Biology subjects

Kuusisto, J.

Publications and source records attributed to Kuusisto, J..

9 recordsLinked to original sources

bioRxiv2026

Length-dependent hypercontractility decoupled from cellular adaptations is the initial phenotype in MYBPC3-haploinsufficient iPSC-derived engineered cardiac tissues

Hypertrophic cardiomyopathy (HCM) is often characterized by a complex landscape of secondary compensatory changes, making it difficult to distinguish primary events triggered by the underlying genetic variants. Here, using patient-derived iPSC-engineered heart tissues cultured in conditions minimizing external stimuli, we identify the initial phenotype of the HCM inducing variant MYBPC3-Q1061X. We demonstrate that initially MYBPC3 haploinsufficiency leads to robust, muscle length-dependent hypercontractility with emerging diastolic dysfunction at higher contraction rates. This phenotype is entirely decoupled from early transcriptional and metabolic stress responses, as evidenced by physiological phenotype and metabolite and cell type-specific transcriptional profiles. Mathematical modeling reveals that the hypercontractile phenotype can be translated to adult cardiac muscle with a small change in myosin availability, amplified by inherent cooperativity within the sarcomere that is instrumental in enhancing the force and the speed of the contraction cycle. Despite the apparent calcium sensitization, these sarcomeric changes do not affect excitation-contraction coupling or calcium buffering in this proximal stage. These results suggest that the primary biophysical defect in MYBPC3-haploinsufficiency manifests as a latent mechanical hypersensitivity that precedes secondary cellular remodeling and functional instability. This initial phenotype provides a window for preventive therapeutic intervention before the onset of permanent cellular remodeling in the heart.

physiology↗

bioRxiv2024

Human iPSC-derived pericyte-like cells carrying APP Swedish mutation overproduce beta-amyloid and induce cerebral amyloid angiopathy-like changes

Alzheimers disease (AD) patients often exhibit cerebral amyloid angiopathy (CAA), i.e beta-amyloid (A{beta}) accumulation within cerebral blood vessels causing cerebrovascular dysfunction. Pericytes wrap around vascular capillaries, thus regulating cerebral blood flow, angiogenesis, and vessel stability. Vascular dysfunction can promote the development and progression of neurodegenerative diseases, yet the specific contribution of pericytes to AD pathology remains unclear. Here we show that human induced pluripotent stem cell (iPSC)-derived pericyte-like cells (iPLCs) can generate A{beta} peptides, and that the cells carrying Swedish mutation in amyloid precursor protein (APPswe) secrete 10 times more A{beta}1-42 than the control cells. Additionally, APPswe iPLCs have an impaired capacity to support angiogenesis and barrier integrity, exhibit a prolonged contractile response, and produce increased levels of pro-inflammatory cytokines upon inflammatory stimulation. These functional alterations in APPswe iPLCs are accompanied by transcriptional upregulation of actin cytoskeleton and extracellular matrix organization-related genes. Therefore, the APPswe mutation in iPLCs recapitulates several features of CAA pathology in vitro. Our iPSC-based vascular cell model may thus serve as a platform for drug discovery targeting vascular dysfunction in AD.

cell biology↗

bioRxiv2023

Adipose tissue eQTL meta-analysis reveals the contribution of allelic heterogeneity to gene expression regulation and cardiometabolic traits

Complete characterization of the genetic effects on gene expression is needed to elucidate tissue biology and the etiology of complex traits. Here, we analyzed 2,344 subcutaneous adipose tissue samples and identified 34K conditionally distinct expression quantitative trait locus (eQTL) signals in 18K genes. Over half of eQTL genes exhibited at least two eQTL signals. Compared to primary signals, non-primary signals had lower effect sizes, lower minor allele frequencies, and less promoter enrichment; they corresponded to genes with higher heritability and higher tolerance for loss of function. Colocalization of eQTL with conditionally distinct genome-wide association study signals for 28 cardiometabolic traits identified 3,605 eQTL signals for 1,861 genes. Inclusion of non-primary eQTL signals increased colocalized signals by 46%. Among 30 genes with [≥]2 pairs of colocalized signals, 21 showed a mediating gene dosage effect on the trait. Thus, expanded eQTL identification reveals more mechanisms underlying complex traits and improves understanding of the complexity of gene expression regulation.

genomics↗

bioRxiv2022

SARS-CoV-2 infection of human neurons requires endosomal cell entry and can be blocked by inhibitors of host phosphoinositol-5 kinase

