Gut microbiome of multiple sclerosis patients and paired household healthy controls reveal associations with disease risk and course.

Changes in gut microbiota have been associated with several diseases. Here, the International Multiple Sclerosis Microbiome Study (iMSMS) studied the gut microbiome of 576 MS patients (36% untreated) and genetically unrelated household healthy controls (1,152 total subjects). We observed a significantly increased proportion of Akkermansia muciniphila, Ruthenibacterium lactatiformans, Hungatella hathewayi, and Eisenbergiella tayi and decreased Faecalibacterium prausnitzii and Blautia species. The phytate degradation pathway was over-represented in untreated MS, while pyruvate-producing carbohydrate metabolism pathways were significantly reduced. Microbiome composition, function, and derived metabolites also differed in response to disease-modifying treatments. The therapeutic activity of interferon-ß may in part be associated with upregulation of short-chain fatty acid transporters. Distinct microbial networks were observed in untreated MS and healthy controls. These results strongly support specific gut microbiome associations with MS risk, course and progression, and functional changes in response to treatment.

Dual proteome-scale networks reveal cell-specific remodeling of the human interactome.

Thousands of interactions assemble proteins into modules that impart spatial and functional organization to the cellular proteome. Through affinity-purification mass spectrometry, we have created two proteome-scale, cell-line-specific interaction networks. The first, BioPlex 3.0, results from affinity purification of 10,128 human proteins-half the proteome-in 293T cells and includes 118,162 interactions among 14,586 proteins. The second results from 5,522 immunoprecipitations in HCT116 cells. These networks model the interactome whose structure encodes protein function, localization, and complex membership. Comparison across cell lines validates thousands of interactions and reveals extensive customization. Whereas shared interactions reside in core complexes and involve essential proteins, cell-specific interactions link these complexes, "rewiring" subnetworks within each cell's interactome. Interactions covary among proteins of shared function as the proteome remodels to produce each cell's phenotype. Viewable interactively online through BioPlexExplorer, these networks define principles of proteome organization and enable unknown protein characterization.

Large-Scale Analyses of Human Microbiomes Reveal Thousands of Small, Novel Genes.

Small proteins are traditionally overlooked due to computational and experimental difficulties in detecting them. To systematically identify small proteins, we carried out a comparative genomics study on 1,773 human-associated metagenomes from four different body sites. We describe >4,000 conserved protein families, the majority of which are novel; ∼30% of these protein families are predicted to be secreted or transmembrane. Over 90% of the small protein families have no known domain and almost half are not represented in reference genomes. We identify putative housekeeping, mammalian-specific, defense-related, and protein families that are likely to be horizontally transferred. We provide evidence of transcription and translation for a subset of these families. Our study suggests that small proteins are highly abundant and those of the human microbiome, in particular, may perform diverse functions that have not been previously reported.

Illuminating G-Protein-Coupling Selectivity of GPCRs.

Heterotrimetic G proteins consist of four subfamilies (Gs, Gi/o, Gq/11, and G12/13) that mediate signaling via G-protein-coupled receptors (GPCRs), principally by receptors binding Gα C termini. G-protein-coupling profiles govern GPCR-induced cellular responses, yet receptor sequence selectivity determinants remain elusive. Here, we systematically quantified ligand-induced interactions between 148 GPCRs and all 11 unique Gα subunit C termini. For each receptor, we probed chimeric Gα subunit activation via a transforming growth factor-α (TGF-α) shedding response in HEK293 cells lacking endogenous Gq/11 and G12/13 proteins, and complemented G-protein-coupling profiles through a NanoBiT-G-protein dissociation assay. Interrogation of the dataset identified sequence-based coupling specificity features, inside and outside the transmembrane domain, which we used to develop a coupling predictor that outperforms previous methods. We used the predictor to engineer designer GPCRs selectively coupled to G12. This dataset of fine-tuned signaling mechanisms for diverse GPCRs is a valuable resource for research in GPCR signaling.

Principles of Protein Stability and Their Application in Computational Design.

