Biology subjects

Singer, J. M.

Publications and source records attributed to Singer, J. M..

10 recordsLinked to original sources

bioRxiv2023

Effects of hepatic mitochondrial pyruvate carrier deficiency on de novo lipogenesis and glycerol-mediated gluconeogenesis in mice

The liver coordinates the systemic response to nutrient deprivation and availability by producing glucose from gluconeogenesis during fasting and synthesizing lipids via de novo lipogenesis (DNL) when carbohydrates are abundant. Mitochondrial pyruvate metabolism is thought to play important roles in both gluconeogenesis and DNL. We examined the effects of hepatocyte-specific mitochondrial pyruvate carrier (MPC) deletion on the fasting-refeeding response. Rates of DNL during refeeding were impaired by liver MPC deletion, but this did not reduce intrahepatic lipid content. During fasting, glycerol is converted to glucose by two pathways; a direct cytosolic pathway essentially reversing glycolysis and an indirect mitochondrial pathway requiring the MPC. MPC deletion reduced the incorporation of 13C-glycerol into TCA cycle metabolites but not into newly synthesized glucose. However, suppression of glycerol metabolism did not affect glucose concentrations in fasted hepatocyte-specific MPC-deficient mice. Thus, glucose production by kidney and intestine may compensate for MPC deficiency in hepatocytes.

physiology↗

bioRxiv2023

Adipocyte lipin 1 is positively associated with metabolic health in humans and regulates systemic metabolism in mice.

Dysfunctional adipose tissue is believed to promote the development of hepatic steatosis and systemic insulin resistance, but many of the mechanisms involved are still unclear. Lipin 1 catalyzes the conversion of phosphatidic acid to diacylglycerol (DAG), the penultimate step of triglyceride synthesis, which is essential for lipid storage. Herein we found that adipose tissue LPIN1 expression is decreased in people with obesity compared to lean subjects and low LPIN1 expression correlated with multi-tissue insulin resistance and increased rates of hepatic de novo lipogenesis. Comprehensive metabolic and multi-omic phenotyping demonstrated that adipocyte-specific Lpin1-/- mice had a metabolically-unhealthy phenotype, including liver and skeletal muscle insulin resistance, hepatic steatosis, increased hepatic de novo lipogenesis, and transcriptomic signatures of nonalcoholic steatohepatitis that was exacerbated by high-fat diets. We conclude that adipocyte lipin 1-mediated lipid storage is vital for preserving adipose tissue and systemic metabolic health and its loss predisposes mice to nonalcoholic steatohepatitis.

physiology↗

bioRxiv2022

Comparison and evaluation of data-driven protein stability prediction models

Predicting protein stability is important to protein engineering yet poses unsolved challenges. Computational costs associated with physics-based models, and the limited amount of data available to support data-driven models, have left stability prediction behind the prediction of structure. New data and advancements in modeling approaches now afford greater opportunities to solve this challenge. We evaluate a set of data-driven prediction models using a large, newly published dataset of various synthetic proteins and their experimental stability data. We test the models in two separate tasks, exercising extrapolation to new protein classes and prediction of the effects on stability of small mutations. Small convolutional neural networks trained from scratch on stability data and large protein embedding models passed through simple downstream models trained on stability data are both able to predict stability comparably well. The largest of the embedding models yields the best performance in all tasks and metrics. We also explored the marginal performance gains seen with two ensemble models.

bioinformatics↗

bioRxiv2022

Monoacylglycerol O-acyltransferase 1 is required for adipocyte differentiation in vitro but does not affect adiposity in mice

