Widespread Expansion of Protein Interaction Capabilities by Alternative Splicing.

While alternative splicing is known to diversify the functional characteristics of some genes, the extent to which protein isoforms globally contribute to functional complexity on a proteomic scale remains unknown. To address this systematically, we cloned full-length open reading frames of alternatively spliced transcripts for a large number of human genes and used protein-protein interaction profiling to functionally compare hundreds of protein isoform pairs. The majority of isoform pairs share less than 50% of their interactions. In the global context of interactome network maps, alternative isoforms tend to behave like distinct proteins rather than minor variants of each other. Interaction partners specific to alternative isoforms tend to be expressed in a highly tissue-specific manner and belong to distinct functional modules. Our strategy, applicable to other functional characteristics, reveals a widespread expansion of protein interaction capabilities through alternative splicing and suggests that many alternative "isoforms" are functionally divergent (i.e., "functional alloforms").

Human gene-centered transcription factor networks for enhancers and disease variants.

Gene regulatory networks (GRNs) comprising interactions between transcription factors (TFs) and regulatory loci control development and physiology. Numerous disease-associated mutations have been identified, the vast majority residing in non-coding regions of the genome. As current GRN mapping methods test one TF at a time and require the use of cells harboring the mutation(s) of interest, they are not suitable to identify TFs that bind to wild-type and mutant loci. Here, we use gene-centered yeast one-hybrid (eY1H) assays to interrogate binding of 1,086 human TFs to 246 enhancers, as well as to 109 non-coding disease mutations. We detect both loss and gain of TF interactions with mutant loci that are concordant with target gene expression changes. This work establishes eY1H assays as a powerful addition to the toolkit of mapping human GRNs and for the high-throughput characterization of genomic variants that are rapidly being identified by genome-wide association studies.

Widespread macromolecular interaction perturbations in human genetic disorders.

How disease-associated mutations impair protein activities in the context of biological networks remains mostly undetermined. Although a few renowned alleles are well characterized, functional information is missing for over 100,000 disease-associated variants. Here we functionally profile several thousand missense mutations across a spectrum of Mendelian disorders using various interaction assays. The majority of disease-associated alleles exhibit wild-type chaperone binding profiles, suggesting they preserve protein folding or stability. While common variants from healthy individuals rarely affect interactions, two-thirds of disease-associated alleles perturb protein-protein interactions, with half corresponding to "edgetic" alleles affecting only a subset of interactions while leaving most other interactions unperturbed. With transcription factors, many alleles that leave protein-protein interactions intact affect DNA binding. Different mutations in the same gene leading to different interaction profiles often result in distinct disease phenotypes. Thus disease-associated alleles that perturb distinct protein activities rather than grossly affecting folding and stability are relatively widespread.

Cobalt-electrocatalytic HAT for functionalization of unsaturated C-C bonds.

The study and application of transition metal hydrides (TMHs) has been an active area of chemical research since the early 1960s1, for energy storage, through the reduction of protons to generate hydrogen2,3, and for organic synthesis, for the functionalization of unsaturated C-C, C-O and C-N bonds4,5. In the former instance, electrochemical means for driving such reactivity has been common place since the 1950s6 but the use of stoichiometric exogenous organic- and metal-based reductants to harness the power of TMHs in synthetic chemistry remains the norm. In particular, cobalt-based TMHs have found widespread use for the derivatization of olefins and alkynes in complex molecule construction, often by a net hydrogen atom transfer (HAT)7. Here we show how an electrocatalytic approach inspired by decades of energy storage research can be made use of in the context of modern organic synthesis. This strategy not only offers benefits in terms of sustainability and efficiency but also enables enhanced chemoselectivity and distinct, tunable reactivity. Ten different reaction manifolds across dozens of substrates are exemplified, along with detailed mechanistic insights into this scalable electrochemical entry into Co-H generation that takes place through a low-valent intermediate.

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 or 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.

A physical and regulatory map of host-influenza interactions reveals pathways in H1N1 infection.

During the course of a viral infection, viral proteins interact with an array of host proteins and pathways. Here, we present a systematic strategy to elucidate the dynamic interactions between H1N1 influenza and its human host. A combination of yeast two-hybrid analysis and genome-wide expression profiling implicated hundreds of human factors in mediating viral-host interactions. These factors were then examined functionally through depletion analyses in primary lung cells. The resulting data point to potential roles for some unanticipated host and viral proteins in viral infection and the host response, including a network of RNA-binding proteins, components of WNT signaling, and viral polymerase subunits. This multilayered approach provides a comprehensive and unbiased physical and regulatory model of influenza-host interactions and demonstrates a general strategy for uncovering complex host-pathogen relationships.

