Cancer genomes harbor a catalog of somatic mutations. The type and genomic context of these mutations depend on their causes and allow their attribution to particular mutational signatures. Previous work has shown that mutational signature activities change over the course of tumor development, but investigations of genomic region variability in mutational signatures have been limited. Here, we expand upon this work by constructing regional profiles of mutational signature activities over 2,203 whole genomes across 25 tumor types, using data aggregated by the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium. We present GenomeTrackSig as an extension to the TrackSig R package to construct regional signature profiles using optimal segmentation and the expectation-maximization (EM) algorithm. We find that 426 genomes from 20 tumor types display at least one change in mutational signature activities (changepoint), and 306 genomes contain at least one of 54 recurrent changepoints shared by seven or more genomes of the same tumor type. Five recurrent changepoint locations are shared by multiple tumor types. Within these regions, the particular signature changes are often consistent across samples of the same type and some, but not all, are characterized by signatures associated with subclonal expansion. The changepoints we found cannot strictly be explained by gene density, mutation density, or cell-of-origin chromatin state. We hypothesize that they reflect a confluence of factors including evolutionary timing of mutational processes, regional differences in somatic mutation rate, large-scale changes in chromatin state that may be tissue type-specific, and changes in chromatin accessibility during subclonal expansion. These results provide insight into the regional effects of DNA damage and repair processes, and may help us localize genomic and epigenomic changes that occur during cancer development. Author summary: Somatic mutations accumulate through cancer development. These mutations are the result of DNA damage and DNA repair deficiencies; in some cases these mutation sources can be recovered by attributing mutations to different mutational signatures. Mutational signatures describe patterns of co-occurring substitution types (e.g., C to A) that are associated with various mutagenic processes. For example, UV radiation causes CpC to TpT substitutions, and smoking mutagens are associated with C to A substitution. Previous work has shown that mutational signature activities in cancer genomes change over the course of a cancer’s development. Less is known about how their activities change across the entire genome, which is relevant for understanding the regional effects of DNA damage and repair deficiencies. We have developed a bioinformatic tool GenomeTrackSig, which uses a segmentation algorithm to estimate mutational signature activities across large chromosomal domains and identify changepoints where activities vary. We apply GenomeTrackSig to 2,203 cancer genomes and find that 426 contain changepoints. Some changepoints recur across multiple samples of the same type, and some also exhibit activity tradeoffs between signatures active in either early or late cancer development. Changepoints cannot be explained well by genomic factors that typically contribute to mutation rate variation. We hypothesize that they are driven by a confluence of factors, including large-scale changes in chromatin state that may occur over cancer evolution.

Identifying host factors that influence infectious disease transmission is an important step toward developing interventions to reduce disease incidence. Recent advances in methods for reconstructing infectious disease transmission events using pathogen genomic and epidemiological data open the door for investigation of host factors that affect onward transmission. While most transmission reconstruction methods are designed to work with densely sampled outbreaks, these methods are making their way into surveillance studies, where the fraction of sampled cases with sequenced pathogens could be relatively low. Surveillance studies that use transmission event reconstruction then use the reconstructed events as response variables (i.e., infection source status of each sampled case) and use host characteristics as predictors (e.g., presence of HIV infection) in regression models. We use simulations to study estimation of the effect of a host factor on probability of being an infection source via this multi-step inferential procedure. Using TransPhylo—a widely-used method for Bayesian estimation of infectious disease transmission events—and logistic regression, we find that low sensitivity of identifying infection sources leads to dilution of the signal, biasing logistic regression coefficients toward zero. We show that increasing the proportion of sampled cases improves sensitivity and some, but not all properties of the logistic regression inference. Application of these approaches to real world data from a population-based TB study in Botswana fails to detect an association between HIV infection and probability of being a TB infection source. We conclude that application of a pipeline, where one first uses TransPhylo and sparsely sampled surveillance data to infer transmission events and then estimates effects of host characteristics on probabilities of these events, should be accompanied by a realistic simulation study to better understand biases stemming from imprecise transmission event inference. Author summary: Factors that affect infectious disease transmission are poorly understood, which impede efforts to prevent the spread of infectious diseases. Recently, software packages have been developed to infer transmission histories of infectious disease outbreaks using data from infectious disease genetics and epidemiology. These software packages have been used as part of methods to identify individual characteristics that affect infectious disease transmission. We used computer simulation to explore whether a statistical pipeline using the software package TransPhylo can successfully identify individual risk factors for being an infection source in a realistic public health setting where only a small proportion of pathogens are sequenced. We simulated tuberculosis (TB) outbreaks with different odds of being an infection source for TB transmission between people living with and without HIV. We found that the TransPhylo-based pipeline consistently underestimated the odds ratio for the association between HIV and being an infection source for TB transmission. We then applied this method to data from a TB study from Botswana and found no evidence of an association between HIV and being an infection source for TB transmission. Identification of transmission risk factors may be difficult in settings with low sampling proportion for genetic data.

