A Pan-plant Protein Complex Map Reveals Deep Conservation and Novel Assemblies.

Plants are foundational for global ecological and economic systems, but most plant proteins remain uncharacterized. Protein interaction networks often suggest protein functions and open new avenues to characterize genes and proteins. We therefore systematically determined protein complexes from 13 plant species of scientific and agricultural importance, greatly expanding the known repertoire of stable protein complexes in plants. By using co-fractionation mass spectrometry, we recovered known complexes, confirmed complexes predicted to occur in plants, and identified previously unknown interactions conserved over 1.1 billion years of green plant evolution. Several novel complexes are involved in vernalization and pathogen defense, traits critical for agriculture. We also observed plant analogs of animal complexes with distinct molecular assemblies, including a megadalton-scale tRNA multi-synthetase complex. The resulting map offers a cross-species view of conserved, stable protein assemblies shared across plant cells and provides a mechanistic, biochemical framework for interpreting plant genetics and mutant phenotypes.

Systematic mapping of TF-mediated cell fate changes by a pooled induction coupled with scRNA-seq and multi-omics approaches.

Transcriptional regulation controls cellular functions through interactions between transcription factors (TFs) and their chromosomal targets. However, understanding the fate conversion potential of multiple TFs in an inducible manner remains limited. Here, we introduce iTF-seq as a method for identifying individual TFs that can alter cell fate toward specific lineages at a single-cell level. iTF-seq enables time course monitoring of transcriptome changes, and with biotinylated individual TFs, it provides a multi-omics approach to understanding the mechanisms behind TF-mediated cell fate changes. Our iTF-seq study in mouse embryonic stem cells identified multiple TFs that trigger rapid transcriptome changes indicative of differentiation within a day of induction. Moreover, cells expressing these potent TFs often show a slower cell cycle and increased cell death. Further analysis using bioChIP-seq revealed that GCM1 and OTX2 act as pioneer factors and activators by increasing gene accessibility and activating the expression of lineage specification genes during cell fate conversion. iTF-seq has utility in both mapping cell fate conversion and understanding cell fate conversion mechanisms.

A census of human soluble protein complexes.

Cellular processes often depend on stable physical associations between proteins. Despite recent progress, knowledge of the composition of human protein complexes remains limited. To close this gap, we applied an integrative global proteomic profiling approach, based on chromatographic separation of cultured human cell extracts into more than one thousand biochemical fractions that were subsequently analyzed by quantitative tandem mass spectrometry, to systematically identify a network of 13,993 high-confidence physical interactions among 3,006 stably associated soluble human proteins. Most of the 622 putative protein complexes we report are linked to core biological processes and encompass both candidate disease genes and unannotated proteins to inform on mechanism. Strikingly, whereas larger multiprotein assemblies tend to be more extensively annotated and evolutionarily conserved, human protein complexes with five or fewer subunits are far more likely to be functionally unannotated or restricted to vertebrates, suggesting more recent functional innovations.

Systematic bromodomain protein screens identify homologous recombination and R-loop suppression pathways involved in genome integrity.

Bromodomain proteins (BRD) are key chromatin regulators of genome function and stability as well as therapeutic targets in cancer. Here, we systematically delineate the contribution of human BRD proteins for genome stability and DNA double-strand break (DSB) repair using several cell-based assays and proteomic interaction network analysis. Applying these approaches, we identify 24 of the 42 BRD proteins as promoters of DNA repair and/or genome integrity. We identified a BRD-reader function of PCAF that bound TIP60-mediated histone acetylations at DSBs to recruit a DUB complex to deubiquitylate histone H2BK120, to allowing direct acetylation by PCAF, and repair of DSBs by homologous recombination. We also discovered the bromo-and-extra-terminal (BET) BRD proteins, BRD2 and BRD4, as negative regulators of transcription-associated RNA-DNA hybrids (R-loops) as inhibition of BRD2 or BRD4 increased R-loop formation, which generated DSBs. These breaks were reliant on topoisomerase II, and BRD2 directly bound and activated topoisomerase I, a known restrainer of R-loops. Thus, comprehensive interactome and functional profiling of BRD proteins revealed new homologous recombination and genome stability pathways, providing a framework to understand genome maintenance by BRD proteins and the effects of their pharmacological inhibition.

