Stem Cells in the Nervous System

Given their capacity to regenerate cells lost through injury or disease, stem cells offer new vistas into possible treatments for degenerative diseases and their underlying causes. As such, stem cell biology is emerging as a driving force behind many studies in regenerative medicine. This review focuses on the current understanding of the applications of stem cells in treating ailments of the human brain, with an emphasis on neurodegenerative diseases. Two types of neural stem cells are discussed: endogenous neural stem cells residing within the adult brain and pluripotent stem cells capable of forming neural cells in culture. Endogenous neural stem cells give rise to neurons throughout life, but they are restricted to specialized regions in the brain. Elucidating the molecular mechanisms regulating these cells is key in determining their therapeutic potential as well as finding mechanisms to activate dormant stem cells outside these specialized microdomains. In parallel, patient-derived stem cells can be used to generate neural cells in culture, providing new tools for disease modeling, drug testing, and cell-based therapies. Turning these technologies into viable treatments will require the integration of basic science with clinical skills in rehabilitation

Mx2 expression is associated with reduced susceptibility to HIV infection in highly exposed HIV seronegative Kenyan sex workers.

CAPRISA, 2015.Abstract available in pdf

Gain, Loss and Divergence in Primate Zinc-Finger Genes: A Rich Resource for Evolution of Gene Regulatory Differences between Species

The molecular changes underlying major phenotypic differences between humans and other primates are not well understood, but alterations in gene regulation are likely to play a major role. Here we performed a thorough evolutionary analysis of the largest family of primate transcription factors, the Krüppel-type zinc finger (KZNF) gene family. We identified and curated gene and pseudogene models for KZNFs in three primate species, chimpanzee, orangutan and rhesus macaque, to allow for a comparison with the curated set of human KZNFs. We show that the recent evolutionary history of primate KZNFs has been complex, including many lineage-specific duplications and deletions. We found 213 species-specific KZNFs, among them 7 human-specific and 23 chimpanzee-specific genes. Two human-specific genes were validated experimentally. Ten genes have been lost in humans and 13 in chimpanzees, either through deletion or pseudogenization. We also identified 30 KZNF orthologs with human-specific and 42 with chimpanzee-specific sequence changes that are predicted to affect DNA binding properties of the proteins. Eleven of these genes show signatures of accelerated evolution, suggesting positive selection between humans and chimpanzees. During primate evolution the most extensive re-shaping of the KZNF repertoire, including most gene additions, pseudogenizations, and structural changes occurred within the subfamily homininae. Using zinc finger (ZNF) binding predictions, we suggest potential impact these changes have had on human gene regulatory networks. The large species differences in this family of TFs stands in stark contrast to the overall high conservation of primate genomes and potentially represents a potent driver of primate evolution

The implications of a Silurian and other thylacocephalan crustaceans for the functional morphology and systematic affinities of the group

Background: Thylacocephala is a group of enigmatic extinct arthropods. Here we provide a full description of the oldest unequivocal thylacocephalan, a new genus and species Thylacares brandonensis, which is present in the Silurian Waukesha fauna from Wisconsin, USA. We also present details of younger, Jurassic specimens, from the Solnhofen lithographic limestones, which are crucial to our interpretation of the systematic position of Thylacocephala. In the past, Thylacocephala has been interpreted as a crustacean ingroup and as closely related to various groups such as cirripeds, decapods or remipeds. Results: The Waukesha thylacocephalan, Thylacares brandonensis n. gen. n. sp., bears compound eyes and raptorial appendages that are relatively small compared to those of other representatives of the group. As in other thylacocephalans the large bivalved shield encloses much of the entire body. The shield lacks a marked optical notch. The eyes, which project just beyond the shield margin, appear to be stalked. Head appendages, which may represent antennulae, antennae and mandibles, appear to be present. The trunk is comprised of up to 22 segments. New details observed on thylacocephalans from the Jurassic Solnhofen lithographic limestones include antennulae and antennae of Mayrocaris bucculata, and endites on the raptorial appendages and an elongate last trunk appendage in Clausocaris lithographica. Preserved features of the internal morphology in C. lithographica include the muscles of the raptorial appendage and trunk. Conclusions: Our results indicate that some `typical' thylacocephalan characters are unique to the group; these autapomorphies contribute to the difficulty of determining thylacocephalan affinities. While the new features reported here are consistent with a eucrustacean affinity, most previous hypotheses for the position of Thylacocephala within Eucrustacea (as Stomatopoda, Thecostraca or Decapoda) are shown to be unlikely. A sister group relationship to Remipedia appears compatible with the observed features of Thylacocephala but more fossil evidence is required to test this assertion. The raptorial appendages of Thylacocephala most likely projected 45 degrees abaxially instead of directly forward as previously reconstructed. The overall morphology of thylacocephalans supports a predatory mode of life

A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns.