Neural circuits exhibit complex activity patterns, both spontaneously and evoked by external stimuli. Information encoding and learning in neural circuits depend on how well time-varying stimuli can control spontaneous network activity. We show that in firing-rate networks in the balanced state, external control of recurrent dynamics, i.e., the suppression of internally-generated chaotic variability, strongly depends on correlations in the input. A distinctive feature of balanced networks is that, because common external input is dynamically canceled by recurrent feedback, it is far more difficult to suppress chaos with common input into each neuron than through independent input. To study this phenomenon, we develop a non-stationary dynamic mean-field theory for driven networks. The theory explains how the activity statistics and the largest Lyapunov exponent depend on the frequency and amplitude of the input, recurrent coupling strength, and network size, for both common and independent input. We further show that uncorrelated inputs facilitate learning in balanced networks. Author summary: Information in the brain is processed by a deeply-layered structure of local recurrent neural circuits. Recurrent neural networks often exhibit spontaneous irregular activity patterns that arise generically through the disordered interactions between neurons. Understanding under which conditions one circuit can control the activity patterns in another circuit and suppress spontaneous, chaotic fluctuations is crucial to unravel information flow and learning input-output tasks.

Identifying cell clusters is a critical step for single-cell transcriptomics study. Despite the numerous clustering tools developed recently, the rapid growth of scRNA-seq volumes prompts for a more (computationally) efficient clustering method. Here, we introduce Secuer, a Scalable and Efficient speCtral clUstERing algorithm for scRNA-seq data. By employing an anchor-based bipartite graph representation algorithm, Secuer enjoys reduced runtime and memory usage over one order of magnitude for datasets with more than 1 million cells. Meanwhile, Secuer also achieves better or comparable accuracy than competing methods in small and moderate benchmark datasets. Furthermore, we showcase that Secuer can also serve as a building block for a new consensus clustering method, Secuer-consensus, which again improves the runtime and scalability of state-of-the-art consensus clustering methods while also maintaining the accuracy. Overall, Secuer is a versatile, accurate, and scalable clustering framework suitable for small to ultra-large single-cell clustering tasks. Author summary: Recently, single-cell RNA sequencing (scRNA-seq) has enabled profiling of thousands to millions of cells, spurring the development of efficient clustering algorithms for large or ultra-large datasets. In this work, we developed an ultrafast clustering method, Secuer, for small to ultra-large scRNA-seq data. Using simulation and real datasets, we demonstrated that Secuer yields high accuracy, while saving runtime and memory usage by orders of magnitude, and that it can be efficiently scaled up to ultra-large datasets. Additionally, with Secuer as a subroutine, we proposed Secuer-consensus, a consensus clustering algorithm. Our results show that Secuer-consensus performs better in terms of clustering accuracy and runtime.

