Cellular landscape of adrenocortical carcinoma at single-nuclei resolution.

Adrenocortical carcinoma (ACC) is a rare yet devastating tumour of the adrenal gland with a molecular pathology that remains incompletely understood. To gain novel insights into the cellular landscape of ACC, we generated single-nuclei RNA sequencing (snRNA-seq) data sets from twelve ACC tumour samples and analysed these alongside snRNA-seq data sets from normal adrenal glands (NAGs). We find the ACC tumour microenvironment to be relatively devoid of immune cells compared to NAG tissues, consistent with known high tumour purity values for ACC as an immunologically "cold" tumour. Our analysis identifies three separate groups of ACC samples that are characterised by different relative compositions of adrenocortical cell types. These include cell populations that are specifically enriched in the most clinically aggressive and hormonally active tumours, displaying hallmarks of reorganised cell mechanobiology and dysregulated steroidogenesis, respectively. We also identified and validated a population of mitotically active adrenocortical cells that strongly overexpress genes POLQ, DIAPH3 and EZH2 to support tumour expansion alongside an LGR4+ progenitor-like or cell-of-origin candidate for adrenocortical carcinogenesis. Trajectory inference suggests the fate adopted by malignant adrenocortical cells upon differentiation is associated with the copy number or allelic balance state of the imprinted DLK1/MEG3 genomic locus, which we verified by assessing bulk tumour DNA methylation status. In conclusion, our results therefore provide new insights into the clinical and cellular heterogeneity of ACC, revealing how genetic perturbations to healthy adrenocortical renewal and zonation provide a molecular basis for disease pathogenesis.

Molecular Characterization of the Tumor Microenvironment in Renal Medullary Carcinoma.

Renal medullary carcinoma (RMC) is a highly aggressive disease associated with sickle hemoglobinopathies and universal loss of the tumor suppressor gene SMARCB1. RMC has a relatively low rate of incidence compared with other renal cell carcinomas (RCCs) that has hitherto made molecular profiling difficult. To probe this rare disease in detail we performed an in-depth characterization of the RMC tumor microenvironment using a combination of genomic, metabolic and single-cell RNA-sequencing experiments on tissue from a representative untreated RMC patient, complemented by retrospective analyses of archival tissue and existing published data. Our study of the tumor identifies a heterogenous population of malignant cell states originating from the thick ascending limb of the Loop of Henle within the renal medulla. Transformed RMC cells displayed the hallmarks of increased resistance to cell death by ferroptosis and proteotoxic stress driven by MYC-induced proliferative signals. Specifically, genomic characterization of RMC tumors provides substantiating evidence for the recently proposed dependence of SMARCB1-difficient cancers on proteostasis modulated by an intact CDKN2A-p53 pathway. We also provide evidence that increased cystine-mTORC-GPX4 signaling plays a role in protecting transformed RMC cells against ferroptosis. We further propose that RMC has an immune landscape comparable to that of untreated RCCs, including heterogenous expression of the immune ligand CD70 within a sub-population of tumor cells. The latter could provide an immune-modulatory role that serves as a viable candidate for therapeutic targeting.

Simulating single-cell metabolism using a stochastic flux-balance analysis algorithm.

Stochasticity from gene expression in single cells is known to drive metabolic heterogeneity at the level of cellular populations, which is understood to have important consequences for issues such as microbial drug tolerance and treatment of human diseases like cancer. Despite considerable advancements in profiling the genomes, transcriptomes, and proteomes of single cells, it remains difficult to experimentally characterize their metabolism at the genome scale. Computational methods could bridge this gap toward a systems understanding of single-cell biology. To address this challenge, we developed stochastic simulation algorithm with flux-balance analysis embedded (SSA-FBA), a computational framework for simulating the stochastic dynamics of the metabolism of individual cells using genome-scale metabolic models with experimental estimates of gene expression and enzymatic reaction rate parameters. SSA-FBA extends the constraint-based modeling formalism of metabolic network modeling to the single-cell regime, enabling simulation when experimentation is intractable. We also developed an efficient implementation of SSA-FBA that leverages the topology of embedded flux-balance analysis models to significantly reduce the computational cost of simulation. As a preliminary case study, we built a reduced single-cell model of Mycoplasma pneumoniae and used SSA-FBA to illustrate the role of stochasticity on the dynamics of metabolism at the single-cell level.