Proteins are increasingly used in basic and applied biomedical research. Many proteins, however, are only marginally stable and can be expressed in limited amounts, thus hampering research and applications. Research has revealed the thermodynamic, cellular, and evolutionary principles and mechanisms that underlie marginal stability. With this growing understanding, computational stability design methods have advanced over the past two decades starting from methods that selectively addressed only some aspects of marginal stability. Current methods are more general and, by combining phylogenetic analysis with atomistic design, have shown drastic improvements in solubility, thermal stability, and aggregation resistance while maintaining the protein's primary molecular activity. Stability design is opening the way to rational engineering of improved enzymes, therapeutics, and vaccines and to the application of protein design methodology to large proteins and molecular activities that have proven challenging in the past.

Multi-omics analysis of innate and adaptive responses to BCG vaccination reveals epigenetic cell states that predict trained immunity.

Immune responses are tightly regulated yet highly variable between individuals. To investigate human population variation of trained immunity, we immunized healthy individuals with Bacillus Calmette-Guérin (BCG). This live-attenuated vaccine induces not only an adaptive immune response against tuberculosis but also triggers innate immune activation and memory that are indicative of trained immunity. We established personal immune profiles and chromatin accessibility maps over a 90-day time course of BCG vaccination in 323 individuals. Our analysis uncovered genetic and epigenetic predictors of baseline immunity and immune response. BCG vaccination enhanced the innate immune response specifically in individuals with a dormant immune state at baseline, rather than providing a general boost of innate immunity. This study advances our understanding of BCG's heterologous immune-stimulatory effects and trained immunity in humans. Furthermore, it highlights the value of epigenetic cell states for connecting immune function with genotype and the environment.

A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain.

The diversity of cell types and regulatory states in the brain, and how these change during aging, remains largely unknown. We present a single-cell transcriptome atlas of the entire adult Drosophila melanogaster brain sampled across its lifespan. Cell clustering identified 87 initial cell clusters that are further subclustered and validated by targeted cell-sorting. Our data show high granularity and identify a wide range of cell types. Gene network analyses using SCENIC revealed regulatory heterogeneity linked to energy consumption. During aging, RNA content declines exponentially without affecting neuronal identity in old brains. This single-cell brain atlas covers nearly all cells in the normal brain and provides the tools to study cellular diversity alongside other Drosophila and mammalian single-cell datasets in our unique single-cell analysis platform: SCope (http://scope.aertslab.org). These results, together with SCope, allow comprehensive exploration of all transcriptional states of an entire aging brain.

Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution.

Immune evasion is a hallmark of cancer. Losing the ability to present neoantigens through human leukocyte antigen (HLA) loss may facilitate immune evasion. However, the polymorphic nature of the locus has precluded accurate HLA copy-number analysis. Here, we present loss of heterozygosity in human leukocyte antigen (LOHHLA), a computational tool to determine HLA allele-specific copy number from sequencing data. Using LOHHLA, we find that HLA LOH occurs in 40% of non-small-cell lung cancers (NSCLCs) and is associated with a high subclonal neoantigen burden, APOBEC-mediated mutagenesis, upregulation of cytolytic activity, and PD-L1 positivity. The focal nature of HLA LOH alterations, their subclonal frequencies, enrichment in metastatic sites, and occurrence as parallel events suggests that HLA LOH is an immune escape mechanism that is subject to strong microenvironmental selection pressures later in tumor evolution. Characterizing HLA LOH with LOHHLA refines neoantigen prediction and may have implications for our understanding of resistance mechanisms and immunotherapeutic approaches targeting neoantigens. VIDEO ABSTRACT.

Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing.

Systematic interrogation of tumor-infiltrating lymphocytes is key to the development of immunotherapies and the prediction of their clinical responses in cancers. Here, we perform deep single-cell RNA sequencing on 5,063 single T cells isolated from peripheral blood, tumor, and adjacent normal tissues from six hepatocellular carcinoma patients. The transcriptional profiles of these individual cells, coupled with assembled T cell receptor (TCR) sequences, enable us to identify 11 T cell subsets based on their molecular and functional properties and delineate their developmental trajectory. Specific subsets such as exhausted CD8+ T cells and Tregs are preferentially enriched and potentially clonally expanded in hepatocellular carcinoma (HCC), and we identified signature genes for each subset. One of the genes, layilin, is upregulated on activated CD8+ T cells and Tregs and represses the CD8+ T cell functions in vitro. This compendium of transcriptome data provides valuable insights and a rich resource for understanding the immune landscape in cancers.

