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Results for “molecular biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 325 records · Page 18Linked to original sources

bioRxiv2021

IDENTIFICATION OF BIOCHEMICAL AND MOLECULAR MARKERS OF EARLY AGING IN CHILDHOOD CANCER SURVIVORS

PurposeSurvival rates of Childhood Cancer Patients have improved tremendously over the past four decades. However, cancer treatments are associated with an increased risk of developing an anticipated onset of chronic diseases typical of aging. Thus, we aimed to identify molecular/metabolic cellular alterations responsible for early aging in Childhood Cancer Survivors (CCS). Patients and MethodsBiochemical, proteomic and molecular biology analyses were conducted on mononuclear cells (MNCs) isolated from peripheral blood of 196 CCS, comparing the results with those obtained on MNCs of 154 healthy subjects. ResultsData demonstrate that CCS-MNCs show: i) inefficient oxidative phosphorylation associated with low energy status and a metabolic switch to lactate fermentation compared with age-matched normal controls; ii) increment of lipid peroxidation due to an unbalance among the oxidative stress production and the activation of the antioxidant defenses; (iii) significantly lower expression of genes and proteins involved in mitochondrial biogenesis and metabolism regulation, such as CLUH, PGC1-, and SIRT6 in CCS, not observed in the age-matched healthy or elderly subjects. The application of a mathematical model based on biochemical parameters predicts that CCS have a biological age significantly increased by decades compared to the chronological age. Overall, the results show that the impact of chemo/chemoradiotherapy on mitochondria efficiency in 196 CCS was rather homogeneous, irrespective of cancer type, treatment protocols, and time elapsed from the end of the curative period. ConclusionsOur study identifies some biochemical and molecular alterations possibly contributing to the pathophysiology of anticipated aging and metabolic deficiency described in CCS. These results may be useful in identifying approaches to restore the mitochondrial function, slowing down the aging and the associated pathological conditions in CCS.

cancer biology↗

bioRxiv2022

Mathematical bridge between epidemiological and molecular data on cancer and beyond

BackgroundAt least six different mathematical models of cancer and their count-less variations and combinations have been published to date in the scientific literature that reasonably explain epidemiological prediction of multi-step carcinogenesis. Each one deals with a particular set of problems at a given organizational level ranging from populations to genes. Any of the models adopted in those articles so far do not account for both epidemiological and molecular levels of carcinogenesis. MethodsWe have developed a mathematically rigorous system to derive those equations satisfying the basic assumptions of both epidemiology and molecular biology without incorporating arbitrary numerical coefficients or constants devoid of any causal explanation just to fit the empirical data. The dataset we have used encompasses 21 major categories of cancer, 124 selected populations, 108 cancer registries, 5 continents, and 14,067,894 individual cases. ResultsWe generalized all the epidemiological and molecular data using our derived equations through linear and non-linear regression and found all the necessary coefficients to explain the data. We also tested our equations against non-neoplastic conditions satisfying equivalent mathematical assumptions. ConclusionThe aim of this treatise is not only to provide some novel insight into the mathematical modeling of malignant transformation but also to revive the classical tools we already have at our disposal to pave the way towards novel insight into integrated approaches in cancer research.

cancer biology↗

bioRxiv2023

Molecular Coevolutionary Drive

Most aspects of the molecular biology of cells involve tightly coordinated intermolecular interactions requiring specific recognition at the nucleotide and/or amino-acid levels. This has led to long-standing interest in the degree to which constraints on interacting molecules result in conserved vs. accelerated rates of sequence evolution, with arguments commonly being made that molecular coevolution can proceed at rates exceeding the neutral expectation. Here, a fairly general model is introduced to evaluate the degree to which the rate of evolution at functionally interacting sites is influenced by effective population sizes (Ne), mutation rates, strength of selection, and the magnitude of recombination between sites. This theory is of particular relevance to matters associated with interactions between organelle- and nuclear-encoded proteins, as the two genomic environments often exhibit dramatic differences in the power of mutation and drift. Although genes within low Ne environments can drive the rate of evolution of partner genes experiencing higher Ne, rates exceeding the neutral expectation require that the former also have an elevated mutation rate. Testable predictions, some counterintuitive, are presented on how patterns of coevolutionary rates should depend on the relative intensities of drift, selection, and mutation. Significance StatementA wide variety of features at the cellular level involve precise interactions between participating molecular partners, thereby requiring coordinated co-evolutionary changes within lineages. The same is true for ecological interactions between species. Although there has been much speculation on how such constraints might drive molecular evolutionary rates beyond the neutral expectation, there has been little formal evolutionary theory to evaluate the generality of such claims. Here, a general framework is developed for ascertaining how rates of sequence evolution depend on the population-genetic environments of both interacting partner molecules. Although the features of one site can indeed drive the evolution of the other, only under restrictive conditions does this process push rates beyond the neutral expectation.

