Peter Calabrese

Background: Ventricular shunts divert cerebrospinal fluid (CSF) in patients with hydrocephalus, which can be lifesaving. Untreated shunt failure may lead to increased intracranial pressure and neurological injury. The process of diagnosing shunt malfunction can be complex, and historically, there has not been a simple method for noninvasive and quantitative measurement of CSF flow through shunts. The demonstration of successful clinical application of phase-contrast magnetic resonance imaging (PC-MRI) to noninvasively quantify shunt flow is relatively new and will benefit from standardization across varying types of magnetic resonance (MR) hardware to facilitate implementation at multiple medical sites. PC-MRI CSF flow measurement through ventricular shunts has not yet been compared across different types of MR hardware (ie, differing field strengths, radiofrequency coils, slew rates, gradient strengths, models, and manufacturers). Objective: This study describes a protocol aimed to standardize and optimize PC-MRI shunt flow measurements for widespread use on any MR scanner. Methods: To study shunt flow in a manner that would translate effectively to the patient care setting, a phantom model incorporating a human shunt catheter can be constructed to obtain data over a typical physiological range of CSF flow rates. Calibration curves are used to model data comparing known flow rates to flow measured via PC-MRI. The accuracy of each MR scanner is assessed using linear regression. This protocol will be repeated on 8 MR scanners before multisite data collection. Results: The shunt flow phantom was constructed in November 2024. Single-site phantom calibration began in April 2025 and concluded in December 2025. Registration of clinical data collection sites will take place between November 2025 and July 2026. Approval from the Children’s Hospital of Los Angeles Institutional Review Board was obtained in January 2023 (before scanner calibration), and single-site data collection is ongoing. Multi-institutional clinical data collection will begin in July 2026 and continue until January 2027. Results and statistical analyses are expected by April 2027. Conclusions: This study protocol provides a methodology to test and implement PC-MRI on any MR scanner using a phantom model that (1) represents real flow and catheter conditions, (2) provides a repeatable means for data collection, (3) is easy to assemble, (4) minimizes artifacts, and (5) is transportable. We also describe analytic methods for interscanner calibration across a range of hardware parameters and provide a framework for multisite data collection.

Some spontaneous germline gain-of-function mutations promote spermatogonial stem cell clonal expansion and disproportionate variant sperm production leading to unexpectedly high transmission rates for some human genetic conditions. To measure the frequency and spatial distribution of de novo mutations we divided three testes into 192 pieces each and used error-corrected deep-sequencing on each piece. We focused on PTPN11 (HGNC:9644) Exon 3 that contains 30 different PTPN11 Noonan syndrome (NS) mutation sites. We found 14 of these variants formed clusters among the testes; one testis had 11 different variant clusters. The mutation frequencies of these different clusters were not correlated with their case-recurrence rates nor were case recurrence rates of PTPN11 variants correlated with their tyrosine phosphatase levels thereby confusing PTPN11's role in germline clonal expansion. Six of the PTPN11 exon 3 de novo variants associated with somatic mutation-induced sporadic cancers (but not NS) also formed testis clusters. Further, three of these six variants were observed among fetuses that underwent prenatal ultrasound screening for NS-like features. Mathematical modeling showed that germline selection can explain both the mutation clusters and the high incidence of NS (1/1000-1/2500).

In 2017, the University of Southern California started a new undergraduate major in quantitative biology. This major combines training in the biological sciences, mathematics, and computer science to prepare students for 21st century biology and medicine. In this article I will discuss the curriculum, the first two cohorts of graduates, the current students, and future plans for the major.

