Biology subjects

Holm, L.

Publications and source records attributed to Holm, L..

12 recordsLinked to original sources

bioRxiv2026

Cryo-EM of a Divergent Herpesvirus Reveals Structural Conservation and Novelty Including a Portal-Vertex Tegument Protein with Multiple Macrodomain-like Folds

Ictalurid herpesvirus 1 (channel catfish virus) is an evolutionarily distant relative of human herpesviruses, from which it is thought to have diverged >400M years ago. Using cryogenic electron microscopy (cryo-EM) combined with symmetry-breaking and particle subtraction approaches, we determined structures of both the immature capsid and virion of IcHV-1. Due to limited genome annotation, we used the machine learning-based tool ModelAngelo for de novo model building, enabling unambiguous protein identification even at marginal resolutions. Notably, the IcHV-1 virion was found to have a substantial and elaborate portal-vertex associated tegument (PVAT) complex. Overall, we determined the identities and structures of ten IcHV-1 proteins: the major capsid protein; the triplex proteins; two novel virion-associated inner tegument proteins; the portal protein; and a further four PVAT proteins. Our findings reveal a high degree of fold conservation in the core capsid proteins when compared with those of human herpesviruses, but also considerable structural novelty, including for the first time in a herpesvirus, identification of a protein that has four putative macrodomains.

microbiology↗

bioRxiv2023

Global analysis of aging-related protein structural changes uncovers enzyme polymerization-based control of longevity

Aging is associated with progressive phenotypic changes over time. Virtually all cellular phenotypes are produced by proteins and structural alterations in proteins can lead to age-related diseases. Nonetheless, comprehensive knowledge of proteins undergoing structural-functional changes during cellular aging and their contribution to age-related phenotypes is lacking. Here, we conducted proteome-wide analysis of early age-related protein structural changes in budding yeast using limited proteolysis-mass spectrometry. The results, compiled in online ProtAge-catalog, unravelled age-related functional changes in regulators of translation, protein folding and amino acid metabolism. Mechanistically, we found that folded glutamate synthase Glt1 polymerizes into supramolecular self-assemblies during aging causing breakdown of cellular amino acid homeostasis. Inhibiting Glt1 polymerization by mutating the polymerization interface restored amino acid levels in aged cells, attenuated mitochondrial dysfunction and led to life span extension. Altogether, this comprehensive map of protein structural changes enables identifying novel mechanisms of age-related phenotypes and offers opportunities for their reversal.

cell biology↗

bioRxiv2022

Optimizing InterProScan feature processinggenerates a surprisingly good protein functionprediction method

MotivationAutomated protein Function Prediction (AFP) is an intensively studied topic. Most of this research focuses on methods that combine multiple data sources, while fewer articles look for the most efficient ways to use a single data source. Therefore, we wanted to test how different preprocessing methods and classifiers would perform in the AFP task when we process the output from the InterProscan (IPS). Especially, we present novel preprocessing methods, less used classifiers and inclusion of species taxonomy. We also test classifier stacking for combining tested classifier results. Methods are tested with in-house data and CAFA3 competition evaluation data. ResultsWe show that including IPS localisation and taxonomy to the data improves results. Also the stacking improves the performance. Surprisingly, our best performing methods outperformed all international CAFA3 competition participants in most tests. Altogether, the results show how preprocessing and classifier combinations are beneficial in the AFP task. Contactpetri.toronen(AT)helsinki.fi Supplementary informationSupplementary text is available at the project web site http://ekhidna2.biocenter.helsinki.fi/AFP/ and at the end of this document.

bioinformatics↗

bioRxiv2022

Fragmented habitat compensates for the adverse effects of genetic bottleneck

In the face of human-caused biodiversity crisis, understanding the theoretical basis of conservation efforts of endangered species and populations has become increasingly important. According to population genetics theory, population subdivision helps organisms retain genetic diversity, crucial for adaptation in a changing environment. Habitat shape is thought to be important for generating and maintaining population subdivision, but empirical cases are needed to test this assumption. We studied Saimaa ringed seals, landlocked in a labyrinthine lake and recovering from a drastic bottleneck, by whole-genome sequencing 105 individuals and additional individuals from three other ringed seal subspecies. We analyzed the distribution of variation and genetic relatedness among the individuals in relation to the habitat shape. Despite a severe history of a genetic bottleneck with prevalent homozygosity in Saimaa ringed seals, we found evidence for population structure mirroring the subregions of the lake. Highlighting the significance of habitat connectivity in conservation biology and the power of genomic tools in understanding its impact, genome-wide analyses showed that the subpopulations had retained unique variation and largely complementary patterns of homozygosity. Integration of genetic analyses in conservation decisions gives hope to Saimaa ringed seals and other endangered species in fragmented habitats.