Structured States of Disordered Proteins from Genomic Sequences.

Protein flexibility ranges from simple hinge movements to functional disorder. Around half of all human proteins contain apparently disordered regions with little 3D or functional information, and many of these proteins are associated with disease. Building on the evolutionary couplings approach previously successful in predicting 3D states of ordered proteins and RNA, we developed a method to predict the potential for ordered states for all apparently disordered proteins with sufficiently rich evolutionary information. The approach is highly accurate (79%) for residue interactions as tested in more than 60 known disordered regions captured in a bound or specific condition. Assessing the potential for structure of more than 1,000 apparently disordered regions of human proteins reveals a continuum of structural order with at least 50% with clear propensity for three- or two-dimensional states. Co-evolutionary constraints reveal hitherto unseen structures of functional importance in apparently disordered proteins.

Lack of Cas13a inhibition by anti-CRISPR proteins from Leptotrichia prophages.

CRISPR systems are prokaryotic adaptive immune systems that use RNA-guided Cas nucleases to recognize and destroy foreign genetic elements. To overcome CRISPR immunity, bacteriophages have evolved diverse families of anti-CRISPR proteins (Acrs). Recently, Lin et al. (2020) described the discovery and characterization of 7 Acr families (AcrVIA1-7) that inhibit type VI-A CRISPR systems. We detail several inconsistencies that question the results reported in the Lin et al. (2020) study. These include inaccurate bioinformatics analyses and bacterial strains that are impossible to construct. Published strains were provided by the authors, but MS2 bacteriophage plaque assays did not support the published results. We also independently tested the Acr sequences described in the original report, in E. coli and mammalian cells, but did not observe anti-Cas13a activity. Taken together, our data and analyses prompt us to question the claim that AcrVIA1-7 reported in Lin et al. are type VI anti-CRISPR proteins.

RNA-DNA triplexes: molecular mechanisms and functional relevance.

Interactions of RNA with DNA are principles of gene expression control that have recently gained considerable attention. Among RNA-DNA interactions are R-loops and RNA-DNA hybrid G-quadruplexes, as well as RNA-DNA triplexes. It is proposed that RNA-DNA triplexes guide RNA-associated regulatory proteins to specific genomic locations, influencing transcription and epigenetic decision making. Although triplex formation initially was considered solely an in vitro event, recent progress in computational, biochemical, and biophysical methods support in vivo functionality with relevance for gene expression control. Here, we review the central methodology and biology of triplexes, outline paradigms required for triplex function, and provide examples of physiologically important triplex-forming long non-coding RNAs.

Advances in methods for tRNA sequencing and quantification.

In the past decade tRNA sequencing (tRNA-seq) has attracted considerable attention as an important tool for the development of novel approaches to quantify highly modified tRNA species and to propel tRNA research aimed at understanding the cellular physiology and disease and development of tRNA-based therapeutics. Many methods are available to quantify tRNA abundance while accounting for modifications and tRNA charging/acylation. Advances in both library preparation methods and bioinformatic workflows have enabled developments in next-generation sequencing (NGS) workflows. Other approaches forgo NGS applications in favor of hybridization-based approaches. In this review we provide a brief comparative overview of various tRNA quantification approaches, focusing on the advantages and disadvantages of these methods, which together facilitate reliable tRNA quantification.

An integrative framework to prioritize genes in more than 500 loci associated with body mass index.

