Biology subjects

Yang, W.

Publications and source records attributed to Yang, W..

At least 199 records · Page 11Linked to original sources

bioRxiv2023

Epigenomic mapping in B-cell acute lymphoblastic leukemia identifies transcriptional regulators and noncoding variants promoting distinct chromatin architectures

B-cell lineage acute lymphoblastic leukemia (B-ALL) is comprised of diverse molecular subtypes and while transcriptional and DNA methylation profiling of B-ALL subtypes has been extensively examined, the accompanying chromatin landscape is not well characterized for many subtypes. We therefore mapped chromatin accessibility using ATAC-seq for 10 B-ALL molecular subtypes in primary ALL cells from 154 patients. Comparisons with B-cell progenitors identified candidate B-ALL cell-of-origin and AP-1-associated cis-regulatory rewiring in B-ALL. Cis-regulatory rewiring promoted B-ALL-specific gene regulatory networks impacting oncogenic signaling pathways that perturb normal B-cell development. We also identified that over 20% of B-ALL accessible chromatin sites exhibit strong subtype enrichment, with transcription factor (TF) footprint profiling identifying candidate TFs that maintain subtype-specific chromatin architectures. Over 9000 inherited genetic variants were further uncovered that contribute to variability in chromatin accessibility among individual patient samples. Overall, our data suggest that distinct chromatin architectures are driven by diverse TFs and inherited genetic variants which promote unique gene regulatory networks that contribute to transcriptional differences among B-ALL subtypes. HIGHLIGHTSO_LIPro-B progenitor cells as the most common cell-of-origin for B-ALL C_LIO_LIAP-1 TF-associated cis-regulatory rewiring in B-ALL C_LIO_LISubtype-specific accessible chromatin signatures representing 20% of all B-ALL sites C_LIO_LIRole for distinct TFs in promoting subtype-specific chromatin architectures C_LIO_LIThousands of inherited genetic variants identified impacting chromatin state C_LI

cancer biology↗

bioRxiv2023

Semi-conservative transmission of DNA N6-adenine methylation in a unicellular eukaryote

While DNA N6-adenine methylation (6mA) is best known in prokaryotes, its presence in eukaryotes has generated great interest recently. Biochemical and genetic evidence supports that AMT1, a MT-A70 family methyltransferase (MTase), is crucial for 6mA deposition in unicellular eukaryotes. Nonetheless, 6mA transmission mechanism remains to be elucidated. Taking advantage of Single Molecule Real-Time Circular Consensus Sequencing (SMRT CCS), here we provide definitive evidence for semi-conservative transmission of 6mA, showcased in the unicellular eukaryote Tetrahymena thermophila. In wildtype (WT) cells, 6mA occurs at the self-complementary ApT dinucleotide, mostly in full methylation (full-6mApT); hemi-methylation (hemi-6mApT) is transiently present on the parental strand of newly replicated DNA. In {Delta}AMT1 cells, 6mA predominantly occurs as hemi-6mApT. Hemi-to-full conversion in WT cells is fast, robust, and likely processive, while de novo 6mA deposition in {Delta}AMT1 cells is slow and sporadic. In Tetrahymena, regularly spaced 6mA clusters coincide with linker DNA of the canonical nucleosome arrays in the gene body. Importantly, in vitro methylation of human chromatin by reconstituted AMT1 complex recapitulates preferential targeting of hemi-6mApT sites in linker DNA, supporting AMT1s intrinsic and autonomous role in maintenance methylation. We conclude that 6mA is transmitted by a semi-conservative mechanism: full-6mApT is split by DNA replication into hemi-6mApT, which is restored to full-6mApT by AMT1-dependent maintenance methylation. Our study dissects AMT1-dependent maintenance methylation and AMT1-independent de novo methylation, reveals a molecular pathway for 6mA transmission with striking similarity to 5-methyl cytosine (5mC) transmission at the CpG dinucleotide, and establishes 6mA as a bona fide eukaryotic epigenetic mark.

molecular biology↗

bioRxiv2023

Changes in physicochemical properties and microbial community succession during leaf stacking fermentation

