A synthetic differentiation circuit in Escherichia coli for suppressing mutant takeover.

Differentiation is crucial for multicellularity. However, it is inherently susceptible to mutant cells that fail to differentiate. These mutants outcompete normal cells by excessive self-renewal. It remains unclear what mechanisms can resist such mutant expansion. Here, we demonstrate a solution by engineering a synthetic differentiation circuit in Escherichia coli that selects against these mutants via a biphasic fitness strategy. The circuit provides tunable production of synthetic analogs of stem, progenitor, and differentiated cells. It resists mutations by coupling differentiation to the production of an essential enzyme, thereby disadvantaging non-differentiating mutants. The circuit selected for and maintained a positive differentiation rate in long-term evolution. Surprisingly, this rate remained constant across vast changes in growth conditions. We found that transit-amplifying cells (fast-growing progenitors) underlie this environmental robustness. Our results provide insight into the stability of differentiation and demonstrate a powerful method for engineering evolutionarily stable multicellular consortia.

Transition to invasive breast cancer is associated with progressive changes in the structure and composition of tumor stroma.

Ductal carcinoma in situ (DCIS) is a pre-invasive lesion that is thought to be a precursor to invasive breast cancer (IBC). To understand the changes in the tumor microenvironment (TME) accompanying transition to IBC, we used multiplexed ion beam imaging by time of flight (MIBI-TOF) and a 37-plex antibody staining panel to interrogate 79 clinically annotated surgical resections using machine learning tools for cell segmentation, pixel-based clustering, and object morphometrics. Comparison of normal breast with patient-matched DCIS and IBC revealed coordinated transitions between four TME states that were delineated based on the location and function of myoepithelium, fibroblasts, and immune cells. Surprisingly, myoepithelial disruption was more advanced in DCIS patients that did not develop IBC, suggesting this process could be protective against recurrence. Taken together, this HTAN Breast PreCancer Atlas study offers insight into drivers of IBC relapse and emphasizes the importance of the TME in regulating these processes.

A Synthetic Bacterial Cell-Cell Adhesion Toolbox for Programming Multicellular Morphologies and Patterns.

Synthetic multicellular systems hold promise as models for understanding natural development of biofilms and higher organisms and as tools for engineering complex multi-component metabolic pathways and materials. However, such efforts require tools to adhere cells into defined morphologies and patterns, and these tools are currently lacking. Here, we report a 100% genetically encoded synthetic platform for modular cell-cell adhesion in Escherichia coli, which provides control over multicellular self-assembly. Adhesive selectivity is provided by a library of outer membrane-displayed nanobodies and antigens with orthogonal intra-library specificities, while affinity is controlled by intrinsic adhesin affinity, competitive inhibition, and inducible expression. We demonstrate the resulting capabilities for quantitative rational design of well-defined morphologies and patterns through homophilic and heterophilic interactions, lattice-like self-assembly, phase separation, differential adhesion, and sequential layering. Compatible with synthetic biology standards, this adhesion toolbox will enable construction of high-level multicellular designs and shed light on the evolutionary transition to multicellularity.

An Integrated Multi-omic Single-Cell Atlas of Human B Cell Identity.

B cells are capable of a wide range of effector functions including antibody secretion, antigen presentation, cytokine production, and generation of immunological memory. A consistent strategy for classifying human B cells by using surface molecules is essential to harness this functional diversity for clinical translation. We developed a highly multiplexed screen to quantify the co-expression of 351 surface molecules on millions of human B cells. We identified differentially expressed molecules and aligned their variance with isotype usage, VDJ sequence, metabolic profile, biosynthesis activity, and signaling response. Based on these analyses, we propose a classification scheme to segregate B cells from four lymphoid tissues into twelve unique subsets, including a CD45RB+CD27- early memory population, a class-switched CD39+ tonsil-resident population, and a CD19hiCD11c+ memory population that potently responds to immune activation. This classification framework and underlying datasets provide a resource for further investigations of human B cell identity and function.

4-bit adhesion logic enables universal multicellular interface patterning.

