Biology subjects

Hathaway, C.

Publications and source records attributed to Hathaway, C..

3 recordsLinked to original sources

bioRxiv2024

Patterns of extreme outlier RNA expression in population data reveal sporadic over-activation of genes with co-regulated modules in subsets of individuals

BackgroundMost RNA-Seq datasets harbor genes with extreme expression levels in some samples. Such extreme outliers are usually treated as technical errors and are removed from the data before further statistical analysis. Here we focus on the patterns of such outlier expression. ResultsOur study is based on multiple datasets, including outbred and inbred mice, humans from the GTEx dataset, different Drosophila species and single-nuclei sequencing experiments from human brain tissues. All show comparable general patterns of outlier expression. Different individuals can harbor very different numbers of outliers, with some individuals showing extreme numbers in only one out of several organs of the respective individual. A three-generation family analysis in mice was generated and analyzed for the inheritance of outlier patterns. We find that most extreme over-expressions are not inherited, i.e., appear to be sporadically generated in individuals. Still, co-regulated outlier expressions are identifiable in various gene groups, and some correspond with known pathways. Among the co-regulated genes with extreme outlier expression are also the hormone genes prolactin and growth hormone, both in mice and humans, for which we include also protein level data from human cohorts. ConclusionsWe show that outlier patterns of gene expression are a biological reality occurring universally across tissues and species. Most of the outlier expressions are spontaneous and not inherited. We discuss the possibility that the outlier patterns reflect edge of chaos effects that are expected for systems of non-linear interactions and feedback loops, such as gene regulatory networks.

genomics↗

bioRxiv2022

Defining tertiary sulci in lateral prefrontal cortex in chimpanzees using human predictions

Similarities and differences in brain structure and function across species is of major interest in systems neuroscience, comparative biology, and brain mapping. Recently, increased emphasis has been placed on tertiary sulci, which are shallow indentations of the cerebral cortex that appear last in gestation, continue to develop after birth, and are largely either human- or hominoid-specific. While tertiary sulcal morphology in lateral prefrontal cortex (LPFC) has been linked to functional representations and cognition in humans, it is presently unknown if LPFC tertiary sulci also exist in non-human hominoids. To fill this gap in knowledge, we leveraged two freely available multimodal datasets to address the following main question: Can LPFC tertiary sulci be defined in chimpanzee cortical surfaces from human predictions? We found that 1-3 components of the posterior middle frontal sulcus (pmfs) in the posterior middle frontal gyrus are identifiable in nearly all chimpanzee hemispheres. In stark contrast to the consistency of the pmfs components, we could only identify components of the paraintermediate frontal sulcus (pimfs) in two chimpanzee hemispheres. LPFC tertiary sulci were relatively smaller and shallower in chimpanzees compared to humans. In both species, two of the pmfs components were deeper in the right compared to the left hemisphere. As these results have direct implications for future studies interested in the functional and cognitive role of LPFC tertiary sulci across species, we share probabilistic predictions of the three pmfs components to guide the definitions of these sulci in future studies.

neuroscience↗

bioRxiv2019

Spatial Correlation and Breast Cancer Risk

We present a novel method for evaluating the spatial correlation structure in two-dimensional (2D) mammograms and evaluate its merits for risk prediction. Two matched case-control studies were analyzed. Study 1 included women (N = 588 pairs) with mammograms acquired with either Hologic Selenia full field digital mammography (FFDM) units or Hologic Dimensions digital breast tomosynthesis units. Study 2 included women (N =180 pairs) with mammograms acquired with a General Electric Senographe 2000D FFDM unit. Matching variables included age, HRT usage/duration, screening history, and mammography unit. The local autocorrelation function was determined with Fourier analysis and compared with template defined as 2D double-sided exponential function with one spatial extent parameter: n = 4, 12, 24, 50, 74, 100, and 124 defined in pixel widths. The difference between local correlation and template was gauged within a kernel with an adjustable parameter and summarized, producing two measures: the mean (mn+1), and standard (sn+1). Both adjustable parameters were varied in Study 1. Select measures that produced significant associations with breast cancer were translated to Study 2. Breast cancer associations were evaluated with conditional logistic regression, adjusted for body mass index and ethnicity. Odds ratios (ORs) were estimated as per standard increment with 95% confidence intervals (CIs). Two measures were selected for breast cancer association analysis in Study 1: m75 and s25. Both measures revealed significant associations with breast cancer: OR = 1.45 (1.23, 1.66) for m75 and OR = 1.30 (1.14, 1.49) for s25. When translating to Study 2, these measures also revealed significant associations: OR = 1.49 (1.12, 1.96) for m75 and OR = 1.34 (1.06, 1.69) for s25. Novel correlation metrics presented in this work revealed significant associations with breast cancer risk. This approach is general and may have applications beyond mammography.

bioengineering↗