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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 19 recordsLinked to original sources

bioRxiv2021

Sablefish (Anoplopoma fimbria) plasma biochemistry and hematology reference intervals including blood cell morphology

Plasma biochemistry and hematology reference intervals are integral health assessment tools in all medical fields, including aquatic animal health. As sablefish (Anoplopoma fimbria) are becoming aquaculturally and economically more important, this manuscript provides essential reference intervals (RI) for their plasma biochemistry and hematology along with reference photomicrographs of blood cells in healthy, fasted sablefish. Blood cell morphology can differ between fish species. In addition, blood cell counts and blood chemistry can vary between fish species, demographics, water conditions, seasons, diets, and culture systems, which precludes the use of RIs from other fish species. For this study, blood was collected for plasma biochemistry and hematology analysis between June 20 and July 18, 2019, from healthy, yearling sablefish, hatched and reared in captivity on a commercial diet. Overnight fast of 16-18 hours did not sufficiently reduce lipids in the blood, which led to visible lipemia and frequent rupture of blood cells during analysis. Therefore, sablefish should be fasted for 24 to 36 hours before blood is collected to reduce hematology artifacts or possible reagent interference in plasma biochemistry analysis. Lymphocytes were the most dominant leukocytes (98%), while eosinophils were rare, and basophils were not detected in sablefish. Neutrophils were very large cells with Dohle bodies. In mammals and avian species, Dohle bodies are usually signs of toxic change from inflammation, but no such association was found in these fish. In conclusion, lipemia can interfere with sablefish blood analysis, and available removal methods should be evaluated as fasting for up to 36 h might not always be feasible. Also, more studies are required to establish RI for different developmental stages and rearing conditions.

zoology↗

bioRxiv2023

Metabolic evolution of pyranopterin-dependent biochemistry

Molybdenum (Mo)-dependent biochemistry is essential for many key metabolic pathways. However, theory and geological evidence suggests that its solubility during long intervals with low dioxygen would have limited its availability on early Earth. We developed models of metabolic evolution and found that reactions employing tungsten (W)-dependent biochemistry likely preceded Mo-dependent reactions, where Mo-usage increased dramatically after the production of dioxygen. Consistent with this finding, we analyzed genomes from over 65,000 phylogenetically diverse microbes and metagenomes from an environmental dataset, and we observed that dioxygen-utilizing prokaryotes living in aerobic niches are enriched with Mo-dependent enzymes as compared to anaerobic microbes. As an independent evaluation of this hypothesis, we combined protein language models, machine learning, and phylogenomic analysis to build a classifier for W- or Mo-pterin dependence in the DMSO reductase superfamily, and we found that W-pterin-dependent enzymes cluster near the root of the tree and that a subset of late-evolving aldehyde oxidoreductases (AORs) from aerobes are predicted to rely on Mo instead of W. Overall, our combination of metabolic modeling, phenotypic analysis, machine learning, and phylogenomic analysis suggest that Mo-pterin-dependent biochemistry likely derived from W-pterin-dependent biochemistry, and that Mo-usage increased drastically after the rise of oxygen.

evolutionary biology↗

bioRxiv2024

Pivoting Back to the In-Person Capstone Biochemistry Lab: Lessons Learned from the COVID-19 Online Modality to Improve the Course-based Undergraduate Research Experience

The restructuring of an upper division biochemistry lab capstone course intended for biochemistry students with a range of laboratory experience was explored. A goal of the course was to give students practice with necessary skills in biochemical and biological techniques, especially for an entry level general position in biotechnology. The immediate impact of the online capstone course mandated by the COVID-19 pandemic limited students on learning essential hands-on research skills but evolved during the transition back to in-person instruction to include more elements of in-person practice. This article highlights the evolution of the capstone biochemistry lab to an in-person CURE capstone lab, with lessons learned and resources successfully used during the COVID-19 remote course. These include changes in the way information was disseminated, access to online resources, and modifications in student assessments. This article documents how course evolution resulted in a shift in pedagogical strategies leading to building a community of biochemistry learners that could be used to help college faculty in developing a CURE capstone lab.