Selenomethionine as an expressible handle for bioconjugations.

Site-selective chemical bioconjugation reactions are enabling tools for the chemical biologist. Guided by a careful study of the selenomethionine (SeM) benzylation, we have refined the reaction to meet the requirements of practical protein bioconjugation. SeM is readily introduced through auxotrophic expression and exhibits unique nucleophilic properties that allow it to be selectively modified even in the presence of cysteine. The resulting benzylselenonium adduct is stable at physiological pH, is selectively labile to glutathione, and embodies a broadly tunable cleavage profile. Specifically, a 4-bromomethylphenylacetyl (BrMePAA) linker has been applied for efficient conjugation of complex organic molecules to SeM-containing proteins. This expansion of the bioconjugation toolkit has broad potential in the development of chemically enhanced proteins.

The HTLV-1 viral oncoproteins Tax and HBZ reprogram the cellular mRNA splicing landscape.

Viral infections are known to hijack the transcription and translation of the host cell. However, the extent to which viral proteins coordinate these perturbations remains unclear. Here we used a model system, the human T-cell leukemia virus type 1 (HTLV-1), and systematically analyzed the transcriptome and interactome of key effectors oncoviral proteins Tax and HBZ. We showed that Tax and HBZ target distinct but also common transcription factors. Unexpectedly, we also uncovered a large set of interactions with RNA-binding proteins, including the U2 auxiliary factor large subunit (U2AF2), a key cellular regulator of pre-mRNA splicing. We discovered that Tax and HBZ perturb the splicing landscape 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/lymphoma (ATLL) samples from two independent patient cohorts, and in well-known cancer census genes. Our interactome mapping approach, applicable to other viral oncogenes, has identified spliceosome perturbation as a novel mechanism coordinated by Tax and HBZ to reprogram the transcriptome.

A mitochondrial protein compendium elucidates complex I disease biology.

Mitochondria are complex organelles whose dysfunction underlies a broad spectrum of human diseases. Identifying all of the proteins resident in this organelle and understanding how they integrate into pathways represent major challenges in cell biology. Toward this goal, we performed mass spectrometry, GFP tagging, and machine learning to create a mitochondrial compendium of 1098 genes and their protein expression across 14 mouse tissues. We link poorly characterized proteins in this inventory to known mitochondrial pathways by virtue of shared evolutionary history. Using this approach, we predict 19 proteins to be important for the function of complex I (CI) of the electron transport chain. We validate a subset of these predictions using RNAi, including C8orf38, which we further show harbors an inherited mutation in a lethal, infantile CI deficiency. Our results have important implications for understanding CI function and pathogenesis and, more generally, illustrate how our compendium can serve as a foundation for systematic investigations of mitochondria.

A protein domain-based interactome network for C. elegans early embryogenesis.

Many protein-protein interactions are mediated through independently folding modular domains. Proteome-wide efforts to model protein-protein interaction or "interactome" networks have largely ignored this modular organization of proteins. We developed an experimental strategy to efficiently identify interaction domains and generated a domain-based interactome network for proteins involved in C. elegans early-embryonic cell divisions. Minimal interacting regions were identified for over 200 proteins, providing important information on their domain organization. Furthermore, our approach increased the sensitivity of the two-hybrid system, resulting in a more complete interactome network. This interactome modeling strategy revealed insights into C. elegans centrosome function and is applicable to other biological processes in this and other organisms.

A massively parallel barcoded sequencing pipeline enables generation of the first ORFeome and interactome map for rice.

Systematic mappings of protein interactome networks have provided invaluable functional information for numerous model organisms. Here we develop PCR-mediated Linkage of barcoded Adapters To nucleic acid Elements for sequencing (PLATE-seq) that serves as a general tool to rapidly sequence thousands of DNA elements. We validate its utility by generating the ORFeome for Oryza sativa covering 2,300 genes and constructing a high-quality protein-protein interactome map consisting of 322 interactions between 289 proteins, expanding the known interactions in rice by roughly 50%. Our work paves the way for high-throughput profiling of protein-protein interactions in a wide range of organisms.

Scaling-up a fragment-based protein-protein interaction method using a human reference interaction set.