evolutionary biology↗

bioRxiv2016

Benchmarks for Measurement of Duplicate Detection Methods in Nucleotide Databases

Duplication of information in databases is a major data quality challenge. The presence of duplicates, implying either redundancy or inconsistency, can have a range of impacts on the quality of analyses that use the data. To provide a sound basis for research on this issue in databases of nucleotide sequences, we have developed new, large-scale validated collections of duplicates, which can be used to test the effectiveness of duplicate detection methods. Previous collections were either designed primarily to test efficiency, or contained only a limited number of duplicates of limited kinds. To date, duplicate detection methods have been evaluated on separate, inconsistent benchmarks, leading to results that cannot be compared and, due to limitations of the benchmarks, of questionable generality.\n\nIn this study we present three nucleotide sequence database benchmarks, based on information drawn from a range of resources, including information derived from mapping to Swiss-Prot and TrEMBL. Each benchmark has distinct characteristics. We quantify these characteristics and argue for their complementary value in evaluation. The benchmarks collectively contain a vast number of validated biological duplicates; the largest has nearly half a billion duplicate pairs (although this is probably only a tiny fraction of the total that is present). They are also the first benchmarks targeting the primary nucleotide databases. The records include the 21 most heavily studied organisms in molecular biology research. Our quantitative analysis shows that duplicates in the different benchmarks, and in different organisms, have different characteristics. It is thus unreliable to evaluate duplicate detection methods against any single benchmark. For example, the benchmark derived from Swiss-Prot mappings identifies more diverse types of duplicates, showing the importance of expert curation, but is limited to coding sequences. Overall, these benchmarks form a resource that we believe will be of great value for development and evaluation of the duplicate detection methods that are required to help maintain these essential resources.\n\nAvailability: The benchmark data sets are available at https://bitbucket.org/biodbqual/benchmarks.

bioinformatics↗

bioRxiv2025

Machine Learning-Assisted Decoding of Temporal Transcriptional Dynamics via Fluorescent Timer

Investigating the temporal dynamics of gene expression is crucial for understanding gene regulation across various biological processes. Using the Fluorescent Timer protein (Timer), the Timer-of-cell-kinetics-and-activity (Tocky) system enables analysis of transcriptional dynamics at the single-cell level. However, the complexity of Timer data has limited its broader application. Here, we introduce an integrative approach combining molecular biology and machine learning to elucidate Foxp3 transcriptional dynamics through flow cytometric Timer analysis. We have developed a Convolutional Neural Networks (ConvNet) approach that incorporates image conversion and Gradient-weighted Class Activation Mapping (Grad-CAM) for class-specific feature identification at the single-cell level. Biologically, we developed a novel CRISPR mutant of Foxp3-Tocky lacking the Conserved Non-coding Sequence 2 (CNS2), which has successfully elucidated CNS2-dependent Foxp3 transcription dynamics, revealing novel roles of CNS2 in regulating Foxp3 transcription frequency under specific conditions. Furthermore, generating new data from WT Foxp3 Tocky mice at various ages, the Grad-CAM methods successfully revealed distinct dynamics of Foxp3 expression from neonatal to aged mice, highlighting prominent thymus-like features of neonatal splenic Foxp3+ T cells. In conclusion, our study uncovers previously unrecognised Foxp3 transcriptional dynamics, establishing a proof-of-concept for integrating CRISPR, Tocky, and machine learning methods as advanced techniques to understand transcriptional dynamics in vivo.