Dynamic reorganization of metabolic enzymes into intracellular bodies.

Both focused and large-scale cell biological and biochemical studies have revealed that hundreds of metabolic enzymes across diverse organisms form large intracellular bodies. These proteinaceous bodies range in form from fibers and intracellular foci--such as those formed by enzymes of nitrogen and carbon utilization and of nucleotide biosynthesis--to high-density packings inside bacterial microcompartments and eukaryotic microbodies. Although many enzymes clearly form functional mega-assemblies, it is not yet clear for many recently discovered cases whether they represent functional entities, storage bodies, or aggregates. In this article, we survey intracellular protein bodies formed by metabolic enzymes, asking when and why such bodies form and what their formation implies for the functionality--and dysfunctionality--of the enzymes that comprise them. The panoply of intracellular protein bodies also raises interesting questions regarding their evolution and maintenance within cells. We speculate on models for how such structures form in the first place and why they may be inevitable.

Alternative proteoforms and proteoform-dependent assemblies in humans and plants.

The variability of proteins at the sequence level creates an enormous potential for proteome complexity. Exploring the depths and limits of this complexity is an ongoing goal in biology. Here, we systematically survey human and plant high-throughput bottom-up native proteomics data for protein truncation variants, where substantial regions of the full-length protein are missing from an observed protein product. In humans, Arabidopsis, and the green alga Chlamydomonas, approximately one percent of observed proteins show a short form, which we can assign by comparison to RNA isoforms as either likely deriving from transcript-directed processes or limited proteolysis. While some detected protein fragments align with known splice forms and protein cleavage events, multiple examples are previously undescribed, such as our observation of fibrocystin proteolysis and nuclear translocation in a green alga. We find that truncations occur almost entirely between structured protein domains, even when short forms are derived from transcript variants. Intriguingly, multiple endogenous protein truncations of phase-separating translational proteins resemble cleaved proteoforms produced by enteroviruses during infection. Some truncated proteins are also observed in both humans and plants, suggesting that they date to the last eukaryotic common ancestor. Finally, we describe novel proteoform-specific protein complexes, where the loss of a domain may accompany complex formation.

The emerging landscape of single-molecule protein sequencing technologies.

Single-cell profiling methods have had a profound impact on the understanding of cellular heterogeneity. While genomes and transcriptomes can be explored at the single-cell level, single-cell profiling of proteomes is not yet established. Here we describe new single-molecule protein sequencing and identification technologies alongside innovations in mass spectrometry that will eventually enable broad sequence coverage in single-cell profiling. These technologies will in turn facilitate biological discovery and open new avenues for ultrasensitive disease diagnostics.

Amino acid sequence assignment from single molecule peptide sequencing data using a two-stage classifier.

We present a machine learning-based interpretive framework (whatprot) for analyzing single molecule protein sequencing data produced by fluorosequencing, a recently developed proteomics technology that determines sparse amino acid sequences for many individual peptide molecules in a highly parallelized fashion. Whatprot uses Hidden Markov Models (HMMs) to represent the states of each peptide undergoing the various chemical processes during fluorosequencing, and applies these in a Bayesian classifier, in combination with pre-filtering by a k-Nearest Neighbors (kNN) classifier trained on large volumes of simulated fluorosequencing data. We have found that by combining the HMM based Bayesian classifier with the kNN pre-filter, we are able to retain the benefits of both, achieving both tractable runtimes and acceptable precision and recall for identifying peptides and their parent proteins from complex mixtures, outperforming the capabilities of either classifier on its own. Whatprot's hybrid kNN-HMM approach enables the efficient interpretation of fluorosequencing data using a full proteome reference database and should now also enable improved sequencing error rate estimates.

Disorder, promiscuity, and toxic partnerships.

Many genes are toxic when overexpressed, but general mechanisms for this toxicity have proven elusive. Vavouri et al. (2009) find that intrinsic protein disorder and promiscuous molecular interactions are strong determinants of dosage sensitivity, explaining in part the toxicity of dosage-sensitive oncogenes in mice and humans.

A synthetic genetic edge detection program.

Edge detection is a signal processing algorithm common in artificial intelligence and image recognition programs. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E. coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. The algorithm is implemented using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks.

Paternal chromosome loss and metabolic crisis contribute to hybrid inviability in Xenopus.