Multicellular systems, from bacterial biofilms to human organs, form interfaces (or boundaries) between different cell collectives to spatially organize versatile functions1,2. The evolution of sufficiently descriptive genetic toolkits probably triggered the explosion of complex multicellular life and patterning3,4. Synthetic biology aims to engineer multicellular systems for practical applications and to serve as a build-to-understand methodology for natural systems5-8. However, our ability to engineer multicellular interface patterns2,9 is still very limited, as synthetic cell-cell adhesion toolkits and suitable patterning algorithms are underdeveloped5,7,10-13. Here we introduce a synthetic cell-cell adhesin logic with swarming bacteria and establish the precise engineering, predictive modelling and algorithmic programming of multicellular interface patterns. We demonstrate interface generation through a swarming adhesion mechanism, quantitative control over interface geometry and adhesion-mediated analogues of developmental organizers and morphogen fields. Using tiling and four-colour-mapping concepts, we identify algorithms for creating universal target patterns. This synthetic 4-bit adhesion logic advances practical applications such as human-readable molecular diagnostics, spatial fluid control on biological surfaces and programmable self-growing materials5-8,14. Notably, a minimal set of just four adhesins represents 4 bits of information that suffice to program universal tessellation patterns, implying a low critical threshold for the evolution and engineering of complex multicellular systems3,5.

Common and rare variant associations with clonal haematopoiesis phenotypes.

Clonal haematopoiesis involves the expansion of certain blood cell lineages and has been associated with ageing and adverse health outcomes1-5. Here we use exome sequence data on 628,388 individuals to identify 40,208 carriers of clonal haematopoiesis of indeterminate potential (CHIP). Using genome-wide and exome-wide association analyses, we identify 24 loci (21 of which are novel) where germline genetic variation influences predisposition to CHIP, including missense variants in the lymphocytic antigen coding gene LY75, which are associated with reduced incidence of CHIP. We also identify novel rare variant associations with clonal haematopoiesis and telomere length. Analysis of 5,041 health traits from the UK Biobank (UKB) found relationships between CHIP and severe COVID-19 outcomes, cardiovascular disease, haematologic traits, malignancy, smoking, obesity, infection and all-cause mortality. Longitudinal and Mendelian randomization analyses revealed that CHIP is associated with solid cancers, including non-melanoma skin cancer and lung cancer, and that CHIP linked to DNMT3A is associated with the subsequent development of myeloid but not lymphoid leukaemias. Additionally, contrary to previous findings from the initial 50,000 UKB exomes6, our results in the full sample do not support a role for IL-6 inhibition in reducing the risk of cardiovascular disease among CHIP carriers. Our findings demonstrate that CHIP represents a complex set of heterogeneous phenotypes with shared and unique germline genetic causes and varied clinical implications.

Tradeoffs in bacterial physiology determine the efficiency of antibiotic killing.

Antibiotic effectiveness depends on a variety of factors. While many mechanistic details of antibiotic action are known, the connection between death rate and bacterial physiology is poorly understood. A common observation is that death rate in antibiotics rises linearly with growth rate; however, it remains unclear how other factors, such as environmental conditions and whole-cell physiological properties, affect bactericidal activity. To address this, we developed a high-throughput assay to precisely measure antibiotic-mediated death. We found that death rate is linear in growth rate, but the slope depends on environmental conditions. Growth under stress lowers death rate compared to nonstressed environments with similar growth rate. To understand stress's role, we developed a mathematical model of bacterial death based on resource allocation that includes a stress-response sector; we identify this sector using RNA-seq. Our model accurately predicts the minimal inhibitory concentration (MIC) with zero free parameters across a wide range of growth conditions. The model also quantitatively predicts death and MIC when sectors are experimentally modulated using cyclic adenosine monophosphate (cAMP), including protection from death at very low cAMP levels. The present study shows that different conditions with equal growth rate can have different death rates and establishes a quantitative relation between growth, death, and MIC that suggests approaches to improve antibiotic efficacy.

Construction and validation of an algorithm to separate focal and generalised epilepsy using clinical variables: A comparison of machine learning approaches.

