Distinct Classes of Complex Structural Variation Uncovered across Thousands of Cancer Genome Graphs.

Cancer genomes often harbor hundreds of somatic DNA rearrangement junctions, many of which cannot be easily classified into simple (e.g., deletion) or complex (e.g., chromothripsis) structural variant classes. Applying a novel genome graph computational paradigm to analyze the topology of junction copy number (JCN) across 2,778 tumor whole-genome sequences, we uncovered three novel complex rearrangement phenomena: pyrgo, rigma, and tyfonas. Pyrgo are "towers" of low-JCN duplications associated with early-replicating regions, superenhancers, and breast or ovarian cancers. Rigma comprise "chasms" of low-JCN deletions enriched in late-replicating fragile sites and gastrointestinal carcinomas. Tyfonas are "typhoons" of high-JCN junctions and fold-back inversions associated with expressed protein-coding fusions, breakend hypermutation, and acral, but not cutaneous, melanomas. Clustering of tumors according to genome graph-derived features identified subgroups associated with DNA repair defects and poor prognosis.

PHENSIM: Phenotype Simulator.

Despite the unprecedented growth in our understanding of cell biology, it still remains challenging to connect it to experimental data obtained with cells and tissues' physiopathological status under precise circumstances. This knowledge gap often results in difficulties in designing validation experiments, which are usually labor-intensive, expensive to perform, and hard to interpret. Here we propose PHENSIM, a computational tool using a systems biology approach to simulate how cell phenotypes are affected by the activation/inhibition of one or multiple biomolecules, and it does so by exploiting signaling pathways. Our tool's applications include predicting the outcome of drug administration, knockdown experiments, gene transduction, and exposure to exosomal cargo. Importantly, PHENSIM enables the user to make inferences on well-defined cell lines and includes pathway maps from three different model organisms. To assess our approach's reliability, we built a benchmark from transcriptomics data gathered from NCBI GEO and performed four case studies on known biological experiments. Our results show high prediction accuracy, thus highlighting the capabilities of this methodology. PHENSIM standalone Java application is available at https://github.com/alaimos/phensim, along with all data and source codes for benchmarking. A web-based user interface is accessible at https://phensim.tech/.

Rates and mechanisms of bacterial mutagenesis from maximum-depth sequencing.

In 1943, Luria and Delbrück used a phage-resistance assay to establish spontaneous mutation as a driving force of microbial diversity. Mutation rates are still studied using such assays, but these can only be used to examine the small minority of mutations conferring survival in a particular condition. Newer approaches, such as long-term evolution followed by whole-genome sequencing, may be skewed by mutational 'hot' or 'cold' spots. Both approaches are affected by numerous caveats. Here we devise a method, maximum-depth sequencing (MDS), to detect extremely rare variants in a population of cells through error-corrected, high-throughput sequencing. We directly measure locus-specific mutation rates in Escherichia coli and show that they vary across the genome by at least an order of magnitude. Our data suggest that certain types of nucleotide misincorporation occur 10(4)-fold more frequently than the basal rate of mutations, but are repaired in vivo. Our data also suggest specific mechanisms of antibiotic-induced mutagenesis, including downregulation of mismatch repair via oxidative stress, transcription­replication conflicts, and, in the case of fluoroquinolones, direct damage to DNA.

Algorithmic methods to infer the evolutionary trajectories in cancer progression.

The genomic evolution inherent to cancer relates directly to a renewed focus on the voluminous next-generation sequencing data and machine learning for the inference of explanatory models of how the (epi)genomic events are choreographed in cancer initiation and development. However, despite the increasing availability of multiple additional -omics data, this quest has been frustrated by various theoretical and technical hurdles, mostly stemming from the dramatic heterogeneity of the disease. In this paper, we build on our recent work on the "selective advantage" relation among driver mutations in cancer progression and investigate its applicability to the modeling problem at the population level. Here, we introduce PiCnIc (Pipeline for Cancer Inference), a versatile, modular, and customizable pipeline to extract ensemble-level progression models from cross-sectional sequenced cancer genomes. The pipeline has many translational implications because it combines state-of-the-art techniques for sample stratification, driver selection, identification of fitness-equivalent exclusive alterations, and progression model inference. We demonstrate PiCnIc's ability to reproduce much of the current knowledge on colorectal cancer progression as well as to suggest novel experimentally verifiable hypotheses.

TRONCO: an R package for the inference of cancer progression models from heterogeneous genomic data.