evolutionary biology↗

bioRxiv2021

Ubiquitin E3 ligases Atrogin-1 and MuRF1 protein contents are differentially regulated in the rapamycin-sensitive mTOR-S6K1 signaling pathway in C2C12 myotubes

Muscle-specific ubiquitin E3 ligases, Atrogin-1 and MuRF1, are highly expressed in multiple conditions of skeletal muscle atrophy. The PI3K/Akt/FoxO signaling pathway is well known to regulate Atrogin-1 and MuRF1 gene expressions. Evidence supporting this is largely based on stimuli by insulin and IGF-1, that activate anabolic signaling, including Akt and Akt-dependent transcription factors. However, Akt activation also activates the mammalian target of rapamycin complex 1 (mTORC1) which induces skeletal muscle hypertrophy. However, whether mTORC1-dependent signaling has a role in regulating Atrogin-1 and/or MuRF1 gene and protein expression is currently unclear. In this study, we confirmed that activation of insulin-mediated Akt signaling suppresses both Atrogin-1 and MuRF1 protein content and that inhibition of Akt increases both Atrogin-1 and MuRF1 protein content in C2C12 myotubes. Interestingly, inhibition of mTORC1 using a specific mTORC1 inhibitor, rapamycin, increased Atrogin-1, but not MuRF1, protein content. Furthermore, activation of AMP-activated protein kinase (AMPK), a negative regulator of the mTORC1 signaling pathway, also showed distinct time-dependent changes between Atrogin-1 and MuRF1 protein content, suggesting differential regulatory mechanisms between Atrogin-1 and MuRF1 protein content. To further explore the downstream of mTORC1 signaling, we employed a specific S6K1 inhibitor, PF-4708671, and found that Atrogin-1 protein content was dose-dependently increased with PF-4708671 treatment, whereas MuRF1 protein content was not significantly altered. Overall, our results indicate that Atrogin-1 and MuRF1 protein contents are regulated by different mechanisms, the downstream of Akt, and that Atrogin-1 protein content can be regulated by rapamycin-sensitive mTOR-S6K1 dependent signaling pathway.

cell biology↗

bioRxiv2021

Gut microbiome and its cofactors are linked tolipoprotein distribution profiles

Increasing evidence indicates that the gut microbiome (GM) plays an important role in the etiology of dyslipidemia. To date, however, no in-depth characterization of the associations between GM and its metabolic attributes with deep profiling of lipoproteins distributions (LPD) among healthy individuals has been conducted. To determine associations and contributions of GM composition and its cofactors with distribution profiles of lipoprotein subfractions, we studied blood plasma LPD, fecal short-chain fatty acids (SCFA) and GM of 262 healthy Danish subjects aged 19-89 years. Stratification of LPD segregated subjects into three clusters of profiles that reflected differences in the lipoprotein subclasses, corresponded well with limits of recommended levels of main lipoprotein fractions and were largely explained by host characteristics such as age and body mass index. Higher levels of HDL, particularly driven by large subfractions (HDL2a and HDL2b), were associated with a higher relative abundance of Ruminococcaceae and Christensenellaceae. Increasing levels of total cholesterol and LDL, which were primarily associated with large 1 and 2 subclasses, were positively associated with Lachnospiraceae and Coriobacteriaceae, and negatively with Bacteroidaceae and Bifidobacteriaceae. Metagenome sequencing showed a higher abundance of genes involved in the biosynthesis of multiple B-vitamins and SCFA metabolism among subjects with healthier LPD profiles. Metagenomic assembled genomes (MAGs) affiliated mainly to Eggerthellaceae and Clostridiales were identified as the contributors of these genes and whose relative abundance correlated positively with larger subfractions of HDL. The results of this study demonstrate that remarkable differences in composition and metabolic traits of the GM are associated with variations in LPD among healthy subjects. Findings from this study provide evidence for GM considerations in future research aiming to shade light on mechanisms of the GM - dyslipidemia axis.