Cooperative metabolic resource allocation in spatially-structured systems.

Natural selection has shaped the evolution of cells and multi-cellular organisms such that social cooperation can often be preferred over an individualistic approach to metabolic regulation. This paper extends a framework for dynamic metabolic resource allocation based on the maximum entropy principle to spatiotemporal models of metabolism with cooperation. Much like the maximum entropy principle encapsulates 'bet-hedging' behaviour displayed by organisms dealing with future uncertainty in a fluctuating environment, its cooperative extension describes how individuals adapt their metabolic resource allocation strategy to further accommodate limited knowledge about the welfare of others within a community. The resulting theory explains why local regulation of metabolic cross-feeding can fulfil a community-wide metabolic objective if individuals take into consideration an ensemble measure of total population performance as the only form of global information. The latter is likely supplied by quorum sensing in microbial systems or signalling molecules such as hormones in multi-cellular eukaryotic organisms.

Dynamic metabolic resource allocation based on the maximum entropy principle.

Organisms have evolved a variety of mechanisms to cope with the unpredictability of environmental conditions, and yet mainstream models of metabolic regulation are typically based on strict optimality principles that do not account for uncertainty. This paper introduces a dynamic metabolic modelling framework that is a synthesis of recent ideas on resource allocation and the powerful optimal control formulation of Ramkrishna and colleagues. In particular, their work is extended based on the hypothesis that cellular resources are allocated among elementary flux modes according to the principle of maximum entropy. These concepts both generalise and unify prior approaches to dynamic metabolic modelling by establishing a smooth interpolation between dynamic flux balance analysis and dynamic metabolic models without regulation. The resulting theory is successful in describing 'bet-hedging' strategies employed by cell populations dealing with uncertainty in a fluctuating environment, including heterogenous resource investment, accumulation of reserves in growth-limiting conditions, and the observed behaviour of yeast growing in batch and continuous cultures. The maximum entropy principle is also shown to yield an optimal control law consistent with partitioning resources between elementary flux mode families, which has important practical implications for model reduction, selection, and simulation.

Energetic substrate availability regulates synchronous activity in an excitatory neural network.

Neural networks are required to meet significant metabolic demands associated with performing sophisticated computational tasks in the brain. The necessity for efficient transmission of information imposes stringent constraints on the metabolic pathways that can be used for energy generation at the synapse, and thus low availability of energetic substrates can reduce the efficacy of synaptic function. Here we study the effects of energetic substrate availability on global neural network behavior and find that glucose alone can sustain excitatory neurotransmission required to generate high-frequency synchronous bursting that emerges in culture. In contrast, obligatory oxidative energetic substrates such as lactate and pyruvate are unable to substitute for glucose, indicating that processes involving glucose metabolism form the primary energy-generating pathways supporting coordinated network activity. Our experimental results are discussed in the context of the role that metabolism plays in supporting the performance of individual synapses, including the relative contributions from postsynaptic responses, astrocytes, and presynaptic vesicle cycling. We propose a simple computational model for our excitatory cultures that accurately captures the inability of metabolically compromised synapses to sustain synchronous bursting when extracellular glucose is depleted.

Geometric phase shifts in biological oscillators.

Many intracellular processes continue to oscillate during the cell cycle. Although it is not well-understood how they are affected by discontinuities in the cellular environment, the general assumption is that oscillations remain robust provided the period of cell divisions is much larger than the period of the oscillator. Here, I will show that under these conditions a cell will in fact have to correct for an additional quantity added to the phase of oscillation upon every repetition of the cell cycle. The resulting phase shift is an analogue of the geometric phase, a curious entity first discovered in quantum mechanics. In this letter, I will discuss the theory of the geometric phase shift and demonstrate its relevance to biological oscillations.

RIEMANN ZEROS AND THE INVERSE PHASE PROBLEM.

Finding a universal method of crystal structure solution and proving the Riemann hypothesis are two outstanding challenges in apparently unrelated fields. For centrosymmetric crystals however, a connection arises as the result of a statistical approach to the inverse phase problem. It is shown that parameters of the phase distribution are related to the non-trivial Riemann zeros by a Mellin transform.

Elongation factor G bound to the ribosome in an intermediate state of translocation.