Hybridization of eggs and sperm from closely related species can give rise to genetic diversity, or can lead to embryo inviability owing to incompatibility. Although central to evolution, the cellular and molecular mechanisms underlying post-zygotic barriers that drive reproductive isolation and speciation remain largely unknown. Species of the African clawed frog Xenopus provide an ideal system to study hybridization and genome evolution. Xenopus laevis is an allotetraploid with 36 chromosomes that arose through interspecific hybridization of diploid progenitors, whereas Xenopus tropicalis is a diploid with 20 chromosomes that diverged from a common ancestor approximately 48 million years ago. Differences in genome size between the two species are accompanied by organism size differences, and size scaling of the egg and subcellular structures such as nuclei and spindles formed in egg extracts. Nevertheless, early development transcriptional programs, gene expression patterns, and protein sequences are generally conserved. Whereas the hybrid produced when X. laevis eggs are fertilized by X. tropicalis sperm is viable, the reverse hybrid dies before gastrulation. Here we apply cell biological tools and high-throughput methods to study the mechanisms underlying hybrid inviability. We reveal that two specific X. laevis chromosomes are incompatible with the X. tropicalis cytoplasm and are mis-segregated during mitosis, leading to unbalanced gene expression at the maternal to zygotic transition, followed by cell-autonomous catastrophic embryo death. These results reveal a cellular mechanism underlying hybrid incompatibility that is driven by genome evolution and contributes to the process by which biological populations become distinct species.

HumanNet v3: an improved database of human gene networks for disease research.

Network medicine has proven useful for dissecting genetic organization of complex human diseases. We have previously published HumanNet, an integrated network of human genes for disease studies. Since the release of the last version of HumanNet, many large-scale protein-protein interaction datasets have accumulated in public depositories. Additionally, the numbers of research papers and functional annotations for gene-phenotype associations have increased significantly. Therefore, updating HumanNet is a timely task for further improvement of network-based research into diseases. Here, we present HumanNet v3 (https://www.inetbio.org/humannet/, covering 99.8% of human protein coding genes) constructed by means of the expanded data with improved network inference algorithms. HumanNet v3 supports a three-tier model: HumanNet-PI (a protein-protein physical interaction network), HumanNet-FN (a functional gene network), and HumanNet-XC (a functional network extended by co-citation). Users can select a suitable tier of HumanNet for their study purpose. We showed that on disease gene predictions, HumanNet v3 outperforms both the previous HumanNet version and other integrated human gene networks. Furthermore, we demonstrated that HumanNet provides a feasible approach for selecting host genes likely to be associated with COVID-19.

A Humanized CB1R Yeast Biosensor Enables Facile Screening of Cannabinoid Compounds.

Yeast expression of human G-protein-coupled receptors (GPCRs) can be used as a biosensor platform for the detection of pharmaceuticals. Cannabinoid receptor type 1 (CB1R) is of particular interest, given the cornucopia of natural and synthetic cannabinoids being explored as therapeutics. We show for the first time that engineering the N-terminus of CB1R allows for efficient signal transduction in yeast, and that engineering the sterol composition of the yeast membrane modulates its performance. Using an engineered cannabinoid biosensor, we demonstrate that large libraries of synthetic cannabinoids and terpenes can be quickly screened to elucidate known and novel structure-activity relationships. The biosensor strains offer a ready platform for evaluating the activity of new synthetic cannabinoids, monitoring drugs of abuse, and developing therapeutic molecules.

Molecular complex detection in protein interaction networks through reinforcement learning.