MOTIVATION: We introduce TRanslational ONCOlogy (TRONCO), an open-source R package that implements the state-of-the-art algorithms for the inference of cancer progression models from (epi)genomic mutational profiles. TRONCO can be used to extract population-level models describing the trends of accumulation of alterations in a cohort of cross-sectional samples, e.g. retrieved from publicly available databases, and individual-level models that reveal the clonal evolutionary history in single cancer patients, when multiple samples, e.g. multiple biopsies or single-cell sequencing data, are available. The resulting models can provide key hints for uncovering the evolutionary trajectories of cancer, especially for precision medicine or personalized therapy. AVAILABILITY AND IMPLEMENTATION: TRONCO is released under the GPL license, is hosted at http://bimib.disco.unimib.it/ (Software section) and archived also at bioconductor.org. CONTACT: tronco@disco.unimib.it SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

High-Speed Atomic Force Microscopy Revealing Contamination in DNA Purification Systems.

Motivated by reports of low-level DNA contamination in popular commercial DNA purification kits, we employed a novel high-speed atomic force microscopy (HS-AFM) method to detect and characterize particulate and polymeric contaminants in four such systems: Qiagen MinElute PCR Purification, Zymo Research DNA Clean and Concentrator-5, Invitrogen ChargeSwitch-Pro PCR Purification, and Beckman Coulter AMPure XP. HS-AFM avoids amplification artifacts present in PCR or in the sequencing of amplified products, and it requires no chemical labels and easily achieves near-single-molecule sensitivity. Using this technique, we found trace levels of filamentous contamination, similar in appearance to dsDNA, in eluates from the Zymo, Qiagen, and ChargeSwitch kits. Conversely, we detected no contaminants in magnetic bead-based AMPure XP solutions. Eluates from the Zymo kits also tested positive for DNA in fluorescent intercalator dye and whole genome amplification (WGA) assays. Qiagen kits tested positive in the fluorescence assay but negative in the WGA assay. Both ChargeSwitch and AMPure XP tested negative in the fluorescence assay while the WGA results for these two kits were ambiguous. Taken together, our findings suggest AMPure XP would be the best choice for analyses requiring very high analytical stringency. While HS-AFM alone does not provide chemical specificity, it is a potentially valuable tool for characterizing and quantifying trace contaminants in molecular biology reagents and instruments in cases where conventional techniques fail.

CAPRI: efficient inference of cancer progression models from cross-sectional data.

UNLABELLED: We devise a novel inference algorithm to effectively solve the cancer progression model reconstruction problem. Our empirical analysis of the accuracy and convergence rate of our algorithm, CAncer PRogression Inference (CAPRI), shows that it outperforms the state-of-the-art algorithms addressing similar problems. MOTIVATION: Several cancer-related genomic data have become available (e.g. The Cancer Genome Atlas, TCGA) typically involving hundreds of patients. At present, most of these data are aggregated in a cross-sectional fashion providing all measurements at the time of diagnosis. Our goal is to infer cancer 'progression' models from such data. These models are represented as directed acyclic graphs (DAGs) of collections of 'selectivity' relations, where a mutation in a gene A 'selects' for a later mutation in a gene B. Gaining insight into the structure of such progressions has the potential to improve both the stratification of patients and personalized therapy choices. RESULTS: The CAPRI algorithm relies on a scoring method based on a probabilistic theory developed by Suppes, coupled with bootstrap and maximum likelihood inference. The resulting algorithm is efficient, achieves high accuracy and has good complexity, also, in terms of convergence properties. CAPRI performs especially well in the presence of noise in the data, and with limited sample sizes. Moreover CAPRI, in contrast to other approaches, robustly reconstructs different types of confluent trajectories despite irregularities in the data. We also report on an ongoing investigation using CAPRI to study atypical Chronic Myeloid Leukemia, in which we uncovered non trivial selectivity relations and exclusivity patterns among key genomic events. AVAILABILITY AND IMPLEMENTATION: CAPRI is part of the TRanslational ONCOlogy R package and is freely available on the web at: http://bimib.disco.unimib.it/index.php/Tronco CONTACT: daniele.ramazzotti@disco.unimib.it SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Atomic force microscopic detection enabling multiplexed low-cycle-number quantitative polymerase chain reaction for biomarker assays.

Quantitative polymerase chain reaction is the current "golden standard" for quantification of nucleic acids; however, its utility is constrained by an inability to easily and reliably detect multiple targets in a single reaction. We have successfully overcome this problem with a novel combination of two widely used approaches: target-specific multiplex amplification with 15 cycles of polymerase chain reaction (PCR), followed by single-molecule detection of amplicons with atomic force microscopy (AFM). In test experiments comparing the relative expression of ten transcripts in two different human total RNA samples, we find good agreement between our single reaction, multiplexed PCR/AFM data, and data from 20 individual singleplex quantitative PCR reactions. This technique can be applied to virtually any analytical problem requiring sensitive measurement concentrations of multiple nucleic acid targets.