systems biology↗

bioRxiv2021

Human blood lipoprotein predictions from 1H NMR spectra: protocol, model performances and cage of covariance

Lipoprotein subfractions are biomarkers for early diagnosis of cardiovascular diseases. The reference method, ultracentrifugation, for measuring lipoproteins is time consuming and there is a need to develop a rapid method for cohort screenings. Here we present partial least squares regression models developed using 1H-NMR spectra and concentrations of lipoproteins as measured by ultracentrifugation on 316 healthy Danes. Different regions of the 1H-NMR spectra representing signals of the lipoproteins and different lipid species were investigated to develop parsimonious, reliable and best performing prediction models. 65 LP main and subfractions were predictable with an accuracy Q2 of > 0.6 on test set samples. The models were tested on an independent cohort of 290 healthy Swedes with predicted and reference values matching by up to 85-95%. The software was developed to predict lipoproteins in human blood using 1H-NMR spectra and made freely available to be applied for future cohort screenings.

systems biology↗

bioRxiv2020

Novel NGS Pipeline for Virus Discovery from a Wide Spectrum of Hosts and Sample Types

The study of the microbiome data holds great potential for elucidating the biological and metabolic functioning of living organisms and their role in the environment. Metagenomic analyses have shown that humans, along with e.g. domestic animals, wildlife and arthropods, are colonized by an immense community of viruses. The current Coronavirus pandemic (COVID-19) heightens the need to rapidly detect previously unknown viruses in an unbiased way. The increasing availability of metagenomic data in this era of next-generation sequencing (NGS), along with increasingly affordable sequencing technologies, highlight the need for reliable and comprehensive methods to manage such data. In this article, we present a novel stand-alone pipeline called LAZYPIPE for identifying both previously known and novel viruses in host-associated or environmental samples and give examples of virus discovery based on it. LAZYPIPE is a Unix-based pipeline for automated assembling and taxonomic profiling of NGS libraries implemented as a collection of C++, Perl, and R scripts.

bioinformatics↗

bioRxiv2020

Gut Mycobiome Dysbiosis Is Linked to Hypertriglyceridemia among Home Dwelling Elderly Danes

Gut microbial dysbiosis have been linked to frailty in elderly, yet the presence of fungal communities and their possible association with host health are little understood. This study attempts to identify gut microbial fungal associations with the progression of atherogenic dyslipidemia in a population of older adults by investigating the interplay between dietary intake, gut mycobiome composition, plasma and fecal metabolome and anthropometric/body-composition measurements of 99 Danes aged 65 to 81 (69.57 {+/-} 3.64) years. The gut mycobiome composition were determined by high-throughput sequencing of internal transcribed spacer (ITS2) gene amplicons, while the plasma and fecal metabolome was determined by GC-MS. The gut microbiome of the subjects investigated is home to three main eukaryotic phyla, namely Ascomyco-ta, Basidiomycota and Zygomycota, with genera Penicillium, Candida, and Aspergillus being particularly common. Hypertriglyceridemia was associated with fewer observed fungal species, and Bray-Curtis dissimilarity matrix-based analysis showed significant (p < 0.05) clustering according to fasting levels of circulating plasma triglycerides (Tg) and very low-density lipoprotein (VLDL) cholesterol fasting levels, respectively. Higher levels of Tg and VLDL cholesterol significantly associates with increased relative abundance of genus Penicillium, and Saccha:ramyces likely mediated by a higher dietary fatty acids intake (p < 0.05), and Sac-charomyces, Debaryomyces, Candida, Agaricus and Starmerella were moderately associated with SCF As groups. Collectively, these findings suggest that gut mycobiome dysbiosis on older adults is associated with hypertriglyceridemia, a known risk factor for development of cardiovascular disease.

microbiology↗

bioRxiv2019

Physical fitness in community dwelling older adults is linked to dietary intake, gut microbiota and metabolomic signatures