Leaf stacking fermentation involves enzymatic actions of many microorganisms and is an efficient and environmentally benign process for degrading macromolecular organic compounds. We investigated the dynamics of metabolite profiles, bacterial and fungal communities and their interactions during fermentation using cigar leaves from three geographic regions. The results showed that the contents of total sugar, reducing sugar, starch, cellulose, lignin, pectin, polyphenol and protein in cigar tobacco leaves was significantly decreased during fermentation. Notably, the furfural, neophytadiene, pyridine, benzyl alcohol, geranyl acetone, 2-butanone, 3-hydroxy-, N-hexanal, isoamylalcohol and 2,3-pentanedione were important features volatile aroma compounds during fermentation. The -diversity of fungi and bacteria initially increased and then decreased during fermentation. The microbial diversity was influenced by fermentation stages and growing locations, in which the fermentation stages had greater impacts on the microbial diversity than locations. Microbiome profiling had identified several core bacteria including Sphingomonas, Bacillus, Staphylococcus, Pseudomonas, Ralstonia, Massilia and Fibrobacter. Fungal biomarkers included Aspergillus, Penicillium, Fusarium, Cladosporium and Trichomonascus. Interestingly, the molecular ecological networks showed that the core taxa had significant correlations with metabolic enzymes and physicochemical properties; bacteria and fungi jointly participated in the carbohydrate and nitrogen compound degrading and volatile aroma compound chemosynthesis processes during fermentation. These studies provide insights into the coupling of material conversion and microbial community succession during leaf fermentation. IMPORTANCECigar tobacco leaves are still crude after air curing. Fermentation can eliminate the pungent and bitter taste of the leaves and promote the accumulations of aromatic compounds. Hitherto, there has been no systematic study on the material conversion, metabolic enzymes and microbial community changes and their interactions in fermented leaves. The significance of our research is in identifying the potential metabolic cooperation related to microbial community, which will greatly enhance our understanding of microorganism to convert macromolecular organic to small molecules during the leaf stacking fermentation process.

microbiology↗

bioRxiv2023

Insm1 regulates the development of mTECs and immune tolerance

The Insm1 gene encodes a zinc finger protein with known functions in neuroendocrine cells and neurons. Here we characterized the expression and function of Insm1 in medullary thymic epithelial cells (mTECs). Insm1 is co-expressed with Aire in majority of Insm1 or Aire positive cells, while a few Insm1 positive cells did not express Aire. Mutation of Insm1 impair the expression of Aire and the generation of normal numbers of Aire-expressing mTECs during development. We detected downregulation of genes that expressed specifically in Aire-expressing mTEC and mimetic cells in Insm1 mutant mTECs. Conversely, when Insm1 was overexpressed in thymic epithelial cells in vivo, the size of the mTECs compartment was enlarged and the expression of Aire and genes expressed specifically in the neuroendocrine mimetic cells were increased. Mechanistically, Insm1 bound DNA in mTECs and the majority of the Insm1 binding sites were co-occupied by Aire. These Insm1 binding sites were enriched on super-enhancer regions and thus may contributed to remoted regulation. Both, mice with a thymus-specific mutation in Insm1 or nude mice transplanted with Insm1 mutant thymus, displayed autoimmune responses in multiple peripheral tissues. Together, our data demonstrate a role of Insm1 in development of mTECs and immune tolerance.

immunology↗

bioRxiv2023

Single-cell transcriptional analysis of lamina propria lymphocytes in the jejunum reveals ILC-like cells in pigs

Pigs are the most suitable model to study various therapeutic strategies and drugs for human beings, while knowledge about tissue- and cell-type-specific transcriptomes and heterogeneity is poorly available. Here, we focused on the intestinal immunity of pigs. Through single-cell sequencing (scRNA-seq) and flow cytometry analysis of the types of immune cells in the jejunum of pigs, we found that innate lymphoid cells (ILCs) existed in the lamina propria lymphocytes (LPLs) of the jejunum. Then, through flow sorting of Live/Dead (L/D)-Lineage(LIN)-CD45+ cells and scRNA-seq, we found that ILCs in the porcine jejunum were mainly ILC3s, with a small number of ILC1s, ILC2s, and NK cells. Through a gene expression map, we found that ILCs coexpressed IL-7R, ID2, and other genes and differentially expressed RORC, GATA3, and other genes but did not express the CD3 gene. According to their gene expression profiles, ILC3s can be divided into four subgroups, and genes such as CXCL8, CXCL2, IL-22, IL-17, and NCR2 are differentially expressed. To further detect and identify ILC3s, we prepared RORC monoclonal antibodies and verified the classification of ILCs in the porcine jejunum subgroup and the expression of related hallmark genes at the protein level by flow cytometry. For systematically characterizing of ILCs in the porcine intestines, we combined our pig ILCs dataset with publicly available human and mice ILCs data and identified that the humans and pigs ILCs shared more common features than those mice ILCs in gene signatures and cell states. For the first time, our results showed in detail the gene expression of porcine jejunal ILCs, the subtype classification of ILCs, and the markers of various ILCs, which provides a basis for an in-depth exploration of porcine intestinal mucosal immunity. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/522424v3_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@184fd80org.highwire.dtl.DTLVardef@1efc00org.highwire.dtl.DTLVardef@328a04org.highwire.dtl.DTLVardef@3296e_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