During COVID-19 pandemic, mutations of SARS-CoV-2 produce new strains that can be more infectious or evade vaccines. Viral RNA mutations can arise from misincorporation by RNA-polymerases and modification by host factors. Analysis of SARS-CoV-2 sequence from patients showed a strong bias toward C-to-U mutation, suggesting a potential mutational role by host APOBEC cytosine deaminases that possess broad anti-viral activity. We report the first experimental evidence demonstrating that APOBEC3A, APOBEC1, and APOBEC3G can edit on specific sites of SARS-CoV-2 RNA to produce C-to-U mutations. However, SARS-CoV-2 replication and viral progeny production in Caco-2 cells are not inhibited by the expression of these APOBECs. Instead, expression of wild-type APOBEC3 greatly promotes viral replication/propagation, suggesting that SARS-CoV-2 utilizes the APOBEC-mediated mutations for fitness and evolution. Unlike the random mutations, this study suggests the predictability of all possible viral genome mutations by these APOBECs based on the UC/AC motifs and the viral genomic RNA structure. One-sentence summary: Efficient Editing of SARS-CoV-2 genomic RNA by Host APOBEC deaminases and Its Potential Impacts on the Viral Replication and Emergence of New Strains in COVID-19 Pandemic

Some de novo human mutations arise at frequencies far exceeding the genome average mutation rate. Examples include the common mutations at one or a few sites in the genes that cause achondroplasia, Apert syndrome, multiple endocrine neoplasia type 2B, and Noonan syndrome. These mutations are recurrent, provide a gain of function, are paternally derived, and are more likely to be transmitted as the father ages. Recent experiments have tested whether the high mutation frequencies are due to an elevated mutation rate per cell division, as expected, or to an advantage of the mutant spermatogonial stem cells over wild-type stem cells. The evidence, which includes the surprising discovery of testis mutation clusters, rules out the former model but not the latter. We propose how the mutations might alter spermatogonial stem cell function and discuss how germline selection contributes to the paternal age effect, the human mutational load, and adaptive evolution.

We used targeted next generation deep-sequencing (Safe Sequencing System) to measure ultra-rare de novo mutation frequencies in the human male germline by attaching a unique identifier code to each target DNA molecule. Segments from three different human genes (FGFR3, MECP2 and PTPN11) were studied. Regardless of the gene segment, the particular testis donor or the 73 different testis pieces used, the frequencies for any one of the six different mutation types were consistent. Averaging over the C>T/G>A and G>T/C>A mutation types the background mutation frequency was 2.6x10^-5 per base pair, while for the four other mutation types the average background frequency was lower at 1.5x10^-6 per base pair. These rates far exceed the well documented human genome average frequency per base pair (~10⁻⁸) suggesting a non-biological explanation for our data. By computational modeling and a new experimental procedure to distinguish between pre-mutagenic lesion base mismatches and a fully mutated base pair in the original DNA molecule, we argue that most of the base-dependent variation in background frequency is due to a mixture of deamination and oxidation during the first two PCR cycles. Finally, we looked at a previously studied disease mutation in the PTPN11 gene and could easily distinguish true mutations from the SSS background. We also discuss the limits and possibilities of this and other methods to measure exceptionally rare mutation frequencies, and we present calculations for other scientists seeking to design their own such experiments.