When humans age, changes in body composition arise along with lifestyle-associated disorders influencing fitness and physical decline. Here we provide a comprehensive view of dietary intake, physical activity, gut microbiota (GM) and host metabolome in relation to physical fitness of 207 community dwelling subjects aged +65 years. Stratification on anthropometric/body-composition/physical-performance measurements (ABPm) variables identified two phenotypes (high/low-fitness) clearly linked to dietary intake, physical activity, GM and host metabolome patterns. Strikingly, despite a higher energy intake high-fitness subjects were characterized by leaner bodies and lower fasting proinsulin-C-peptide/blood glucose levels in a mechanism likely driven by higher dietary-fiber intake, physical activity and increased abundance of Bifidobacteriales and Clostridiales species in GM and associated metabolites (i.e. enterolactone). These factors explained 50.1% of the individual variation in physical fitness. We propose that targeting dietary strategies for modulation of GM and host metabolome interactions may allow establishing therapeutic approaches to delay and possibly revert comorbidities of aging.

cell biology↗

bioRxiv2018

Novel Comparison of Evaluation Metrics for Gene Ontology Classifiers Reveals Drastic Performance Differences

Automated protein annotation using the Gene Ontology (GO) plays an important role in the biosciences. Evaluation has always been considered central to developing novel annotation methods, but little attention has been paid to the evaluation metrics themselves. Evaluation metrics define how well an annotation method performs and allows for them to be ranked against one another. Unfortunately, most of these metrics were adopted from the machine learning literature without establishing whether they were appropriate for GO annotations.\n\nWe propose a novel approach for comparing GO evaluation metrics called Artificial Dilution Series (ADS). Our approach uses existing annotation data to generate a series of annotation sets with different levels of correctness (referred to as their signal level). We calculate the evaluation metric being tested for each annotation set in the series, allowing us to identify whether it can separate different signal levels. Finally, we contrast these results with several false positive annotation sets, which are designed to expose systematic weaknesses in GO assessment.\n\nWe compared 37 evaluation metrics for GO annotation using ADS and identified drastic differences between metrics. We show that some metrics struggle to differentiate between different signal levels, while others give erroneously high scores to the false positive data sets. Based on our findings, we provide guidelines on which evaluation metrics perform well with the Gene Ontology and propose improvements to several well-known evaluation metrics. In general, we argue that evaluation metrics should be tested for their performance and we provide software for this purpose (https://bitbucket.org/plyusnin/ads/). ADS is applicable to other areas of science where the evaluation of prediction results is non-trivial.\n\nAuthor SummaryIn the biosciences, predictive methods are becoming increasingly necessary as novel sequences are generated at an ever-increasing rate. The volume of sequence data necessitates Automated Function Prediction (AFP) as manual curation is often impossible. Unfortunately, selecting the best AFP method is complicated by researchers using different evaluation metrics. Furthermore, many commonly-used metrics can give misleading results. We argue that the use of poor metrics in AFP evaluation is a result of the lack of methods to benchmark the metrics themselves. We propose an approach called Artificial Dilution Series (ADS). ADS uses existing data sets to generate multiple artificial AFP results, where each result has a controlled error rate. We use ADS to understand whether different metrics can distinguish between results with known quantities of error. Our results highlight dramatic differences in performance between evaluation metrics.

bioinformatics↗

bioRxiv2018

Bracketing phenotypic limits of mammalian hybridization

An increasing number of mammalian species have been shown to have a history of hybridization and introgression based on genetic analyses. Only relatively few fossils, however, preserve genetic material and morphology must be used to identify the species and determine whether morphologically intermediate fossils could represent hybrids. Because dental and cranial fossils are typically the key body parts studied in mammalian paleontology, here we bracket the potential for phenotypically extreme hybridizations by examining uniquely preserved cranio-dental material of a captive hybrid between gray and ringed seals. We analyzed how distinct these species are genetically and morphologically, how easy it is to identify the hybrids using morphology, and whether comparable hybridizations happen in the wild. We show that the genetic distance between these species is more than twice the modern human-Neanderthal distance, but still within that of morphologically similar species-pairs known to hybridize. In contrast, morphological and developmental analyses show gray and ringed seals to be highly disparate, and that the hybrid is a predictable intermediate. Genetic analyses of the parent populations reveal introgression in the wild, suggesting that gray-ringed seal hybridization is not limited to captivity. Taken together, gray and ringed seals appear to be in an adaptive radiation phase of evolution, showing large morphological differences relative to their comparatively modest genetic distance. Because morphological similarity does not always correlate with genetic distance in nature, we postulate that there is considerable potential for mammalian hybridization between phenotypically disparate taxa.

evolutionary biology↗