How Mitochondrial Signaling Games May Shape and Stabilize the Nuclear-Mitochondrial Symbiosis.

The eukaryotic lineage has enjoyed a long-term "stable" mutualism between nucleus and mitochondrion, since mitochondrial endosymbiosis began about 2 billion years ago. This mostly cooperative interaction has provided the basis for eukaryotic expansion and diversification, which has profoundly altered the forms of life on Earth. While we ignore the exact biochemical details of how the alpha-proteobacterial ancestor of mitochondria entered into endosymbiosis with a proto-eukaryote, in more general terms, we present a signaling games perspective of how the cooperative relationship became established, and has been maintained. While games are used to understand organismal evolution, information-asymmetric games at the molecular level promise novel insights into endosymbiosis. Using a previously devised biomolecular signaling games approach, we model a sender-receiver information asymmetric game, in which the informed mitochondrial sender signals and the uninformed nuclear receiver may take actions (involving for example apoptosis, senescence, regeneration and autophagy/mitophagy). The simulation shows that cellularization is a stabilizing mechanism for Pareto efficient sender/receiver strategic interaction. In stark contrast, the extracellular environment struggles to maintain efficient outcomes, as senders are indifferent to the effects of their signals upon the receiver. Our hypothesis has translational implications, such as in cellular therapy, as mitochondrial medicine matures. It also inspires speculative conjectures about how an analogous human-AI endosymbiosis may be engineered.

C1QA is an invariant biomarker for tissue macrophages.

Macrophages play a pivotal role in immune responses, particularly in the context of combating microbial threats within tissues. The identification of reliable biomarkers associated with macrophage function is essential for understanding their diverse roles in host defense. This study investigates the potential of C1QA as an invariant biomarker for tissue macrophages, focusing on its correlation with the anti-microbial pathway. C1QA, a component of the complement system, has been previously implicated in various immune functions. Our research delves into the specific association of C1QA with tissue-resident macrophages and its implications in the context of anti-microbial responses. Through comprehensive systems biology and Boolean analysis of gene expression, we aim to establish C1QA as a consistent and reliable marker for identifying tissue macrophages. Furthermore, we explore the functional significance of C1QA in the anti-microbial pathway. This research seeks to provide valuable insights into the molecular mechanisms underlying the anti-microbial functions of tissue macrophages, with C1QA emerging as a potential key player in this intricate regulatory network. Understanding the relationship between C1QA, tissue macrophages, and the anti-microbial pathway could pave the way for the development of targeted therapeutic strategies aimed at enhancing the host's ability to combat infections. Ultimately, our findings contribute to the expanding knowledge of macrophage biology and may have implications for the diagnosis and treatment of infectious diseases.

Reverse engineering dynamic temporal models of biological processes and their relationships.

Biological processes such as circadian rhythms, cell division, metabolism, and development occur as ordered sequences of events. The synchronization of these coordinated events is essential for proper cell function, and hence the determination of critical time points in biological processes is an important component of all biological investigations. In particular, such critical time points establish logical ordering constraints on subprocesses, impose prerequisites on temporal regulation and spatial compartmentalization, and situate dynamic reorganization of functional elements in preparation for subsequent stages. Thus, building temporal phenomenological representations of biological processes from genome-wide datasets is relevant in formulating biological hypotheses on: how processes are mechanistically regulated; how the regulations vary on an evolutionary scale, and how their inadvertent disregulation leads to a diseased state or fatality. This paper presents a general framework (GOALIE) to reconstruct temporal models of cellular processes from time-course gene expression data. We mathematically formulate the problem as one of optimally segmenting datasets into a succession of "informative" windows such that time points within a window expose concerted clusters of gene action whereas time points straddling window boundaries constitute points of significant restructuring. We illustrate here how GOALIE successfully brings out the interplay between multiple yeast processes, inferred from combined experimental datasets for the cell cycle and the metabolic cycle.

Do Place Cells Dream of Deceptive Moves in a Signaling Game?

