the complementary factor for APOBEC-1 (A1CF), respectively. Tax and HBZ perturb the splicing landscape in T-cells by altering cassette exons in opposing manners, with Tax inducing exon inclusion while HBZ induces exon exclusion. Among Tax- and HBZ-dependent splicing changes, we identify events that are also altered in Adult T cell leukemia (ATL) patients, and in well-known cancer census genes. Our interactome mapping approach, applicable to other viral oncogenes, has identified spliceosome perturbation as a novel mechanism coordinately used by Tax and HBZ to reprogram the transcriptome. HighlightsO_LITax and HBZ interact with RNA-binding proteins as well as transcription factors C_LIO_LIHTLV-1 encoded proteins Tax and HBZ alter the splicing landscape in T-cells C_LIO_LITax and HBZ expression affect alternative splicing of 33 and 63 cancer genes, respectively C_LIO_LIOpposing roles for Tax and HBZ in deregulation of gene expression C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=158 HEIGHT=200 SRC="FIGDIR/small/427104v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@b5f6f0org.highwire.dtl.DTLVardef@6725b3org.highwire.dtl.DTLVardef@1dc32fborg.highwire.dtl.DTLVardef@19728c6_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