immunology↗

bioRxiv2022

Flexible and scalable control of T cell memory by a reversible epigenetic switch

The immune system encodes information about the severity of a pathogenic threat in the quantity and type of memory cell populations formed in response. This encoding emerges from the decisions of lymphocytes to maintain or lose self-renewal and memory potential during a challenge. By tracking responding CD8 T cells at the single-cell and clonal lineage level using time-resolved transcriptomics and quantitative live imaging, we identify a remarkably flexible decision-making strategy, whereby T cells initially choose whether to maintain or lose memory potential early after antigen recognition, but following pathogen clearance may regain memory potential if initially lost. Mechanistically, this flexibility is implemented by a cis-epigenetic switch that silences the memory regulator TCF1 in a stochastic and reversible manner in response to stimulatory inputs. Mathematical modeling shows how this strategy allows memory T cell numbers to scale robustly with pathogen virulence and immune response magnitudes. We propose that flexibility and stochasticity in cellular decision making ensures optimal immune responses against diverse threats.

immunology↗

bioRxiv2022

A pathogen effector FOLD diversified in symbiotic fungi

Pathogenic fungi use secreted effector proteins to suppress immunity and support their infection, but effectors have also been reported from fungi that engage in nutritional symbioses with plants. Sequence based effector comparisons between pathogens and symbiotic arbuscular mycorrhizal (AM) fungi are hampered by the huge diversity of effector sequences even within closely related microbes. Here we used a systematic protein structure modelling approach to classify the secretome of the AM fungus Rhizophagus irregularis. We identified secreted proteins with high structural similarity to Fusarium oxysporum f. sp. lycopersici dual domain (FOLD) effectors, which occur in low numbers in fungal pathogen genomes. Contrastingly, genes encoding FOLD proteins from AM fungi (MycFOLDs) are found in enlarged and diversified gene families. Our structure-model comparison suggests that MycFOLDs are similar to carbohydrate binding motifs. Different MycFOLD genes are expressed during colonisation of different hosts and MycFOLD-17 transcripts accumulate in plant intracellular arbuscules. The exclusive presence of MycFOLDs across unrelated plant-colonising fungi, their inducible expression, lineage specific sequence diversification, and transcripts in arbuscules support the hypothesis that FOLD proteins act as effectors during plant colonisation by symbiotic and pathogenic fungi.

microbiology↗

bioRxiv2022

Spatial transcriptome profiling of in vitro 3D tumouroids to study tumour-stroma interactions

Bioengineering facets of the tumour microenvironment (TME) are essential in 3D tissue models to accurately recapitulate tumour progression. Stromal cells are key components of the TME and their incorporation into 3D biomimetic bioengineered tumour-stroma models is essential to be able to mimic the TME. By engineering tumouroids with distinct tumour and stromal compartments, it has been possible to identify how gene expression is altered by the presence of different stromal cells using spatial transcriptomics. Ameloblastoma is a benign epithelial tumour of the jawbone and in engineered multi-compartment tumouroids increased expression of oncogenes was found where osteoblasts (bone stroma) were present. Engineering a gingival fibroblast stroma resulted in increased matrix remodelling genes in the ameloblastoma tumour. This study provides evidence to show the stromal specific effect on tumour behaviour and illustrates the importance of engineering biologically relevant stroma for engineered tumour models. Our novel results show that an engineered fibroblast stroma causes the upregulation of matrix remodelling genes in ameloblastoma which directly correlates to measured invasion in the model. In contrast the presence of an osteoblast/bone stroma increases the expression of oncogenes by ameloblastoma cells.