ObjectiveMonoacylglycerol O-acyltransferase 1 (Mogat1), a lipogenic enzyme that converts monoacylglycerol to diacylglycerol, is highly expressed in adipocytes and may regulate lipolysis by re-esterifying fatty acids released during times when lipolytic rates are low. However, the role of Mogat1 in regulating adipocyte fat storage during differentiation and diet-induced obesity is relatively understudied. MethodsHere we generated adipocyte-specific Mogat1 knockout mice and subjected them to a high-fat diet to determine the effects of Mogat1 deficiency on diet-induced obesity. We also used Mogat1 floxed mice to develop preadipocyte cell lines wherein Mogat1 could be conditionally knocked out to study adipocyte differentiation in vitro. ResultsIn preadipocytes, we found that Mogat1 knockout at the onset of preadipocyte differentiation prevented the accumulation of glycerolipids and reduced the differentiation capacity of preadipocytes. However, the loss of adipocyte Mogat1 did not affect weight gain or fat mass induced by high-fat diet in mice. Furthermore, loss of Mogat1 in adipocytes did not affect plasma lipid or glucose concentrations or insulin tolerance. ConclusionsThese data suggest Mogat1 may play a role in adipocyte differentiation in vitro but not adipose tissue expansion in response to nutrient overload in mice. STUDY IMPORTANCEO_ST_ABSWhat is already known?C_ST_ABSO_LIAdipose tissue expansion through adipocyte precursor cell differentiation is critical for proper lipid storage during nutrient overload. C_LIO_LIMonoacylglycerol O-acyltransferase 1 (Mogat1), a lipogenic enzyme, is highly induced during adipocyte differentiation of human and mouse precursor cells and is reduced in patients with obesity and metabolic dysfunction. C_LI What does this study add?O_LIMogat1 deletion during early adipocyte differentiation reduces differentiation capacity, adipogenic gene expression and lowers glycerolipid content of differentiated adipocytes. C_LIO_LIAdipocyte Mogat1 expression is dispensable for adiposity and metabolic outcomes high-fat fed mice and suggests compensation from other glycerolipid synthesis enzymes. C_LI How might these results change the direction of research?O_LIUnderstanding the molecular mechanisms of glycerolipid metabolism healthy adipose tissue expansion. C_LI

molecular biology↗

bioRxiv2021

Silencing alanine transaminase 2 in diabetic liver attenuates hyperglycemia by reducing gluconeogenesis from amino acids

Hepatic gluconeogenesis from amino acids contributes significantly to diabetic hyperglycemia, but the molecular mechanisms involved are incompletely understood. Alanine transaminases (ALT1 and ALT2) catalyze the interconversion of alanine and pyruvate, which is required for gluconeogenesis from alanine. We found that ALT2 was overexpressed in liver of diet-induced obese and db/db mice and that the expression of the gene encoding ALT2 (GPT2) was downregulated following bariatric surgery in people with obesity. The increased hepatic expression of Gpt2 in db/db liver was mediated by activating transcription factor 4; an endoplasmic reticulum stress-activated transcription factor. Hepatocyte-specific knockout of Gpt2 attenuated incorporation of 13C-alanine into newly synthesized glucose by hepatocytes. In vivo Gpt2 knockdown or knockout in liver had no effect on glucose concentrations in lean mice, but Gpt2 suppression alleviated hyperglycemia in db/db mice. These data suggest that ALT2 plays a significant role in hepatic gluconeogenesis from amino acids in diabetes.

physiology↗

bioRxiv2021

Improved data sets and evaluation methods for the automatic prediction of DNA-binding proteins

MotivationAccurate automatic annotation of protein function relies on both innovative models and robust datasets. Due to their importance in biological processes, the identification of DNA-binding proteins directly from protein sequence has been the focus of many studies. However, the data sets used to train and evaluate these methods have suffered from substantial flaws. We describe some of the weaknesses of the data sets used in previous DNA-binding protein literature and provide several new data sets addressing these problems. We suggest new evaluative benchmark tasks that more realistically assess real-world performance for protein annotation models. We propose a simple new model for the prediction of DNA-binding proteins and compare its performance on the improved data sets to two previously published models. Additionally, we provide extensive tests showing how the best models predict across taxonomies. ResultsOur new gradient boosting model, which uses features derived from a published protein language model, outperforms the earlier models. Perhaps surprisingly, so does a baseline nearest neighbor model using BLAST percent identity. We evaluate the sensitivity of these models to perturbations of DNA-binding regions and control regions of protein sequences. The successful data-driven models learn to focus on DNA-binding regions. When predicting across taxonomies, the best models are highly accurate across species in the same kingdom and can provide some information when predicting across kingdoms. Code and Data AvailabilityAll the code and data for this paper can be found at https://github.com/AZaitzeff/tools_for_dna_binding_proteins. Contactalexander.zaitzeff@twosixtech.com