Obesity is a major risk factor for a myriad of diseases, affecting >600 million people worldwide. Genome-wide association studies (GWASs) have identified hundreds of genetic variants that influence body mass index (BMI), a commonly used metric to assess obesity risk. Most variants are non-coding and likely act through regulating genes nearby. Here, we apply multiple computational methods to prioritize the likely causal gene(s) within each of the 536 previously reported GWAS-identified BMI-associated loci. We performed summary-data-based Mendelian randomization (SMR), FINEMAP, DEPICT, MAGMA, transcriptome-wide association studies (TWASs), mutation significance cutoff (MSC), polygenic priority score (PoPS), and the nearest gene strategy. Results of each method were weighted based on their success in identifying genes known to be implicated in obesity, ranking all prioritized genes according to a confidence score (minimum: 0; max: 28). We identified 292 high-scoring genes (≥11) in 264 loci, including genes known to play a role in body weight regulation (e.g., DGKI, ANKRD26, MC4R, LEPR, BDNF, GIPR, AKT3, KAT8, MTOR) and genes related to comorbidities (e.g., FGFR1, ISL1, TFAP2B, PARK2, TCF7L2, GSK3B). For most of the high-scoring genes, however, we found limited or no evidence for a role in obesity, including the top-scoring gene BPTF. Many of the top-scoring genes seem to act through a neuronal regulation of body weight, whereas others affect peripheral pathways, including circadian rhythm, insulin secretion, and glucose and carbohydrate homeostasis. The characterization of these likely causal genes can increase our understanding of the underlying biology and offer avenues to develop therapeutics for weight loss.

The peptide woods are lovely, dark and deep: Hunting for novel cancer antigens.

Harnessing the patient's immune system to control a tumor is a proven avenue for cancer therapy. T cell therapies as well as therapeutic vaccines, which target specific antigens of interest, are being explored as treatments in conjunction with immune checkpoint blockade. For these therapies, selecting the best suited antigens is crucial. Most of the focus has thus far been on neoantigens that arise from tumor-specific somatic mutations. Although there is clear evidence that T-cell responses against mutated neoantigens are protective, the large majority of these mutations are not immunogenic. In addition, most somatic mutations are unique to each individual patient and their targeting requires the development of individualized approaches. Therefore, novel antigen types are needed to broaden the scope of such treatments. We review high throughput approaches for discovering novel tumor antigens and some of the key challenges associated with their detection, and discuss considerations when selecting tumor antigens to target in the clinic.

An energy-conserving reaction in amino acid metabolism catalyzed by arginine synthetase.

All forms of life are presumed to synthesize arginine from citrulline via a two-step pathway consisting of argininosuccinate synthetase and argininosuccinate lyase using citrulline, adenosine 5'-triphosphate (ATP), and aspartate as substrates. Conversion of arginine to citrulline predominantly proceeds via hydrolysis. Here, from the hyperthermophilic archaeon Thermococcus kodakarensis, we identified an enzyme which we designate "arginine synthetase". In arginine synthesis, the enzyme converts citrulline, ATP, and free ammonia to arginine, adenosine 5'-diphosphate (ADP), and phosphate. In the reverse direction, arginine synthetase conserves the energy of arginine deimination and generates ATP from ADP and phosphate while releasing ammonia. The equilibrium constant of this reaction at pH 7.0 is [Cit][ATP][NH3]/[Arg][ADP][Pi] = 10.1 ± 0.7 at 80 °C, corresponding to a ΔG°' of -6.8 ± 0.2 kJ mol-1. Growth of the gene disruption strain was compared to the host strain in medium composed of amino acids. The results suggested that arginine synthetase is necessary in providing ornithine, the precursor for proline biosynthesis, as well as in generating ATP. Growth in medium supplemented with citrulline indicated that arginine synthetase can function in the direction of arginine synthesis. The enzyme is widespread in nature, including bacteria and eukaryotes, and catalyzes a long-overlooked energy-conserving reaction in microbial amino acid metabolism. Along with ornithine transcarbamoylase and carbamate kinase, the pathway identified here is designated the arginine synthetase pathway.

Context-dependent design of induced-fit enzymes using deep learning generates well-expressed, thermally stable and active enzymes.