immunology↗

bioRxiv2019

The origin of the central dogma through conflicting multi-level selection

The central dogma of molecular biology rests on two kinds of asymmetry between genomes and enzymes: informatic asymmetry, where information flows from genomes to enzymes but not from enzymes to genomes; and catalytic asymmetry, where enzymes provide chemical catalysis but genomes do not. How did these asymmetries originate? Here we show that these asymmetries can spontaneously arise from conflict between selection at the molecular level and selection at the cellular level. We developed a model consisting of a population of protocells, each containing a population of replicating catalytic molecules. The molecules are assumed to face a trade-off between serving as catalysts and serving as templates. This trade-off causes conflicting multilevel selection: serving as catalysts is favoured by selection between protocells, whereas serving as templates is favoured by selection between molecules within protocells. This conflict induces informatic and catalytic symmetry breaking, whereby the molecules differentiate into genomes and enzymes, establishing the central dogma. We show mathematically that the symmetry breaking is caused by a positive feedback between Fishers reproductive values and the relative impact of selection at different levels. This feedback induces a division of labour between genomes and enzymes, provided variation at the molecular level is sufficiently large relative to variation at the cellular level, a condition that is expected to hinder the evolution of altruism. Taken together, our results suggest that the central dogma is a logical consequence of conflicting multilevel selection.

evolutionary biology↗

bioRxiv2023

TReSR: A PCR-compatible DNA sequence design method for engineering proteins containing tandem repeats

Protein tandem repeats (TRs) are motifs comprised of near-identical contiguous sequence duplications. They are found in approximately 14% of all proteins and are implicated in diverse biological functions facilitating both structured and disordered protein-protein and protein-DNA interactions. These functionalities make protein TR domains an attractive component for the modular design of protein constructs. However, the repetitive nature of DNA sequences encoding TR motifs complicates their synthesis and mutagenesis by traditional molecular biology workflows commonly employed by protein engineers and synthetic biologists. To address this challenge, we developed a computational protocol to significantly reduce the complementarity of DNA sequences encoding TRs called TReSR (for Tandem Repeat DNA Sequence Redesign). The utility of TReSR was demonstrated by constructing a novel constitutive repressor synthesized by duplicating the LacI DNA binding domain into a single-chain TR construct by assembly PCR. Repressor function was evaluated by expression of a fluorescent reporter delivered on a single plasmid encoding a three-component genetic circuit. The successful application of TReSR to construct a novel TR-containing repressor with a DNA sequence that is amenable to PCR-based construction and manipulation will enable the incorporation of a wide range of TR-containing proteins for protein engineering and synthetic biology applications.

synthetic biology↗

bioRxiv2022

Molecular dynamics simulations reveal the impact of NUTD15 variants in structural conformation and dynamics

NUDT15 or MTH2 is a member of NUDIX protein family that catalyze the hydrolysis of nucleotides and deoxynucleotides, including thioguanine analogues. NUTD15 has been reported as a DNA sanitizer in humans, and more recent studies have proved that genomic variants are related to a poor prognosis in inmoplastic and immunologic diseases with thioguanines. Despite of this, the role of NUTD15 in physiology and molecular biology is quite unclear, as well as the mechanism of action of this enzyme. The existence of clinically relevant variants has prompted the study of this enzymes, whose capacity to bound and hydrolyze thioguanine nucleotides is still poor understood. By using a combination of biomolecular modelling techniques together with molecular dynamics we have studied the monomeric wild type NUTD15, as well as two important variants R139C and R139H. Our findings reveal not only how nucleotide binding stabilizes the enzyme, but also how two loops are responsible for keeping the enzyme in a packed close conformation. Mutations in 2 helix affect a network of hydrophobic and {pi}-interactions that are responsible of active site enclosing.

biophysics↗

bioRxiv2024

Novel insights into IKur modulation by Lgi3-4: Implications in atrial fibrillation

