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1. Strong selective effects of mitochondrial DNA on the nuclear genome

   https://doi.org/10.1073/pnas.1910141117  

   Healy, Timothy M. ; Burton, Ronald S. ( March 2020 , Proceedings of the National Academy of Sciences)

   Oxidative phosphorylation, the primary source of cellular energy in eukaryotes, requires gene products encoded in both the nuclear and mitochondrial genomes. As a result, functional integration between the genomes is essential for efficient adenosine triphosphate (ATP) generation. Although within populations this integration is presumably maintained by coevolution, the importance of mitonuclear coevolution in key biological processes such as speciation and mitochondrial disease has been questioned. In this study, we crossed populations of the intertidal copepodTigriopus californicusto disrupt putatively coevolved mitonuclear genotypes in reciprocal F₂hybrids. We utilized interindividual variation in developmental rate among these hybrids as a proxy for fitness to assess the strength of selection imposed on the nuclear genome by alternate mitochondrial genotypes. Developmental rate varied among hybrid individuals, and in vitro ATP synthesis rates of mitochondria isolated from high-fitness hybrids were approximately two-fold greater than those of mitochondria isolated from low-fitness individuals. We then used Pool-seq to compare nuclear allele frequencies for high- or low-fitness hybrids. Significant biases for maternal alleles were detected on 5 (of 12) chromosomes in high-fitness individuals of both reciprocal crosses, whereas maternal biases were largely absent in low-fitness individuals. Therefore, the most fit hybrids were those with nuclear alleles that matched their mitochondrialmore »genotype on these chromosomes, suggesting that mitonuclear effects underlie individual-level variation in developmental rate and that intergenomic compatibility is critical for high fitness. We conclude that mitonuclear interactions can have profound impacts on both physiological performance and the evolutionary trajectory of the nuclear genome.

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2. Phenotypic Variation in Mitochondria-Related Performance Traits Across New Zealand Snail Populations

   https://doi.org/10.1093/icb/icaa066  

   Greimann, Emma S ; Ward, Samuel F ; Woodell, James D ; Hennessey, Samantha ; Kline, Michael R ; Moreno, Jorge A ; Peters, Madeline ; Cruise, Jennifer L ; Montooth, Kristi L ; Neiman, Maurine ; et al ( June 2020 , Integrative and Comparative Biology)

   Synopsis Mitochondrial function is critical for energy homeostasis and should shape how genetic variation in metabolism is transmitted through levels of biological organization to generate stability in organismal performance. Mitochondrial function is encoded by genes in two distinct and separately inherited genomes—the mitochondrial genome and the nuclear genome—and selection is expected to maintain functional mito-nuclear interactions. The documented high levels of polymorphism in genes involved in these mito-nuclear interactions and wide variation for mitochondrial function demands an explanation for how and why variability in such a fundamental trait is maintained. Potamopyrgus antipodarum is a New Zealand freshwater snail with coexisting sexual and asexual individuals and, accordingly, contrasting systems of separate vs. co-inheritance of nuclear and mitochondrial genomes. As such, this snail provides a powerful means to dissect the evolutionary and functional consequences of mito-nuclear variation. The lakes inhabited by P. antipodarum span wide environmental gradients, with substantial across-lake genetic structure and mito-nuclear discordance. This situation allows us to use comparisons across reproductive modes and lakes to partition variation in cellular respiration across genetic and environmental axes. Here, we integrated cellular, physiological, and behavioral approaches to quantify variation in mitochondrial function across a diverse set of wild P. antipodarum lineages. Wemore »found extensive across-lake variation in organismal oxygen consumption and behavioral response to heat stress and differences across sexes in mitochondrial membrane potential but few global effects of reproductive mode. Taken together, our data set the stage for applying this important model system for sexual reproduction and polyploidy to dissecting the complex relationships between mito-nuclear variation, performance, plasticity, and fitness in natural populations.« less
3. Collagen density modulates triple-negative breast cancer cell metabolism through adhesion-mediated contractility

   https://doi.org/10.1038/s41598-018-35381-9  

   Mah, Emma J. ; Lefebvre, Austin E. Y. T. ; McGahey, Gabrielle E. ; Yee, Albert F. ; Digman, Michelle A. ( November 2018 , Scientific Reports)