bioengineering↗

bioRxiv2022

TLS2trees: a scalable tree segmentation pipeline for TLS data

Above Ground Biomass (AGB) is an important metric used to quantify the mass of carbon stored in terrestrial ecosystems. For forests, this is routinely estimated at the plot scale (typically [≥]1 ha) using inventory measurements and allometry. In recent years, Terrestrial Laser Scanning (TLS) has appeared as a disruptive technology that can generate a more accurate assessment of tree and plot scale AGB; however, operationalising TLS methods has had to overcome a number of challenges. One such challenge is the segmentation of individual trees from plot level point clouds that are required to estimate woody volume, this is often done manually (e.g. with interactive point cloud editing software) and can be very time consuming. Here we present TLS2trees, an automated processing pipeline and set of Python command line tools that aims to redress this processing bottleneck. TLS2trees consists of existing and new methods and is specifically designed to be horizontally scalable. The processing pipeline is demonstrated across 10 plots of 7 forest types; from open savanna to dense tropical rainforest, where a total of 10,557 trees are segmented. TLS2trees segmented trees are compared to 1,281 manually segmented trees. Results indicate that TLS2trees performs well, particularly for larger trees (i.e. the cohort of largest trees that comprise 50% of total plot volume), where plot-wise tree volume bias is {+/-}0.4 m3 and %RMSE is ~60%. To facilitate improvements to the presented methods as well as modification for other laser scanning modes (e.g. mobile and UAV laser scanning), TLS2trees is a free and open-source software (FOSS).

ecology↗

bioRxiv2022

A Community Challenge to Predict Clinical Outcomes After Immune Checkpoint Blockade in Non-Small Cell Lung Cancer

PurposePredictive biomarkers of immune checkpoint inhibitors (ICIs) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. MethodsParticipants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. ResultsA total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The bestperforming models showed improved predictive power over reference variables, including TMB or PD-L1. ConclusionThis DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICIs clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICIs efficacy. Context summaryO_ST_ABSKey objectiveC_ST_ABSNot all patients with non-small cell lung cancer (NSCLC) eligible for immune checkpoint inhibitor (ICIs) respond to treatment, but accurate predictive biomarkers of ICIs clinical outcomes are currently lacking. This crowdsourced initiative enabled the robust assessment of predictive models using data from two randomized clinical trials of first-line ICI in metastatic NSCLC. Knowledge generatedModels submitted indicate that a combination of programmed death ligand 1 (PD-L1), tumor mutational burden (TMB), and immune gene signatures might be able to identify patients more likely to respond to ICIs. TMB and PD-L1 seemed important to predict progression-free survival and overall survival. Mechanisms including apoptosis, T-cell crosstalk, and adaptive immune resistance appeared essential to predict response. Relevance

cancer biology↗

bioRxiv2022

Region- and variance-based DNA methylation analyses reveal novel disease genes and pathways for systemic lupus erythematosus

BackgroundSystemic lupus erythematosus (SLE) is a prototype autoimmune disease with unclear pathogenesis. DNA methylation is an important regulatory mechanism on gene expression, providing a key angle to understand disease mechanisms. To understand the pathways involved in SLE, and to develop biomarkers for its diagnosis and treatment, we analyzed DNA methylation profiles on blood cells from SLE patients and healthy controls. ResultsWe identified most differentially methylated regions (DMRs) in T cells, while majority of differentially variable sites (DVSs) were found in B cells, featuring hypervariability in enhancers. We observed a prominent T cell receptor (TCR) signaling cluster with consistent hypermethylation and a B cell receptor (BCR) cluster with highly increased variability in SLE. Genes involved in innate immunity were often found hypomethylated, while adaptive immunity genes were featured with hypermethylation. Using a machine learning approach, we identified 60 genes that accurately distinguished SLE patients from healthy individuals, which also showed correlation with disease activities. ConclusionsThis study highlights the role of lymphocyte receptor aberrations in the disease and identified a list of genes showing great potential as biomarkers and shedding new light on disease mechanisms, through novel analyses of methylation data.

