Exploring a general multi-pronged activation strategy for natural product discovery in Actinomycetes.

Natural products possess significant therapeutic potential but remain underutilized despite advances in genomics and bioinformatics. While there are approaches to activate and upregulate natural product biosynthesis in both native and heterologous microbial strains, a comprehensive strategy to elicit production of natural products as well as a generalizable and efficient method to interrogate diverse native strains collection, remains lacking. Here, we explore a flexible and robust integrase-mediated multi-pronged activation approach to reliably perturb and globally trigger antibiotics production in actinobacteria. Across 54 actinobacterial strains, our approach yielded 124 distinct activator-strain combinations which consistently outperform wild type. Our approach expands accessible metabolite space by nearly two-fold and increases selected metabolite yields by up to >200-fold, enabling discovery of Gram-negative bioactivity in tetramic acid analogs. We envision these findings as a gateway towards a more streamlined, accelerated, and scalable strategy to unlock the full potential of Nature's chemical repertoire.

A Malassezia pseudoprotease dominates the secreted hydrolase landscape and is a potential allergen on skin.

Malassezia globosa is abundant and prevalent on sebaceous areas of the human skin. Genome annotation reveals that M. globosa possesses a repertoire of secreted hydrolytic enzymes relevant for lipid and protein metabolism. However, the functional significance of these enzymes is uncertain and presence of these genes in the genome does not always translate to expression at the cutaneous surface. In this study we utilized targeted RNA sequencing from samples isolated directly from the skin to quantify gene expression of M. globosa secreted proteases, lipases, phospholipases and sphingomyelinases. Our findings indicate that the expression of these enzymes is dynamically regulated by the environment in which the fungus resides, as different growth phases of the planktonic culture of M. globosa show distinct expression levels. Furthermore, we observed significant differences in the expression of these enzymes in culture compared to healthy sebaceous skin sites. By examining the in situ gene expression of M. globosa's secreted hydrolases, we identified a predicted aspartyl protease, MGL_3331, which is highly expressed on both healthy and disease-affected dermatological sites. However, molecular modeling and biochemical studies revealed that this protein has a non-canonical active site motif and lacks measurable proteolytic activity. This pseudoprotease MGL_3331 elicits a heightened IgE-reactivity in blood plasma isolated from patients with atopic dermatitis compared to healthy individuals and invokes a pro-inflammatory response in peripheral blood mononuclear cells. Overall, our study highlights the importance of studying fungal proteins expressed in physiologically relevant environments and underscores the notion that secreted inactive enzymes may have important functions in influencing host immunity.

Expression and engineering of PET-degrading enzymes from Microbispora, Nonomuraea, and Micromonospora.

IMPORTANCE: Mismanagement of PET plastic waste significantly threatens human and environmental health. Together with the relentless increase in plastic production, plastic pollution is an issue of rising concern. In response to this challenge, scientists are investigating eco-friendly approaches, such as bioprocessing and microbial factories, to sustainably manage the growing quantity of plastic waste in our ecosystem. Industrial applicability of enzymes capable of degrading PET is limited by numerous factors, including their scarcity in nature. The objective of this study is to enhance our understanding of this group of enzymes by identifying and characterizing novel enzymes that can facilitate the breakdown of PET waste. This data will expand the enzymatic repertoire and provide valuable insights into the prerequisites for successful PET degradation.

A sweeter future: Using protein language models for exploring sweeter brazzein homologs.

With growing concerns over the health impact of sugar, brazzein offers a viable alternative due to its sweetness, thermostability, and low risk profile. Here, we demonstrated the ability of protein language models to design new brazzein homologs with improved thermostability and potentially higher sweetness, resulting in new diverse optimized amino acid sequences that improve structural and functional features beyond what conventional methods could achieve. This innovative approach resulted in the identification of unexpected mutations, thereby generating new possibilities for protein engineering. To facilitate the characterization of the brazzein mutants, a simplified procedure was developed for expressing and analyzing related proteins. This process involved an efficient purification method using Lactococcus lactis (L. lactis), a generally recognized as safe (GRAS) bacterium, as well as taste receptor assays to evaluate sweetness. The study successfully demonstrated the potential of computational design in producing a more heat-resistant and potentially more palatable brazzein variant, V23.

Towards a Rapid-Turnaround Low-Depth Unbiased Metagenomics Sequencing Workflow on the Illumina Platforms.