BackgroundPatients with atrial fibrillation (AF) exhibit a reduction in the ultrarapid outward potassium current (IKur) conducted by KV1.5 channels. Ion channels are closely modulated by regulatory subunits, forming macromolecular complexes known as channelosomes. One such regulatory family is the leucine-rich glioma-inactivated protein family (Lgi1-4), which has been shown to interact with KV1, modifying their trafficking and/or biophysical properties in neurons. However, the expression and impact of these proteins in the heart is still unknown. We investigated the role of Lgi3-4 proteins in cardiac electrophysiology, focusing specifically on IKur, and their potential contribution to the pathophysiology of AF. MethodsWe used three complementary biological systems, including heterologous COS-7, HEK297 and CHO cells, AAV-mediated cardiac-specific Lgi4 gene transfer in mice (Lgi4 mice), and human samples from patients in sinus rhythm and AF. Our multidisciplinary approach included immunolocalization, patch clamping, surface ECG, transvenous catheter-mediated intracardiac stimulation, and molecular biology techniques. ResultsOnly Lgi3 and Lgi4 were expressed in the human heart. In human atrial tissue and heterologous cells, Lgi3 and Lgi4 interacted with KV1.5 channels. In HEK293 cells, Lgi3-4 impaired KV1.5/KV{beta} association, partially reversing the KV{beta}-induced N-type inactivation and reducing IKur amplitude. On surface ECG, the QRS interval was prolonged, and impulse conduction was impaired in cardiac-specific Lgi4 mice compared with control. In isolated ventricular cardiomyocytes from Lgi4 mice, early action potential repolarization was prolonged compared to control cardiomyocytes. These results correlated with the reduced KV1.5 membrane expression and IKur density observed in Lgi4 cardiomyocytes and HEK293 cells. Notably, Lgi4 protein expression was lower in atrial tissue from patients with AF than sinus rhythm patients. The reduction in Lgi4 protein levels in AF was also associated with an altered colocalization with KV1.5 channels, suggesting potential disruptions in their functional interactions. ConclusionsLgi3-4 proteins are new components of the KV1.5 channelosome. They modulate IKur by interfering with KV1.5 interaction with the KV{beta} subunit. Importantly, Lgi4 is dysregulated differently in paroxysmal versus permanent AF. The results improved the understanding of this most common type of arrhythmia and identified Lgi proteins as a new potential target for treatment. NOVELTY AND SIGNIFICANCEWhat is known? O_LILeucine-rich glioma-inactivated protein family (Lgi1-4) exert an important role in the nervous system and neurological diseases. In neurons, certain Lgi proteins interact with KV1 channels, modifying their trafficking and/or biophysical properties. C_LIO_LIIn cardiomyocytes, the activation of KV1.5 channels generates the ultrarapid outward potassium current (IKur), which is essential for the initial phase of human atrial repolarization, and it is dysregulated in AF. C_LIO_LIChanges in the properties or functional expression of some KV1.5 interacting proteins have crucial pathophysiological consequences. C_LI What new information does this article contribute? O_LIWe demonstrate that Lgi3-4 are novel components of KV1.5 channelosome, modulating IKur and hence human atrial electrophysiology. Lgi3-4 proteins decrease IKur by interfering with the interaction between KV1.5 and KV{beta} subunits. C_LIO_LIThe decrease in IKur in cardiac-specific mouse model expressing Lgi4 slows the early repolarization in the action potential, as well as produce electrophysiological changes in the surface ECG and the cardiac conduction system. C_LIO_LILgi4 is dysregulated differently in paroxysmal (PX) versus permanent (PM) AF, thus shedding light into the mechanisms underlying this cardiac arrhythmia. C_LI

physiology↗

bioRxiv2017

A molecular model of the mitochondrial genome segregation machinery in Trypanosoma brucei

In almost all eukaryotes mitochondria maintain their own genome. Despite the discovery more than 50 years ago still very little is known about how the genome is properly segregated during cell division. The protozoan parasite Trypanosoma brucei contains a single mitochondrion with a singular genome the kinetoplast DNA (kDNA). Electron microscopy studies revealed the tripartite attachment complex (TAC) to physically connect the kDNA to the basal body of the flagellum and to ensure proper segregation of the mitochondrial genome via the basal bodies movement, during cell cycle. Using super-resolution microscopy we precisely localize each of the currently known unique TAC components. We demonstrate that the TAC is assembled in a hierarchical order from the base of the flagellum towards the mitochondrial genome and that the assembly is not dependent on the kDNA itself. Based on biochemical analysis the TAC consists of several non-overlapping subcomplexes suggesting an overall size of the TAC exceeding 2.8 mDa. We furthermore demonstrate that the TAC has an impact on mitochondrial organelle positioning however is not required for proper organelle biogenesis or segregation.\n\nSignificance StatementMitochondrial genome replication and segregation are essential processes in most eukaryotic cells. While replication has been studied in some detail much less is known about the molecular machinery required distribute the replicated genomes. Using super-resolution microscopy in combination with molecular biology and biochemistry we show for the first time in which order the segregation machinery is assembled and that it is assembled de novo rather than in a semi conservative fashion in the single celled parasite Trypanosoma brucei. Furthermore, we demonstrate that the mitochondrial genome itself is not required for assembly to occur. It seems that the physical connection of the mitochondrial genome to cytoskeletal elements is a conserved feature in most eukaryotes, however the molecular components are highly diverse.\n\nAbbreviation