bioinformatics↗

bioRxiv2022

Accurate Computational Design of 3D Protein Crystals

Protein crystallization plays a central role in structural biology1, with broad impact2 in pharmaceutical formulation3, drug delivery4, biosensing5, and biocatalysis6,7. Despite this importance, the process of protein crystallization remains poorly understood and highly empirical8-10, with largely unpredictable crystal contacts, lattice packing arrangements, and space group preferences, and the programming of protein crystallization through precisely engineered sidechain-sidechain interactions across multiple protein-protein interfaces is an outstanding challenge. Here we develop a general computational approach to designing three-dimensional (3D) protein crystals with pre-specified lattice architectures at atomic accuracy that hierarchically constrains the overall degree of freedoms (DOFs) of the system. We use the approach to design three pairs of oligomers that can be individually purified, and upon mixing, spontaneously self-assemble into large 3D crystals (>100 m). Small-angle X-ray scattering and X-ray crystallography show these crystals are nearly identical to the computational design models, with the design target F4132 and I432 space groups and closely corresponding overall architectures and protein-protein interfaces. The crystal unit cell dimensions can be systematically redesigned while retaining space group symmetry and overall architecture, and the crystals are both extremely porous and highly stable, enabling the robust scaffolding of inorganic nanoparticle arrays. Our approach thus enables the computational design of protein crystals with high accuracy, and since both structure and assembly are encoded in the primary sequence, provides a powerful new platform for biological material engineering.

synthetic biology↗

bioRxiv2022

ppGpp is Present in and Functions to Regulate Sleep in Drosophila

Sleep is essential for animals, and receives inputs from circadian, homeostasis, and environment, yet the mechanisms of sleep regulation remain elusive. Discovery of molecules in living systems and demonstration of their functional roles are pivotal in furthering our understanding of the molecular basis of biology. Here we report that ppGpp (guanosine-5-diphosphate, 3-diphosphate), a molecule that has been detected in prokaryotes for more than five decades, is present in Drosophila, and plays an important role in regulation of sleep and SISL (starvation induced sleep loss). ppGpp is detected in germ-free Drosophila and hydrolyzed by an enzyme encoded by the mesh1 gene in Drosophila. Nighttime sleep and SISL were defected in mesh1 mutant flies, and rescued by expression of wildtype Mesh1, but not the enzymatically defective mutant Mesh1E66A. Ectopic expression of RelA, the E. coli synthetase for ppGpp, phenocopied mesh1 knockout mutants, whereas overexpression of Mesh1 resulted in the opposite phenotypes, supporting that ppGpp is both necessary and sufficient in sleep regulation. A chemoconnectomic screen followed by genetic intersection experiments implicate the Dilp2 neurons in the pars intercerebralis (PI) brain region as the site of ppGpp function. Our results have thus supported that ppGpp is present in animals after long lag since its discovery in bacteria, and revealed a physiological role of ppGpp in sleep regulation for the first time.

genetics↗

bioRxiv2022

DeepCCI: a deep learning framework for identifying cell-cell interactions from single-cell RNA sequencing data

With the rapid development of high throughput single-cell RNA sequencing (scRNA-seq) technologies, it is of high importance to identify Cell-cell interactions (CCIs) from the ever-increasing scRNA-seq data. However, limited by the algorithmic constraints, current computational methods based on statistical strategies ignore some key latent information contained in scRNA-seq data with high sparsity and heterogeneity. To address the issue, here, we developed a deep learning framework named DeepCCI to identify meaningful CCIs from scRNA-seq data. Applications of DeepCCI to a wide range of publicly available datasets from diverse technologies and platforms demonstrate its ability to predict significant CCIs accurately and effectively.

bioinformatics↗

bioRxiv2022

A compendium of genetic regulatory effects across pig tissues

The Farm animal Genotype-Tissue Expression (FarmGTEx, https://www.farmgtex.org/) project has been established to develop a comprehensive public resource of genetic regulatory variants in domestic animal species, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biology discovery and exploitation in animal breeding and human biomedicine. Here we present results from the pilot phase of PigGTEx (http://piggtex.farmgtex.org/), where we processed 9,530 RNA-sequencing and 1,602 whole-genome sequencing samples from pigs. We build a pig genotype imputation panel, characterize the transcriptional landscape across over 100 tissues, and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We study interactions between genotype and breed/cell type, evaluate tissue specificity of regulatory effects, and elucidate the molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying about 80% of the genetic associations for 207 pig complex phenotypes, and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, corroborating the importance of pigs as a human biomedical model.