Unbiased metagenomic sequencing is conceptually well-suited for first-line diagnosis as all known and unknown infectious entities can be detected, but costs, turnaround time and human background reads in complex biofluids, such as plasma, hinder widespread deployment. Separate preparations of DNA and RNA also increases costs. In this study, we developed a rapid unbiased metagenomics next-generation sequencing (mNGS) workflow with a human background depletion method (HostEL) and a combined DNA/RNA library preparation kit (AmpRE) to address this issue. We enriched and detected bacterial and fungal standards spiked in plasma at physiological levels with low-depth sequencing (<1 million reads) for analytical validation. Clinical validation also showed 93% of plasma samples agreed with the clinical diagnostic test results when the diagnostic qPCR had a Ct < 33. The effect of different sequencing times was evaluated with the 19 h iSeq 100 paired end run, a more clinically palatable simulated iSeq 100 truncated run and the rapid 7 h MiniSeq platform. Our results demonstrate the ability to detect both DNA and RNA pathogens with low-depth sequencing and that iSeq 100 and MiniSeq platforms are compatible with unbiased low-depth metagenomics identification with the HostEL and AmpRE workflow.

Novelty Search Promotes Antigenic Diversity in Microbial Pathogens.

Driven by host-pathogen coevolution, cell surface antigens are often the fastest evolving parts of a microbial pathogen. The persistent evolutionary impetus for novel antigen variants suggests the utility of novelty-seeking algorithms in predicting antigen diversification in microbial pathogens. In contrast to traditional genetic algorithms maximizing variant fitness, novelty-seeking algorithms optimize variant novelty. Here, we designed and implemented three evolutionary algorithms (fitness-seeking, novelty-seeking, and hybrid) and evaluated their performances in 10 simulated and 2 empirically derived antigen fitness landscapes. The hybrid walks combining fitness- and novelty-seeking strategies overcame the limitations of each algorithm alone, and consistently reached global fitness peaks. Thus, hybrid walks provide a model for microbial pathogens escaping host immunity without compromising variant fitness. Biological processes facilitating novelty-seeking evolution in natural pathogen populations include hypermutability, recombination, wide dispersal, and immune-compromised hosts. The high efficiency of the hybrid algorithm improves the evolutionary predictability of novel antigen variants. We propose the design of escape-proof vaccines based on high-fitness variants covering a majority of the basins of attraction on the fitness landscape representing all potential variants of a microbial antigen.

The human pathobiont Malassezia furfur secreted protease Mfsap1 regulates cell dispersal and exacerbates skin inflammation.

Malassezia form the dominant eukaryotic microbial community on the human skin. The Malassezia genus possesses a repertoire of secretory hydrolytic enzymes involved in protein and lipid metabolism which alter the external cutaneous environment. The exact role of most Malassezia secreted enzymes, including those in interaction with the epithelial surface, is not well characterized. In this study, we compared the expression level of secreted proteases, lipases, phospholipases, and sphingomyelinases of Malassezia globosa in healthy subjects and seborrheic dermatitis or atopic dermatitis patients. We observed upregulated gene expression of the previously characterized secretory aspartyl protease MGSAP1 in both diseased groups, in lesional and non-lesional skin sites, as compared to healthy subjects. To explore the functional roles of MGSAP1 in skin disease, we generated a knockout mutant of the homologous protease MFSAP1 in the genetically tractable Malassezia furfur. We observed the loss of MFSAP1 resulted in dramatic changes in the cell adhesion and dispersal in both culture and a human 3D reconstituted epidermis model. In a murine model of Malassezia colonization, we further demonstrated Mfsap1 contributes to inflammation as observed by reduced edema and inflammatory cell infiltration with the knockout mutant versus wildtype. Taken together, we show that this dominant secretory Malassezia aspartyl protease has an important role in enabling a planktonic cellular state that can potentially aid in colonization and additionally as a virulence factor in barrier-compromised skin, further highlighting the importance of considering the contextual relevance when evaluating the functions of secreted microbial enzymes.

Expression of Staphylococcus aureus Virulence Factors in Atopic Dermatitis.

