Mega-scale experimental analysis of protein folding stability in biology and design.

Advances in DNA sequencing and machine learning are providing insights into protein sequences and structures on an enormous scale1. However, the energetics driving folding are invisible in these structures and remain largely unknown2. The hidden thermodynamics of folding can drive disease3,4, shape protein evolution5-7 and guide protein engineering8-10, and new approaches are needed to reveal these thermodynamics for every sequence and structure. Here we present cDNA display proteolysis, a method for measuring thermodynamic folding stability for up to 900,000 protein domains in a one-week experiment. From 1.8 million measurements in total, we curated a set of around 776,000 high-quality folding stabilities covering all single amino acid variants and selected double mutants of 331 natural and 148 de novo designed protein domains 40-72 amino acids in length. Using this extensive dataset, we quantified (1) environmental factors influencing amino acid fitness, (2) thermodynamic couplings (including unexpected interactions) between protein sites, and (3) the global divergence between evolutionary amino acid usage and protein folding stability. We also examined how our approach could identify stability determinants in designed proteins and evaluate design methods. The cDNA display proteolysis method is fast, accurate and uniquely scalable, and promises to reveal the quantitative rules for how amino acid sequences encode folding stability.

Transfer learning enables predictions in network biology.

Mapping gene networks requires large amounts of transcriptomic data to learn the connections between genes, which impedes discoveries in settings with limited data, including rare diseases and diseases affecting clinically inaccessible tissues. Recently, transfer learning has revolutionized fields such as natural language understanding1,2 and computer vision3 by leveraging deep learning models pretrained on large-scale general datasets that can then be fine-tuned towards a vast array of downstream tasks with limited task-specific data. Here, we developed a context-aware, attention-based deep learning model, Geneformer, pretrained on a large-scale corpus of about 30 million single-cell transcriptomes to enable context-specific predictions in settings with limited data in network biology. During pretraining, Geneformer gained a fundamental understanding of network dynamics, encoding network hierarchy in the attention weights of the model in a completely self-supervised manner. Fine-tuning towards a diverse panel of downstream tasks relevant to chromatin and network dynamics using limited task-specific data demonstrated that Geneformer consistently boosted predictive accuracy. Applied to disease modelling with limited patient data, Geneformer identified candidate therapeutic targets for cardiomyopathy. Overall, Geneformer represents a pretrained deep learning model from which fine-tuning towards a broad range of downstream applications can be pursued to accelerate discovery of key network regulators and candidate therapeutic targets.

Human studies of mitochondrial biology demonstrate an overall lack of binary sex differences: A multivariate meta-analysis.

Mitochondria are maternally inherited organelles that play critical tissue-specific roles, including hormone synthesis and energy production, that influence human development, health, and aging. However, whether mitochondria from women and men exhibit consistent biological differences remains unclear, representing a major gap in knowledge. This meta-analysis systematically examined four domains and six subdomains of mitochondrial biology (total 39 measures), including mitochondrial content, respiratory capacity, reactive oxygen species (ROS) production, morphometry, and mitochondrial DNA copy number. Standardized effect sizes (Hedge's g) of sex differences were computed for each measure using data in 2258 participants (51.5% women) from 50 studies. Only two measures demonstrated aggregate binary sex differences: higher mitochondrial content in women's WAT and isolated leukocyte subpopulations (g = 0.20, χ2 p = .01), and higher ROS production in men's skeletal muscle (g = 0.49, χ2 p < .0001). Sex differences showed weak to no correlation with age or BMI. Studies with small sample sizes tended to overestimate effect sizes (r = -.17, p < .001), and sex differences varied by tissue examined. Our findings point to a wide variability of findings in the literature concerning possible binary sex differences in mitochondrial biology. Studies specifically designed to capture sex- and gender-related differences in mitochondrial biology are needed, including detailed considerations of physical activity and sex hormones.

Historicity at the heart of biology.

Most mathematical modeling in biology relies either implicitly or explicitly on the epistemology of physics. The underlying conception is that the historicity of biological objects would not matter to understand a situation here and now, or, at least, historicity would not impact the method of modeling. We analyze that it is not the case with concrete examples. Historicity forces a conceptual reconfiguration where equations no longer play a central role. We argue that all observations depend on objects defined by their historical origin instead of their relations as in physics. Therefore, we propose that biological variations and historicity come first, and regularities are constraints with limited validity in biology. Their proper theoretical and empirical use requires specific rationales.

Systematic analysis of noise reduction properties of coupled and isolated feed-forward loops.

Cells can maintain their homeostasis in a noisy environment since their signaling pathways can filter out noise somehow. Several network motifs have been proposed for biological noise filtering and, among these, feed-forward loops have received special attention. Specific feed-forward loops show noise reducing capabilities, but we notice that this feature comes together with a reduced signal transducing performance. In posttranslational signaling pathways feed-forward loops do not function in isolation, rather they are coupled with other motifs to serve a more complex function. Feed-forward loops are often coupled to other feed-forward loops, which could affect their noise-reducing capabilities. Here we systematically study all feed-forward loop motifs and all their pairwise coupled systems with activation-inactivation kinetics to identify which networks are capable of good noise reduction, while keeping their signal transducing performance. Our analysis shows that coupled feed-forward loops can provide better noise reduction and, at the same time, can increase the signal transduction of the system. The coupling of two coherent 1 or one coherent 1 and one incoherent 4 feed-forward loops can give the best performance in both of these measures.