We consider the possibility of applying game theory to analysis and modeling of neurobiological systems. Specifically, the basic properties and features of information asymmetric signaling games are considered and discussed as having potential to explain diverse neurobiological phenomena; we focus on neuronal action potential discharge that can represent cognitive variables in memory and purposeful behavior. We begin by arguing that there is a pressing need for conceptual frameworks that can permit analysis and integration of information and explanations across many scales of biological function including gene regulation, molecular and biochemical signaling, cellular and metabolic function, neuronal population, and systems level organization to generate plausible hypotheses across these scales. Developing such integrative frameworks is crucial if we are to understand cognitive functions like learning, memory, and perception. The present work focuses on systems neuroscience organized around the connected brain regions of the entorhinal cortex and hippocampus. These areas are intensely studied in rodent subjects as model neuronal systems that undergo activity-dependent synaptic plasticity to form neuronal circuits and represent memories and spatial knowledge used for purposeful navigation. Examples of cognition-related spatial information in the observed neuronal discharge of hippocampal place cell populations and medial entorhinal head-direction cell populations are used to illustrate possible challenges to information maximization concepts. It may be natural to explain these observations using the ideas and features of information asymmetric signaling games.

Application of the PHENotype SIMulator for rapid identification of potential candidates in effective COVID-19 drug repurposing.

The current, rapidly diversifying pandemic has accelerated the need for efficient and effective identification of potential drug candidates for COVID-19. Knowledge on host-immune response to SARS-CoV-2 infection, however, remains limited with few drugs approved to date. Viable strategies and tools are rapidly arising to address this, especially with repurposing of existing drugs offering significant promise. Here we introduce a systems biology tool, the PHENotype SIMulator, which -by leveraging available transcriptomic and proteomic databases-allows modeling of SARS-CoV-2 infection in host cells in silico to i) determine with high sensitivity and specificity (both>96%) the viral effects on cellular host-immune response, resulting in specific cellular SARS-CoV-2 signatures and ii) utilize these cell-specific signatures to identify promising repurposable therapeutics. Powered by this tool, coupled with domain expertise, we identify several potential COVID-19 drugs including methylprednisolone and metformin, and further discern key cellular SARS-CoV-2-affected pathways as potential druggable targets in COVID-19 pathogenesis.

Scoring-and-unfolding trimmed tree assembler: concepts, constructs and comparisons.

MOTIVATION: Mired by its connection to a well-known -complete combinatorial optimization problem-namely, the Shortest Common Superstring Problem (SCSP)-historically, the whole-genome sequence assembly (WGSA) problem has been assumed to be amenable only to greedy and heuristic methods. By placing efficiency as their first priority, these methods opted to rely only on local searches, and are thus inherently approximate, ambiguous or error prone, especially, for genomes with complex structures. Furthermore, since choice of the best heuristics depended critically on the properties of (e.g. errors in) the input data and the available long range information, these approaches hindered designing an error free WGSA pipeline. RESULTS: We dispense with the idea of limiting the solutions to just the approximated ones, and instead favor an approach that could potentially lead to an exhaustive (exponential-time) search of all possible layouts. Its computational complexity thus must be tamed through a constrained search (Branch-and-Bound) and quick identification and pruning of implausible overlays. For his purpose, such a method necessarily relies on a set of score functions (oracles) that can combine different structural properties (e.g. transitivity, coverage, physical maps, etc.). We give a detailed description of this novel assembly framework, referred to as Scoring-and-Unfolding Trimmed Tree Assembler (SUTTA), and present experimental results on several bacterial genomes using next-generation sequencing technology data. We also report experimental evidence that the assembly quality strongly depends on the choice of the minimum overlap parameter k. AVAILABILITY AND IMPLEMENTATION: SUTTA's binaries are freely available to non-profit institutions for research and educational purposes at http://www.bioinformatics.nyu.edu.

TotalReCaller: improved accuracy and performance via integrated alignment and base-calling.

MOTIVATION: Currently, re-sequencing approaches use multiple modules serially to interpret raw sequencing data from next-generation sequencing platforms, while remaining oblivious to the genomic information until the final alignment step. Such approaches fail to exploit the full information from both raw sequencing data and the reference genome that can yield better quality sequence reads, SNP-calls, variant detection, as well as an alignment at the best possible location in the reference genome. Thus, there is a need for novel reference-guided bioinformatics algorithms for interpreting analog signals representing sequences of the bases ({A, C, G, T}), while simultaneously aligning possible sequence reads to a source reference genome whenever available. RESULTS: Here, we propose a new base-calling algorithm, TotalReCaller, to achieve improved performance. A linear error model for the raw intensity data and Burrows-Wheeler transform (BWT) based alignment are combined utilizing a Bayesian score function, which is then globally optimized over all possible genomic locations using an efficient branch-and-bound approach. The algorithm has been implemented in soft- and hardware [field-programmable gate array (FPGA)] to achieve real-time performance. Empirical results on real high-throughput Illumina data were used to evaluate TotalReCaller's performance relative to its peers-Bustard, BayesCall, Ibis and Rolexa-based on several criteria, particularly those important in clinical and scientific applications. Namely, it was evaluated for (i) its base-calling speed and throughput, (ii) its read accuracy and (iii) its specificity and sensitivity in variant calling. AVAILABILITY: A software implementation of TotalReCaller as well as additional information, is available at: http://bioinformatics.nyu.edu/wordpress/projects/totalrecaller/ CONTACT: fabian.menges@nyu.edu.