Identifying the interrelations among cancer driver genes and the patterns in which the driver genes get mutated is critical for understanding cancer. In this paper, we study cross-sectional data from cohorts of tumors to identify the cancer-type (or subtype) specific process in which the cancer driver genes accumulate critical mutations. We model this mutation accumulation process using a tree, where each node includes a driver gene or a set of driver genes. A mutation in each node enables its children to have a chance of mutating. This model simultaneously explains the mutual exclusivity patterns observed in mutations in specific cancer genes (by its nodes) and the temporal order of events (by its edges). We introduce a computationally efficient dynamic programming procedure for calculating the likelihood of our noisy datasets and use it to build our Markov Chain Monte Carlo (MCMC) inference algorithm, ToMExO. Together with a set of engineered MCMC moves, our fast likelihood calculations enable us to work with datasets with hundreds of genes and thousands of tumors, which cannot be dealt with using available cancer progression analysis methods. We demonstrate our method’s performance on several synthetic datasets covering various scenarios for cancer progression dynamics. Then, a comparison against two state-of-the-art methods on a moderate-size biological dataset shows the merits of our algorithm in identifying significant and valid patterns. Finally, we present our analyses of several large biological datasets, including colorectal cancer, glioblastoma, and pancreatic cancer. In all the analyses, we validate the results using a set of method-independent metrics testing the causality and significance of the relations identified by ToMExO or competing methods. Author summary: Cancer progression is an evolutionary process where somatic mutations in so-called driver genes provide the harboring cells with certain selective advantages. Identifying the interplay among the driver genes is critical for understanding how cancer evolves. In this paper, we introduce a method for analyzing cohorts of tumors. Our approach is based on a novel probabilistic model, which can identify the temporal order of mutations in driver genes, and how they may exhaust each other’s selective advantages. We introduce an efficient likelihood calculation procedure and build an MCMC algorithm for making inferences based on our model. Our computationally efficient inference algorithm enables us to work with hundreds of genes and thousands of tumors. Using a broad set of synthetic data experiments, we demonstrate the performance of our inference algorithm in various scenarios. We also present our analyses of several biological datasets. Our results agree with a set of well-known relations among the driver genes and suggest new interesting such relationships.

Stress-induced cell death, mainly apoptosis, and its subsequent tissue repair is interlinked although our knowledge of this connection is still very limited. An intriguing finding is apoptosis-induced proliferation (AiP), an evolutionary conserved mechanism employed by apoptotic cells to trigger compensatory proliferation of their neighboring cells. Studies using Drosophila as a model organism have revealed that apoptotic caspases and c-Jun N-terminal kinase (JNK) signaling play critical roles to activate AiP. For example, the initiator caspase Dronc, the caspase-9 ortholog in Drosophila, promotes activation of JNK leading to release of mitogenic signals and AiP. Recent studies further revealed that Dronc relocates to the cell cortex via Myo1D, an unconventional myosin, and stimulates production of reactive oxygen species (ROS) to trigger AiP. During this process, ROS can attract hemocytes, the Drosophila macrophages, which further amplify JNK signaling cell non-autonomously. However, the intrinsic components connecting Dronc, ROS and JNK within the stressed signal-producing cells remain elusive. Here, we identified LIM domain kinase 1 (LIMK1), a kinase promoting cellular F-actin polymerization, as a novel regulator of AiP. F-actin accumulates in a Dronc-dependent manner in response to apoptotic stress. Suppression of F-actin polymerization in stressed cells by knocking down LIMK1 or expressing Cofilin, an inhibitor of F-actin elongation, blocks ROS production and JNK activation, hence AiP. Furthermore, Dronc and LIMK1 genetically interact. Co-expression of Dronc and LIMK1 drives F-actin accumulation, ROS production and JNK activation. Interestingly, these synergistic effects between Dronc and LIMK1 depend on Myo1D. Therefore, F-actin remodeling plays an important role mediating caspase-driven ROS production and JNK activation in the process of AiP. Author summary: In multicellular organisms, damaged cells are frequently removed via apoptosis, the major form of programmed cell death. Intriguingly, these apoptotic cells can emit signals to induce proliferation of their neighboring cells for the maintenance of tissue homeostasis, a phenomenon termed apoptosis-induced proliferation (AiP). Caspases, a family of cysteine proteases well known to execute apoptosis, also play a critical role to trigger AiP via activation of JNK, a stress response kinase. In this study, we identified the actin cytoskeleton, a dynamic structural network of the cell, as a key mediator of JNK activation in AiP. During this process, actin filaments undergo increased polymerization which depends on LIM domain kinase 1 (LIMK1). Genetically, caspases and LIMK1 work together to promote actin polymerization, which in turn drives production of reactive oxygen species (ROS) and the subsequent activation of JNK. Therefore, our study discovered a role of actin remodeling in dying cells which mediates the non-apoptotic roles of caspases in AiP and tissue repair.