Acute Coronary Syndromes (ACS)-Unravelling Biology to Identify New Therapies-The Microcirculation as a Frontier for New Therapies in ACS.

In acute coronary syndrome (ACS) patients, restoring epicardial culprit vessel patency and flow with percutaneous coronary intervention or coronary artery bypass grafting has been the mainstay of treatment for decades. However, there is an emerging understanding of the crucial role of coronary microcirculation in predicting infarct burden and subsequent left ventricular remodelling, and the prognostic significance of coronary microvascular obstruction (MVO) in mortality and morbidity. This review will elucidate the multifaceted and interconnected pathophysiological processes which underpin MVO in ACS, and the various diagnostic modalities as well as challenges, with a particular focus on the invasive but specific and reproducible index of microcirculatory resistance (IMR). Unfortunately, a multitude of purported therapeutic strategies to address this unmet need in cardiovascular care, outlined in this review, have so far been disappointing with conflicting results and a lack of hard clinical end-point benefit. There are however a number of exciting and novel future prospects in this field that will be evaluated over the coming years in large adequately powered clinical trials, and this review will briefly appraise these.

Biologically grounded scientific methods: The challenges ahead for combating epidemics.

The protracted COVID 19 pandemic may indicate failures of scientific methodologies. Hoping to facilitate the evaluation and/or update of methods relevant in Biomedicine, several aspects of scientific processes are here explored. First, the background is reviewed. In particular, eight topics are analyzed: (i) the history of Higher Education models in reference to the pursuit of science and the type of student cognition pursued, (ii) whether explanatory or actionable knowledge is emphasized depending on the well- or ill-defined nature of problems, (iii) the role of complexity and dynamics, (iv) how differences between Biology and other fields influence methodologies, (v) whether theory, hypotheses or data drive scientific research, (vi) whether Biology is reducible to one or a few factors, (vii) the fact that data, to become actionable knowledge, require structuring, and (viii) the need of inter-/trans-disciplinary knowledge integration. To illustrate how these topics interact, a second section describes four temporal stages of scientific methods: conceptualization, operationalization, validation and evaluation. They refer to the transition from abstract (non-measurable) concepts (such as 'health') to the selection of concrete (measurable) operations (such as 'quantification of ́anti-virus specific antibody titers'). Conceptualization is the process that selects concepts worth investigating, which continues as operationalization when data-producing variables viewed to reflect critical features of the concepts are chosen. Because the operations selected are not necessarily valid, informative, and may fail to solve problems, validations and evaluations are critical stages, which require inter/trans-disciplinary knowledge integration. It is suggested that data structuring can substantially improve scientific methodologies applicable in Biology, provided that other aspects here mentioned are also considered. The creation of independent bodies meant to evaluate biologically oriented scientific methods is recommended.

Causal and non-causal explanations in code biology.

In the philosophy of science, we can consider debates about the nature of non-causal explanations in general (e.g. Reutlinger, Saatsi 2018; Lange 2017) and then especially those in the life sciences (e.g. Huneman, 2018; Kostic 2020). These debates are accompanied by the development of a new mechanism that is becoming the major response to the nature of scientific explanation in the life sciences (e.g. Craver, Darden 2013; Craver 2006); and also by the development of a design explanation (e.g. Eck, Mennes 2016) that represents a modern variant of a functional explanation. In this paper, we will methodically: 1. evaluate the plurality of explanatory strategies in contemporary science (chapter 2). 2. describe the mechanical philosophy and mechanistic explanation (Glennan 2016; Craver, Darden 2013, etc.) (chapter 3). 3. explicate the role of mechanisms in code biology (Barbieri 2015, 2002, etc.) and its relation to the new mechanism (chapter 4). 4. fulfill the main goal of the paper - to apply mechanistic explanations in code biology (Barbieri 2019, etc.) and to apply their suitability for this scientific domain (chapter 5).

Defining life and evolution: Essay on the origin, expansion, and evolution of living matter.

This essay aims to define the origin, expansion, and evolution of living matter. The first formations, identified as remains, fossils, traces etc. of life are almost as old as the Earth itself. During four billion years, life on the Earth has continuously existed and been implemented in the range of conditions, ensuring the liquid state of water. During the entire period of life existence, its evolution was proceeding with the tendency of multidirectionality, after each catastrophe tending to the diversity and vastness of distribution, and all the currently living species, regardless of their complexity, have the same evolutionary age. The property of reproductive surplus (multiplication) is inherent in all the living matter. The reproduction of all the living matter is implemented via the "development" - a process of continuous occurrence of something new that did not exist in the previous moment in the reproduced individual at each specific moment of time with the tendency towards the reproduction of a "copy". In its fundamental basis, Life is based on a programme, its material support is implemented and exists not in the field of causative-consecutive events, but in the field of programmed-causative-consecutive events. This predetermines the "biology laws", the behaviour of the material constituent of Life at each time period, and the future of the material constituent of life.

