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.

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.

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/.

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 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.

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.

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.

On the Origin of Biomolecular Networks.

Biomolecular networks have already found great utility in characterizing complex biological systems arising from pairwise interactions amongst biomolecules. Here, we explore the important and hitherto neglected role of information asymmetry in the genesis and evolution of such pairwise biomolecular interactions. Information asymmetry between sender and receiver genes is identified as a key feature distinguishing early biochemical reactions from abiotic chemistry, and a driver of network topology as biomolecular systems become more complex. In this context, we review how graph theoretical approaches can be applied not only for a better understanding of various proximate (mechanistic) relations, but also, ultimate (evolutionary) structures encoded in such networks from among all types of variations they induce. Among many possible variations, we emphasize particularly the essential role of gene duplication in terms of signaling game theory, whereby sender and receiver gene players accrue benefit from gene duplication, leading to a preferential attachment mode of network growth. The study of the resulting dynamics suggests many mathematical/computational problems, the majority of which are intractable yet yield to efficient approximation algorithms, when studied through an algebraic graph theoretic lens. We relegate for future work the role of other possible generalizations, additionally involving horizontal gene transfer, sexual recombination, endo-symbiosis, etc., which enrich the underlying graph theory even further.

Origin of biomolecular games: deception and molecular evolution.

Biological macromolecules encode information: some of it to endow the molecule with structural flexibility, some of it to enable molecular actions as a catalyst or a substrate, but a residual part can be used to communicate with other macromolecules. Thus, macromolecules do not need to possess information only to survive in an environment, but also to strategically interact with others by sending signals to a receiving macromolecule that can properly interpret the signal and act suitably. These sender-receiver signalling games are sustained by the information asymmetry that exists among the macromolecules. In both biochemistry and molecular evolution, the important role of information asymmetry remains largely unaddressed. Here, we provide a new unifying perspective on the impact of information symmetry between macromolecules on molecular evolutionary processes, while focusing on molecular deception. Biomolecular games arise from the ability of biological macromolecules to exert precise recognition, and their role as units of selection, meaning that they are subject to competition and cooperation with other macromolecules. Thus, signalling game theory can be used to better understand fundamental features of living systems such as molecular recognition, molecular mimicry, selfish elements and 'junk' DNA. We show how deceptive behaviour at the molecular level indicates a conflict of interest, and so provides evidence of genetic conflict. This model proposes that molecular deception is diagnostic of selfish behaviour, helping to explain the evasive behaviour of transposable elements in 'junk' DNA, for example. Additionally, in this broad review, a range of major evolutionary transitions are shown to be associated with the establishment of signalling conventions, many of which are susceptible to molecular deception. These perspectives allow us to assign rudimentary behaviour to macromolecules, and show how participation in signalling games differentiates biochemistry from abiotic chemistry.

DNA nanomapping using CRISPR-Cas9 as a programmable nanoparticle.

Progress in whole-genome sequencing using short-read (e.g., <150 bp), next-generation sequencing technologies has reinvigorated interest in high-resolution physical mapping to fill technical gaps that are not well addressed by sequencing. Here, we report two technical advances in DNA nanotechnology and single-molecule genomics: (1) we describe a labeling technique (CRISPR-Cas9 nanoparticles) for high-speed AFM-based physical mapping of DNA and (2) the first successful demonstration of using DVD optics to image DNA molecules with high-speed AFM. As a proof of principle, we used this new "nanomapping" method to detect and map precisely BCL2-IGH translocations present in lymph node biopsies of follicular lymphoma patents. This HS-AFM "nanomapping" technique can be complementary to both sequencing and other physical mapping approaches.

Simulating Heterogeneous Tumor Cell Populations.

Certain tumor phenomena, like metabolic heterogeneity and local stable regions of chronic hypoxia, signify a tumor's resistance to therapy. Although recent research has shed light on the intracellular mechanisms of cancer metabolic reprogramming, little is known about how tumors become metabolically heterogeneous or chronically hypoxic, namely the initial conditions and spatiotemporal dynamics that drive these cell population conditions. To study these aspects, we developed a minimal, spatially-resolved simulation framework for modeling tissue-scale mixed populations of cells based on diffusible particles the cells consume and release, the concentrations of which determine their behavior in arbitrarily complex ways, and on stochastic reproduction. We simulate cell populations that self-sort to facilitate metabolic symbiosis, that grow according to tumor-stroma signaling patterns, and that give rise to stable local regions of chronic hypoxia near blood vessels. We raise two novel questions in the context of these results: (1) How will two metabolically symbiotic cell subpopulations self-sort in the presence of glucose, oxygen, and lactate gradients? We observe a robust pattern of alternating striations. (2) What is the proper time scale to observe stable local regions of chronic hypoxia? We observe the stability is a function of the balance of three factors related to O2-diffusion rate, local vessel release rate, and viable and hypoxic tumor cell consumption rate. We anticipate our simulation framework will help researchers design better experiments and generate novel hypotheses to better understand dynamic, emergent whole-tumor behavior.

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.

Cancer megafunds with in silico and in vitro validation: accelerating cancer drug discovery via financial engineering without financial crisis.

Advances in financial engineering are radically reshaping the biomedical marketplace. For instance, new methods of pooling diversified drug development programs by placing them in a special purpose vehicle (SPV) have been proposed to create a securitized cancer megafund allowing for debt and equity participation. In this study, we perform theoretical and numerical simulations that highlight the role of empirical validation of the projects comprising a cancer megafund. We quantify the degree to which the deliberately designed structure of derivatives and investments is key to its liquidity. Research megafunds with comprehensive in silico and laboratory validation protocols and ability to issue both debt, and equity as well as hybrid financial products may enable conservative investors including pension funds and sovereign government funds to profit from unique securitization opportunities. Thus, while hedging investor's longevity risk, such well-validated megafunds will contribute to health, well being and longevity of the global population.

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.

Histological Image Processing Features Induce a Quantitative Characterization of Chronic Tumor Hypoxia.

Hypoxia in tumors signifies resistance to therapy. Despite a wealth of tumor histology data, including anti-pimonidazole staining, no current methods use these data to induce a quantitative characterization of chronic tumor hypoxia in time and space. We use image-processing algorithms to develop a set of candidate image features that can formulate just such a quantitative description of xenographed colorectal chronic tumor hypoxia. Two features in particular give low-variance measures of chronic hypoxia near a vessel: intensity sampling that extends radially away from approximated blood vessel centroids, and multithresholding to segment tumor tissue into normal, hypoxic, and necrotic regions. From these features we derive a spatiotemporal logical expression whose truth value depends on its predicate clauses that are grounded in this histological evidence. As an alternative to the spatiotemporal logical formulation, we also propose a way to formulate a linear regression function that uses all of the image features to learn what chronic hypoxia looks like, and then gives a quantitative similarity score once it is trained on a set of histology images.