PURPOSE: Epilepsy type, whether focal or generalised, is important in deciding anti-seizure medication (ASM). In resource-limited settings, investigations are usually not available, so a clinical separation is required. We used a naïve Bayes approach to devise an algorithm to do this, and compared its accuracy with algorithms devised by five other machine learning methods. METHODS: We used data on 28 clinical variables from 503 patients attending an epilepsy clinic in India with defined epilepsy type, as determined by an epileptologist with access to clinical, imaging, and EEG data. We adopted a machine learning approach to select the most relevant variables based on mutual information, to train the model on part of the data, and then to evaluate it on the remaining data (testing set). We used a naïve Bayes approach and compared the results in the testing set with those obtained by several other machine learning algorithms by measuring sensitivity, specificity, accuracy, area under the curve, and Cohen's kappa. RESULTS: The six machine learning methods produced broadly similar results. The best naïve Bayes algorithm contained eleven variables, and its accuracy was 92.2% in determining epilepsy type (sensitivity 92.0%, specificity 92.7%). An algorithm incorporating the best eight of these variables was only slightly less accurate - 91.0% (sensitivity 89.6%, and specificity 95.1%) - and easier for clinicians to use. CONCLUSION: A clinical algorithm with eight variables is effective and accurate at separating focal from generalised epilepsy. It should be useful in resource-limited settings, by epilepsy-inexperienced doctors, to help determine epilepsy type and therefore optimal ASMs for individual patients, without the need for EEG or neuroimaging.

A brief history of the hypothesis.

Two terms used as frameworks for scientific experimentation--the "hypothesis" and the "model"--carry distinct philosophical assumptions, with important consequences for the practicing scientist.

How good is an explanation?

How good is an explanation and when is one explanation better than another? In this paper, I address these questions by exploring probabilistic measures of explanatory power in order to defend a particular Bayesian account of explanatory goodness. Critical to this discussion is a distinction between weak and strong measures of explanatory power due to Good (Br J Philos Sci 19:123-143, 1968). In particular, I argue that if one is interested in the overall goodness of an explanation, an appropriate balance needs to be struck between the weak explanatory power and the complexity of a hypothesis. In light of this, I provide a new defence of a strong measure proposed by Good by providing new derivations of it, comparing it with other measures and exploring its connection with information, confirmation and explanatory virtues. Furthermore, Good really presented a family of strong measures, whereas I draw on a complexity criterion that favours a specific measure and hence provides a more precise way to quantify explanatory goodness.

Frequent template switching in postreplication gaps: suppression of deleterious consequences by the Escherichia coli Uup and RadD proteins.

When replication forks encounter template DNA lesions, the lesion is simply skipped in some cases. The resulting lesion-containing gap must be converted to duplex DNA to permit repair. Some gap filling occurs via template switching, a process that generates recombination-like branched DNA intermediates. The Escherichia coli Uup and RadD proteins function in different pathways to process the branched intermediates. Uup is a UvrA-like ABC family ATPase. RadD is a RecQ-like SF2 family ATPase. Loss of both functions uncovers frequent and RecA-independent deletion events in a plasmid-based assay. Elevated levels of crossing over and repeat expansions accompany these deletion events, indicating that many, if not most, of these events are associated with template switching in postreplication gaps as opposed to simple replication slippage. The deletion data underpin simulations indicating that multiple postreplication gaps may be generated per replication cycle. Both Uup and RadD bind to branched DNAs in vitro. RadD protein suppresses crossovers and Uup prevents nucleoid mis-segregation. Loss of Uup and RadD function increases sensitivity to ciprofloxacin. We present Uup and RadD as genomic guardians. These proteins govern two pathways for resolution of branched DNA intermediates such that potentially deleterious genome rearrangements arising from frequent template switching are averted.

The role of GDF11 in aging and skeletal muscle, cardiac and bone homeostasis.

