Biology subjects

Cong, Q.

Publications and source records attributed to Cong, Q..

36 records · Page 2Linked to original sources

bioRxiv2024

Orthogonalized human protease control of secreted signals

Synthetic circuits that regulate protein secretion in human cells could support cell-based therapies by enabling control over local environments. While protein-level circuits enable such potential clinical applications, featuring orthogonality and compactness, their non-human origin poses a potential immunogenic risk. Here, we developed Humanized Drug Induced Regulation of Engineered CyTokines (hDIRECT) as a platform to control cytokine activity exclusively using human-derived proteins. We sourced a specific human protease and its FDA-approved inhibitor. We engineered cytokines (IL-2, IL-6, and IL-10) whose activities can be activated and abrogated by proteolytic cleavage. We utilized species specificity and re-localization strategies to orthogonalize the cytokines and protease from the human context that they would be deployed in. hDIRECT should enable local cytokine activation to support a variety of cell-based therapies such as muscle regeneration and cancer immunotherapy. Our work offers a proof of concept for the emerging appreciation of humanization in synthetic biology for human health.

synthetic biology↗

bioRxiv2023

ECOD domain classification of 48 whole proteomes from AlphaFold Structure Database using DPAM

Protein structure prediction has now been deployed widely across several different large protein sets. Large-scale domain annotation of these predictions can aid in the development of biological insights. Using our Evolutionary Classification of Protein Domains (ECOD) from experimental structures as a basis for classification, we describe the detection and cataloging of domains from 48 whole proteomes deposited in the AlphaFold Database. On average, we can provide positive classification (either of domains or other identifiable non-domain regions) for 90% of residues in all proteomes. We classified 746,349 domains from 536,808 proteins comprised of over 226,424,000 amino acid residues. We examine the varying populations of homologous groups in both eukaryotes and bacteria. In addition to containing a higher fraction of disordered regions and unassigned domains, eukaryotes show a higher proportion of repeated proteins, both globular and small repeats. We enumerate those highly populated domains that are shared in both eukaryotes and bacteria, such as the Rossmann domains, TIM barrels, and P-loop domains. Additionally, we compare the sampling of homologous groups from this whole proteome set against our stable ECOD reference and discuss groups that have been enriched by structure predictions. Finally, we discuss the implication of these results for protein target selection for future classification strategies for very large protein sets. Author SummaryProteins can contain one or more domains, regions that are evolutionary independent and convey fiction and function. Here we present our classification of proteins within 48 proteomes provided by the AlphaFold Structural Database. These proteomes span multiple model organisms used in research as a common ground for studying biological principles as well as organisms involved in prevalent human infectious diseases. We classify these domains by our AlphaFold-specific Domain Parser for AlphaFold Models (DPAM), which was previously tested on the human proteome. We find that eukaryotic and bacterial proteomes can be classified to different degrees, with significantly more disordered and low-confident regions in eukaryotic proteins.

bioinformatics↗

bioRxiv2023

Efficient and accurate prediction of protein structure using RoseTTAFold2

AlphaFold2 and RoseTTAFold predict protein structures with very high accuracy despite substantial architecture differences. We sought to develop an improved method combining features of both. The resulting method, RoseTTAFold2, extends the original three-track architecture of RoseTTAFold over the full network, incorporating the concepts of Frame-aligned point error, recycling during training, and the use of a distillation set from AlphaFold2. We also took from AlphaFold2 the idea of structurally coherent attention in updating pair features, but using a more computationally efficient structure-biased attention as opposed to triangle attention. The resulting model has the accuracy of AlphaFold2 on monomers, and AlphaFold2-multimer on complexes, with better computational scaling for large proteins and complexes. This excellent performance is achieved without hallmark features of AlphaFold2, invariant point attention and triangle attention, indicating that these are not essential for high accuracy prediction. Almost all recent work on protein structure prediction has re-used the basic AlphaFold2 architecture; our results show that excellent performance can be achieved with a broader class of models, opening the door for further exploration.

biochemistry↗

bioRxiv2023

Genetic manipulation of candidate phyla radiation bacteria provides functional insights into microbial dark matter