genomics↗

bioRxiv2022

Discovery of benzophenanthridine derivatives with potent activity against multidrug resistant Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb), the pathogen responsible for tuberculosis (TB), is the leading cause of bacterial disease-related death worldwide. Current antibiotic regimens for the treatment of TB remain dated and suffer from long treatment times as well as the development of drug-resistance. As such, the search for novel chemical modalities that have selective or potent anti-Mtb properties remains an urgent priority, particularly against multidrug resistant (MDR) Mtb strains. Herein, we design and synthesize 35 novel benzo[c]phenanthridine derivatives (BPD). The two most potent compounds, BPD-6 and BPD-9, accumulated within the bacterial cell and exhibited strong inhibitory activity (MIC90 [~] 2-10 M) against multiple Mycobacterium strains, while remaining inactive against a range of other Gram-negative and Gram-positive bacteria. BPD-6 and BPD-9 were also effective in reducing Mtb viability within infected macrophages. The two BPD compounds displayed comparable efficacy to rifampicin, a critical frontline antibiotic used for the prevention and treatment of TB. Importantly, BPD-6 and BPD-9 inhibited the growth of multiple MDR Mtb clinical isolates, suggesting a completely novel mechanism of action compared to existing frontline TB dugs. The discovery of BPDs provides novel chemical scaffolds for anti-TB drug discovery. TOC/GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/515485v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@1536acaorg.highwire.dtl.DTLVardef@1881e83org.highwire.dtl.DTLVardef@18b2f58org.highwire.dtl.DTLVardef@d15f1a_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2022

Reconstitution of human adrenocortical specification and steroidogenesis using induced pluripotent stem cells

The mechanisms leading to adrenal cortex development and steroid synthesis in humans remain poorly understood due to the paucity of model systems. Herein, we faithfully recapitulate human fetal adrenal cortex specification processes through stepwise induction of human induced pluripotent stem cells through posterior intermediate mesoderm-like and adrenal progenitor-like states to ultimately generate fetal zone adrenal cortex-like cells (FZLCs), as evidenced by histomorphological, ultrastructural, and transcriptome features and adrenocorticotropic hormone (ACTH)-independent {Delta}5 steroid biosynthesis. Furthermore, FZLC generation is promoted by SHH and inhibited by NOTCH, ACTIVIN and WNT signaling, and that steroid synthesis is amplified by ACTH/PKA signaling and blocked by inhibitors of {Delta}5 steroid synthesis enzymes. Finally, NR5A1 promotes FZLC survival and steroidogenesis. Together, these findings provide a framework for understanding and reconstituting human adrenocortical development in vitro paving the way for cell-based therapies of adrenal insufficiency.

developmental biology↗

bioRxiv2022

A frameshift in Yersinia pestis rcsD leads to expression of a small HPt variant that alters canonical Rcs signalling to preserve flea-mammal plague transmission cycles

Multiple genetic changes in the enteric pathogen Yersinia pseudotuberculosis have driven the emergence of Yesinia pestis, the arthropod-borne, etiological agent of plague. These include developing the capacity for biofilm-dependent blockage of the flea foregut to enable transmission by flea bite. Previously, we showed that pseudogenisation of rcsA, encoding a component of the Rcs signalling pathway, is an important evolutionary step facilitating Y. pestis flea-borne transmission. Additionally, rcsD, another important gene in the Rcs system, harbours a frameshift mutation. Here, we demonstrated that this rcsD mutation resulted in predominant production of a small protein composing the C-terminal RcsD histidine-phosphotransferase domain (designated RcsD-Hpt) and low levels of full-length RcsD. Genetic analysis revealed that the rcsD frameshift mutation followed the emergence of rcsA pseudogenisation. It further altered the canonical Rcs phosphorylation signal cascade, fine-tuning biofilm production to be conducive with retention of the pgm locus in modern lineages of Y. pestis. Taken together, our findings suggest that a frameshift mutation in rcsD, is an important evolutionary step that fine-tuned biofilm production to ensure perpetuation of flea-mammal plague transmission cycles.

microbiology↗