Coming of Age: Cryo-Electron Tomography as a Versatile Tool to Generate High-Resolution Structures at Cellular/Biological Interfaces.

Over the last few years, cryo electron microscopy has become the most important method in structural biology. While 80% of deposited maps are from single particle analysis, electron tomography has grown to become the second most important method. In particular sub-tomogram averaging has matured as a method, delivering structures between 2 and 5 Å from complexes in cells as well as in vitro complexes. While this resolution range is not standard, novel developments point toward a promising future. Here, we provide a guide for the workflow from sample to structure to gain insight into this emerging field.

Integrative biology defines novel biomarkers of resistance to strongylid infection in horses.

The widespread failure of anthelmintic drugs against nematodes of veterinary interest requires novel control strategies. Selective treatment of the most susceptible individuals could reduce drug selection pressure but requires appropriate biomarkers of the intrinsic susceptibility potential. To date, this has been missing in livestock species. Here, we selected Welsh ponies with divergent intrinsic susceptibility (measured by their egg excretion levels) to cyathostomin infection and found that their divergence was sustained across a 10-year time window. Using this unique set of individuals, we monitored variations in their blood cell populations, plasma metabolites and faecal microbiota over a grazing season to isolate core differences between their respective responses under worm-free or natural infection conditions. Our analyses identified the concomitant rise in plasma phenylalanine level and faecal Prevotella abundance and the reduction in circulating monocyte counts as biomarkers of the need for drug treatment (egg excretion above 200 eggs/g). This biological signal was replicated in other independent populations. We also unravelled an immunometabolic network encompassing plasma beta-hydroxybutyrate level, short-chain fatty acid producing bacteria and circulating neutrophils that forms the discriminant baseline between susceptible and resistant individuals. Altogether our observations open new perspectives on the susceptibility of equids to strongylid infection and leave scope for both new biomarkers of infection and nutritional intervention.

Machine Learning in Epigenomics: Insights into Cancer Biology and Medicine.

The recent deluge of genome-wide technologies for the mapping of the epigenome and resulting data in cancer samples has provided the opportunity for gaining insights into and understanding the roles of epigenetic processes in cancer. However, the complexity, high-dimensionality, sparsity, and noise associated with these data pose challenges for extensive integrative analyses. Machine Learning (ML) algorithms are particularly suited for epigenomic data analyses due to their flexibility and ability to learn underlying hidden structures. We will discuss four overlapping but distinct major categories under ML: dimensionality reduction, unsupervised methods, supervised methods, and deep learning (DL). We review the preferred use cases of these algorithms in analyses of cancer epigenomics data with the hope to provide an overview of how ML approaches can be used to explore fundamental questions on the roles of epigenome in cancer biology and medicine.

Foundational concepts in the biology of bacterial keratitis.

Bacterial infections of the cornea, or bacterial keratitis (BK), are notorious for causing rapidly fulminant disease and permanent vision loss, even among treated patients. In the last sixty years, dramatic upward trajectories in the frequency of BK have been observed internationally, driven in large part by the commercialization of hydrogel contact lenses in the late 1960s. Despite this worsening burden of disease, current evidence-based therapies for BK - including broad-spectrum topical antibiotics and, if indicated, topical corticosteroids - fail to salvage vision in a substantial proportion of affected patients. Amid growing concerns of rapidly diminishing antibiotic utility, there has been renewed interest in urgently needed novel treatments that may improve clinical outcomes on an individual and public health level. Bridging the translational gap in the care of BK requires the identification of new therapeutic targets and rational treatment design, but neither of these aims can be achieved without understanding the complex biological processes that determine how bacterial corneal infections arise, progress, and resolve. In this chapter, we synthesize the current wealth of human and animal experimental data that now inform our understanding of basic BK pathophysiology, in context with modern concepts in ocular immunology and microbiology. By identifying the key molecular determinants of clinical disease, we explore how novel treatments can be developed and translated into routine patient care.

Plasma Biology.

It is now more than 90 years since Irving Langmuir used the technical term "plasma" to describe an ionized gas [...].

Mediated Electrochemical Probing: A Systems-Level Tool for Redox Biology.

Biology uses well-known redox mechanisms for energy harvesting (e.g., respiration), biosynthesis, and immune defense (e.g., oxidative burst), and now we know biology uses redox for systems-level communication. Currently, we have limited abilities to "eavesdrop" on this redox modality, which can be contrasted with our abilities to observe and actuate biology through its more familiar ionic electrical modality. In this Perspective, we argue that the coupling of electrochemistry with diffusible mediators (electron shuttles) provides a unique opportunity to access the redox communication modality through its electrical features. We highlight previous studies showing that mediated electrochemical probing (MEP) can "communicate" with biology to acquire information and even to actuate specific biological responses (i.e., targeted gene expression). We suggest that MEP may reveal an extent of redox-based communication that has remained underappreciated in nature and that MEP could provide new technological approaches for redox biology, bioelectronics, clinical care, and environmental sciences.