The study of bacteria has yielded fundamental insights into cellular biology and physiology, biotechnological advances and many therapeutics. Yet due to a lack of suitable tools, the significant portion of bacterial diversity held within the candidate phyla radiation (CPR) remains inaccessible to such pursuits. Here we show that CPR bacteria belonging to the phylum Saccharibacteria exhibit natural competence. We exploit this property to develop methods for their genetic manipulation, including the insertion of heterologous sequences and the construction of targeted gene deletions. Imaging of fluorescent protein-labeled Saccharibacteria provides high spatiotemporal resolution of phenomena accompanying epibiotic growth and a transposon insertion sequencing genome-wide screen reveals the contribution of enigmatic Saccharibacterial genes to growth on their Actinobacteria hosts. Finally, we leverage metagenomic data to provide cutting-edge protein structure-based bioinformatic resources that support the strain Southlakia epibionticum and its corresponding host, Actinomyces israelii, as a model system for unlocking the molecular underpinnings of the epibiotic lifestyle.

microbiology↗

bioRxiv2022

A tissue injury repair pathway distinct but parallel to host pathogen defense

Pathogen infection and tissue injury are universal insults that disrupt homeostasis. Innate immunity senses microbial infections and induces interferons (IFNs) to activate resistance mechanisms. Applying unbiased phylogenetic analysis, we show that interleukin-24 (IL24) is among the closest evolutionary homologs to the IFN family and shares a common ancestral origin. However, in contrast to IFNs, IL24 induction occurs specifically in barrier epithelial progenitors after injury and is independent of microbiome or adaptive immunity. Surprisingly, Il24 ablation impedes not only epidermal proliferation and re-epithelialization, but also capillary and fibroblast regeneration within the dermal wound bed. Conversely, ectopic Il24 induction in homeostatic epidermis triggers global epithelial-mesenchymal tissue repair responses. Mechanistically, sustained Il24 expression depends upon both IL24 receptor/STAT3 signaling and also hypoxia-stabilized HIF1, which converge following injury. Thus, parallel to the IFN-mediated innate immune sensing of pathogens to resolve infections, epithelial stem cells sense injury signals to orchestrate IL24-mediated tissue repair.

immunology↗

bioRxiv2022

DPAM: A Domain Parser for AlphaFold Models

The recent breakthroughs in structure prediction, where methods such as AlphaFold demonstrated near atomic accuracy, herald a paradigm shift in structure biology. The 200 million high-accuracy models released in the AlphaFold Database are expected to guide protein science in the coming decades. Partitioning these AlphaFold models into domains and subsequently assigning them to our evolutionary hierarchy provides an efficient way to gain functional insights of proteins. However, classifying such a large number of predicted structures challenges the infrastructure of current structure classifications, including our Evolutionary Classification of protein Domains (ECOD). Better computational tools are urgently needed to automatically parse and classify domains from AlphaFold models. Here we present a Domain Parser for AlphaFold Models (DPAM) that can automatically recognize globular domains from these models based on predicted aligned errors, inter-residue distances in 3D structures, and ECOD domains found by sequence (HHsuite) and structural (DALI) similarity searches. Based on a benchmark of 18,759 AlphaFold models, we demonstrated that DPAM could recognize 99.5% domains and assign correct boundaries for 85.2% of them, significantly outperforming structure-based domain parsers and homology-based domain assignment using ECOD domains found by HHsuite or DALI. Application of DPAM to the massive set of AlphaFold models will allow for more efficient classification of domains, providing evolutionary contexts and facilitating functional studies.

bioinformatics↗

bioRxiv2022

Assessing risk for butterflies in the context of climate change, demographic uncertainty, and heterogenous data sources

AbstractOngoing declines in insect populations have led to substantial concern and calls for conservation action. However, even for relatively well-studied groups, like butterflies, information relevant to species-specific status and risk is scattered across field guides, the scientific literature, and agency reports. Consequently, attention and resources have been spent on a miniscule fraction of insect diversity, including a few well-studied butterflies. Here we bring together heterogenous sources of information for 396 butterfly species to provide the first regional assessment of butterflies for the 11 western US states. For 184 species, we use monitoring data to characterize historical and projected trends in population abundance. For another 212 species (for which monitoring data are not available, but other types of information can be collected), we use exposure to climate change, development, geographic range, number of host plants, and other factors to rank species for conservation concern. A phylogenetic signal is apparent, with concentrations of declining and at-risk species in the families Lycaenidae and Hesperiidae. A geographic bias exists in that many species that lack monitoring data occur in more southern states where we expect that impacts of warming and drying trends will be most severe. Legal protection is rare among the taxa with the highest risk values: of the top 100 species, one is listed as threatened under the US Endangered Species Act and one is a candidate for listing. Among the many taxa not currently protected, we highlight a short list of species in decline, including Vanessa annabella, Thorybes mexicanus, Euchloe ausonides, and Pholisora catullus. Notably, many of these species have broad geographic ranges, which perhaps highlights a new era of insect conservation in which small or fragmented ranges will not be the only red flags that attract conservation attention.