cell biology↗

bioRxiv2026

Striping artifact removal in VisiumHD data through nuclear counts modeling

Motivation10x Genomics VisiumHD enables spatial transcriptomics at 2 m x 2 m resolution but exhibits slide-specific, non-periodic striping artifacts due to lane-width variability. These multiplicative row/column effects distort bin total counts and can bias downstream analyses. The state-of-the-art destriping approach is the normalization procedure used as a preprocessing step in bin2cell; it applies sequential high-quantile row- then column-wise normalization, which is asymmetric and can introduce edge effects/macro-stripes and distortions of large-scale total-count structure. ResultsWe propose a statistical destriping approach that leverages nuclei segmentation from the coregistered H&E image. Assuming transcript abundance is constant within each nucleus, we model bin counts with a negative binomial distribution whose mean is a product of a nucleus-specific concentration and row- and column-specific stripe-factors reflecting lane-width variation. We fit all parameters in a generalized linear modeling framework with cross-validated regularization on stripe-factors and iterative dispersion estimation, and use the fitted parameters to correct the observed counts into a destriped image. On synthetic data with known ground truth, our method improves stripe-factor estimation accuracy and reduces error in corrected counts relative to bin2cell and bin2cell-derived baselines. Across four public VisiumHD slides, it consistently lowers striping intensity while substantially better preserving biological signal present in the large-scale global count structure and avoiding the artifacts introduced by other methods. Availability and ImplementationAll source code and links to publicly available data used for this study are available at https://github.com/paolamalsot/destriping-GLM. Contactpaola.malsot@inf.ethz.ch, raetsch@inf.ethz.ch NoteThis manuscript extends the version submitted to Intelligent Systems for Molecular Biology (ISMB) 2026 by describing a new optimization algorithm that yields an approximately tenfold speedup. All plots and benchmarks in this manuscript use the updated implementation.

bioinformatics↗

bioRxiv2020

Water-mediated interactions determine helix formation of peptides in open nanotubes

Water-mediated interactions (WMIs) play diverse roles in molecular biology. They are particularly relevant in geometrically confined spaces such as the interior of the chaperonin, at the interface between ligands and their binding partners, and in the ribosome tunnel. Inspired in part by the geometry of the ribosome tunnel, we consider confinement effects on the stability of peptides. We describe results from replica exchange molecular dynamics simulations of a system containing a 23-alanine or 23-serine polypeptide confined to non-polar and polar nanotubes in the gas phase and when open to a water reservoir. We quantify the effect of water in determining the preferred conformational states of these polypeptides by calculating the difference in the solvation free energy for the helix and coil states in the open nanotube in the two phases. Our simulations reveal several possibilities. We find that nanoscopic confinement preferentially stabilizes the helical state of polypeptides with hydrophobic side chains, which is explained by the entropic stabilization mechanism proposed on the basis of polymer physics. Polypeptide chains with hydrophilic side chains can adopt helical structures within nanotubes, but helix formation is sensitive to the nature of the nanotube due to WMIs. We elaborate on the potential implications of our findings to the stability of peptides in the ribosome tunnel.

biophysics↗

bioRxiv2021

Towards a Theory of Evolution as Multilevel Learning

We apply the theory of learning to physically renormalizable systems in an attempt to develop a theory of biological evolution, including the origin of life, as multilevel learning. We formulate seven fundamental principles of evolution that appear to be necessary and sufficient to render a universe observable and show that they entail the major features of biological evolution, including replication and natural selection. These principles also follow naturally from the theory of learning. We formulate the theory of evolution using the mathematical framework of neural networks, which provides for detailed analysis of evolutionary phenomena. To demonstrate the potential of the proposed theoretical framework, we derive a generalized version of the Central Dogma of molecular biology by analyzing the flow of information during learning (back-propagation) and predicting (forward-propagation) the environment by evolving organisms. The more complex evolutionary phenomena, such as major transitions in evolution, in particular, the origin of life, have to be analyzed in the thermodynamic limit, which is described in detail in the accompanying paper. Significance statementModern evolutionary theory gives a detailed quantitative description of microevolutionary processes that occur within evolving populations of organisms, but evolutionary transitions and emergence of multiple levels of complexity remain poorly understood. Here we establish correspondence between the key features of evolution, renormalizability of physical theories and learning dynamics, to outline a theory of evolution that strives to incorporate all evolutionary processes within a unified mathematical framework of the theory of learning. Under this theory, for example, natural selection readily arises from the learning dynamics, and in sufficiently complex systems, the same learning phenomena occur on multiple levels or on different scales, similar to the case of renormalizable physical theories.