COVID-19 is a disease caused by coronavirus SARS-CoV-2. In addition to respiratory illness, COVID-19 patients exhibit neurological symptoms that can last from weeks to months (long COVID). It is unclear whether these neurological manifestations are due to infection of brain cells. We found that a small fraction of cortical neurons, but not astrocytes, were naturally susceptible to SARS-CoV-2. Based on the inhibitory effect of blocking antibodies, the infection seemed to depend on the receptor angiotensin-converting enzyme 2 (ACE2), which was expressed at very low levels. Although only a limited number of neurons was infectable, the infection was productive, as demonstrated by the presence of double-stranded RNA in the cytoplasm (the hallmark of viral replication), abundant synthesis of viral late genes localized throughout the neuronal cell, and an increase in viral RNA in the culture medium within the first 48 h of infection (viral release). The productive entry of SARS-CoV-2 requires the fusion of the viral and cellular membranes, which results in the delivery of viral genome into the cytoplasm of the target cell. The fusion is triggered by proteolytic cleavage of the viral surface protein spike, which can occur at the plasma membrane or from endo/lysosomes. Using specific combinations of small-molecule inhibitors, we found that SARS-CoV-2 infection of human neurons was insensitive to nafamostat and camostat, which inhibit cellular serine proteases found on the cell surface, including TMPRSS2. In contrast, the infection was blocked by apilimod, an inhibitor of phosphatidyl-inositol 5 kinase (PIK5K) that regulates endosomal maturation. ImportanceCOVID-19 is a disease caused by coronavirus SARS-CoV-2. Millions of patients display neurological symptoms, including headache, impairment of memory, seizures and encephalopathy, as well as anatomical abnormalities such as changes in brain morphology. Whether these symptoms are linked to brain infection is not clear. The mechanism of the virus entry into neurons has also not been characterized. Here we investigated SARS-CoV-2 infection using a human iPSC-derived neural cell model and found that a small fraction of cortical neurons was naturally susceptible to infection. The infection depended on the ACE2 receptor and was productive. We also found that the virus used the late endosomal/lysosomal pathway for cell entry and that the infection could be blocked by apilimod, an inhibitor of the cellular phosphatidyl-inositol 5 kinase.

neuroscience↗

bioRxiv2021

Multiparametric assessment of cellular lipid metabolism in hypercholesterolemia

Systematic insight into cellular dysfunctions can improve understanding of disease etiology, risk assessment and patient stratification. We present a multiparametric high-content imaging platform enabling quantification of low-density lipoprotein (LDL) uptake and lipid storage in cytoplasmic droplets of primary leukocyte subpopulations. We validated this platform with samples from 65 individuals with variable blood LDL-cholesterol (LDL-c) levels, including familial hypercholesterolemia (FH) and non-FH subjects. We integrated lipid storage data into a novel readout, lipid mobilization, measuring the efficiency with which cells deplete lipid reservoirs. Lipid mobilization correlated positively with LDL uptake and negatively with hypercholesterolemia and age, improving differentiation of individuals with normal and elevated LDL-c. Moreover, combination of cell-based readouts with a polygenic risk score for LDL-c explained hypercholesterolemia better than the genetic risk score alone. This platform provides functional insights into cellular lipid trafficking from a few mls of blood and is applicable to dissect metabolic disorders, such as hypercholesterolemia. MotivationWe have limited information on how cellular lipid uptake and processing differ between individuals and influence the development of metabolic diseases, such as hypercholesterolemia. Available assays are labor intensive, require skilled personnel and are difficult to scale to higher throughput, making it challenging to obtain systematic functional cell-based data from individuals. To overcome this problem, we established a scalable automated analysis pipeline enabling reliable quantification of multiple cellular readouts, including lipid uptake, storage and mobilization, from different white blood cell populations. This approach provides new personalized insights into the cellular basis of hypercholesterolemia and obesity. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/440471v4_ufig1.gif" ALT="Figure 1"> View larger version (84K): org.highwire.dtl.DTLVardef@9222c1org.highwire.dtl.DTLVardef@27e289org.highwire.dtl.DTLVardef@89ba82org.highwire.dtl.DTLVardef@33c9cd_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2020

Association of Structural Variation with Cardiometabolic Traits in Finns

The contribution of genome structural variation (SV) to quantitative traits associated with cardiometabolic diseases remains largely unknown. Here, we present the results of a study examining genetic association between SVs and cardiometabolic traits in the Finnish population. We used sensitive methods to identify and genotype 129,166 high-confidence SVs from deep whole genome sequencing (WGS) data of 4,848 individuals. We tested the 64,572 common and low frequency SVs for association with 116 quantitative traits, and tested candidate associations using exome sequencing and array genotype data from an additional 15,205 individuals. We discovered 31 genome-wide significant associations at 15 loci, including two novel loci at which SVs have strong phenotypic effects: (1) a deletion of the ALB gene promoter that is greatly enriched in the Finnish population and causes decreased serum albumin level in carriers (p=1.47x10-54), and is also associated with increased levels of total cholesterol (p=1.22x10-28) and 14 additional cholesterol-related traits, and (2) a multiallelic copy number variant (CNV) at PDPR that is strongly associated with pyruvate (p=4.81x10-21) and alanine (p=6.14x10-12) levels and resides within a structurally complex genomic region that has accumulated many rearrangements over evolutionary time. We also confirmed six previously reported associations, including five led by stronger signals in single nucleotide variants (SNVs), and one linking recurrent HP gene deletion and cholesterol levels (p=6.24x10-10), which was also found to be strongly associated with increased glycoprotein level (p=3.53x10-35). Our study confirms that integrating SVs in trait-mapping studies will expand our knowledge of genetic factors underlying disease risk.