ecology↗

bioRxiv2022

Co-Component Signal Transduction Systems: fast-evolving virulence regulation cassettes discovered in enteric bacteria

Bacterial signal transduction systems sense changes in the environment and transmit these signals to control cellular responses. The simplest one-component signal transduction systems include an input sensor domain and an output response domain encoded in a single protein chain. Alternately, two-component signal transduction systems transmit signals by phosphorelay between input and output domains from separate proteins. The membrane tethered periplasmic bile acid sensor that activates the Vibrio parahaemolyticus type III secretion system adopts an obligate heterodimer of two proteins encoded by partially overlapping VtrA and VtrC genes. This co-component signal transduction system binds bile acid using a lipocalin-like domain in VtrC and transmits the signal through the membrane to a cytoplasmic DNA-binding transcription factor in VtrA. Using the domain and operon organization of VtrA/VtrC, we identify a fast-evolving superfamily of co-component systems in enteric bacteria. Accurate machine learning-based fold predictions for the candidate co-components support their homology in the twilight zone of rapidly evolving sequence and provide mechanistic hypotheses about previously unrecognized lipid-sensing functions. Significance statementUsing the domain and operon organization of VtrA/VtrC, combined with fold predictions, we identify new co-component signal transduction systems in enteric bacteria that likely regulate virulence. We observe that the heterodimeric VtrA/VtrC periplasmic bile acid receptor controlling Vibrio parahaemolyticus T3SS2 is a distant homolog of the ToxR/ToxS master regulator of virulence and has evolved beyond confident sequence recognition. Exploiting the newly developed machine learning methods for structure prediction, we observe a VtrC-like lipocalin fold for both the ToxS periplasmic domain and for other detected periplasmic sensor components. This structure prediction supports the divergent evolution of VtrA/VtrC-like co-component signal transduction systems and suggests a role for lipid sensing in regulating virulence in enteric bacteria.

evolutionary biology↗

bioRxiv2022

Towards a structurally resolved cancer interactome

Protein-protein interactions (PPIs) are involved in almost all essential cellular processes. Perturbation of PPI networks plays critical roles in tumorigenesis, cancer progression and metastasis. While numerous high-throughput experiments have produced a vast amount of data for PPIs, these datasets suffer from high false positive rates and exhibit a high degree of discrepancy. Coevolution of amino acid positions between protein pairs has proven to be useful in identifying interacting proteins and providing structural details of the interaction interfaces with the help of deep learning methods like AlphaFold (AF). In this study, we applied AF to investigate the cancer protein-protein interactome. We predicted 1,798 PPIs for cancer driver proteins involved in diverse cellular processes such as transcription regulation, signal transduction, DNA repair and cell cycle. We modeled the spatial structure for the predicted binary protein complexes, 1,087 of which lacked previous 3D structure information. Our predictions offer novel structural insight into many cancer-related processes such as the MAP kinase cascade and Fanconi anemia pathway. We further investigated the cancer mutation landscape by mapping somatic missense mutations (SMMs) in cancer to the predicted PPI interfaces and performing enrichment and depletion analyses. Interfaces enriched or depleted with SMMs exhibit different preferences for functional categories. Interfaces enriched in mutations tend to function in pathways that are deregulated in cancers and they may help explain the molecular mechanisms of cancers in patients; interfaces lacking mutations appear to be essential for the survival of cancer cells and thus may be future targets for PPI modulating drugs.

bioinformatics↗

bioRxiv2021

Structures of core eukaryotic protein complexes

Protein-protein interactions play critical roles in biology, but despite decades of effort, the structures of many eukaryotic protein complexes are unknown, and there are likely many interactions that have not yet been identified. Here, we take advantage of recent advances in proteome-wide amino acid coevolution analysis and deep-learning-based structure modeling to systematically identify and build accurate models of core eukaryotic protein complexes, as represented within the Saccharomyces cerevisiae proteome. We use a combination of RoseTTAFold and AlphaFold to screen through paired multiple sequence alignments for 8.3 million pairs of S. cerevisiae proteins and build models for strongly predicted protein assemblies with two to five components. Comparison to existing interaction and structural data suggests that these predictions are likely to be quite accurate. We provide structure models spanning almost all key processes in Eukaryotic cells for 104 protein assemblies which have not been previously identified, and 608 which have not been structurally characterized. One-sentence summaryWe take advantage of recent advances in proteome-wide amino acid coevolution analysis and deep-learning-based structure modeling to systematically identify and build accurate models of core eukaryotic protein complexes.

biochemistry↗

bioRxiv2021

Human mitochondrial protein complexes revealed by large-scale coevolution analysis and deep learning-based structure modeling