evolutionary biology↗

bioRxiv2024

Edinger-Westphal ghrelin receptor signalling regulates binge alcohol consumption in a sex specific manner

BackgroundRates of risky drinking are continuing to rise, particularly in women, yet sex as a biological variable has been largely ignored. An emerging yet understudied potential component of this circuitry is the central projecting Edinger-Westphal (EWcp), which is made up of two prominent, but distinct cell populations expressing either an array of neuropeptides (including cocaine and amphetamine regulated transcript; CART) or vGlut2 (glutamatergic). MethodsHere, we use a combination of approaches including genetic, molecular biology, behavioural testing, and electrophysiology to understand how the EWcp contributes to alcohol consumption in female versus male mice. ResultsChemogenetic inhibition of EWcpCART cells reduced binge drinking specifically in female, but not male mice. Further, inhibition of EWcpCART cells prevented ghrelin induced drinking, and viral-mediated ghrelin receptor (Ghsr) knockdown in the EWcp reduced binge drinking in female, but not male mice. RNAscope revealed Ghsr expression across peptidergic (marked by CART) and glutamatergic populations in the EWcp, with neurons from female mice more sensitive to bath application of ghrelin than male mice. Targeted knockdown of Ghsr from distinct EWcp populations revealed GHSR signalling on peptidergic, but not glutamatergic cells mediate binge drinking in female mice. Finally, both a GHSR inverse agonist and antagonist delivered directly within the EWcp reduced binge drinking in female mice. ConclusionsThese findings suggest the EWcp is a region mediating excessive alcohol bingeing through GHSR actions on peptidergic cells (CART-expressing) in female mice and expand our understanding of the neural mechanism(s) underpinning how the ghrelin system mediates alcohol consumption.

neuroscience↗

bioRxiv2019

Robust latent-variable interpretation of in vivo regression models by nested resampling

Simple multilinear methods, such as partial least squares regression (PLSR), are effective at interrelating dynamic, multivariate datasets of cell-molecular biology through high-dimensional arrays. However, data collected in vivo are more difficult, because animal-to-animal variability is often high, and each time-point measured is usually a terminal endpoint for that animal. Observations are further complicated by the nesting of cells within tissues or tissue sections, which themselves are nested within animals. Here, we introduce principled resampling strategies that preserve the tissue-animal hierarchy of individual replicates and compute the uncertainty of multidimensional decompositions applied to global averages. Using molecular-phenotypic data from the mouse aorta and colon, we find that interpretation of decomposed latent variables (LVs) changes when PLSR models are resampled. Lagging LVs, which statistically improve global-average models, are unstable in resampled iterations that preserve nesting relationships, arguing that these LVs should not be mined for biological insight. Interestingly, resampling is less discriminatory for multidimensional regressions of in vitro data, suggesting it is unnecessary when replicate-to-replicate variance is low. Our work illustrates the challenges and opportunities in translating systems-biology approaches from cultured cells to living organisms. Nested resampling adds a straightforward quality-control step aiding the interpretability of in vivo regression models.

systems biology↗

bioRxiv2020

Genome-scale metabolic model of the diatom Thalassiosira pseudonana highlights the importance of nitrogen and sulfur metabolism in redox balance

Diatoms are unicellular photosynthetic algae known to secrete organic matter that fuels secondary production in the ocean, though our knowledge of how their physiology impacts the character of dissolved organic matter remains limited. Like all photosynthetic organisms, their use of light for energy and reducing power creates the challenge of avoiding cellular damage. To better understand the interplay between redox balance and organic matter secretion, we reconstructed a genome-scale metabolic model of Thalassiosira pseudonana strain CCMP 1335, a model for diatom molecular biology and physiology, with a 60-year history of studies. The model simulates the metabolic activities of 1,432 genes via a network of 2,792 metabolites produced through 6,079 reactions distributed across six subcellular compartments. Growth was simulated under different steady-state light conditions (5-200 mol photons m-2 s-1) and in a batch culture progressing from exponential growth to nitrate-limitation and nitrogen-starvation. We used the model to examine the dissipation of reductants generated through light-dependent processes and found that when available, nitrate assimilation is an important means of dissipating reductants in the plastid; under nitrate-limiting conditions, sulfate assimilation plays a similar role. The use of either nitrate or sulfate uptake to balance redox reactions leads to the secretion of distinct organic nitrogen and sulfur compounds. Such compounds can be accessed by bacteria in the surface ocean. The model of the diatom Thalassiosira pseudonana provides a mechanistic explanation for the production of ecologically and climatologically relevant compounds that may serve as the basis for intricate, cross-kingdom microbial networks. Diatom metabolism has an important influence on global biogeochemistry; metabolic models of marine microorganisms link genes to ecosystems and may be key to integrating molecular data with models of ocean biogeochemistry.