Cell membranes are flexible and often undergo large-scale morphological changes during processes like mitosis, protrusion and retraction, or vesicle fusion. Mathematical modeling of cell membranes depends on a representation of the free-form surface by discrete meshes. During morphological changes, these meshes must be adjusted under the minimization of the total free energy. Current methodology for meshing is limited in one of two ways: 1) Free energy-dependent methods have no restriction on the mesh geometry. The resulting irregular meshes cause artifacts in follow-up models of morphodynamics. 2) Geometry-dependent methods maintain mesh quality but violate the physics of free energy minimization. To fill this gap, we regulate mesh geometries via a free-energy-determined remeshing process: adding and removing mesh elements upon morphological changes based on barrier crossings in a double-barrier potential between neighboring vertices in the meshes. We test the method’s robustness by reproducing the morphodynamics of red blood cells and vesicle fusions; and we demonstrate the method’s adaptability by simulating the formation of filopodia, lamellipodia and invaginations. Finally, we use the method to study a mechanical decoupling effect of two connected membrane tethers that has been recently observed experimentally, but has not been mechanistically explained in the context of a complete membrane surface. We propose a biophysical model that strengthens the decoupling effect and broadens the original interpretation of the experiment. The method is developed in C/Matlab and distributed via https://github.com/DanuserLab/biophysicsModels. Author summary: Many cellular functions require morphological features such as tubulations, protrusions, and invaginations. These features emerge from membranes reshaped by curvature-inducing proteins, osmotic pressure, and other mechanical factors. To elucidate these morphodynamics mathematical models describing the relation between morphology, mechanics, and molecular compoisition are indispensable. The key to such models is discretizing the membrane. One popular discretization relies on meshing membranes into fully connected triangles. When necessary, the mesh configuration is updated via remeshing operations. These include adjusting the number of meshes, rearranging the mesh-to-mesh connectivity, and keeping the individual triangles nearly equilateral. Such flexibility and geometry is necessary accuracy wise. Current remeshing algorithms lack geometrical consistency or rely on user-defined rules that defy physical laws. Here, we propose a new algorithm that marries the needs for geometrical and physical accuracy in a double-barrier potential Vin. The two barriers confine the edges in the triangles to ensure their geometrical consistency. Large enough mechanical perturbations cause barrier crossings that trigger remeshing to restore the perturbed geometry. This physics-based algorithm is robust and more efficient than rule-based algorithms. We implement the algorithm to simulate distinct morphologies and to examine how lipid diffusion controls the mechanical coupling of distant locations on the membrane.

Activin and inhibin are both dimeric proteins sharing the same β subunits that belong to the TGF-β superfamily. They are well known for stimulating and inhibiting pituitary FSH secretion, respectively, in mammals. In addition, activin also acts as a mesoderm-inducing factor in frogs. However, their functions in development and reproduction of other species are poorly defined. In this study, we disrupted all three activin/inhibin β subunits (βAa, inhbaa; βAb, inhbab; and βB, inhbb) in zebrafish using CRISPR/Cas9. The loss of βAa/b but not βB led to a high mortality rate in the post-hatching stage. Surprisingly, the expression of fshb but not lhb in the pituitary increased in the female βA mutant together with aromatase (cyp19a1a) in the ovary. The single mutant of βAa/b showed normal folliculogenesis in young females; however, their double mutant (inhbaa-/-;inhbab-/-) showed delayed follicle activation, granulosa cell hypertrophy, stromal cell accumulation and tissue fibrosis. The ovary of inhbaa-/- deteriorated progressively after 180 dpf with reduced fecundity and the folliculogenesis ceased completely around 540 dpf. In addition, tumor- or cyst-like tissues started to appear in the inhbaa-/- ovary after about one year. In contrast to females, activin βAa/b mutant males showed normal spermatogenesis and fertility. As for activin βB subunit, the inhbb-/- mutant exhibited normal folliculogenesis, spermatogenesis and fertility in both sexes; however, the fecundity of mutant females decreased dramatically at 270 dpf with accumulation of early follicles. In summary, the activin-inhibin system plays an indispensable role in fish reproduction, in particular folliculogenesis and ovarian homeostasis. Author summary: Activin and inhibin are important growth factors that play essential roles in animal development and reproduction. Activin and inhibin share the same β subunits, but they are antagonistic in function. Using gene editing technology, we studied the functional roles and importance of all three β subunits (βAs: inhbaa and inhbab; βB: inhbb) in zebrafish development and reproduction. Our results showed that the loss of all three subunits resulted in early death in zebrafish larvae, indicating functional importance of these molecules in development. Further analysis demonstrated essential roles of the β subunits especially βΑs (inhbaa and inhbab) in controlling female follicle development in the ovary. The loss of βΑs especially inhbaa showed delayed follicle activation and ovarian fibrosis. Without inhbaa gene, the ovary deteriorated progressively after 6 months and the formation of eggs stopped completely after 18 months. In summary, the present study provided comprehensive evidence for the relative importance of each β subunit in zebrafish reproduction.

