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Rapid accumulation and availability of gene expression datasets in public repositories have enabled large-scale meta-analyses of combined data. The richness of cross-experiment data has provided new biological insights, including identification of new cancer genes. In this study, we compiled a human gene expression dataset from ∼40,000 publicly available Affymetrix HG-U133Plus2 arrays. After strict quality control and data normalisation the data was quantified in an expression matrix of ∼20,000 genes and ∼28,000 samples. To enable different ways of sample grouping, existing annotations where subjected to systematic ontology assisted categorisation and manual curation. Groups like normal tissues, neoplasmic tissues, cell lines, homoeotic cells and incompletely differentiated cells were created. Unsupervised analysis of the data confirmed global structure of expression consistent with earlier analysis but with more details revealed due to increased resolution. A suitable mixed-effects linear model was used to further investigate gene expression in solid tissue tumours, and to compare these with the respective healthy solid tissues. The analysis identified 1,285 genes with systematic expression change in cancer. The list is significantly enriched with known cancer genes from large, public, peer-reviewed databases, whereas the remaining ones are proposed as new cancer gene candidates. The compiled dataset is publicly available in the ArrayExpress Archive. It contains the most diverse collection of biological samples, making it the largest systematically annotated gene expression dataset of its kind in the public domain.

How genomic diversity within bacterial populations originates and is maintained in the presence of frequent recombination is a central problem in understanding bacterial evolution. Natural populations of Borrelia burgdorferi, the bacterial agent of Lyme disease, consist of diverse genomic groups co-infecting single individual vertebrate hosts and tick vectors. To understand mechanisms of sympatric genome differentiation in B. burgdorferi, we sequenced and compared 23 genomes representing major genomic groups in North America and Europe. Linkage analysis of >13,500 single-nucleotide polymorphisms revealed pervasive horizontal DNA exchanges. Although three times more frequent than point mutation, recombination is localized and weakly affects genome-wide linkage disequilibrium. We show by computer simulations that, while enhancing population fitness, recombination constrains neutral and adaptive divergence among sympatric genomes through periodic selective sweeps. In contrast, simulations of frequency-dependent selection with recombination produced the observed pattern of a large number of sympatric genomic groups associated with major sequence variations at the selected locus. We conclude that negative frequency-dependent selection targeting a small number of surface-antigen loci (ospC in particular) sufficiently explains the maintenance of sympatric genome diversity in B. burgdorferi without adaptive divergence. We suggest that pervasive recombination makes it less likely for local B. burgdorferi genomic groups to achieve host specialization. B. burgdorferi genomic groups in the northeastern United States are thus best viewed as constituting a single bacterial species, whose generalist nature is a key to its rapid spread and human virulence.

Three billion years of evolution has produced a tremendous diversity of protein molecules 1 , but the full potential of proteins is likely to be much greater. Accessing this potential has been challenging for both computation and experiments because the space of possible protein molecules is much larger than the space of those likely to have functions. Here we introduce Chroma, a generative model for proteins and protein complexes that can directly sample novel protein structures and sequences, and that can be conditioned to steer the generative process towards desired properties and functions. To enable this, we introduce a diffusion process that respects the conformational statistics of polymer ensembles, an efficient neural architecture for molecular systems that enables long-range reasoning with sub-quadratic scaling, layers for efficiently synthesizing three-dimensional structures of proteins from predicted inter-residue geometries and a general low-temperature sampling algorithm for diffusion models. Chroma achieves protein design as Bayesian inference under external constraints, which can involve symmetries, substructure, shape, semantics and even natural-language prompts. The experimental characterization of 310 proteins shows that sampling from Chroma results in proteins that are highly expressed, fold and have favourable biophysical properties. The crystal structures of two designed proteins exhibit atomistic agreement with Chroma samples (a backbone root-mean-square deviation of around 1.0 Å). With this unified approach to protein design, we hope to accelerate the programming of protein matter to benefit human health, materials science and synthetic biology. Evolution has produced a range of diverse proteins, and now a generative model called Chroma can expand that set by allowing the user to design new proteins and protein complexes with desired properties and functions.