   Extracellular matrix (ECM) mechanical properties upregulate cancer invasion, cell contractility, and focal adhesion formation. Alteration in energy metabolism is a known characteristic of cancer cells (i.e., Warburg effect) and modulates cell invasion. There is little evidence to show if collagen density can alter cancer cell metabolism. We investigated changes in energy metabolism due to collagen density in five breast cell lines by measuring the fluorescence lifetime of NADH. We found that only triple-negative breast cancer cells, MDA-MB231 and MDA-MB468 cells, had an increased population of bound NADH, indicating an oxidative phosphorylation (OXPHOS) signature, as collagen density decreased. When inhibiting ROCK and cell contractility, MDA-MB231 cells on glass shifted from glycolysis (GLY) to OXPHOS, confirming the intricate relationship between mechanosensing and metabolism. MCF10A cells showed less significant changes in metabolism, shifting towards GLY as collagen density decreased. The MCF-7 and T-47D, less invasive breast cancer cells, compared to the MDA-MB231 and MDA-MB468 cells, showed no changes regardless of substrate. In addition, OXPHOS or GLY inhibitors in MDA-MB231 cells showed dramatic shifts from OXPHOS to GLY orvice versa. These results provide an important link between cellular metabolism, contractility, and collagen density in human breast cancer.
4. A Deep Learning Framework for Sickle Cell Disease Microfluidic Biomarker Assays

   https://doi.org/10.1182/blood-2020-141693  

   Praljak, Niksa ; Shipley, Brandon ; Gonzales, Ayesha ; Goreke, Utku ; Iram, Shamreen ; Singh, Gundeep ; Hill, Ailis ; Gurkan, Umut A. ; Hinczewski, Michael ( November 2020 , Blood)

   Introduction: Vaso-occlusive crises (VOCs) are a leading cause of morbidity and early mortality in individuals with sickle cell disease (SCD). These crises are triggered by sickle red blood cell (sRBC) aggregation in blood vessels and are influenced by factors such as enhanced sRBC and white blood cell (WBC) adhesion to inflamed endothelium. Advances in microfluidic biomarker assays (i.e., SCD Biochip systems) have led to clinical studies of blood cell adhesion onto endothelial proteins, including, fibronectin, laminin, P-selectin, ICAM-1, functionalized in microchannels. These microfluidic assays allow mimicking the physiological aspects of human microvasculature and help characterize biomechanical properties of adhered sRBCs under flow. However, analysis of the microfluidic biomarker assay data has so far relied on manual cell counting and exhaustive visual morphological characterization of cells by trained personnel. Integrating deep learning algorithms with microscopic imaging of adhesion protein functionalized microfluidic channels can accelerate and standardize accurate classification of blood cells in microfluidic biomarker assays. Here we present a deep learning approach into a general-purpose analytical tool covering a wide range of conditions: channels functionalized with different proteins (laminin or P-selectin), with varying degrees of adhesion by both sRBCs and WBCs, and in both normoxic and hypoxic environments. Methods: Our neuralmore »networks were trained on a repository of manually labeled SCD Biochip microfluidic biomarker assay whole channel images. Each channel contained adhered cells pertaining to clinical whole blood under constant shear stress of 0.1 Pa, mimicking physiological levels in post-capillary venules. The machine learning (ML) framework consists of two phases: Phase I segments pixels belonging to blood cells adhered to the microfluidic channel surface, while Phase II associates pixel clusters with specific cell types (sRBCs or WBCs). Phase I is implemented through an ensemble of seven generative fully convolutional neural networks, and Phase II is an ensemble of five neural networks based on a Resnet50 backbone. Each pixel cluster is given a probability of belonging to one of three classes: adhered sRBC, adhered WBC, or non-adhered / other. Results and Discussion: We applied our trained ML framework to 107 novel whole channel images not used during training and compared the results against counts from human experts. As seen in Fig. 1A, there was excellent agreement in counts across all protein and cell types investigated: sRBCs adhered to laminin, sRBCs adhered to P-selectin, and WBCs adhered to P-selectin. Not only was the approach able to handle surfaces functionalized with different proteins, but it also performed well for high cell density images (up to 5000 cells per image) in both normoxic and hypoxic conditions (Fig. 1B). The average uncertainty for the ML counts, obtained from accuracy metrics on the test dataset, was 3%. This uncertainty is a significant improvement on the 20% average uncertainty of the human counts, estimated from the variance in repeated manual analyses of the images. Moreover, manual classification of each image may take up to 2 hours, versus about 6 minutes per image for the ML analysis. Thus, ML provides greater consistency in the classification at a fraction of the processing time. To assess which features the network used to distinguish adhered cells, we generated class activation maps (Fig. 1C-E). These heat maps indicate the regions of focus for the algorithm in making each classification decision. Intriguingly, the highlighted features were similar to those used by human experts: the dimple in partially sickled RBCs, the sharp endpoints for highly sickled RBCs, and the uniform curvature of the WBCs. Overall the robust performance of the ML approach in our study sets the stage for generalizing it to other endothelial proteins and experimental conditions, a first step toward a universal microfluidic ML framework targeting blood disorders. Such a framework would not only be able to integrate advanced biophysical characterization into fast, point-of-care diagnostic devices, but also provide a standardized and reliable way of monitoring patients undergoing targeted therapies and curative interventions, including, stem cell and gene-based therapies for SCD. Disclosures Gurkan: Dx Now Inc.: Patents & Royalties; Xatek Inc.: Patents & Royalties; BioChip Labs: Patents & Royalties; Hemex Health, Inc.: Consultancy, Current Employment, Patents & Royalties, Research Funding.« less
5. METABOLIC: high-throughput profiling of microbial genomes for functional traits, metabolism, biogeochemistry, and community-scale functional networks