bioinformatics↗

bioRxiv2021

Large-scale design and refinement of stable proteins using sequence-only models

Engineered proteins generally must possess a stable structure in order to achieve their designed function. Stable designs, however, are astronomically rare within the space of all possible amino acid sequences. As a consequence, many designs must be tested computationally and experimentally in order to find stable ones, which is expensive in terms of time and resources. Here we report a neural network model that predicts protein stability based only on sequences of amino acids, and demonstrate its performance by evaluating the stability of almost 200,000 novel proteins. These include a wide range of sequence perturbations, providing a baseline for future work in the field. We also report a second neural network model that is able to generate novel stable proteins. Finally, we show that the predictive model can be used to substantially increase the stability of both expert-designed and model-generated proteins.

biophysics↗

bioRxiv2020

Antisense oligonucleotides against monoacylglycerol acyltransferase 1 (Mogat1) improve glucose metabolism independently of Mogat1

ObjectiveMonoacylglycerol acyltransferase (MGAT) enzymes catalyze the synthesis of diacylglycerol from monoacylglycerol. Previous work has suggested the importance of MGAT activity in the development of obesity-related hepatic insulin resistance. Indeed, antisense oligonucleotide (ASO)-mediated knockdown of the gene encoding MGAT1, Mogat1, reduced hepatic MGAT activity and improved glucose tolerance and insulin resistance in high fat diet (HFD) fed mice. However, recent work has suggested that some ASOs may have off-target effects on body weight and metabolic parameters via activation of the interferon alpha/beta receptor 1 (IFNAR-1) pathway. MethodsMice with whole-body Mogat1 knockout or a floxed allele for Mogat1 to allow for liver-specific Mogat1-knockout (by either a liver-specific transgenic or adeno-associated virus-driven Cre recombinase) were generated. These mice were placed on a high fat diet and glucose metabolism and insulin sensitivity was assessed after 16 weeks on diet. In some experiments, mice were treated with control or Mogat1 or control ASOs in the presence or absence of IFNAR-1 neutralizing antibody. ResultsGenetic deletion of hepatic Mogat1, either acutely or chronically, did not improve hepatic steatosis, glucose tolerance, or insulin sensitivity in HFD-fed mice. Furthermore, constitutive Mogat1 knockout in all tissues actually exacerbated HFD-induced weight gain, insulin resistance, and glucose intolerance on a HFD. Despite markedly reduced Mogat1 expression, liver MGAT activity was unaffected in all knockout mouse models. Mogat1 overexpression hepatocytes increased liver MGAT activity and TAG content in low-fat fed mice, but did not cause insulin resistance. Interestingly, Mogat1 ASO treatment improved glucose tolerance in both wild-type and Mogat1 null mice, suggesting an off target effect. Inhibition of IFNAR-1 did not block the effect of Mogat1 ASO on glucose homeostasis. ConclusionThese results indicate that genetic loss of Mogat1 does not affect hepatic MGAT activity or metabolic homeostasis on HFD and show that Mogat1 ASOs improve glucose metabolism through effects independent of targeting Mogat1 or activation of IFNAR-1 signaling. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=65 SRC="FIGDIR/small/238535v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@18d43c4org.highwire.dtl.DTLVardef@15c1974org.highwire.dtl.DTLVardef@18b396dorg.highwire.dtl.DTLVardef@16745bf_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIMogat1 liver-specific KO or KD does not improve metabolism in HFD fed mice. C_LIO_LIWhole-body Mogat1-deletion impairs insulin tolerance in HFD fed mice. C_LIO_LIMogat1 ASOs improves whole body metabolism independently of gene knockdown. C_LIO_LIBlockade of the INFR response does not prevent off-target effects of Mogat1 ASOs. C_LI