Many applications in protein engineering require optimizing multiple protein properties simultaneously, such as binding one target but not others or binding a target while maintaining stability. Such multistate design problems require navigating a high-dimensional space to find proteins with desired characteristics. A model that relates protein sequence to functional attributes can guide design to solutions that would be hard to discover via screening. In this work, we measured thousands of protein–peptide binding affinities with the high-throughput interaction assay amped SORTCERY and used the data to parameterize a model of the alpha-helical peptide-binding landscape for three members of the Bcl-2 family of proteins: Bcl-xL, Mcl-1, and Bfl-1. We applied optimization protocols to explore extremes in this landscape to discover peptides with desired interaction profiles. Computational design generated 36 peptides, all of which bound with high affinity and specificity to just one of Bcl-xL, Mcl-1, or Bfl-1, as intended. We designed additional peptides that bound selectively to two out of three of these proteins. The designed peptides were dissimilar to known Bcl-2–binding peptides, and high-resolution crystal structures confirmed that they engaged their targets as expected. Excellent results on this challenging problem demonstrate the power of a landscape modeling approach, and the designed peptides have potential uses as diagnostic tools or cancer therapeutics.

Small cell lung cancer (SCLC) is an aggressive neuroendocrine tumor characterized by a high metastatic potential with an overall survival rate of ~5%. The transcription factor signal transducer and activator of transcription 3 (STAT3) is overexpressed by >50% of tumors, including SCLC, but its role in SCLC development and metastasis is unclear. Here, we show that, while STAT3 deletion restricts primary tumor growth, it paradoxically enhances metastatic spread by promoting immune evasion. This occurs because STAT3 is crucial for maintaining the immune sensor stimulator of interferon (IFN) genes (STING). Without STAT3, the cyclic adenosine monophosphate–guanosine monophosphate synthase–STING pathway is inactive, resulting in decreased type I IFN secretion and an IFN gene signature. Importantly, restoration of IFN signaling through re-expression of endogenous STING, enforced expression of IFN response factor 7 or administration of recombinant type I IFN re-established antitumor immunity, inhibiting metastatic SCLC in vivo. These data show the potential of augmenting the innate immune response to block metastatic SCLC. Here, the authors show that signal transducer and activator of transcription 3 (STAT3) has dual functions in small cell lung cancer as its deletion inhibited primary tumor growth but boosted metastatic spread. These seemingly opposing functions are a result of the requirement of STAT3 for stimulator of interferon genes–interferon signaling in metastasis and the authors show how it can be targeted in mice.

Background It is a great challenge of modern biology to determine the functional roles of non-synonymous Single Nucleotide Polymorphisms (nsSNPs) on complex phenotypes. Statistical and machine learning techniques establish correlations between genotype and phenotype, but may fail to infer the biologically relevant mechanisms. The emerging paradigm of Network-based Association Studies aims to address this problem of statistical analysis. However, a mechanistic understanding of how individual molecular components work together in a system requires knowledge of molecular structures, and their interactions. Results To address the challenge of understanding the genetic, molecular, and cellular basis of complex phenotypes, we have, for the first time, developed a structural systems biology approach for genome-wide multiscale modeling of nsSNPs - from the atomic details of molecular interactions to the emergent properties of biological networks. We apply our approach to determine the functional roles of nsSNPs associated with hypoxia tolerance in Drosophila melanogaster . The integrated view of the functional roles of nsSNP at both molecular and network levels allows us to identify driver mutations and their interactions (epistasis) in H, Rad51D, Ulp1, Wnt5, HDAC4, Sol, Dys, GalNAc-T2, and CG33714 genes, all of which are involved in the up-regulation of Notch and Gurken/EGFR signaling pathways. Moreover, we find that a large fraction of the driver mutations are neither located in conserved functional sites, nor responsible for structural stability, but rather regulate protein activity through allosteric transitions, protein-protein interactions, or protein-nucleic acid interactions. This finding should impact future Genome-Wide Association Studies. Conclusions Our studies demonstrate that the consolidation of statistical, structural, and network views of biomolecules and their interactions can provide new insight into the functional role of nsSNPs in Genome-Wide Association Studies, in a way that neither the knowledge of molecular structures nor biological networks alone could achieve. Thus, multiscale modeling of nsSNPs may prove to be a powerful tool for establishing the functional roles of sequence variants in a wide array of applications.