Recent development of deep-learning methods has led to a breakthrough in the prediction accuracy of 3-dimensional protein structures. Extending these methods to protein pairs is expected to allow large-scale detection of protein-protein interactions and modeling protein complexes at the proteome level. We applied RoseTTAFold and AlphaFold2, two of the latest deep-learning methods for structure predictions, to analyze coevolution of human proteins residing in mitochondria, an organelle of vital importance in many cellular processes including energy production, metabolism, cell death, and antiviral response. Variations in mitochondrial proteins have been linked to a plethora of human diseases and genetic conditions. RoseTTAFold, with high computational speed, was used to predict the coevolution of about 95% of mitochondrial protein pairs. Top-ranked pairs were further subject to the modeling of the complex structures by AlphaFold2, which also produced contact probability with high precision and in many cases consistent with RoseTTAFold. Most of the top ranked pairs with high contact probability were supported by known protein-protein interactions and/or similarities to experimental structural complexes. For high-scoring pairs without experimental complex structures, our coevolution analyses and structural models shed light on the details of their interfaces, including CHCHD4-AIFM1, MTERF3-TRUB2, FMC1-ATPAF2, ECSIT-NDUFAF1 and COQ7-COQ9, among others. We also identified novel PPIs (PYURF-NDUFAF5, LYRM1-MTRF1L and COA8-COX10) for several proteins without experimentally characterized interaction partners, leading to predictions of their molecular functions and the biological processes they are involved in.

bioinformatics↗

bioRxiv2021

Accurate prediction of protein structures and interactions using a 3-track network

DeepMind presented remarkably accurate protein structure predictions at the CASP14 conference. We explored network architectures incorporating related ideas and obtained the best performance with a 3-track network in which information at the 1D sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The 3-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables rapid solution of challenging X-ray crystallography and cryo-EM structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate models of protein-protein complexes from sequence information alone, short circuiting traditional approaches which require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research. One-Sentence SummaryAccurate protein structure modeling enables rapid solution of structure determination problems and provides insights into biological function.

bioinformatics↗

bioRxiv2021

A covariation analysis reveals elements of selectivity in quorum sensing systems

Many bacteria communicate with kin and coordinate group behaviors through a form of cell-cell signaling called acyl-homoserine lactone (AHL) quorum sensing (QS). In these systems, a signal synthase produces an AHL to which its paired receptor selectively responds. Selectivity is fundamental to cell signaling. Despite its importance, it has been challenging to determine how this selectivity is achieved and how AHL QS systems evolve and diversify. We hypothesized that we could use covariation within the protein sequences of AHL synthases and receptors to identify selectivity residues. We began by identifying about 6,000 unique synthase-receptor pairs. We then used the protein sequences of these pairs to identify covariation patterns and mapped the patterns onto the LasI/R system from Pseudomonas aeruginosa PAO1. The covarying residues in both proteins cluster around the ligand binding sites. We demonstrate that these residues are involved in system selectivity toward the cognate signal and go on to engineer the Las system to both produce and respond to an alternate AHL signal. We have thus demonstrated a new application for covariation methods and have deepened our understanding of how communication systems evolve and diversify.

microbiology↗

bioRxiv2020

The meadow jumping mouse genome and transcriptome suggest mechanisms of hibernation

Hibernating mammals exhibit medically relevant phenotypes, but the genetic basis of hibernation remains poorly understood. Using the meadow jumping mouse (Zapus hudsonius), we investigated the genetic underpinnings of hibernation by uniting experimental and comparative genomic approaches. We assembled a Z. hudsonius genome and identified widespread expression changes during hibernation in genes important for circadian rhythm, membrane fluidity, and cell cycle arrest. Tissue-specific gene expression changes during torpor encompassed Wnt signaling in the brain and structural and transport functions in the kidney brush border. Using genomes from the closely related Zapus oregonus (previously classified as Z. princeps) and leveraging a panel of hibernating and non-hibernating rodents, we found selective pressure on genes involved in feeding behavior, metabolism, and cell biological processes potentially important for function at low body temperature. Leptin stands out with elevated conservation in hibernating rodents, implying a role for this metabolic hormone in triggering fattening and hibernation. These findings illustrate that mammalian hibernation requires adaptation at all levels of organismal form and function and lay the groundwork for future study of hibernation phenotypes.

genomics↗

bioRxiv2020

The relationship between gene flow and population difference during secondary contact between butterfly sister species