BACKGROUND: Proteins often assemble into higher-order complexes to perform their biological functions. Such protein-protein interactions (PPI) are often experimentally measured for pairs of proteins and summarized in a weighted PPI network, to which community detection algorithms can be applied to define the various higher-order protein complexes. Current methods include unsupervised and supervised approaches, often assuming that protein complexes manifest only as dense subgraphs. Utilizing supervised approaches, the focus is not on how to find them in a network, but only on learning which subgraphs correspond to complexes, currently solved using heuristics. However, learning to walk trajectories on a network to identify protein complexes leads naturally to a reinforcement learning (RL) approach, a strategy not extensively explored for community detection. Here, we develop and evaluate a reinforcement learning pipeline for community detection on weighted protein-protein interaction networks to detect new protein complexes. The algorithm is trained to calculate the value of different subgraphs encountered while walking on the network to reconstruct known complexes. A distributed prediction algorithm then scales the RL pipeline to search for novel protein complexes on large PPI networks. RESULTS: The reinforcement learning pipeline is applied to a human PPI network consisting of 8k proteins and 60k PPI, which results in 1,157 protein complexes. The method demonstrated competitive accuracy with improved speed compared to previous algorithms. We highlight protein complexes such as C4orf19, C18orf21, and KIAA1522 which are currently minimally characterized. Additionally, the results suggest TMC04 be a putative additional subunit of the KICSTOR complex and confirm the involvement of C15orf41 in a higher-order complex with HIRA, CDAN1, ASF1A, and by 3D structural modeling. CONCLUSIONS: Reinforcement learning offers several distinct advantages for community detection, including scalability and knowledge of the walk trajectories defining those communities. Applied to currently available human protein interaction networks, this method had comparable accuracy with other algorithms and notable savings in computational time, and in turn, led to clear predictions of protein function and interactions for several uncharacterized human proteins.

Twinfilin1 controls lamellipodial protrusive activity and actin turnover during vertebrate gastrulation.

The dynamic control of the actin cytoskeleton is a key aspect of essentially all animal cell movements. Experiments in single migrating cells and in vitro systems have provided an exceptionally deep understanding of actin dynamics. However, we still know relatively little of how these systems are tuned in cell-type-specific ways, for example in the context of collective cell movements that sculpt the early embryo. Here, we provide an analysis of the actin-severing and depolymerization machinery during vertebrate gastrulation, with a focus on Twinfilin1 (Twf1) in Xenopus. We find that Twf1 is essential for convergent extension, and loss of Twf1 results in a disruption of lamellipodial dynamics and polarity. Moreover, Twf1 loss results in a failure to assemble polarized cytoplasmic actin cables, which are essential for convergent extension. These data provide an in vivo complement to our more-extensive understanding of Twf1 action in vitro and provide new links between the core machinery of actin regulation and the specialized cell behaviors of embryonic morphogenesis.

Humanization of yeast genes with multiple human orthologs reveals functional divergence between paralogs.

Despite over a billion years of evolutionary divergence, several thousand human genes possess clearly identifiable orthologs in yeast, and many have undergone lineage-specific duplications in one or both lineages. These duplicated genes may have been free to diverge in function since their expansion, and it is unclear how or at what rate ancestral functions are retained or partitioned among co-orthologs between species and within gene families. Thus, in order to investigate how ancestral functions are retained or lost post-duplication, we systematically replaced hundreds of essential yeast genes with their human orthologs from gene families that have undergone lineage-specific duplications, including those with single duplications (1 yeast gene to 2 human genes, 1:2) or higher-order expansions (1:>2) in the human lineage. We observe a variable pattern of replaceability across different ortholog classes, with an obvious trend toward differential replaceability inside gene families, and rarely observe replaceability by all members of a family. We quantify the ability of various properties of the orthologs to predict replaceability, showing that in the case of 1:2 orthologs, replaceability is predicted largely by the divergence and tissue-specific expression of the human co-orthologs, i.e., the human proteins that are less diverged from their yeast counterpart and more ubiquitously expressed across human tissues more often replace their single yeast ortholog. These trends were consistent with in silico simulations demonstrating that when only one ortholog can replace its corresponding yeast equivalent, it tends to be the least diverged of the pair. Replaceability of yeast genes having more than 2 human co-orthologs was marked by retention of orthologous interactions in functional or protein networks as well as by more ancestral subcellular localization. Overall, we performed >400 human gene replaceability assays, revealing 50 new human-yeast complementation pairs, thus opening up avenues to further functionally characterize these human genes in a simplified organismal context.

Spatiotemporal transcriptional dynamics of the cycling mouse oviduct.