genetics↗

bioRxiv2020

Microglia orchestrate neuronal activity in brain organoids

Human stem cell-derived brain organoids provide a physiologically relevant in vitro 3D brain model for studies of neurological development that are unique to the human nervous system. Prior studies have reported protocols that support the maturation of microglia from mesodermal progenitors leading to innately developing microglia within the organoids. However, although microglia are known to support neuronal development in rodents, none of the previous studies have reported what is the impact of microglia on neuronal growth and maturation in human brain organoids. Here we show that incorporating microglial progenitors into the developing organoid supports neuronal maturation, the emergence of neurons capable of firing repetitive action potentials and the appearance of synaptic and neuronal bursting activity. Immunocompetent organoids enable experimental strategies for interrogating fundamental questions on microglial and neuronal diversity and function during human brain development.

neuroscience↗

bioRxiv2020

Machine learning reveals time-varying microbial predictors with complex effects on glucose regulation

The incidence of type 2 diabetes (T2D) has been increasing globally and a growing body of evidence links type 2 diabetes with altered microbiota composition. Type 2 diabetes is preceded by a long pre-diabetic state characterized by changes in various metabolic parameters. We tested whether the gut microbiome could have predictive potential for T2D development during the healthy and pre-diabetic disease stages. We used prospective data of 608 well-phenotyped Finnish men collected from the population-based Metabolic Syndrome In Men (METSIM) study to build machine learning models for predicting continuous glucose and insulin measures in a shorter (1.5 year) and longer (4.5 year) period. Our results show that the inclusion of gut microbiome improves prediction accuracy for modelling T2D associated parameters such as glycosylated hemoglobin and insulin measures. We identified novel microbial biomarkers and described their effects on the predictions using interpretable machine learning techniques, which revealed complex linear and non-linear associations. Additionally, the modelling strategy carried out allowed us to compare the stability of model performances and biomarker selection, also revealing differences in short-term and long-term predictions. The identified microbiome biomarkers provide a predictive measure for various metabolic traits related to T2D, thus providing an additional parameter for personal risk assessment. Our work also highlights the need for robust modelling strategies and the value of interpretable machine learning. ImportanceRecent studies have shown a clear link between gut microbiota and type 2 diabetes. However, current results are based on cross-sectional studies that aim to determine the microbial dysbiosis when the disease is already prevalent. In order to consider microbiome as a factor in disease risk assessment, prospective studies are needed. Our study is the first study that assesses the gut microbiome as a predictive measure for several type 2 diabetes associated parameters in a longitudinal study setting. Our results revealed a number of novel microbial biomarkers that can improve the prediction accuracy for continuous insulin measures and glycosylated hemoglobin levels. These results make the prospect of using microbiome in personalized medicine promising.

microbiology↗

bioRxiv2017

Epigenome-wide association in adipose tissue from the METSIM cohort identifies novel loci and the involvement of adipocytes and macrophages in diabetes traits

Most epigenome-wide association studies to date have been conducted in blood. However, metabolic syndrome is mediated by a dysregulation of adiposity and therefore it is critical to study adipose tissue in order to understand the effects of this syndrome on epigenomes. To determine if natural variation in DNA methylation was associated with metabolic syndrome traits, we profiled global methylation levels in subcutaneous abdominal adipose tissue. We measured association between 32 clinical traits related to diabetes and obesity in 201 people from the Metabolic Syndrome In Men cohort. We performed epigenome-wide association studies between DNA methylation levels and traits, and identified associations for 13 clinical traits in 21 loci. We prioritized candidate genes in these loci using eQTL, and identified 18 high confidence candidate genes, including known and novel genes associated with diabetes and obesity traits. Using methylation deconvolution, we examined which cell types may be mediating the associations, and concluded that most of the loci we identified were specific to adipocytes. We determined whether the abundance of cell types varies with metabolic traits, and found that macrophages increased in abundance with the severity of metabolic syndrome traits. Finally, we developed a DNA methylation based biomarker to assess type II diabetes risk in adipose tissue. In conclusion, our results demonstrate that profiling DNA methylation in adipose tissue is a powerful tool for understanding the molecular effects of metabolic syndrome on adipose tissue, and can be used in conjunction with traditional genetic analyses to further characterize this disorder.

genetics↗