Atopic dermatitis (AD) is a skin inflammatory disease in which the opportunistic pathogen Staphylococcus aureus is prevalent and abundant. S. aureus harbors several secreted virulence factors that have well-studied functions in infection models, but it is unclear whether these extracellular microbial factors are relevant in the context of AD. To address this question, we designed a culture-independent method to detect and quantify S. aureus virulence factors expressed at the skin sites. We utilized RNase-H‒dependent multiplex PCR for preamplification of reverse-transcribed RNA extracted from tape strips of patients with AD sampled at skin sites with differing severity and assessed the expression of a panel of S. aureus virulence factors using qPCR. We observed an increase in viable S. aureus abundance on sites with increased severity of disease, and many virulence factors were expressed at the AD skin sites. Surprisingly, we did not observe any significant upregulation of the virulence factors at the lesional sites compared with those at the nonlesional control. Overall, we utilized a robust assay to directly detect and quantify viable S. aureus and its associated virulence factors at the site of AD skin lesions. This method can be extended to study the expression of skin microbial genes at the sites of various dermatological conditions.

MapCell: Learning a Comparative Cell Type Distance Metric With Siamese Neural Nets With Applications Toward Cell-Type Identification Across Experimental Datasets.

Large collections of annotated single-cell RNA sequencing (scRNA-seq) experiments are being generated across different organs, conditions and organisms on different platforms. The diversity, volume and complexity of this aggregated data requires new analysis techniques to extract actionable knowledge. Fundamental to most analysis are key abilities such as: identification of similar cells across different experiments and transferring annotations from an annotated dataset to an unannotated one. There have been many strategies explored in achieving these goals, and they focuses primarily on aligning and re-clustering datasets of interest. In this work, we are interested in exploring the applicability of deep metric learning methods as a form of distance function to capture similarity between cells and facilitate the transfer of cell type annotation for similar cells across different experiments. Toward this aim, we developed MapCell, a few-shot training approach using Siamese Neural Networks (SNNs) to learn a generalizable distance metric that can differentiate between single cell types. Requiring only a small training set, we demonstrated that SNN derived distance metric can perform accurate transfer of annotation across different scRNA-seq platforms, batches, species and also aid in flagging novel cell types.

Non-Invasive Characterization of the Pancreas During Bariatric Surgery via Circulating Pancreatic Specific Cell-free Messenger RNA.

Bariatric surgery results in sustained weight loss and improvement in glucose homeostasis. However, the lack of accessible non-invasive tools to examine molecular alterations occurring in the pancreas limits our understanding of the causes and recovery of glucose homeostasis. Here, we describe the use of a circulating cell free mRNA (cfmRNA) based multiplex qPCR assay to selectively amplify and quantify circulating pancreatic specific transcripts levels within plasma. We applied this assay to a cohort of 58 plasma samples consisting of 10 patients that tracks multiple time points including pre and post-bariatric surgery. In our targeted multiplex screen of 14 selected pancreatic specific circulating transcripts, we identified 13 pancreatic specific transcripts that can be amplified from plasma. Furthermore, when quantifying the amplicons obtained in the short-term post-surgery (2 weeks-1 month) and long-term (3-12 months), we observed a consistent reduction of circulating GCG transcripts during short term post-surgery. Across the cohort, GCG cfmRNA levels correlated significantly with common metrics of improvement following bariatric surgery such as: haemoglobin A1c levels (R: -0.41, p-value: 0.0039) and percentage of excess weight loss (R: 0.29, p-value: 0.046).

Single Cell Transcriptomes Derived from Human Cervical and Uterine Tissue during Pregnancy.

This work presents the workflow for generating single cell transcriptomes derived from primary human uterine and cervical tissue obtained during planned cesarean hysterectomies. In total, a catalogue of 310 single cell transcriptomes are obtained, cell types present in these biopsies are inferred, and specific genes defining each of the cellular types present in the tissue are identified. Further validation of the inferred cell identity is also demonstrated via meta-analysis of independent repositories in literature generated by bulk sequenced data of fluorescence-activated cell sorting sorted cells.

High-throughput full-length single-cell mRNA-seq of rare cells.