scientific communication and education↗

bioRxiv2020

Using Metacognitive Strategies to Improve Academic Performance in Biochemistry

Growing evidence suggests that students self-beliefs about the ability to alter their academic abilities can directly influence long-term achievement. These self-beliefs or mindsets can either be fixed (unchangeable) or growth oriented. Students with growth mindsets believe their academic abilities can change, which leads to higher grades and academic persistence in contrast to students with fixed mindsets. However, less is known about how these attributes affect student learning, particularly in college level biochemistry courses. In this study, we utilized social-psychological interventions to promote growth mindset among third and fourth year undergraduate students enrolled in a one semester Biochemistry survey course. Using a mixed-methods study design we evaluated student mindset, attitudes towards learning, and academic performance over four semesters. Our results suggest that although students mindsets did not change as a result of metacognitive interventions, their positive perceptions about learning vs performance did increase. Furthermore, students receiving growth mindset interventions significantly outperformed students who did not receive interventions on the final cumulative exam that assessed critical thinking skills. These results suggest that metacognitive interventions can be an effective tool to improve student academic performance in a biochemistry course.

scientific communication and education↗

bioRxiv2026

Spindle biochemistry responds to compressive force from thenuclear envelope to tune spindle dynamics during closed mitosis

During closed mitosis in S. pombe, the nuclear envelope and mitotic spindle must work together mechanically and biochemically to ensure successful nuclear division. Previous work has demonstrated that mechanical force from the nuclear envelope, transmitted through spindle pole bodies, can re-shape the spindle. However, it remains unclear how force might alter spindle biochemistry. Here, we investigate how force reprograms the spindle with two approaches: chronically increasing nuclear envelope tension via the lipid synthesis inhibitor cerulenin, and acutely applying force through an optical trap. Both perturbations slow elongation dynamics and reduce microtubule number. Despite this reduction, key spindle proteins Ase1 and Klp5 increase their density at the spindle midzone, indicating inward force from the nuclear envelope can alter spindle biochemistry. We find that while motor proteins Klp5 and Klp6 only minimally affect the spindles response to increased nuclear envelope force, the combination of removing Ase1 and increasing nuclear envelope force together rescue spindle stability. Together, our findings reveal that nuclear force on the spindle does not merely alter its shape, but is key in regulating its biochemistry to maintain force balance.

biophysics↗

bioRxiv2026

The ModelSEED Biochemistry Database, 2026 update: grading multi-source thermodynamics

The ModelSEED Biochemistry Database (https://modelseed.org) supplies foundational mass- and charge-balanced reaction networks for metabolic reconstructions. Here we present an update on the biochemistry where the database has been expanded to [~] 46,000 compounds, and [~] 56,000 reactions, featuring [~] 37,000 metabolic structures. We have expanded our approach for handling thermodynamic data, enabling multiple sources of data to be derived, integrated, and presented to the wider research community. We now publish predictions of pKa, reaction energy (and respective uncertainties), and estimates of reaction direction from multiple independent sources. Each reaction is graded gold, silver or bronze according to the strength of the evidence behind it, so that users can weigh its reliability directly. We also release reaction directions predicted by an ensemble of large language models. This multi-source approach exposes agreements and discrepancies between sources for [~] 33,000 reactions. Finally, to ensure data integrity, a new conflict-resolution pipeline reconciles structures across sources, documenting input from curators. Our work is publicly available at https://github.com/ModelSEED/ModelSEEDDatabase.

biochemistry↗

bioRxiv2021

uPIC-M: efficient and scalable preparation of clonal single mutant libraries for high-throughput protein biochemistry

New high-throughput biochemistry techniques complement selection-based approaches and provide quantitative kinetic and thermodynamic data for thousands of protein variants in parallel. With these advances, library generation rather than data collection has become rate limiting. Unlike pooled selection approaches, high-throughput biochemistry requires mutant libraries in which individual sequences are rationally designed, efficiently recovered, sequence-validated, and separated from one another, but current strategies are unable to produce these libraries at the needed scale and specificity at reasonable cost. Here, we present a scalable, rapid, and inexpensive approach for creating User-designed Physically Isolated Clonal-Mutant (uPIC-M) libraries that utilizes recent advances in oligo synthesis, high-throughput sample preparation, and next-generation sequencing. To demonstrate uPIC-M, we created a scanning mutant library of SpAP, a 541 amino acid alkaline phosphatase, and recovered 94% of desired mutants in a single iteration. uPIC-M uses commonly available equipment and freely downloadable custom software and can produce a 5000 mutant library at 1/3 the cost and 1/5 the time of traditional techniques.

