Surender Rajasekaran

Insulin is amongst the human genome’s most well-studied genes/proteins due to its connection to metabolic health. Within this article, we review literature and data to build a knowledge base of Insulin (INS) genetics that influence transcription, transcript processing, translation, hormone maturation, secretion, receptor binding, and metabolism while highlighting the future needs of insulin research. The INS gene region has 2076 unique variants from population genetics. Several variants are found near the transcriptional start site, enhancers, and following the INS transcripts that might influence the readthrough fusion transcript INS–IGF2. This INS–IGF2 transcript splice site was confirmed within hundreds of pancreatic RNAseq samples, lacks drift based on human genome sequencing, and has possible elevated expression due to viral regulation within the liver. Moreover, a rare, poorly characterized African population-enriched variant of INS–IGF2 results in a loss of the stop codon. INS transcript UTR variants rs689 and rs3842753, associated with type 1 diabetes, are found in many pancreatic RNAseq datasets with an elevation of the 3′UTR alternatively spliced INS transcript. Finally, by combining literature, evolutionary profiling, and structural biology, we map rare missense variants that influence preproinsulin translation, proinsulin processing, dimer/hexamer secretory storage, receptor activation, and C-peptide detection for quasi-insulin blood measurements.

The SOX transcription factor family is pivotal in controlling aspects of development. To identify genotype–phenotype relationships of SOX proteins, we performed a non-biased study of SOX using 1890 open-reading frame and 6667 amino acid sequences in combination with structural dynamics to interpret 3999 gnomAD, 485 ClinVar, 1174 Geno2MP, and 4313 COSMIC human variants. We identified, within the HMG (High Mobility Group)- box, twenty-seven amino acids with changes in multiple SOX proteins annotated to clinical pathologies. These sites were screened through Geno2MP medical phenotypes, revealing novel SOX15 R104G associated with musculature abnormality and SOX8 R159G with intellectual disability. Within gnomAD, SOX18 E137K (rs201931544), found within the HMG box of ~0.8% of Latinx individuals, is associated with seizures and neurological complications, potentially through blood–brain barrier alterations. A total of 56 highly conserved variants were found at sites outside the HMG-box, including several within the SOX2 HMG-box-flanking region with neurological associations, several in the SOX9 dimerization region associated with Campomelic Dysplasia, SOX14 K88R (rs199932938) flanking the HMG box associated with cardiovascular complications within European populations, and SOX7 A379V (rs143587868) within an SOXF conserved far C-terminal domain heterozygous in 0.716% of African individuals with associated eye phenotypes. This SOX data compilation builds a robust genotype-to-phenotype association for a gene family through more robust ortholog data integration.

A large percentage of infants develop viral bronchiolitis needing medical intervention and often develop further airway disease such as asthma. To characterize metabolic perturbations in acute respiratory syncytial viral (RSV) bronchiolitis, we compared metabolomic profiles of moderate and severe RSV patients versus sedation controls. RSV patients were classified as moderate or severe based on the need for invasive mechanical ventilation. Whole blood and urine samples were collected at two time points (baseline and 72 h). Plasma and urinary metabolites were extracted in cold methanol and analyzed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS), and data from the two biofluids were combined for multivariate data analysis. Metabolite profiles were clustered according to severity, characterized by unique metabolic changes in both plasma and urine. Plasma metabolites that correlated with severity included intermediates in the sialic acid biosynthesis, while urinary metabolites included citrate as well as multiple nucleotides. Furthermore, metabolomic profiles were predictive of future development of asthma, with urinary metabolites exhibiting higher predictive power than plasma. These metabolites may offer unique insights into the pathology of RSV bronchiolitis and may be useful in identifying patients at risk for developing asthma.