Comparisons of genomes from recently diverged butterfly populations along a suture zone in central Texas have revealed high levels of divergence on the Z chromosome relative to autosomes, as measured by fixation index, Fst. The pattern of divergence appears to result from accumulation of incompatible alleles, obstructing introgression on the Z chromosome in hybrids. However, it is unknown whether this mechanism is sufficient to explain the data. Here, we simulate the effects of hybrid incompatibility on interbreeding butterfly populations using a model in which populations accumulate cross-incompatible alleles in allopatry prior to contact. We compute statistics for introgression and population divergence during contact between model butterfly populations and compare them to statistics obtained for 15 pairs of butterfly species interbreeding along the Texas suture zone. For populations that have evolved sufficiently in allopatry, the model exhibits high levels of divergence on the Z chromosome relative to autosomes in populations inter-breeding on time scales comparable to periods of interglacial contact between butterfly populations in central Texas. Levels of divergence on the Z chromosome increase when interacting groups of genes are closely linked, consistent with interacting clusters of functionally related genes in butterfly genomes. Results for various periods in allopatry are in qualitative agreement with the pattern of data for butterflies, supporting a picture of speciation in which populations are subjected to cycles of divergence in glacial isolation, and partial fusion during interglacial contact.

biophysics↗

bioRxiv2019

Genomic determinants of speciation

Studies of life rely on classifying organisms into species. However, since Darwin, there is no agreement about how to separate species from varieties. Contrary to a frequent belief, quantitative standards for species delineation are lacking and overdue, and debates about species delimitation create obstacles for conservation biology, agriculture, legislation, and education. To tackle this key biological question, we have chosen butterflies as model organisms. We sequenced and analyzed transcriptomes of 188 specimens representing pairs of close but clearly distinct species, populations, and taxa that are debated among experts. We find that species are robustly separated from populations by the combination of two measures computed on Z-linked genes: fixation index that detects hiatus between species, and the extent of gene flow that quantifies reproductive isolation. These criteria suggest that all 9 butterfly pairs that caused experts disagreement are distinct species, not populations. When applied to Homo, our criteria agree that all modern humans are the same species distinct from Neanderthals, suggesting relevance of this study beyond butterflies. Furthermore, we found that proteins involved in interactions with DNA (including proteins encoded by trans-regulatory elements), circadian clock, pheromone sensing, development, and immune response recurrently correlate with speciation. A significant fraction of these proteins is encoded by the Z-chromosome, which appears to be resistant to introgression. Taken together, we find common speciation mechanisms in butterflies, reveal the central role of Z-chromosome in speciation, and suggest quantitative criteria for species delimitation using genomic data, which is vital for the exploration of biodiversity.

evolutionary biology↗

bioRxiv2019

Genomics of a complete butterfly continent

Never before have we had the luxury of choosing a continent, picking a large phylogenetic group of animals, and obtaining genomic data for its every species. Here, we sequence all 845 species of butterflies recorded from North America north of Mexico. Our comprehensive approach reveals the pattern of diversification and adaptation occurring in this phylogenetic lineage as it has spread over the continent, which cannot be seen on a sample of selected species. We observe bursts of diversification that generated taxonomic ranks: subfamily, tribe, subtribe, genus, and species. The older burst around 70 Mya resulted in the butterfly subfamilies, with the major evolutionary inventions being unique phenotypic traits shaped by high positive selection and gene duplications. The recent burst around 5 Mya is caused by explosive radiation in diverse butterfly groups associated with diversification in transcription and mRNA regulation, morphogenesis, and mate selection. Rapid radiation correlates with more frequent introgression of speciation-promoting and beneficial genes among radiating species. Radiation and extinction patterns over the last 100 million years suggest the following general model of animal evolution. A population spreads over the land, adapts to various conditions through mutations, and diversifies into several species. Occasional hybridization between these species results in accumulation of beneficial alleles in one, which eventually survives, while others become extinct. Not only butterflies, but also the hominids may have followed this path.

genomics↗

bioRxiv2019

Genomics reveals the origins of ancient specimens

Centuries of zoological studies amassed billions of specimens in collections worldwide. Genomics of these specimens promises to rejuvenate biodiversity research. The obstacles stem from DNA degradation with specimen age. Overcoming this challenge, we set out to resolve a series of long-standing controversies involving a group of butterflies. We deduced geographical origins of several ancient specimens of uncertain provenance that are at the heart of these debates. Here, genomics tackles one of the greatest problems in zoology: countless old, poorly documented specimens that serve as irreplaceable embodiments of species concepts. The ability to figure out where they were collected will resolve many on-going disputes. More broadly, we show the utility of genomics applied to ancient museum specimens to delineate the boundaries of species and populations, and to hypothesize about genotypic determinants of phenotypic traits.

zoology↗