molecular biology↗

bioRxiv2017

A cell-free synthetic biochemistry platform for raspberry ketone production

Cell-free synthetic biochemistry provides a green solution to replace traditional petroleum or agricultural based methods for production of fine chemicals. 4-(4-hydroxyphenyl)-butan-2-one, also known as raspberry ketone, is the major fragrance component of raspberry fruit and is utilised as a natural additive in the food and sports industry. Current industrial processing standards involve chemical extraction with a yield of 1-4 mg per kilo of fruit. As such its market price can fluctuate up to $20,000 per kg. Metabolic engineering approaches to synthesise this molecule by microbial fermentation have only resulted in low yields of up to 5 mg L-1. In contrast, cell-free synthetic biochemistry offers an intriguing compromise to the engineering constraints provided by the living cell. Using purified enzymes or a two-step semisynthetic route, an optimised pathway was formed for raspberry ketone synthesis leading up to 100% yield conversion. The semi-synthetic route is potentially scalable and cost-efficient for industrial synthesis of raspberry ketone.

synthetic biology↗

bioRxiv2017

Knowledge of the Neighborhood of the Reactive Site up to Three Atoms Can Predict Biochemistry and Protein Sequences

Thousands of biochemical reactions with characterized activities are orphan, meaning they cannot be assigned to a specific enzyme, leaving gaps in metabolic pathways. Novel reactions predicted by pathway-generation tools also lack associated sequences, limiting protein engineering applications. Associating orphan and novel reactions with known biochemistry and suggesting enzymes to catalyze them is a daunting problem. We propose a new method, BridgIT, to identify candidate genes and protein sequences for these reactions, and this method introduces, for the first time, information about the enzyme binding pocket into reaction similarity comparisons. BridgIT assesses the similarity of two reactions, one orphan and one well-characterized, nonorphan reaction, using their substrate reactive sites, their surrounding structures, and the structures of the generated products to suggest protein sequences and genes that catalyze the most similar non-orphan reactions as candidates for also catalyzing the orphan ones.\n\nWe performed two large-scale validation studies to test BridgIT predictions against experimental biochemical evidence. For the 234 orphan reactions from KEGG 2011 (a comprehensive enzymatic reaction database) that became non-orphan in KEGG 2018, BridgIT predicted the exact or a highly related enzyme for 211 of them. Moreover, for 334 out of 379 novel reactions in 2014 that were later catalogued in KEGG 2018, BridgIT predicted the exact or highly similar enzyme sequences.\n\nBridgIT requires knowledge about only three connecting bonds around the atoms of the reactive sites to correctly identify protein sequences for 93% of analyzed enzymatic reactions. Increasing to six connecting bonds allowed for the accurate identification of a sequence for nearly all known enzymatic reactions.\n\nSIGNIFICANCE STATEMENTRecent advances in synthetic biochemistry have resulted in a wealth of novel hypothetical enzymatic reactions that are not matched to protein-encoding genes, deeming them \"orphan\". Nearly half of known metabolic enzymes are also orphan, leaving important gaps in metabolic network maps. Proposing genes for the catalysis of orphan reactions is critical for applications ranging from biotechnology to medicine. In this work, a novel computational method, BridgIT, identified a potential enzyme sequence to orphan reactions and nearly all theoretically possible biochemical transformations, providing candidate genes to catalyze these reactions to the research community. BridgIT online tool will allow researchers to fill the knowledge gaps in metabolic networks and will act as a starting point for designing novel enzymes to catalyze non-natural transformations.

bioinformatics↗

bioRxiv2017

Hematology, biochemistry, and toxicology of wild hawksbill turtles (Eretmochelys imbricata) nesting in mangrove estuaries in the eastern Pacific Ocean