The feasibility of gastrointestinal (GI) microbiome work in a pediatric intensive care unit (PICU) to determine the GI microbiota composition of infants as compared to control infants from the same hospital was investigated. In a single-site observational study at an urban quaternary care children’s hospital in Western Michigan, subjects less than 6 months of age, admitted to the PICU with severe respiratory syncytial virus (RSV) bronchiolitis, were compared to similarly aged control subjects undergoing procedural sedation in the outpatient department. GI microbiome samples were collected at admission (n = 20) and 72 h (n = 19) or at time of sedation (n = 10). GI bacteria were analyzed by sequencing the V4 region of the 16S rRNA gene. Alpha and beta diversity were calculated. Mechanical ventilation was required for the majority (n = 14) of study patients, and antibiotics were given at baseline (n = 8) and 72 h (n = 9). Control subjects’ bacterial communities contained more Porphyromonas, and Prevotella (p = 0.004) than those of PICU infants. The ratio of Prevotella to Bacteroides was greater in the control than the RSV infants (mean ± SD—1.27 ± 0.85 vs. 0.61 ± 0.75: p = 0.03). Bacterial communities of PICU infants were less diverse than those of controls with a loss of potentially protective populations.

The immune response to COVID-19 infection is variable. How COVID-19 influences clinical outcomes in hospitalized patients needs to be understood through readily obtainable biological materials, such as blood. We hypothesized that a high-density analysis of host (and pathogen) blood RNA in hospitalized patients with SARS-CoV-2 would provide mechanistic insights into the heterogeneity of response amongst COVID-19 patients when combined with advanced multidimensional bioinformatics for RNA. We enrolled 36 hospitalized COVID-19 patients (11 died) and 15 controls, collecting 74 blood PAXgene RNA tubes at multiple timepoints, one early and in 23 patients after treatment with various therapies. Total RNAseq was performed at high-density, with >160 million paired-end, 150 base pair reads per sample, representing the most sequenced bases per sample for any publicly deposited blood PAXgene tube study. There are 770 genes significantly altered in the blood of COVID-19 patients associated with antiviral defense, mitotic cell cycle, type I interferon signaling, and severe viral infections. Immune genes activated include those associated with neutrophil mechanisms, secretory granules, and neutrophil extracellular traps (NETs), along with decreased gene expression in lymphocytes and clonal expansion of the acquired immune response. Therapies such as convalescent serum and dexamethasone reduced many of the blood expression signatures of COVID-19. Severely ill or deceased patients are marked by various secondary infections, unique gene patterns, dysregulated innate response, and peripheral organ damage not otherwise found in the cohort. High-density transcriptomic data offers shared gene expression signatures, providing unique insights into the immune system and individualized signatures of patients that could be used to understand the patient’s clinical condition. Whole blood transcriptomics provides patient-level insights for immune activation, immune repertoire, and secondary infections that can further guide precision treatment.

Glycero- and sphingo-lipids are important in plasma membrane structure, caloric storage and signaling. An un-targeted lipidomics approach for a cohort of critically ill pediatric intensive care unit (PICU) patients undergoing multi-organ dysfunction syndrome (MODS) was compared to sedation controls. After IRB approval, patients meeting the criteria for MODS were screened, consented (n = 24), and blood samples were collected from the PICU at HDVCH, Michigan; eight patients needed veno-arterial extracorporeal membrane oxygenation (VA ECMO). Sedation controls were presenting for routine sedation (n = 4). Plasma lipid profiles were determined by nano-electrospray (nESI) direct infusion high resolution/accurate mass spectrometry (MS) and tandem mass spectrometry (MS/MS). Biostatistics analysis was performed using R v 3.6.0. Sixty-one patient samples over three time points revealed a ceramide metabolite, hexosylceramide (Hex-Cer) was high across all time points (mean 1.63–3.19%; vs. controls 0.22%). Fourteen species statistically differentiated from sedation controls (p-value ≤ 0.05); sphingomyelin (SM) [SM(d18:1/23:0), SM(d18:1/22:0), SM(d18:1/23:1), SM(d18:1/21:0), SM(d18:1/24:0)]; and glycerophosphotidylcholine (GPC) [GPC(36:01), GPC(18:00), GPC(O:34:02), GPC(18:02), GPC(38:05), GPC(O:34:03), GPC(16:00), GPC(40:05), GPC(O:36:03)]. Hex-Cer has been shown to be involved in viral infection and may be at play during acute illness. GPC(36:01) was elevated in all MODS patients at all time points and is associated with inflammation and brain injury.