systems biology↗

bioRxiv2019

Robinson-Foulds Reticulation Networks

Phylogenetic (hybridization) networks allow investigation of evolutionary species histories that involve complex phylogenetic events other than speciation, such as reassortment in virus evolution or introgressive hybridization in invertebrates and mammals. Reticulation networks can be inferred by solving the reticulation network problem, typically known as the hybridization network problem. Given a collection of phylogenetic input trees, this problem seeks a minimum reticulation network with the smallest number of reticulation vertices into which the input trees can be embedded exactly. Unfortunately, this problem is limited in practice, since minimum reticulation networks can be easily obfuscated by even small topological errors that typically occur in input trees inferred from biological data. We adapt the reticulation network problem to address erroneous input trees using the classic Robinson-Foulds distance. The RF embedding cost allows trees to be embedded into reticulation networks inexactly, but up to a measurable error. The adapted problem, called the Robinson-Foulds reticulation network (RF-Network) problem is, as we show and like many other problems applied in molecular biology, NP-hard. To address this, we employ local search strategies that have been successfully applied in other NP-hard phylogenetic problems. Our local search method benefits from recent theoretical advancements in this area. Further, we introduce inpractice effective algorithms for the computational challenges involved in our local search approach. Using simulations we experimentally validate the ability of our method, RF-Net, to reconstruct correct phylogenetic networks in the presence of error in input data. Finally, we demonstrate how RF-networks can help identify reassortment in influenza A viruses, and provide insight into the evolutionary history of these viruses. RF-Net was able to estimate a large and credible reassortment network with 164 taxa.

evolutionary biology↗

bioRxiv2021

Directed Evolution of Aptamer Discovery Technologies

ConspectusAlthough antibodies are a powerful tool for molecular biology and clinical diagnostics, there are many emerging applications for which nucleic acid-based aptamers can be advantageous. However, generating high-quality aptamers with sufficient affinity and specificity for biomedical applications is a challenging feat for most research laboratories. In this Account, we describe four techniques developed in our lab to accelerate the discovery of high quality aptamer reagents that can achieve robust binding even for challenging molecular targets. The first method is particle display, in which we convert solution-phase aptamers into aptamer particles that can be screened via fluorescence-activated cell sorting (FACS) to quantitatively isolate individual aptamer particles based on their affinity. This enables the efficient isolation of high-affinity aptamers in fewer selection rounds than conventional methods, thereby minimizing selection biases and reducing the emergence of artifacts in the final aptamer pool. We subsequently developed the multi-parametric particle display (MPPD) method, which employs two-color FACS to isolate aptamer particles based on both affinity and specificity, yielding aptamers that exhibit excellent target binding even in complex matrices like serum. The third method is a click chemistry-based particle display (click-PD) that enables the generation and high-throughput screening of "non-nattural" aptamers with a wide range of base modifications. We have shown that these base-modified aptamers can achieve robust affinity and specificity for targets that have proven challenging or inaccessible with natural nucleotide-based aptamer libraries. Lastly, we describe the non-natural aptamer array (N2A2) platform, in which a modified benchtop sequencing instrument is used to characterize base-modified aptamers in a massively parallel fashion, enabling the efficient identification of molecules with excellent affinity and specificity for their targets. This system first generates aptamer clusters on the flow-cell surface that incorporate alkyne-modified nucleobases, and then performs a click reaction to couple those nucleobases to an azide-modified chemical moiety. This yields a sequence-defined array of tens of millions of base-modified sequences, which can then be characterized in a high-throughput fashion. Collectively, we believe that these advancements are helping to make aptamer technology more accessible, efficient, and robust, thereby enabling the use of these affinity reagents for a wider range of molecular recognition and detection-based applications.

bioengineering↗