Three billion years of evolution have produced a tremendous diversity of protein molecules, and yet the full potential of this molecular class is likely far greater. Accessing this potential has been challenging for computation and experiments because the space of possible protein molecules is much larger than the space of those likely to host function. Here we introduce Chroma, a generative model for proteins and protein complexes that can directly sample novel protein structures and sequences and that can be conditioned to steer the generative process towards desired properties and functions. To enable this, we introduce a diffusion process that respects the conformational statistics of polymer ensembles, an efficient neural architecture for molecular systems based on random graph neural networks that enables long-range reasoning with sub-quadratic scaling, equivariant layers for efficiently synthesizing 3D structures of proteins from predicted inter-residue geometries, and a general low-temperature sampling algorithm for diffusion models. We suggest that Chroma can effectively realize protein design as Bayesian inference under external constraints, which can involve symmetries, substructure, shape, semantics, and even natural language prompts. With this unified approach, we hope to accelerate the prospect of programming protein matter for human health, materials science, and synthetic biology.Competing Interest StatementThe authors have declared no competing interest.

Achieving a functional and durable non-platinum group metal-based methanol oxidation catalyst is critical for a cost-effective direct methanol fuel cell. While Ni(OH) 2 has been widely studied as methanol oxidation catalyst, the initial process of oxidizing Ni(OH) 2 to NiOOH requires a high potential of 1.35 V vs. RHE. Such potential would be impractical since the theoretical potential of the cathodic oxygen reduction reaction is at 1.23 V. Here we show that a four-coordinated nickel atom is able to form charge-transfer orbitals through delocalization of electrons near the Fermi energy level. As such, our previously reported periodically arranged four-six-coordinated nickel hydroxide nanoribbon structure (NR-Ni(OH) 2 ) is able to show remarkable methanol oxidation activity with an onset potential of 0.55 V vs. RHE and suggests the operability in direct methanol fuel cell configuration. Thus, this strategy offers a gateway towards the development of high performance and durable non-platinum direct methanol fuel cell. Development of suitable methanol oxidation reaction catalysts for direct methanol fuel cells is challenging due to sluggish kinetics. Herein, authors show that four-coordinate nickel atoms form charge-transfer orbitals near the Fermi energy level, leading to remarkable methanol oxidation activity.

Large spin–orbit torques (SOTs) generated by topological materials and heavy metals interfaced with ferromagnets are promising for next-generation magnetic memory and logic devices. SOTs generated from y spin originating from spin Hall and Edelstein effects can realize field-free magnetization switching only when the magnetization and spin are collinear. Here we circumvent the above limitation by utilizing unconventional spins generated in a MnPd3 thin film grown on an oxidized silicon substrate. We observe conventional SOT due to y spin, and out-of-plane and in-plane anti-damping-like torques originated from z spin and x spin, respectively, in MnPd3/CoFeB heterostructures. Notably, we have demonstrated complete field-free switching of perpendicular cobalt via out-of-plane anti-damping-like SOT. Density functional theory calculations show that the observed unconventional torques are due to the low symmetry of the (114)-oriented MnPd3 films. Altogether our results provide a path toward realization of a practical spin channel in ultrafast magnetic memory and logic devices.The authors address spin–orbit torques and magnetization switching in MnPd3/CoFeB hetrostructures.

The history of clinical resistance to tetracycline antibiotics is characterized by cycles whereby the deployment of a new generation of drug molecules is quickly followed by the discovery of a new mechanism of resistance. This suggests mechanism-specific selection by each tetracycline generation; however, the evolutionary dynamics of this remain unclear. Here, we evaluate 24 recombinant Escherichia coli strains expressing tetracycline resistance genes from each mechanism (efflux pumps, ribosomal protection proteins, and enzymatic inactivation) in the context of each tetracycline generation. We employ a high-throughput barcode sequencing protocol that can discriminate between strains in mixed culture and quantify their relative abundances. We find that each mechanism is preferentially selected for by specific antibiotic generations, leading to their expansion. Remarkably, the minimum inhibitory concentration associated with individual genes is secondary to resistance mechanism for inter-mechanism relative fitness, but it does explain intra-mechanism relative fitness. These patterns match the history of clinical deployment of tetracycline drugs and resistance discovery in pathogens. Bacteria can use different mechanisms to become resistant to the same antibiotic class. Here, the authors study bacterial strains that express genes conferring tetracycline resistance via different mechanisms, and find that each mechanism is preferentially selected for by specific tetracycline generations.