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Hexokinases (HKs) convert hexose sugars to hexose-6-phosphate, thus trapping them inside cells to meet the synthetic and energetic demands. HKs participate in various standard and altered physiological processes, including cancer, primarily through the reprogramming of cellular metabolism. Four canonical HKs have been identified with different expression patterns across tissues. HKs 1–3 play a role in glucose utilization, whereas HK 4 (glucokinase, GCK) also acts as a glucose sensor. Recently, a novel fifth HK, hexokinase domain containing 1 (HKDC1), has been identified, which plays a role in whole-body glucose utilization and insulin sensitivity. Beyond the metabolic functions, HKDC1 is differentially expressed in many forms of human cancer. This review focuses on the role of HKs, particularly HKDC1, in metabolic reprogramming and cancer progression.

The gut microbiome has emerged as a novel determinant of type 1 diabetes (T1D), but the underlying mechanisms are unknown. In this context, major gut microbial metabolites, short-chain fatty acids (SCFAs), are considered to be an important link between the host and gut microbiome. We, along with other laboratories, have explored how SCFAs and their cognate receptors affect various metabolic conditions, including obesity, type 2 diabetes, and metabolic syndrome. Though gut microbiome and SCFA-level changes have been reported in T1D and in mouse models of the disease, the role of SCFA receptors in T1D remains under explored. In this review article, we will highlight the existing and possible roles of these receptors in T1D pathology. We conclude with a discussion of SCFA receptors as therapeutic targets for T1D, exploring an exciting new potential for novel treatments of glucometabolic disorders.

The transition from β-cell compensation to β-cell failure is not well understood. Previous works by our group and others have demonstrated a role for Prostaglandin EP3 receptor (EP3), encoded by the Ptger3 gene, in the loss of functional β-cell mass in Type 2 diabetes (T2D). The primary endogenous EP3 ligand is the arachidonic acid metabolite prostaglandin E2 (PGE2). Expression of the pancreatic islet EP3 and PGE2 synthetic enzymes and/or PGE2 excretion itself have all been shown to be upregulated in primary mouse and human islets isolated from animals or human organ donors with established T2D compared to nondiabetic controls. In this study, we took advantage of a rare and fleeting phenotype in which a subset of Black and Tan BRachyury (BTBR) mice homozygous for the Leptinob/ob mutation—a strong genetic model of T2D—were entirely protected from fasting hyperglycemia even with equal obesity and insulin resistance as their hyperglycemic littermates. Utilizing this model, we found numerous alterations in full-body metabolic parameters in T2D-protected mice (e.g., gut microbiome composition, circulating pancreatic and incretin hormones, and markers of systemic inflammation) that correlate with improvements in EP3-mediated β-cell dysfunction.

This study explored adipocytokine associations with acute and chronic hyperglycemia in African-American men (AAM). Fourteen adipocytokines were measured from men with normal glucose tolerance (NGT) or type 2 diabetes (T2D, drug-naïve MF(−) or using metformin MF(+)). Acute and chronic hyperglycemia were evaluated by 120 min oral glucose tolerance test (OGTT) and glycohemoglobin A1c (HbA1c). AAM with T2D (n = 21) compared to NGT (n = 20) were older, had higher BMI and slightly higher glucose and insulin. In the fasted state, TNF-α, IL-6, PAI-1, IL-13, adiponectin, adipsin, and lipocalin were lower in T2D vs. NGT. At 120 min post-glucose load, TNF-α, IL-6, IL-13, IL-8, PAI-1, adiponectin, adipsin, lipocalin, and resistin were lower in T2D vs. NGT. There were no statistical differences for GM-CSF, IL-7, IL-10, IP-10, and MCP-1. Regression analysis showed that fasting IL-8, TNF-α, adiponectin, lipocalin, resistin, adipsin, and PAI-1 were associated with HbA1c. After adjusting for age, BMI, glucose tolerance, and metformin use, only adipsin remained significantly associated with HbA1c (p = 0.021). The model including adipsin, TNF-α, age, BMI, and group designation (i.e., NGT, MF(−), MF(+)) explained 86% of HbA1c variability. The data suggested that adipsin could be associated with HbA1c in AAM with varied glucose tolerance.

Gut microbiota and their biomarkers may be associated with obesity. This study evaluated associations of body mass index (BMI) with circulating microbiota biomarkers in African American men (AAM) (n = 75). The main outcomes included fecal microbial community structure (16S rRNA), gut permeability biomarkers (ELISA), and short-chain fatty acids (SCFAs, metabolome analysis). These outcomes were compared between obese and non-obese men, after adjusting for age. The results showed that lipopolysaccharide-binding protein (LBP), the ratio of LBP to CD14 (LBP/CD14), and SCFAs (propionic, butyric, isovaleric) were higher in obese (n = 41, age 58 years, BMI 36 kg/m2) versus non-obese (n = 34, age 55 years, BMI 26 kg/m2) men. BMI correlated positively with LBP, LBP/CD14 (p < 0.05 for both) and SCFAs (propionic, butyric, isovaleric, p < 0.01 for all). In the regression analysis, LBP, LBP/CD14, propionic and butyric acids were independent determinants of BMI. The study showed for the first time that selected microbiota biomarkers (LBP, LBP/CD14, propionic and butyric acids) together with several other relevant risks explained 39%−47% of BMI variability, emphasizing that factors other than microbiota-related biomarkers could be important. Further research is needed to provide clinical and mechanistic insight into microbiota biomarkers and their utility for diagnostic and therapeutic purposes.