Protein-protein interactions (PPIs) are essential in understanding numerous aspects of protein function. Here, we significantly scaled and modified analyses of the recently developed all-vs-all sequencing (AVA-Seq) approach using a gold-standard human protein interaction set (hsPRS-v2) containing 98 proteins. Binary interaction analyses recovered 20 of 47 (43%) binary PPIs from this positive reference set (PRS), comparing favorably with other methods. However, the increase of 20× in the interaction search space for AVA-Seq analysis in this manuscript resulted in numerous changes to the method required for future use in genome-wide interaction studies. We show that standard sequencing analysis methods must be modified to consider the possible recovery of thousands of positives among millions of tested interactions in a single sequencing run. The PRS data were used to optimize data scaling, auto-activator removal, rank interaction features (such as orientation and unique fragment pairs), and statistical cutoffs. Using these modifications to the method, AVA-Seq recovered >500 known and novel PPIs, including interactions between wild-type fragments of tumor protein p53 and minichromosome maintenance complex proteins 2 and 5 (MCM2 and MCM5) that could be of interest in human disease.

Systematic screening reveals a role for BRCA1 in the response to transcription-associated DNA damage.

BRCA1 is a breast and ovarian tumor suppressor. Given its numerous incompletely understood functions and the possibility that more exist, we performed complementary systematic screens in search of new BRCA1 protein-interacting partners. New BRCA1 functions and/or a better understanding of existing ones were sought. Among the new interacting proteins identified, genetic interactions were detected between BRCA1 and four of the interactors: TONSL, SETX, TCEANC, and TCEA2. Genetic interactions were also detected between BRCA1 and certain interactors of TONSL, including both members of the FACT complex. From these results, a new BRCA1 function in the response to transcription-associated DNA damage was detected. Specifically, new roles for BRCA1 in the restart of transcription after UV damage and in preventing or repairing damage caused by stabilized R loops were identified. These roles are likely carried out together with some of the newly identified interactors. This new function may be important in BRCA1 tumor suppression, since the expression of several interactors, including some of the above-noted transcription proteins, is repeatedly aberrant in both breast and ovarian cancers.

Electrochemical Nozaki-Hiyama-Kishi Coupling: Scope, Applications, and Mechanism.

One of the most oft-employed methods for C-C bond formation involving the coupling of vinyl-halides with aldehydes catalyzed by Ni and Cr (Nozaki-Hiyama-Kishi, NHK) has been rendered more practical using an electroreductive manifold. Although early studies pointed to the feasibility of such a process, those precedents were never applied by others due to cumbersome setups and limited scope. Here we show that a carefully optimized electroreductive procedure can enable a more sustainable approach to NHK, even in an asymmetric fashion on highly complex medicinally relevant systems. The e-NHK can even enable non-canonical substrate classes, such as redox-active esters, to participate with low loadings of Cr when conventional chemical techniques fail. A combination of detailed kinetics, cyclic voltammetry, and in situ UV-vis spectroelectrochemistry of these processes illuminates the subtle features of this mechanistically intricate process.

Extensive rewiring and complex evolutionary dynamics in a C. elegans multiparameter transcription factor network.

Gene duplication results in two identical paralogs that diverge through mutation, leading to loss or gain of interactions with other biomolecules. Here, we comprehensively characterize such network rewiring for C. elegans transcription factors (TFs) within and across four newly delineated molecular networks. Remarkably, we find that even highly similar TFs often have different interaction degrees and partners. In addition, we find that most TF families have a member that is highly connected in multiple networks. Further, different TF families have opposing correlations between network connectivity and phylogenetic age, suggesting that they are subject to different evolutionary pressures. Finally, TFs that have similar partners in one network generally do not in another, indicating a lack of pressure to retain cross-network similarity. Our multiparameter analyses provide unique insights into the evolutionary dynamics that shaped TF networks.

Dynamic Role of trans Regulation of Gene Expression in Relation to Complex Traits.

Identifying causal genetic variants and understanding their mechanisms of effect on traits remains a challenge in genome-wide association studies (GWASs). In particular, how genetic variants (i.e., trans-eQTLs) affect expression of remote genes (i.e., trans-eGenes) remains unknown. We hypothesized that some trans-eQTLs regulate expression of distant genes by altering the expression of nearby genes (cis-eGenes). Using published GWAS datasets with 39,165 single-nucleotide polymorphisms (SNPs) associated with 1,960 traits, we explored whole blood gene expression associations of trait-associated SNPs in 5,257 individuals from the Framingham Heart Study. We identified 2,350 trans-eQTLs (at p < 10-7); more than 80% of them were found to have cis-associated eGenes. Mediation testing suggested that for 35% of trans-eQTL-trans-eGene pairs in different chromosomes and 90% pairs in the same chromosome, the disease-associated SNP may alter expression of the trans-eGene via cis-eGene expression. In addition, we identified 13 trans-eQTL hotspots, affecting from ten to hundreds of genes, suggesting the existence of master genetic regulators. Using causal inference testing, we searched causal variants across eight cardiometabolic traits (BMI, systolic and diastolic blood pressure, LDL cholesterol, HDL cholesterol, total cholesterol, triglycerides, and fasting blood glucose) and identified several cis-eGenes (ALDH2 for systolic and diastolic blood pressure, MCM6 and DARS for total cholesterol, and TRIB1 for triglycerides) that were causal mediators for the corresponding traits, as well as examples of trans-mediators (TAGAP for LDL cholesterol). The finding of extensive evidence of genome-wide mediation effects suggests a critical role of cryptic gene regulation underlying many disease traits.