In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA

Mammary Involution and Breast Cancer Risk: Transgenic Models and Clinical Studies

Postlactational involution is the process following weaning during which the mammary gland undergoes massive cell death and tissue remodeling as it returns to the pre-pregnant state. Lobular involution is the process by which the breast epithelial tissue is gradually lost with aging of the mammary gland. While postlactational involution and lobular involution are distinct processes, recent studies have indicated that both are related to breast cancer development. Experiments using a variety of rodent models, as well as observations in human populations, suggest that deregulation of postlactational involution may act to facilitate tumor formation. By contrast, new human studies show that completion of lobular involution protects against subsequent breast cancer incidence

Beyond the Global Brain Differences:Intraindividual Variability Differences in 1q21.1 Distal and 15q11.2 BP1-BP2 Deletion Carriers

BACKGROUND: Carriers of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants exhibit regional and globalbrain differences compared with noncarriers. However, interpreting regional differences is challenging if a globaldifference drives the regional brain differences. Intraindividual variability measures can be used to test for regionaldifferences beyond global differences in brain structure.METHODS: Magnetic resonance imaging data were used to obtain regional brain values for 1q21.1 distal deletion (n =30) and duplication (n = 27) and 15q11.2 BP1-BP2 deletion (n = 170) and duplication (n = 243) carriers and matchednoncarriers (n = 2350). Regional intra-deviation scores, i.e., the standardized difference between an individual’sregional difference and global difference, were used to test for regional differences that diverge from the globaldifference.RESULTS: For the 1q21.1 distal deletion carriers, cortical surface area for regions in the medial visual cortex, posterior cingulate, and temporal pole differed less and regions in the prefrontal and superior temporal cortex differedmore than the global difference in cortical surface area. For the 15q11.2 BP1-BP2 deletion carriers, cortical thicknessin regions in the medial visual cortex, auditory cortex, and temporal pole differed less and the prefrontal andsomatosensory cortex differed more than the global difference in cortical thickness.CONCLUSIONS: We find evidence for regional effects beyond differences in global brain measures in 1q21.1 distaland 15q11.2 BP1-BP2 copy number variants. The results provide new insight into brain profiling of the 1q21.1 distaland 15q11.2 BP1-BP2 copy number variants, with the potential to increase understanding of the mechanismsinvolved in altered neurodevelopment

What Is Stochastic Resonance? Definitions, Misconceptions, Debates, and Its Relevance to Biology

Stochastic resonance is said to be observed when increases in levels of unpredictable fluctuations—e.g., random noise—cause an increase in a metric of the quality of signal transmission or detection performance, rather than a decrease. This counterintuitive effect relies on system nonlinearities and on some parameter ranges being “suboptimal”. Stochastic resonance has been observed, quantified, and described in a plethora of physical and biological systems, including neurons. Being a topic of widespread multidisciplinary interest, the definition of stochastic resonance has evolved significantly over the last decade or so, leading to a number of debates, misunderstandings, and controversies. Perhaps the most important debate is whether the brain has evolved to utilize random noise in vivo, as part of the “neural code”. Surprisingly, this debate has been for the most part ignored by neuroscientists, despite much indirect evidence of a positive role for noise in the brain. We explore some of the reasons for this and argue why it would be more surprising if the brain did not exploit randomness provided by noise—via stochastic resonance or otherwise—than if it did. We also challenge neuroscientists and biologists, both computational and experimental, to embrace a very broad definition of stochastic resonance in terms of signal-processing “noise benefits”, and to devise experiments aimed at verifying that random variability can play a functional role in the brain, nervous system, or other areas of biology

Global wealth disparities drive adherence to COVID-safe pathways in head and neck cancer surgery

Peer reviewe