Sea turtles are a keystone species and are highly sensitive to changes in their environment, making them excellent environmental indicators. In light of environmental and climate changes, species are increasingly threatened by pollution, changes in ocean health, habitat alteration, and plastic ingestion. There may be additional health related threats and understanding these threats is key in directing future management and conservation efforts, particularly for severely reduced sea turtle populations. Hawksbill turtles (Eretmochelys imbricata) are critically endangered, with those in the eastern Pacific Ocean (Mexico-Peru) considered one of the most threatened sea turtle populations in the world. This study establishes baseline health parameters in hematology and blood biochemistry as well as tested for heavy metals and persitent organic pollutants in eastern Pacific hawksbills at a primary nesting colony located in a mangrove estuary. Whereas hematology and biochemistry results are consistent with healthy populations of other species of sea turtles, we identified differences in packed cell volume, heterophils and lympohcyte counts, and glucose when comparing our data to other adult hawksbill analysis (1), (2), (3). Our analysis of heavy metal contamination revealed a mean blood level of 0.245 ppm of arsenic, 0.045 ppm of lead, and 0.008 ppm of mercury. Blood levels of persistent organic pollutants were below the laboratory detection limit for all turtles. Our results suggest that differences in the feeding ecology of eastern Pacific hawksbills in mangrove estuaries may make them less likely to accumulate persistent organic pollutants and heavy metals in their blood. These baseline data on blood values in hawksbills nesting within a mangrove estuary in the eastern Pacific offer important guidance for health assessments of the species in the wild and in clinical rehabilitation facilities, and underscore the importance of preventing contamination from point and non-point sources in mangrove estuaries, which represent primary habitat to hawksbills and myriad other marine species in the eastern Pacific Ocean.

zoology↗

bioRxiv2023

Sequence and structure based deep learning models represent different aspects of protein biochemistry

Deep learning models are seeing increased use as methods to predict mutational effects or allowed mutations in proteins. The models commonly used for these purposes include large language models (LLMs) and 3D Convolutional Neural Networks (CNNs). These two model types have very different architectures and are commonly trained on different representations of proteins. LLMs make use of the transformer architecture and are trained purely on protein sequences whereas 3D CNNs are trained on voxelized representations of local protein structure. While comparable overall prediction accuracies have been reported for both types of models, it is not known to what extent these models make comparable specific predictions and/or generalize protein biochemistry in similar ways. Here, we perform a systematic comparison of two LLMs and two structure-based models (CNNs) and show that the different model types have distinct strengths and weaknesses. The overall prediction accuracies are largely uncorrelated between the sequence- and structure-based models. Overall, the two structure-based models are better at predicting buried aliphatic and hydrophobic residues whereas the two LLMs are better at predicting solvent-exposed polar and charged amino acids. Finally, we find that a combined model that takes the individual model predictions as input can leverage these individual model strengths and results in significantly improved overall prediction accuracy.

biochemistry↗

bioRxiv2025

Understanding of ATP-lid conformational dynamics in the N-terminal domain of Hsp90 and its mutants by use of a computational biochemistry approach

Chaperone Hsp90 regulates the activation and maturation of various protein, and is an attractive target for drug discovery. In catalytic cycle of Hsp90, ATP hydrolysis is a key event that drives structural changes, including the interchange of the dimeric Hsp90 structure between open and closed forms. For ATP hydrolysis, ATP-lid closure in the ATP-binding site from the up- to down-conformation is an indispensable co-ordinated structural change. However, the atomistic mechanism underlying lid closure remains unclear. In this study, a computational biochemistry approach was applied to wild-type apo and ATP-complex structures, and the lid-mutants A107N and T101I ATP-complex, to understand lid closure. A total of 15-s molecular dynamic simulation, including the equilibration and production phases, was conducted for every structure starting from the lid up-conformation, but no lid-closures were observed. However, a very early event, i.e., a sign, of lid closure have been captured. In the simulations of wild-type and A107N ATP-complex structures, lid segment showed conformational fluctuations, and helix-7 (H7) segment in lid segment was unwound. This conformational instability of lid segment energetically weakened its interaction with facing region, suggesting the up-to-down transition was triggered by the instability of lid segment, particularly H7 segment. The interaction energy between lid segment and facing region was ranked in the order A107N > wild-type > T101I, which was correlated with experimental results such as the orders of ATP-hydrolytic activity and rapidity of conformational changes of lid segment, measured in ATP-spiking experiments by fluorescence resonance energy transfer method (i.e., A107N > wild-type > T101I).