Insights into the Role of Transient Chiral Mediators and Pyridone Ligands in Asymmetric Pd-Catalyzed C-H Functionalization.

Mechanistic investigations uncover a novel role for 2-pyridone ligands and interrogate the origin of enantioselectivity in the (+)-norbornene-mediated Pd-catalyzed meta-C(aryl)-H functionalization of diarylmethylamines. Observations from kinetic analysis in concert with in situ 19F NMR monitoring allow us to propose that the pyridone ligand plays a role in enhancing the rate- and enantio-determining insertion of an arylpalladium species into a chiral norbornene derivative. The unprecedented features of 2-pyridone ligands in asymmetric 1,2 migratory insertion, and norbornene as a transient chiral mediator in relay chemistry, provide new insights into this ligand scaffold for future developments in stereoselective transition-metal-catalyzed C-H functionalization.

Multiplex single-molecule interaction profiling of DNA-barcoded proteins.

In contrast with advances in massively parallel DNA sequencing, high-throughput protein analyses are often limited by ensemble measurements, individual analyte purification and hence compromised quality and cost-effectiveness. Single-molecule protein detection using optical methods is limited by the number of spectrally non-overlapping chromophores. Here we introduce a single-molecular-interaction sequencing (SMI-seq) technology for parallel protein interaction profiling leveraging single-molecule advantages. DNA barcodes are attached to proteins collectively via ribosome display or individually via enzymatic conjugation. Barcoded proteins are assayed en masse in aqueous solution and subsequently immobilized in a polyacrylamide thin film to construct a random single-molecule array, where barcoding DNAs are amplified into in situ polymerase colonies (polonies) and analysed by DNA sequencing. This method allows precise quantification of various proteins with a theoretical maximum array density of over one million polonies per square millimetre. Furthermore, protein interactions can be measured on the basis of the statistics of colocalized polonies arising from barcoding DNAs of interacting proteins. Two demanding applications, G-protein coupled receptor and antibody-binding profiling, are demonstrated. SMI-seq enables 'library versus library' screening in a one-pot assay, simultaneously interrogating molecular binding affinity and specificity.

Identifying pathogenicity of human variants via paralog-based yeast complementation.

To better understand the health implications of personal genomes, we now face a largely unmet challenge to identify functional variants within disease-associated genes. Functional variants can be identified by trans-species complementation, e.g., by failure to rescue a yeast strain bearing a mutation in an orthologous human gene. Although orthologous complementation assays are powerful predictors of pathogenic variation, they are available for only a few percent of human disease genes. Here we systematically examine the question of whether complementation assays based on paralogy relationships can expand the number of human disease genes with functional variant detection assays. We tested over 1,000 paralogous human-yeast gene pairs for complementation, yielding 34 complementation relationships, of which 33 (97%) were novel. We found that paralog-based assays identified disease variants with success on par with that of orthology-based assays. Combining all homology-based assay results, we found that complementation can often identify pathogenic variants outside the homologous sequence region, presumably because of global effects on protein folding or stability. Within our search space, paralogy-based complementation more than doubled the number of human disease genes with a yeast-based complementation assay for disease variation.

An extended set of yeast-based functional assays accurately identifies human disease mutations.

We can now routinely identify coding variants within individual human genomes. A pressing challenge is to determine which variants disrupt the function of disease-associated genes. Both experimental and computational methods exist to predict pathogenicity of human genetic variation. However, a systematic performance comparison between them has been lacking. Therefore, we developed and exploited a panel of 26 yeast-based functional complementation assays to measure the impact of 179 variants (101 disease- and 78 non-disease-associated variants) from 22 human disease genes. Using the resulting reference standard, we show that experimental functional assays in a 1-billion-year diverged model organism can identify pathogenic alleles with significantly higher precision and specificity than current computational methods.