A key step of translation by the ribosome is translocation, which involves the movement of messenger RNA (mRNA) and transfer RNA (tRNA) with respect to the ribosome. This allows a new round of protein chain elongation by placing the next mRNA codon in the A site of the 30S subunit. Translocation proceeds through an intermediate state in which the acceptor ends of the tRNAs have moved with respect to the 50S subunit but not the 30S subunit, to form hybrid states. The guanosine triphosphatase (GTPase) elongation factor G (EF-G) catalyzes the subsequent movement of mRNA and tRNA with respect to the 30S subunit. Here, we present a crystal structure at 3 angstrom resolution of the Thermus thermophilus ribosome with a tRNA in the hybrid P/E state bound to EF-G with a GTP analog. The structure provides insights into structural changes that facilitate translocation and suggests a common GTPase mechanism for EF-G and elongation factor Tu.

Crystal structure of a bioactive pactamycin analog bound to the 30S ribosomal subunit.

Biosynthetically and chemically derived analogs of the antibiotic pactamycin and de-6-methylsalicylyl (MSA)-pactamycin have attracted recent interest as potential antiprotozoal and antitumor drugs. Here, we report a 3.1-Å crystal structure of de-6-MSA-pactamycin bound to its target site on the Thermus thermophilus 30S ribosomal subunit. Although de-6-MSA-pactamycin lacks the MSA moiety, it shares the same binding site as pactamycin and induces a displacement of nucleic acid template bound at the E-site of the 30S. The structure highlights unique interactions between this pactamycin analog and the ribosome, which paves the way for therapeutic development of related compounds.

Acinetobacter insertion sequence ISAba11 belongs to a novel family that encodes transposases with a signature HHEK motif.

Experimental and in silico PCR analysis targeting ISAba11 and TnAbaR islands in 196 epidemiologically unrelated Acinetobacter strains representative of ≥19 species were performed. The first two Acinetobacter baumannii ISAba11 elements identified had been found to map to the same site on TnAbaR transposons. However, no further evidence of physical linkage between the two elements was demonstrated. Indeed, examination of 25 definite or putative insertion sites suggested limited sequence specificity. Importantly, an aacC1-tagged version of ISAba11 was shown to actively transpose in A. baumannii. Similarity searches identified nine iso-ISAba11 elements in Acinetobacter and one in Enhydrobacter and single representatives of four distant homologs in bacteria belonging to the phyla "Cyanobacteria" and Proteobacteria. Phylogenetic, sequence, and structural analyses of ISAba11 and/or its associated transposase (Tnp(ISAba11)) suggested that these elements be assigned to a new family. All five homologs encode transposases with a shared extended signature comprising 16 invariant residues within the N2, N3, and C1 regions, four of which constituted the cardinal ISAba11 family HHEK motif that is substituted for the YREK DNA binding motif conserved in the IS4 family. Additionally, ISAba11 family members were associated with either no flanking direct repeat (DR) or an ISAba11-typical 5-bp DR and possessed variable-length terminal inverted repeats that exhibited extensive intrafamily sequence identity. Given the limited pairwise identity among Tnp(ISAba11) homologs and the observed restricted distribution of ISAba11, we propose that substantial gaps persist in the evolutionary record of ISAba11 and that this element represents a recent though potentially highly significant entrant into the A. baumannii gene pool.

Expression, purification, crystallization and preliminary X-ray analysis of wild-type and of an active-site mutant of glyceraldehyde-3-phosphate dehydrogenase from Campylobacter jejuni.

The genome of the enteric pathogen Campylobacter jejuni encodes a single glyceraldehyde-3-phosphate dehydrogenase that can utilize either NADP+ or NAD+ as coenzymes for the oxidative phosphorylation of glyceraldehyde-3-phosphate to 1,3-diphosphoglycerate. Here, the cloning, expression, purification, crystallization and preliminary X-ray analysis of both the wild type and an active-site mutant of the enzyme are presented. Preliminary X-ray analysis revealed that in both cases the crystals diffracted to beyond 1.9 Šresolution. The space group is shown to be I4(1)22, with unit-cell parameters a=90.75, b=90.75, c=225.48 Å, α=90.46, ß=90.46, γ=222.79°; each asymmetric unit contains only one subunit of the tetrameric enzyme.