biochemistry↗

bioRxiv2026

Non-enzymatic assimilation of organosulfur compounds at the interface of geochemistry and biochemistry

Dimethyl sulfide (DMS) and dimethyl sulfoxide (DMSO) have important microbial-driven roles in global nutrient cycling, chemotaxis and/or climate regulation. Microbial DMSO and DMS assimilation were thought to require their enzymatic reduction to methanethiol. However, reactive oxygen species were recently shown to mediate methane production from DMS and DMSO. Here, we show that this oxidative mechanism can sustain microbial growth without the need for enzymes to directly reduce DMS or oxidize DMSO. Both, light and heat were effective geochemical drivers of this mechanism that yielded methanesulfonic acid and sulfite, and which mimicked the stepwise oxidation of DMSO by monooxygenases that only enzymatically enhance this oxidation reaction. Furthermore, we demonstrated intracellular DMSO degradation, in which sulfur and the simultaneously released carbon were both utilized by microbes. This study emphasizes that metabolic pathways are not necessarily enzyme-driven, with putatively ancient, non-enzymatic reaction networks taking place at a transition between geochemistry and biochemistry.

biochemistry↗

bioRxiv2017

Giantin Knockout Models Reveal The Capacity Of The Golgi To Regulate Its Biochemistry By Controlling Glycosyltransferase Expression

The Golgi is the cellular hub for complex glycosylation, controlling accurate processing of complex proteoglycans, receptors, ligands, and glycolipids. Its structure and organisation is dependent on golgins, which tether cisternal membranes and incoming transport vesicles. Here we show that knockout of the largest golgin, giantin, leads to substantial changes in gene expression despite only limited effects on Golgi structure. Notably, 22 Golgi-resident glycosyltransferases, but not glycan processing enzymes or the ER glycosylation machinery, are differentially expressed following giantin ablation. This includes near-complete loss-of-function of GALNT3 in both mammalian cell and zebrafish models. Giantin knockout zebrafish exhibit hyperostosis and ectopic calcium deposits, recapitulating phenotypes of hyperphosphatemic familial tumoral calcinosis, a disease caused by mutations in GALNT3. These data reveal a new feature of Golgi homeostasis, the ability to regulate glycosyltransferase expression to generate a functional proteoglycome.\n\nSummary statementKnockout of giantin in a genome-engineered cell line and zebrafish models reveals the capacity of the Golgi to control its own biochemistry through changes in gene expression.

cell biology↗

bioRxiv2020

The structure of autocatalytic networks, with application to early biochemistry

Metabolism across all known living systems combines two key features. First, all of the molecules that are required are either available in the environment or can be built up from available resources via other reactions within the system. Second, the reactions proceed in a fast and synchronised fashion via catalysts that are also produced within the system. Building on early work by Stuart Kauffman, a precise mathematical model for describing such self-sustaining autocatalytic systems (RAF theory) has been developed to explore the origins and organisation of living systems within a general formal framework. In this paper, we develop this theory further by establishing new relationships between classes of RAFs and related classes of networks, and developing new algorithms to investigate and visualise RAF structures in detail. We illustrate our results by showing how it reveals further details into the structure of archaeal and bacterial metabolism near the origin of life, and provide techniques to study and visualise the core aspects of primitive biochemistry.

evolutionary biology↗

bioRxiv2022

Eight Unexpected Selenoprotein Families in ABC transport, in Organometallic Biochemistry in Clostridium difficile and other anaerobes, and in Methylmercury Biosynthesis.