Viral infections affecting the lower respiratory tract place enormous burdens on hospitals. As neither vaccines nor antiviral agents exist for many viruses, understanding risk factors and outcomes in each patient using minimally invasive analysis, such as blood, can lead to improved health care delivery. A cohort of PAXgene RNA sequencing of infants admitted with moderate or severe acute bronchiolitis and respiratory syncytial virus were compared with case-control statistical analysis and cohort-based outlier mapping for precision transcriptomics. Patients with severe bronchiolitis had signatures connected to the immune system, interferon signaling, and cytokine signaling, with marked sex differences in XIST, RPS4Y1, KDM5D, and LINC00278 for severity. Several patients had unique secondary infections, cytokine activation, immune responses, biological pathways, and immune cell activation, highlighting the need for defining patient-level transcriptomic signatures. Balancing relative contributions of cohort-based biomarker discoveries with patient’s biological responses is needed to understand the totality of mechanisms of adverse outcomes in viral bronchiolitis.

Ornithine decarboxylase 1 (ODC1 gene) has been linked through gain-of-function variants to a rare disease featuring developmental delay, alopecia, macrocephaly, and structural brain anomalies. ODC1 has been linked to additional diseases like cancer, with growing evidence for neurological contributions to schizophrenia, mood disorders, anxiety, epilepsy, learning, and suicidal behavior. The evidence of ODC1 connection to neural disorders highlights the need for a systematic analysis of ODC1 genotype-to-phenotype associations. An analysis of variants from ClinVar, Geno2MP, TOPMed, gnomAD, and COSMIC revealed an intellectual disability and seizure connected loss-of-function variant, ODC G84R (rs138359527, NC_000002.12:g.10444500C > T). The missense variant is found in ~1% of South Asian individuals and results in 2.5-fold decrease in enzyme function. Expression quantitative trait loci (eQTLs) reveal multiple functionally annotated, non-coding variants regulating ODC1 that associate with psychiatric/neurological phenotypes. Further dissection of RNA-Seq during fetal brain development and within cerebral organoids showed an association of ODC1 expression with cell proliferation of neural progenitor cells, suggesting gain-of-function variants with neural over-proliferation and loss-of-function variants with neural depletion. The linkage from the expression data of ODC1 in early neural progenitor proliferation to phenotypes of neurodevelopmental delay and to the connection of polyamine metabolites in brain function establish ODC1 as a bona fide neurodevelopmental disorder gene.

Lipids are molecules involved in metabolism and inflammation. This study investigates the plasma lipidome for markers of severity and nutritional status in critically ill children. Children with multi-organ dysfunction syndrome (MODS) (n = 24) are analyzed at three time-points and cross-referenced to sedation controls (n = 4) for a total of N = 28. Eight of the patients with MODS, needed veno-arterial extracorporeal membrane oxygenation (VA ECMO) support to survive. Blood plasma lipid profiles are quantified by nano-electrospray (nESI), direct infusion high resolution/accurate mass spectrometry (MS), and tandem mass spectrometry (MS/MS), and compared to nutritional profiles and pediatric logistic organ dysfunction (PELOD) scores. Our results show that PELOD scores were not significantly different between MODS and ECMO cases across time-points (p = 0.66). Lipid profiling provides stratification between sedation controls and all MODS patients for total lysophosphatidylserine (lysoPS) (p-value = 0.004), total phosphatidylserine (PS) (p-value = 0.015), and total ether-linked phosphatidylethanolamine (ether-PE) (p-value = 0.03) after adjusting for sex and age. Nutrition intake over time did not correlate with changes in lipid profiles, as measured by caloric and protein intake. Lipid measurement in the intensive care environment shows dynamic changes over an 8-day pediatric intensive care unit (PICU) course, suggesting novel metabolic indicators for defining critically ill children.