   https://doi.org/10.1186/s40168-021-01213-8  

   Zhou, Zhichao ; Tran, Patricia Q. ; Breister, Adam M. ; Liu, Yang ; Kieft, Kristopher ; Cowley, Elise S. ; Karaoz, Ulas ; Anantharaman, Karthik ( February 2022 , Microbiome)

   Advances in microbiome science are being driven in large part due to our ability to study and infer microbial ecology from genomes reconstructed from mixed microbial communities using metagenomics and single-cell genomics. Such omics-based techniques allow us to read genomic blueprints of microorganisms, decipher their functional capacities and activities, and reconstruct their roles in biogeochemical processes. Currently available tools for analyses of genomic data can annotate and depict metabolic functions to some extent; however, no standardized approaches are currently available for the comprehensive characterization of metabolic predictions, metabolite exchanges, microbial interactions, and microbial contributions to biogeochemical cycling.

   We present METABOLIC (METabolic And BiogeOchemistry anaLyses In miCrobes), a scalable software to advance microbial ecology and biogeochemistry studies using genomes at the resolution of individual organisms and/or microbial communities. The genome-scale workflow includes annotation of microbial genomes, motif validation of biochemically validated conserved protein residues, metabolic pathway analyses, and calculation of contributions to individual biogeochemical transformations and cycles. The community-scale workflow supplements genome-scale analyses with determination of genome abundance in the microbiome, potential microbial metabolic handoffs and metabolite exchange, reconstruction of functional networks, and determination of microbial contributions to biogeochemical cycles. METABOLIC can take input genomes from isolates, metagenome-assembled genomes, ormore »single-cell genomes. Results are presented in the form of tables for metabolism and a variety of visualizations including biogeochemical cycling potential, representation of sequential metabolic transformations, community-scale microbial functional networks using a newly defined metric “MW-score” (metabolic weight score), and metabolic Sankey diagrams. METABOLIC takes ~ 3 h with 40 CPU threads to process ~ 100 genomes and corresponding metagenomic reads within which the most compute-demanding part of hmmsearch takes ~ 45 min, while it takes ~ 5 h to complete hmmsearch for ~ 3600 genomes. Tests of accuracy, robustness, and consistency suggest METABOLIC provides better performance compared to other software and online servers. To highlight the utility and versatility of METABOLIC, we demonstrate its capabilities on diverse metagenomic datasets from the marine subsurface, terrestrial subsurface, meadow soil, deep sea, freshwater lakes, wastewater, and the human gut.

   METABOLIC enables the consistent and reproducible study of microbial community ecology and biogeochemistry using a foundation of genome-informed microbial metabolism, and will advance the integration of uncultivated organisms into metabolic and biogeochemical models. METABOLIC is written in Perl and R and is freely available under GPLv3 athttps://github.com/AnantharamanLab/METABOLIC.

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