A novel protein family related to mercury resistance protein MerB, which cleaves Hg-C bonds of organomercurial compounds, is a newly recognized selenoprotein, typically seen truncated in sequence databases at CU (cysteine-selenocysteine) dipeptide sites fifty residues before the true C-terminus. Inspection shows this protein occurs in a nine-gene neighborhood conserved in more than fifty bacterial species, taxonomically diverse but exclusively anaerobic, including spirochetes, deltaproteobacteria, and Gram-positive spore-formers Clostridium difficile and C. botulinum. Three included families are novel selenoproteins in most instances, including two ABC transporter subunits, one a substrate-binding protein with another CU motif, the other a permease subunit with selenocysteine at the substrate-gating position. Phylogenetic profiling shows a strong pattern of co-occurrence with Stickland metabolism selenoproteins, but an even closer link to a group of 8Fe-9S cofactor-type double-cubane proteins. These 8Fe-9S enzymes vary in count and in genome location but frequently sit next to the nine-gene locus. We have named the locus SAO, because of the Selenocysteine-Assisted Organometallic (SAO) biochemistry implied by an uptake ABC transporter with apparent metal-binding selenocysteines, complementary metal efflux pump SaoE, the MerB-like cytosolic enzyme now called SaoL, and comparative genomics signatures suggesting energy metabolism rather than metal resistance. Hypothesizing cycles of formation and dismutation of organometallic compounds involved in fermentative metabolism, we examined methylmercury formation proteins, and discovered most HgcA proteins are selenoproteins as well, with a CU motif N-terminal to the previously predicted start. Seeking additional rare and overlooked selenoproteins, tricky because of their rarity, could help reveal more candidate cryptic biochemical processes.

microbiology↗

bioRxiv2022

ALIBY: ALFA Nanobody-Based Toolkit for Imaging and Biochemistry in Yeast

Specialized epitope tags continue to be an integral component in various biochemical and cell biological applications such as fluorescence microscopy, immunoblotting, immunoprecipitation, and protein purification. However, until recently, no single tag could offer this complete set of functionalities on its own. Here, we present a plasmid-based toolkit named ALIBY (ALFA Toolkit for Imaging and Biochemistry in Yeast) that provides a universal workflow to adopt the versatile ALFA tag/NbALFA system within the well-established model organism Saccharomyces cerevisiae. The kit comprises of tagging plasmids for labelling a protein-of-interest with the ALFA tag, and detection plasmids encoding a fluorescent protein-tagged NbALFA for live-cell imaging purposes. We demonstrate the suitability of ALIBY for visualizing the spatiotemporal localization of yeast proteins (i.e., cytoskeleton, nucleus, centrosome, divisome and exocyst) in live cells. Our approach has yielded an excellent signal-to-noise ratio without off-targeting or effect on cell growth. In summary, our yeast-specific toolkit aims to simplify and further advance the live-cell imaging of differentially abundant yeast proteins while also being suitable for biochemical applications. ImportanceIn yeast research, conventional fluorescent protein tags and small epitope tags are widely used to study the spatiotemporal dynamics and activity of proteins. Though proven to be efficient, these tags lack the versatility for usage across different cell biological and biochemical studies of a given protein-of-interest. Therefore, there is an urgent need for a unified platform for visualization, biochemical, and functional analyses of proteins-of-interest in yeast. Herein, we have engineered ALIBY, a plasmid-based toolkit which expands the benefits of the recently developed ALFA tag/NbALFA system to studies in the well-established model organism Saccharomyces cerevisiae. We demonstrate that ALIBY provides a simple and versatile strain construction workflow for long duration live-cell imaging and biochemical applications in yeast.

cell biology↗

bioRxiv2022

Biochemistry-informed design selects potent siRNAs against SARS-CoV-2

RNA interference (RNAi) offers an efficient way to repress genes of interest, and it is widely used in research settings. Clinical applications emerged more recently, with 5 approved siRNAs (the RNA guides of the RNAi effector complex) against human diseases. The development of siRNAs against the SARS-CoV-2 virus could therefore provide the basis of novel COVID-19 treatments, while being easily adaptable to future variants or to other, unrelated viruses. Because the biochemistry of RNAi is very precisely described, it is now possible to design siRNAs with high predicted activity and specificity using only computational tools. While previous siRNA design algorithms tended to rely on simplistic strategies (raising fully complementary siRNAs against targets of interest), our approach uses the most up-to-date mechanistic description of RNAi to allow mismatches at tolerable positions and to force them at beneficial positions, while optimizing siRNA duplex asymmetry. Our pipeline proposes 8 siRNAs against SARS-CoV-2, and ex vivo assessment confirms the high antiviral activity of 6 out of 8 siRNAs, also achieving excellent variant coverage (with several 3-siRNA combinations recognizing each correctly-sequenced variant as of September 2022). Our approach is easily generalizable to other viruses as long as a variant genome database is available. With siRNA delivery procedures being currently improved, RNAi could therefore become an efficient and versatile antiviral therapeutic strategy.

molecular biology↗