How signalling games explain mimicry at many levels: from viral epidemiology to human sociology.

Mimicry is exhibited in multiple scales, ranging from molecular, to organismal, and then to human society. 'Batesian'-type mimicry entails a conflict of interest between sender and receiver, reflected in a deceptive mimic signal. 'Müllerian'-type mimicry occurs when there is perfect common interest between sender and receiver in a particular type of encounter, manifested by an honest co-mimic signal. Using a signalling games approach, simulations show that invasion by Batesian mimics will make Müllerian mimicry unstable, in a coevolutionary chase. We use these results to better understand the deceptive strategies of SARS-CoV-2 and their key role in the COVID-19 pandemic. At the biomolecular level, we explain how cellularization promotes Müllerian molecular mimicry, and discourages Batesian molecular mimicry. A wide range of processes analogous to cellularization are presented; these might represent a manner of reducing oscillatory instabilities. Lastly, we identify examples of mimicry in human society that might be addressed using a signalling game approach.

Rapid Identification of Druggable Targets and the Power of the PHENotype SIMulator for Effective Drug Repurposing in COVID-19.

The current, rapidly diversifying pandemic has accelerated the need for efficient and effective identification of potential drug candidates for COVID-19. Knowledge on host-immune response to SARS-CoV-2 infection, however, remains limited with very few drugs approved to date. Viable strategies and tools are rapidly arising to address this, especially with repurposing of existing drugs offering significant promise. Here we introduce a systems biology tool, the PHENotype SIMulator, which - by leveraging available transcriptomic and proteomic databases - allows modeling of SARS-CoV-2 infection in host cells in silico to i) determine with high sensitivity and specificity (both > 96%) the viral effects on cellular host-immune response, resulting in a specific cellular SARS-CoV-2 signature and ii) utilize this specific signature to narrow down promising repurposable therapeutic strategies. Powered by this tool, coupled with domain expertise, we have identified several potential COVID-19 drugs including methylprednisolone and metformin, and further discern key cellular SARS-CoV-2-affected pathways as potential new druggable targets in COVID-19 pathogenesis.

How Signaling Games Explain Mimicry at Many Levels: From Viral Epidemiology to Human Sociology.

Mimicry is exhibited in multiple scales, ranging from molecular, to organismal, and then to human society. 'Batesian' type mimicry entails a conflict of interest between sender and receiver, reflected in a deceptive mimic signal. 'Mullerian' type mimicry occurs when there is perfect common interest between sender and receiver, manifested by an honest co-mimic signal. Using a signaling games approach, simulations show that invasion by Batesian mimics will make Mullerian mimicry unstable, in a coevolutionary chase. We use these results to better understand the deceptive strategies of SARS-CoV-2 and their key role in the COVID-19 pandemic. At the biomolecular level, we explain how cellularization promotes Mullerian molecular mimicry, and discourages Batesian molecular mimicry. A wide range of processes analogous to cellularization are presented; these might represent a manner of reducing oscillatory instabilities. Lastly, we identify examples of mimicry in human society, that might be addressed using a signaling game approach.

ANERGY TO SYNERGY-THE ENERGY FUELING THE RXCOVEA FRAMEWORK.

We write to introduce our novel group formed to confront some of the issues raised by the COVID-19 pandemic. Information about the group, which we named "cure COVid for Ever and for All" (RxCOVEA), its dynamic membership (changing regularly), and some of its activities-described in more technical detail for expert perusal and commentary-are available upon request.

Simultaneously segmenting multiple gene expression time courses by analyzing cluster dynamics.

We present a new approach to segmenting multiple time series by analyzing the dynamics of cluster formation and rearrangement around putative segment boundaries. This approach finds application in distilling large numbers of gene expression profiles into temporal relationships underlying biological processes. By directly minimizing information-theoretic measures of segmentation quality derived from Kullback-Leibler (KL) divergences, our formulation reveals clusters of genes along with a segmentation such that clusters show concerted behavior within segments but exhibit significant regrouping across segmentation boundaries. The results of the segmentation algorithm can be summarized as Gantt charts revealing temporal dependencies in the ordering of key biological processes. Applications to the yeast metabolic cycle and the yeast cell cycle are described.