Single-cell characterization techniques, such as mRNA-seq, have been applied to a diverse range of applications in cancer biology, yielding great insight into mechanisms leading to therapy resistance and tumor clonality. While single-cell techniques can yield a wealth of information, a common bottleneck is the lack of throughput, with many current processing methods being limited to the analysis of small volumes of single cell suspensions with cell densities on the order of 107 per mL. In this work, we present a high-throughput full-length mRNA-seq protocol incorporating a magnetic sifter and magnetic nanoparticle-antibody conjugates for rare cell enrichment, and Smart-seq2 chemistry for sequencing. We evaluate the efficiency and quality of this protocol with a simulated circulating tumor cell system, whereby non-small-cell lung cancer cell lines (NCI-H1650 and NCI-H1975) are spiked into whole blood, before being enriched for single-cell mRNA-seq by EpCAM-functionalized magnetic nanoparticles and the magnetic sifter. We obtain high efficiency (> 90%) capture and release of these simulated rare cells via the magnetic sifter, with reproducible transcriptome data. In addition, while mRNA-seq data is typically only used for gene expression analysis of transcriptomic data, we demonstrate the use of full-length mRNA-seq chemistries like Smart-seq2 to facilitate variant analysis of expressed genes. This enables the use of mRNA-seq data for differentiating cells in a heterogeneous population by both their phenotypic and variant profile. In a simulated heterogeneous mixture of circulating tumor cells in whole blood, we utilize this high-throughput protocol to differentiate these heterogeneous cells by both their phenotype (lung cancer versus white blood cells), and mutational profile (H1650 versus H1975 cells), in a single sequencing run. This high-throughput method can help facilitate single-cell analysis of rare cell populations, such as circulating tumor or endothelial cells, with demonstrably high-quality transcriptomic data.

Numerous uncharacterized and highly divergent microbes which colonize humans are revealed by circulating cell-free DNA.

Blood circulates throughout the human body and contains molecules drawn from virtually every tissue, including the microbes and viruses which colonize the body. Through massive shotgun sequencing of circulating cell-free DNA from the blood, we identified hundreds of new bacteria and viruses which represent previously unidentified members of the human microbiome. Analyzing cumulative sequence data from 1,351 blood samples collected from 188 patients enabled us to assemble 7,190 contiguous regions (contigs) larger than 1 kbp, of which 3,761 are novel with little or no sequence homology in any existing databases. The vast majority of these novel contigs possess coding sequences, and we have validated their existence both by finding their presence in independent experiments and by performing direct PCR amplification. When their nearest neighbors are located in the tree of life, many of the organisms represent entirely novel taxa, showing that microbial diversity within the human body is substantially broader than previously appreciated.

Quantification of transplant-derived circulating cell-free DNA in absence of a donor genotype.

Quantification of cell-free DNA (cfDNA) in circulating blood derived from a transplanted organ is a powerful approach to monitoring post-transplant injury. Genome transplant dynamics (GTD) quantifies donor-derived cfDNA (dd-cfDNA) by taking advantage of single-nucleotide polymorphisms (SNPs) distributed across the genome to discriminate donor and recipient DNA molecules. In its current implementation, GTD requires genotyping of both the transplant recipient and donor. However, in practice, donor genotype information is often unavailable. Here, we address this issue by developing an algorithm that estimates dd-cfDNA levels in the absence of a donor genotype. Our algorithm predicts heart and lung allograft rejection with an accuracy that is similar to conventional GTD. We furthermore refined the algorithm to handle closely related recipients and donors, a scenario that is common in bone marrow and kidney transplantation. We show that it is possible to estimate dd-cfDNA in bone marrow transplant patients that are unrelated or that are siblings of the donors, using a hidden Markov model (HMM) of identity-by-descent (IBD) states along the genome. Last, we demonstrate that comparing dd-cfDNA to the proportion of donor DNA in white blood cells can differentiate between relapse and the onset of graft-versus-host disease (GVHD). These methods alleviate some of the barriers to the implementation of GTD, which will further widen its clinical application.

Dynamic ASXL1 Exon Skipping and Alternative Circular Splicing in Single Human Cells.

Circular RNAs comprise a poorly understood new class of noncoding RNA. In this study, we used a combination of targeted deletion, high-resolution splicing detection, and single-cell sequencing to deeply probe ASXL1 circular splicing. We found that efficient circular splicing required the canonical transcriptional start site and inverted AluSx elements. Sequencing-based interrogation of isoforms after ASXL1 overexpression identified promiscuous linear splicing between all exons, with the two most abundant non-canonical linear products skipping the exons that produced the circular isoforms. Single-cell sequencing revealed a strong preference for either the linear or circular ASXL1 isoforms in each cell, and found the predominant exon skipping product is frequently co-expressed with its reciprocal circular isoform. Finally, absolute quantification of ASXL1 isoforms confirmed our findings and suggests that standard methods overestimate circRNA abundance. Taken together, these data reveal a dynamic new view of circRNA genesis, providing additional framework for studying their roles in cellular biology.

Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq.

Direct lineage reprogramming represents a remarkable conversion of cellular and transcriptome states. However, the intermediate stages through which individual cells progress during reprogramming are largely undefined. Here we use single-cell RNA sequencing at multiple time points to dissect direct reprogramming from mouse embryonic fibroblasts to induced neuronal cells. By deconstructing heterogeneity at each time point and ordering cells by transcriptome similarity, we find that the molecular reprogramming path is remarkably continuous. Overexpression of the proneural pioneer factor Ascl1 results in a well-defined initialization, causing cells to exit the cell cycle and re-focus gene expression through distinct neural transcription factors. The initial transcriptional response is relatively homogeneous among fibroblasts, suggesting that the early steps are not limiting for productive reprogramming. Instead, the later emergence of a competing myogenic program and variable transgene dynamics over time appear to be the major efficiency limits of direct reprogramming. Moreover, a transcriptional state, distinct from donor and target cell programs, is transiently induced in cells undergoing productive reprogramming. Our data provide a high-resolution approach for understanding transcriptome states during lineage differentiation.

Single-cell genome sequencing: current state of the science.

The field of single-cell genomics is advancing rapidly and is generating many new insights into complex biological systems, ranging from the diversity of microbial ecosystems to the genomics of human cancer. In this Review, we provide an overview of the current state of the field of single-cell genome sequencing. First, we focus on the technical challenges of making measurements that start from a single molecule of DNA, and then explore how some of these recent methodological advancements have enabled the discovery of unexpected new biology. Areas highlighted include the application of single-cell genomics to interrogate microbial dark matter and to evaluate the pathogenic roles of genetic mosaicism in multicellular organisms, with a focus on cancer. We then attempt to predict advances we expect to see in the next few years.

Dissecting the clonal origins of childhood acute lymphoblastic leukemia by single-cell genomics.

Many cancers have substantial genomic heterogeneity within a given tumor, and to fully understand that diversity requires the ability to perform single cell analysis. We performed targeted sequencing of a panel of single nucleotide variants (SNVs), deletions, and IgH sequences in 1,479 single tumor cells from six acute lymphoblastic leukemia (ALL) patients. By accurately segregating groups of cooccurring mutations into distinct clonal populations, we identified codominant clones in the majority of patients. Evaluation of intraclonal mutation patterns identified clone-specific punctuated cytosine mutagenesis events, showed that most structural variants are acquired before SNVs, determined that KRAS mutations occur late in disease development but are not sufficient for clonal dominance, and identified clones within the same patient that are arrested at varied stages in B-cell development. Taken together, these data order the sequence of genetic events that underlie childhood ALL and provide a framework for understanding the development of the disease at single-cell resolution.

Noninvasive in vivo monitoring of tissue-specific global gene expression in humans.

Circulating cell-free RNA in the blood provides a potential window into the health, phenotype, and developmental programs of a variety of human organs. We used high-throughput methods of RNA analysis such as microarrays and next-generation sequencing to characterize the global landscape circulating RNA in a cohort of human subjects. By focusing on genes whose expression is highly specific to certain tissues, we were able to identify the relative contributions of these tissues to circulating RNA and to monitor changes in tissue development and health. As one application of this approach, we performed a longitudinal study on pregnant women and analyzed their combined cell-free RNA transcriptomes across all three trimesters of pregnancy and after delivery. In addition to the analysis of mRNA, we observed and characterized noncoding species such as long noncoding RNA and circular RNA transcripts whose presence had not been previously observed in human plasma. We demonstrate that it is possible to track specific longitudinal phenotypic changes in both the mother and the fetus and that it is possible to directly measure transcripts from a variety of fetal tissues in the maternal blood sample. We also studied the role of neuron-specific transcripts in the blood of healthy adults and those suffering from the neurodegenerative disorder Alzheimer's disease and showed that disease specific neural transcripts are present at increased levels in the blood of affected individuals. Characterization of the cell-free transcriptome in its entirety may thus provide broad insights into human health and development without the need for invasive tissue sampling.