Multiple endocrine neoplasia type 2B (MEN2B) is a highly aggressive thyroid cancer syndrome. Since almost all sporadic cases are caused by the same nucleotide substitution in the RET proto-oncogene, the calculated disease incidence is 100–200 times greater than would be expected based on the genome average mutation frequency. In order to determine whether this increased incidence is due to an elevated mutation rate at this position (true mutation hot spot) or a selective advantage conferred on mutated spermatogonial stem cells, we studied the spatial distribution of the mutation in 14 human testes. In donors aged 36–68, mutations were clustered with small regions of each testis having mutation frequencies several orders of magnitude greater than the rest of the testis. In donors aged 19–23 mutations were almost non-existent, demonstrating that clusters in middle-aged donors grew during adulthood. Computational analysis showed that germline selection is the only plausible explanation. Testes of men aged 75–80 were heterogeneous with some like middle-aged and others like younger testes. Incorporating data on age-dependent death of spermatogonial stem cells explains the results from all age groups. Germline selection also explains MEN2B's male mutation bias and paternal age effect. Our discovery focuses attention on MEN2B as a model for understanding the genetic and biochemical basis of germline selection. Since RET function in mouse spermatogonial stem cells has been extensively studied, we are able to suggest that the MEN2B mutation provides a selective advantage by altering the PI3K/AKT and SFK signaling pathways. Mutations that are preferred in the germline but reduce the fitness of offspring increase the population's mutational load. Our approach is useful for studying other disease mutations with similar characteristics and could uncover additional germline selection pathways or identify true mutation hot spots. Multiple endocrine neoplasia type 2B (MEN2B) is a highly aggressive thyroid cancer syndrome. MEN2B offspring with unaffected parents almost always received a new mutation from the father. Moreover, this mutation is almost always at the same nucleotide in the RET proto-oncogene. Thus MEN2B's incidence should equal the average single nucleotide mutation frequency, but the observed incidence is 100–200 times greater. One explanation is that the mutation rate at the causal nucleotide is significantly elevated above the genome average. Another is that human testis stem cells acquiring this mutation have a selective advantage over non-mutated ones and this advantage increases the mutation's frequency in the testis. Computational analysis of our testis dissection and mutation assay data rejects the hot spot but not the selective advantage explanation. Because the normal RET gene is known to be critical for mouse testis stem cell function, we now have an important insight into what biochemical pathways are altered by the MEN2B mutation to provide this selective advantage in humans. Germline selection explains the unexpectedly high incidence of MEN2B, why the mutation's origin is almost always in the father, and why the probability a child is born with this disease increases with the father's age.

The purpose of this article is to present a relatively easy to understand cancer model where transformation occurs when the first cell, among many at risk within a colon, accumulates a set of driver mutations. The analysis of this model yields a simple algebraic equation, which takes as inputs the number of stem cells, mutation and division rates, and the number of driver mutations, and makes predictions about cancer epidemiology.

Apert syndrome is almost always caused by a spontaneous mutation of paternal origin in one of two nucleotides in the fibroblast growth factor receptor 2 gene (FGFR2). The incidence of this disease increases with the age of the father (paternal age effect), and this increase is greater than what would be expected based on the greater number of germ-line divisions in older men. We use a highly sensitive PCR assay to measure the frequencies of the two causal mutations in the sperm of over 300 normal donors with a wide range of ages. The mutation frequencies increase with the age of the sperm donors, and this increase is consistent with the increase in the incidence rate. In both the sperm data and the birth data, the increase is non-monotonic. Further, after normalizing for age, the two Apert syndrome mutation frequencies are correlated within individual sperm donors. We consider a mathematical model for germ-line mutation which reproduces many of the attributes of the data. This model, with other evidence, suggests that part of the increase in both the sperm data and the birth data is due to selection for mutated premeiotic cells. It is likely that a number of other genetic diseases have similar features. Epidemiological studies show that the incidence of some genetic diseases increases with the age of the father. This “paternal age effect” is traditionally explained by the fact that, as men age, the male germ-line cells continue to divide, and each division presents an additional chance for mutation. Apert syndrome is an example of such a disease; virtually all cases are caused by spontaneous base substitution mutations of paternal origin at either one of just two sites. In this paper, we measure the frequencies of these two mutations in the sperm of unaffected men of different ages and find a frequency increase with age similar to what has been found in the data on Apert syndrome births. We also find (1) the increase in mutation frequency is not strictly monotonic, featuring a decrease followed by an increase in middle age, and (2) after normalizing for age, the two mutation frequencies are correlated within individual donors. The mutation frequency increase we observed is greater than expected based just on the number of male germ-line divisions. Along with other evidence, our data supports a novel explanation for the paternal age effect whereby Apert syndrome mutations, though harmful to the child, confer an advantage to premeiotic cells in the male germ-line that carry such a mutation. A number of other genetic diseases may exhibit similar features.