Transcriptional coregulator regulates pancreatic β-cells fitness and function

The transcriptional coregulator Swi-independent 3—or Sin3—switches on and off the genes that drive crucial biological processes during prenatal development, including cellular differentiation, maturation, survival, metabolism, and stress responses. Earlier studies reported the postnatal presence of Sin3 in the pancreas, yet its functional attributes were poorly understood.

In recent work published in Diabetes, Xiaodun Yang—from the lab of Associate Professor of Cell and Developmental Biology Guoqiang Gu—clarifies the role of Sin3 in the embryonic development and postnatal function of pancreatic β-cells, which produce insulin. Impaired β-cell function in humans accompanies reduced insulin secretion and the onset of type 1 diabetes.

On an evolutionary scale, Sin3 diverged into paralogues—Sin3a and Sin3b—two distinct forms of a single ancestral gene, each residing in a unique location within the genome, with overlapping as well as redundant functions.

The investigators studied the roles of Sin3a and Sin3b in the pancreas both before and after birth. They found that among transgenic mice lacking Sin3a in the pancreas, Sin3a was not necessary for the prenatal differentiation of the islet cells that produce β-cells; however, it was crucial for the postnatal survival, maturation, and function of β-cells. After birth, the β-cells of the Sin3a-deletion mice failed to produce physiological levels of insulin and the mice succumbed to diabetes.

The team also found that although Sin3b-deletion mice did not show any abnormalities, those bred with neither Sin3a nor Sin3b had expedited onset of diabetes. This finding suggests redundancy in the function of both Sin3 paralogues, with Sin3a being the major contributor to β-cell function. Sin3a and Sin3b may regulate either a similar set of genes or different genes with similar functions in β-cells. In particular, the researchers identified several genes that Sin3a appeared to regulate, including some associated with protein transport, glucose metabolism, oxidative stress response, cell death, and maturation of β-cells.

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Smartphones can predict brain function associated with anxiety and depression

Information on social activity, screen time and location from smartphones can predict connectivity between regions of the brain that are responsible for emotion, according to a study from Dartmouth College.

In the research, data from phone usage was analyzed alongside results from fMRI scans to confirm that passively collected information can mirror activity in the brain linked to traits such as anxiety. Predictions based solely on the phone data matched the brain scans with 80 percent accuracy.

The study, presented at ACM UbiComp, an annual conference on pervasive and ubiquitous computing, represents the first time researchers have been able to predict connectivity between specific brain regions solely based on passive data from smartphones.

“Simple information about how someone is using their smartphone can provide a peek into the complex functioning of the human brain,” said Mikio Obuchi, a Ph.D. student in the Department of Computer Science at Dartmouth and lead author of the study. “Although this research is just beginning, combining data from smartphones—rather than fMRI alone—will hopefully accelerate research to understand better how the human brain works.”

According to the research, how often and how long an individual uses their phone provides information about the functioning between the ventromedial prefrontal cortex and the amygdala—two key centers of the brain related to emotional state.

The ventromedial prefrontal cortex is responsible for self-control, decision making, and risk evaluation. The amygdala triggers the fight or flight response and helps individuals determine the emotions of others.

In addition to data on social activity, screen time and location, information on exercise and sleep patterns was also collected for the study.

The research found that more screen time, regular exercise, earlier bedtimes, higher social interaction and certain location patterns passively inferred from phone data matched a state of higher functional connectivity between the brain regions. This increased activity indicates a more positive emotional state.

“We are not suggesting that phones should replace technology like fMRI, but they can help individuals and health providers learn more about behavior patterns from everyday observations,” said Jeremy Huckins, a lecturer on psychological and brain sciences at Dartmouth and a co-author of the study.

The research result aligns with clinical evidence showing that stronger connectivity between the ventromedial prefrontal cortex and the amygdala to be associated with lower levels of anxiety and depression. Weaker functional connectivity, on the other hand, represents a more negative emotional state.

Anonymous fMRI data from volunteer participants were placed into two categories divided by low and high brain connectivity levels. By matching phone data against the fMRI results the researchers were able to predict which research subjects had higher or lower connectivity between brain regions with 80 percent accuracy.

According to the research team, the use of passive information from a smartphone can help eliminate the subjectivity that often complicates other information-gathering techniques on emotional well-being such as personal interviews and self-reporting on questionnaires.

The phone information allowed researchers to predict the emotional state of individuals at any given time without intrusive data collection. The data also support predictions of the long-term emotional traits in individuals.

“Hopefully, this study shows how mobile sensing can provide deep longitudinal human behavioral data to complement brain scans,” said Andrew Campbell, the Albert Bradley 1915 Third Century Professor of computer science at Dartmouth and the senior researcher on the study. “This could offer new insights into the emotional well-being of subjects that would just not be possible without continuous sensing.”

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Use of cystatin C for precise assessment of kidney function and cardiovascular risk

In many situations, it is essential that the physician knows a patient’s kidney function as precisely as possible. The glomerular filtration rate (GFR) is calculated in order to assess kidney function. Various equations and methods exist in that regard, each of which has its advantages and disadvantages. As a paper published today in Nephrology Dialysis Transplantation has now shown, there are clear scientific findings on how to calculate GFR optimally for best possible precision.

The glomerular filtration rate (GFR) is normally specified as a measure of kidney function. The GFR is the volume of blood that the kidneys filter per minute (the unit of measurement, in relation to a standardized body surface area, is therefore ml/min/1.73 m2). To calculate or estimate GFR (eGFR= estimated GFR), an equation based, inter alia, on the laboratory parameter serum creatinine is mostly applied. Creatinine, a non-protein nitrogenous substance, is a breakdown product of muscle metabolism that is released continuously and excreted in urine (making it a urinary substance). If kidney function is impaired, eGFR decreases and serum creatinine increases. However, because the body’s own creatinine production depends on various factors (e.g. age, gender and muscle mass), the significance of creatinine-based eGFR (eGFRcr) is a recurrent topic of discussion among specialists. For example, the kidney function of a delicate elderly lady (with low muscle mass and correspondingly lower serum creatinine) may be wrongly assessed as normal, based on her creatinine level, even though her kidney function may be significantly reduced. Conversely, the muscular creatinine production in a bodybuilder may cause elevated serum creatinine values and thus lead arithmetically to a low eGFR (despite normal kidney function). The endogenous protein Cystatin C (Cys-C), which is permanently released in the metabolism of almost all body cells, therefore appears to be more suitable as a marker than serum creatinine. The volume of Cys-C amount is independent of age, gender and muscle mass—potential confounding factors in cystatin-based eGFR estimation (eGFRcys) are inflammation, cancer, thyroid dysfunction or steroid therapy. Cys-C measurement is also more expensive than creatinine, and the test is not available in every laboratory.

An equation for estimating eGFR that includes both parameters (eGFRcr-cys) has been shown to provide the most accurate approximation of true GFR, not only in early stages, but also in late stages of kidney disease. This may be due to the fact that the confounding factors of the two parameters are independent of each other and play a less significant role in the combined equation eGFRcr-cys, according to the authors. eGFRcr-cys is particularly suitable, therefore, when it is important to know how well kidneys function as precisely as possible and at an early stage (e.g. to calculate the dosage of certain drugs, for enrolment in studies, or in the case of potential kidney donors).

“Accurate measurement is needed for the early detection of CKD. The ERA-EDTA recommends that eGFRcys and eGFRcr-cys be implemented as the new standard”, emphasizes Professor Denis Fouque, Lyon/France, NDT´s Editor-in-chief.

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