GDF11 is a secreted factor in the TGFß family of cytokines. Its nearest neighbor evolutionarily is myostatin, a factor discovered as being a negative regulator of skeletal muscle growth. High profile studies several years ago suggested that GDF11 declines with age, and that restoration of systemic GDF11 to 'youthful' levels is beneficial for several age-related conditions. Particularly surprising was a report that supplementation of GDF11 aided skeletal muscle regeneration, as its homolog, myostatin, has the opposite role. Given this apparent contradiction in functionality, multiple independent labs sought to discern differences between the two factors and better elucidate age-related changes in circulating GDF11, with most failing to reproduce the initial finding of declining GDF11 levels, and, importantly, all subsequent studies examining the effects of GDF11 on skeletal muscle described an inhibitory effect on regeneration - and that higher doses induce skeletal muscle atrophy and cachexia. There have also been several studies examining the effect of GDF11 and/or the downstream ActRII pathway on cardiac function, along with several interesting reports on bone. A review of the GDF11 literature, as it relates in particular to aging and skeletal muscle, cardiac and bone biology, is presented.

Genetic Analysis of DinG Family Helicase YoaA and Its Interaction with Replication Clamp Loader Protein HolC in Escherichia coli.

The XP-D/DinG family of DNA helicases contributes to genomic stability in all three domains of life. Here, we investigate the role of one of these proteins, YoaA, of Escherichia coli. In E. coli, YoaA aids in tolerance to the nucleoside azidothymidine (AZT), a DNA replication inhibitor, and physically interacts with a subunit of the DNA polymerase III holoenzyme, HolC. We map the residues of YoaA required for HolC interaction to its C terminus by yeast two-hybrid analysis. We propose that this interaction competes with HolC's interaction with HolD and the rest of the replisome; YoaA indeed inhibits growth when overexpressed, dependent on this interaction region. By gene fusions, we show that YoaA is repressed by LexA and induced in response to DNA damage as part of the SOS response. Induction of YoaA by AZT is biphasic, with an immediate response after treatment and a slower response that peaks in the late log phase of growth. This growth-phase-dependent induction by AZT is not blocked by lexA3 (Ind-), which normally negates its self-cleavage, implying another means to induce the DNA damage response that responds to the nutritional state of the cell. We propose that YoaA helicase activity increases access to the 3' nascent strand during replication; consistent with this, YoaA appears to aid in the removal of potential A-to-T transversion mutations in ndk mutants, which are prone to nucleotide misincorporation. We provide evidence that YoaA and its paralog DinG may also initiate template switching that leads to deletions between tandem repeats in DNA. IMPORTANCE Maintaining genomic stability is crucial for all living organisms. Replication of DNA frequently encounters barriers that must be removed to complete genome duplication. Balancing DNA synthesis with its repair is critical and not entirely understood at a mechanistic level. The YoaA protein, studied here, is required for certain types of DNA repair and interacts in an alternative manner with proteins that catalyze DNA replication. YoaA is part of the well-studied LexA-regulated response to DNA damage, the SOS response. We describe an unusual feature of its regulation that promotes induction after DNA damage as the culture begins to experience starvation. Replication fork repair integrates both DNA damage and nutritional signals. We also show that YoaA affects genomic stability.

Five-in-One: Simultaneous isolation of multiple major liver cell types from livers of normal and NASH mice.

NASH is a chronic liver disease that affects 3%-6% of individuals and requires urgent therapeutic developments. Isolating the key cell types in the liver is a necessary step towards understanding their function and roles in disease pathogenesis. However, traditional isolation methods through gradient centrifugation can only collect one or a few cell types simultaneously and pose technical difficulties when applied to NASH livers. Taking advantage of identified cell surface markers from liver single-cell RNAseq, here we established the combination of gradient centrifugation and antibody-based cell sorting techniques to isolate five key liver cell types (hepatocytes, endothelial cells, stellate cells, macrophages and other immune cells) from a single mouse liver. This method yielded high purity of each cell type from healthy and NASH livers. Our five-in-one protocol simultaneously isolates key liver cell types with high purity under normal and NASH conditions, enabling for systematic and accurate exploratory experiments such as RNA sequencing.

Regulation of yeast pyruvate kinase by ultrasensitive allostery independent of phosphorylation.