The potential of engineered enzymes in industrial applications is often limited by their expression levels, thermal stability, and catalytic diversity. De novo enzyme design faces challenges due to the complexity of enzymatic catalysis. An alternative approach involves expanding natural enzyme capabilities for new substrates and parameters. Here, we introduce CoSaNN (Conformation Sampling using Neural Network), an enzyme design strategy using deep learning for structure prediction and sequence optimization. CoSaNN controls enzyme conformations to expand chemical space beyond simple mutagenesis. It employs a context-dependent approach for generating enzyme designs, considering non-linear relationships in sequence and structure space. We also developed SolvIT, a graph NN predicting protein solubility in Escherichia coli, optimizing enzyme expression selection from larger design sets. Using this method, we engineered enzymes with superior expression levels, with 54% expressed in E. coli, and increased thermal stability, with over 30% having higher Tm than the template, with no high-throughput screening. Our research underscores AI's transformative role in protein design, capturing high-order interactions and preserving allosteric mechanisms in extensively modified enzymes, and notably enhancing expression success rates. This method's ease of use and efficiency streamlines enzyme design, opening broad avenues for biotechnological applications and broadening field accessibility.

CURTAIN-A unique web-based tool for exploration and sharing of MS-based proteomics data.

To facilitate analysis and sharing of mass spectrometry (MS)-based proteomics data, we created online tools called CURTAIN (https://curtain.proteo.info) and CURTAIN-PTM (https://curtainptm.proteo.info) with an accompanying series of video tutorials (https://www.youtube.com/@CURTAIN-me6hl). These are designed to enable non-MS experts to interactively peruse volcano plots and deconvolute primary experimental data so that replicates can be visualized in bar charts or violin plots and exported in publication-ready format. They also allow assessment of overall experimental quality by correlation matrix and profile plot analysis. After making a selection of protein "hits", the user can analyze known domain structure, AlphaFold predicted structure, reported interactors, relative expression as well as disease links. CURTAIN-PTM permits analysis of all identified PTM sites on protein(s) of interest with selected databases. CURTAIN-PTM also links with the Kinase Library to predict upstream kinases that may phosphorylate sites of interest. We provide examples of the utility of CURTAIN and CURTAIN-PTM in analyzing how targeted degradation of the PPM1H Rab phosphatase that counteracts the Parkinson's LRRK2 kinase impacts cellular protein levels and phosphorylation sites. We also reanalyzed a ubiquitylation dataset, characterizing the PINK1-Parkin pathway activation in primary neurons, revealing data of interest not highlighted previously. CURTAIN and CURTAIN-PTM are free to use and open source, enabling researchers to share and maximize the impact of their proteomics data. We advocate that MS data published in volcano plot format be reported containing a shareable CURTAIN weblink, thereby allowing readers to better analyze and exploit the data.

Frequent nonhomologous replacement of replicative helicase loaders by viruses in Vibrionaceae.

Several microbial genomes lack textbook-defined essential genes. If an essential gene is absent from a genome, then an evolutionarily independent gene of unknown function complements its function. Here, we identified frequent nonhomologous replacement of an essential component of DNA replication initiation, a replicative helicase loader gene, in Vibrionaceae. Our analysis of Vibrionaceae genomes revealed two genes with unknown function, named vdhL1 and vdhL2, that were substantially enriched in genomes without the known helicase-loader genes. These genes showed no sequence similarities to genes with known function but encoded proteins structurally similar with a viral helicase loader. Analyses of genomic syntenies and coevolution with helicase genes suggested that vdhL1/2 encodes a helicase loader. The in vitro assay showed that Vibrio harveyi VdhL1 and Vibrio ezurae VdhL2 promote the helicase activity of DnaB. Furthermore, molecular phylogenetics suggested that vdhL1/2 were derived from phages and replaced an intrinsic helicase loader gene of Vibrionaceae over 20 times. This high replacement frequency implies the host's advantage in acquiring a viral helicase loader gene.

Exploiting enzyme evolution for computational protein design.

Recent years have seen an explosion of interest in understanding the physicochemical parameters that shape enzyme evolution, as well as substantial advances in computational enzyme design. This review discusses three areas where evolutionary information can be used as part of the design process: (i) using ancestral sequence reconstruction (ASR) to generate new starting points for enzyme design efforts; (ii) learning from how nature uses conformational dynamics in enzyme evolution to mimic this process in silico; and (iii) modular design of enzymes from smaller fragments, again mimicking the process by which nature appears to create new protein folds. Using showcase examples, we highlight the importance of incorporating evolutionary information to continue to push forward the boundaries of enzyme design studies.