Pseudomonas aeruginosa, a major inhabitant of numerous environmental reservoirs, is a momentous opportunistic human pathogen associated with severe infections even death in the patients suffering from immune deficiencies or metabolic diseases. Type III secretion system (T3SS) employed by P. aeruginosa to inject effector proteins into host cells is one of the pivotal virulence factors pertaining to acute infections caused by this pathogen. Previous studies showed that P. aeruginosa T3SS is activated by various environmental cues such as calcium concentration and the host signal spermidine. However, how T3SS is regulated and expressed particularly under the ever-changing environmental conditions remains largely elusive. In this study, we reported that a tRNA modification enzyme PA3980, designated as MiaB, positively regulated T3SS gene expression in P. aeruginosa and was essential for the induced cytotoxicity of human lung epithelial cells. Further genetic assays revealed that MiaB promoted T3SS gene expression by repressing the LadS-Gac/Rsm signaling pathway and through the T3SS master regulator ExsA. Interestingly, ladS, gacA, rsmY and rsmZ in the LadS-Gac/Rsm signaling pathway seemed potential targets under the independent regulation of MiaB. Moreover, expression of MiaB was found to be induced by the cAMP-dependent global regulator Vfr as well as the spermidine transporter-dependent signaling pathway and thereafter functioned to mediate their regulation on the T3SS gene expression. Together, these results revealed a novel regulatory mechanism for MiaB, with which it integrates different environmental cues to modulate T3SS gene expression in this important bacterial pathogen. Author summary: T3SS in P. aeruginosa is a predominant virulence factor causing acute infections in many hosts including human beings. Expression of T3SS genes is usually controlled by an intrinsic ExsECDA partner-switching cascade together with numerous extrinsic regulators with most of them are related to or inducible by different environmental cues. Whether any intermediate players exist to connect environmental cues to the activation of P. aeruginosa T3SS is not known. Here we demonstrated that a tRNA modification enzyme MiaB was upregulated by Vfr and the spermidine transporter-dependent signaling pathway, two important T3SS regulators in response to various host-related environments, to mediate their regulation on T3SS gene expression through the LadS-Gac/Rsm signaling pathway and the T3SS master regulator ExsA. Findings in this study not only advanced our understanding on the complex T3SS gene regulation in response to different environmental cues but also provided important targets for future anti-infective exploitations.

When judging the average value of sample stimuli (e.g., numbers) people tend to either over- or underweight extreme sample values, depending on task context. In a context of overweighting, recent work has shown that extreme sample values were overly represented also in neural signals, in terms of an anti-compressed geometry of number samples in multivariate electroencephalography (EEG) patterns. Here, we asked whether neural representational geometries may also reflect a relative underweighting of extreme values (i.e., compression) which has been observed behaviorally in a great variety of tasks. We used a simple experimental manipulation (instructions to average a single-stream or to compare dual-streams of samples) to induce compression or anti-compression in behavior when participants judged rapid number sequences. Model-based representational similarity analysis (RSA) replicated the previous finding of neural anti-compression in the dual-stream task, but failed to provide evidence for neural compression in the single-stream task, despite the evidence for compression in behavior. Instead, the results indicated enhanced neural processing of extreme values in either task, regardless of whether extremes were over- or underweighted in subsequent behavioral choice. We further observed more general differences in the neural representation of the sample information between the two tasks. Together, our results indicate a mismatch between sample-level EEG geometries and behavior, which raises new questions about the origin of common psychometric distortions, such as diminishing sensitivity for larger values.