physiology↗

bioRxiv2018

Simphony: simulating large-scale, rhythmic data

Simulated data are invaluable for assessing a computational method's ability to distinguish signal from noise. Although many biological systems show rhythmicity, there is no general-purpose tool to simulate large-scale, rhythmic data. Here we present Simphony, an R package for simulating data from experiments in which the abundances of rhythmic and non-rhythmic features (e.g., genes) are measured at multiple time points in multiple conditions. Simphony has parameters for specifying experimental design and each feature's rhythmic properties (e.g., shape, amplitude, and phase). In addition, Simphony can sample measurements from Gaussian and negative binomial distributions, the latter of which approximates read counts from next-generation sequencing data. We show an example of using Simphony to benchmark a method for detecting rhythms. Our results suggest that Simphony can aid experimental design and computational method development. Simphony is thoroughly documented and freely available at https://github.com/hugheylab/simphony.

bioinformatics↗

bioRxiv2017

Plasmodium falciparum infection during pregnancy impairs fetal head growth: prospective and populational-based retrospective studies

BackgroundMalaria in pregnancy is associated with adverse effects on the fetus and newborns. However, the outcome on a newborns head circumference (HC) is still unclear. Here, we show the relation of malaria during pregnancy with fetal head growth.\n\nMethodsClinical and anthropometric data were collected from babies in two cohort studies of malaria-infected and non-infected pregnant women, in the Brazilian Amazon. One enrolled prospectively (PCS, Jan. 2013 to April 2015) through volunteer sampling, and followed until delivery, 600 malaria-infected and non-infected pregnant women. The other assembled retrospectively (RCS, Jan. 2012 to Dec. 2013) clinical and malaria data from 4697 pregnant women selected through population-based sampling. The effects of malaria during pregnancy in the newborns were assessed using a multivariate logistic regression. According with World Health Organization guidelines babies were classified in small head (HC < 1 SD below the median) and microcephaly (HC < 2 SD below the median) using international HC standards.\n\nResultsAnalysis of 251 (PCS) and 232 (RCS) malaria-infected, and 158 (PCS) and 3650 (RCS) non-infected women with clinical data and anthropometric measures of their babies was performed. Among the newborns, 70 (17.1%) in the PCS and 934 (24.1%) in the RCS presented with a small head (SH). Of these, 15 (3.7%) and 161 (4.2%), respectively, showed microcephaly (MC). The prevalence of newborns with a SH (30.7% in PCS and 36.6% in RCS) and MC (8.1% in PCS and 7.3% in RCS) was higher among babies born from women infected with Plasmodium falciparum during pregnancy. Multivariate logistic regression analyses revealed that P. falciparum infection during pregnancy represents a significant increased odds for the occurrence of a SH in newborns (PCS: OR 3.15, 95% CI 1.52-6.53, p=0.002; RCS: OR 1.91, 95% CI 1.21-3.04, p=0.006). Similarly, there is an increased odds of MC in babies born from mothers that were P. falciparum-infected (PCS: OR 5.09, 95% CI 1.12-23.17, p=0.035). Moreover, characterization of placental pathology corroborates the association analysis, particularly through the occurrence of more syncytial nuclear aggregates and inflammatory infiltrates in placentas from babies with the reduced head circumference.\n\nConclusionsThis work indicates that falciparum-malaria during pregnancy presents an increased likelihood of occurring reduction of head circumference in newborns, which is associated with placental malaria.\n\nTrial Registrationregistered as RBR-3yrqfq in the Brazilian Clinical Trials Registry

epidemiology↗