Female fertility in mammals requires iterative remodeling of the entire adult female reproductive tract across the menstrual/estrous cycle. However, while transcriptome dynamics across the estrous cycle have been reported in human and bovine models, no global analysis of gene expression across the estrous cycle has yet been reported for the mouse. Here, we examined the cellular composition and global transcriptional dynamics of the mouse oviduct along the anteroposterior axis and across the estrous cycle. We observed robust patterns of differential gene expression along the anteroposterior axis, but we found surprisingly few changes in gene expression across the estrous cycle. Notable gene expression differences along the anteroposterior axis included a surprising enrichment for genes related to embryonic development, such as Hox and Wnt genes. The relatively stable transcriptional dynamics across the estrous cycle differ markedly from other mammals, leading us to speculate that this is an evolutionarily derived state that may reflect the extremely rapid five-day mouse estrous cycle. This dataset fills a critical gap by providing an important genomic resource for a highly tractable genetic model of mammalian female reproduction.

hu.MAP 2.0: integration of over 15,000 proteomic experiments builds a global compendium of human multiprotein assemblies.

A general principle of biology is the self-assembly of proteins into functional complexes. Characterizing their composition is, therefore, required for our understanding of cellular functions. Unfortunately, we lack knowledge of the comprehensive set of identities of protein complexes in human cells. To address this gap, we developed a machine learning framework to identify protein complexes in over 15,000 mass spectrometry experiments which resulted in the identification of nearly 7,000 physical assemblies. We show our resource, hu.MAP 2.0, is more accurate and comprehensive than previous state of the art high-throughput protein complex resources and gives rise to many new hypotheses, including for 274 completely uncharacterized proteins. Further, we identify 253 promiscuous proteins that participate in multiple complexes pointing to possible moonlighting roles. We have made hu.MAP 2.0 easily searchable in a web interface (http://humap2.proteincomplexes.org/), which will be a valuable resource for researchers across a broad range of interests including systems biology, structural biology, and molecular explanations of disease.

Evaluating the Effect of Dye-Dye Interactions of Xanthene-Based Fluorophores in the Fluorosequencing of Peptides.

A peptide sequencing scheme utilizing fluorescence microscopy and Edman degradation to determine the amino acid position in fluorophore-labeled peptides was recently reported, referred to as fluorosequencing. It was observed that multiple fluorophores covalently linked to a peptide scaffold resulted in a decrease in the anticipated fluorescence output and worsened the single-molecule fluorescence analysis. In this study, we report an improvement in the photophysical properties of fluorophore-labeled peptides by incorporating long and flexible (PEG)10 linkers at the peptide attachment points. Long linkers to the fluorophores were installed using copper-catalyzed azide-alkyne cycloaddition conditions. The photophysical properties of these peptides were analyzed in solution and immobilized on a microscope slide at the single-molecule level under peptide fluorosequencing conditions. Solution-phase fluorescence analysis showed improvements in both quantum yield and fluorescence lifetime with the long linkers. While on the solid support, photometry measurements showed significant increases in fluorescence brightness and 20 to 60% improvements in the ability to determine the amino acid position with fluorosequencing. This spatial distancing strategy demonstrates improvements in the peptide sequencing platform and provides a general approach for improving the photophysical properties in fluorophore-labeled macromolecules.

Systematic Identification of Protein Phosphorylation-Mediated Interactions.

Protein phosphorylation is a key regulatory mechanism involved in nearly every eukaryotic cellular process. Increasingly sensitive mass spectrometry approaches have identified hundreds of thousands of phosphorylation sites, but the functions of a vast majority of these sites remain unknown, with fewer than 5% of sites currently assigned a function. To increase our understanding of functional protein phosphorylation we developed an approach (phospho-DIFFRAC) for identifying the phosphorylation-dependence of protein assemblies in a systematic manner. A combination of nonspecific protein phosphatase treatment, size-exclusion chromatography, and mass spectrometry allowed us to identify changes in protein interactions after the removal of phosphate modifications. With this approach we were able to identify 316 proteins involved in phosphorylation-sensitive interactions. We recovered known phosphorylation-dependent interactors such as the FACT complex and spliceosome, as well as identified novel interactions such as the tripeptidyl peptidase TPP2 and the supraspliceosome component ZRANB2. More generally, we find phosphorylation-dependent interactors to be strongly enriched for RNA-binding proteins, providing new insight into the role of phosphorylation in RNA binding. By searching directly for phosphorylated amino acid residues in mass spectrometry data, we identified the likely regulatory phosphosites on ZRANB2 and FACT complex subunit SSRP1. This study provides both a method and resource for obtaining a better understanding of the role of phosphorylation in native macromolecular assemblies. All mass spectrometry data are available through PRIDE (accession #PXD021422).