Allostery and covalent modification are major means of fast-acting metabolic regulation. Their relative roles in responding to environmental changes remain, however, unclear. Here we examine this issue, using as a case study the rapid decrease in pyruvate kinase flux in yeast upon glucose removal. The main pyruvate kinase isozyme (Cdc19) is phosphorylated in response to environmental cues. It also exhibits positively cooperative (ultrasensitive) allosteric activation by fructose-1,6-bisphosphate (FBP). Glucose removal causes accumulation of Cdc19's substrate, phosphoenolpyruvate. This response is retained in strains with altered protein-kinase-A or AMP-activated-protein-kinase activity or with CDC19 carrying mutated phosphorylation sites. In contrast, yeast engineered with a CDC19 point mutation that ablates FBP-based regulation fail to accumulate phosphoenolpyruvate. They also fail to grow on ethanol and slowly resume growth upon glucose upshift. Thus, while yeast pyruvate kinase is covalently modified in response to glucose availability, its activity is controlled almost exclusively by ultrasensitive allostery.

Blockade of activin type II receptors with a dual anti-ActRIIA/IIB antibody is critical to promote maximal skeletal muscle hypertrophy.

The TGF-ß family ligands myostatin, GDF11, and activins are negative regulators of skeletal muscle mass, which have been reported to primarily signal via the ActRIIB receptor on skeletal muscle and thereby induce muscle wasting described as cachexia. Use of a soluble ActRIIB-Fc "trap," to block myostatin pathway signaling in normal or cachectic mice leads to hypertrophy or prevention of muscle loss, perhaps suggesting that the ActRIIB receptor is primarily responsible for muscle growth regulation. Genetic evidence demonstrates however that both ActRIIB- and ActRIIA-deficient mice display a hypertrophic phenotype. Here, we describe the mode of action of bimagrumab (BYM338), as a human dual-specific anti-ActRIIA/ActRIIB antibody, at the molecular and cellular levels. As shown by X-ray analysis, bimagrumab binds to both ActRIIA and ActRIIB ligand binding domains in a competitive manner at the critical myostatin/activin binding site, hence preventing signal transduction through either ActRII. Myostatin and the activins are capable of binding to both ActRIIA and ActRIIB, with different affinities. However, blockade of either single receptor through the use of specific anti-ActRIIA or anti-ActRIIB antibodies achieves only a partial signaling blockade upon myostatin or activin A stimulation, and this leads to only a small increase in muscle mass. Complete neutralization and maximal anabolic response are achieved only by simultaneous blockade of both receptors. These findings demonstrate the importance of ActRIIA in addition to ActRIIB in mediating myostatin and activin signaling and highlight the need for blocking both receptors to achieve a strong functional benefit.

Protein kinase A activation inhibits DUX4 gene expression in myotubes from patients with facioscapulohumeral muscular dystrophy.

Facioscapulohumeral muscular dystrophy (FSHD) is among the most prevalent of the adult-onset muscular dystrophies. FSHD causes a loss of muscle mass and function, resulting in severe debilitation and reduction in quality of life. Currently, only the symptoms of FSHD can be treated, and such treatments have minimal benefit. The available options are not curative, and none of the treatments address the underlying cause of FSHD. The genetic, epigenetic, and molecular mechanisms triggering FSHD are now quite well-understood, and it has been shown that expression of the transcriptional regulator double homeobox 4 (DUX4) is necessary for disease onset and is largely thought to be the causative factor in FSHD. Therefore, we sought to identify compounds suppressing DUX4 expression in a phenotypic screen using FSHD patient-derived muscle cells, a zinc finger and SCAN domain-containing 4 (ZSCAN4)-based reporter gene assay for measuring DUX4 activity, and ∼3,000 small molecules. This effort identified molecules that reduce DUX4 gene expression and hence DUX4 activity. Among those, ß2-adrenergic receptor agonists and phosphodiesterase inhibitors, both leading to increased cellular cAMP, effectively decreased DUX4 expression by >75% in cells from individuals with FSHD. Of note, we found that cAMP production reduces DUX4 expression through a protein kinase A-dependent mode of action in FSHD patient myotubes. These findings increase our understanding of how DUX4 expression is regulated in FSHD and point to potential areas of therapeutic intervention.