Enumeration of binary trees compatible with a perfect phylogeny.

Evolutionary models used for describing molecular sequence variation suppose that at a non-recombining genomic segment, sequences share ancestry that can be represented as a genealogy-a rooted, binary, timed tree, with tips corresponding to individual sequences. Under the infinitely-many-sites mutation model, mutations are randomly superimposed along the branches of the genealogy, so that every mutation occurs at a chromosomal site that has not previously mutated; if a mutation occurs at an interior branch, then all individuals descending from that branch carry the mutation. The implication is that observed patterns of molecular variation from this model impose combinatorial constraints on the hidden state space of genealogies. In particular, observed molecular variation can be represented in the form of a perfect phylogeny, a tree structure that fully encodes the mutational differences among sequences. For a sample of n sequences, a perfect phylogeny might not possess n distinct leaves, and hence might be compatible with many possible binary tree structures that could describe the evolutionary relationships among the n sequences. Here, we investigate enumerative properties of the set of binary ranked and unranked tree shapes that are compatible with a perfect phylogeny, and hence, the binary ranked and unranked tree shapes conditioned on an observed pattern of mutations under the infinitely-many-sites mutation model. We provide a recursive enumeration of these shapes. We consider both perfect phylogenies that can be represented as binary and those that are multifurcating. The results have implications for computational aspects of the statistical inference of evolutionary parameters that underlie sets of molecular sequences.

Rat skeletal muscle-nerve preparation to teach skeletal muscle physiology.

This sourcebook update describes a variation of a previous sourcebook experiment that used isolated extensor digitorum longus muscle from mouse to teach skeletal muscle properties (Head SI, Arber MS. Adv Physiol Educ 37: 405-414, 2013). Gastrocnemius-sciatic nerve preparation in an anaesthetized rat was developed and muscle contractions were recorded in a computerized data acquisition system using an isometric force transducer. Teachers and students in physiology or biology can use this preparation to demonstrate skeletal muscle properties like simple muscle twitch, quantal summation, wave summation, superposition, incomplete tetanus, complete tetanus, treppe, fatigue, and length-tension relationship.

Teaching bioelectricity and neurophysiology to medical students using LabAXON simulations.

Patch-clamp electrophysiological recordings of neuronal activity require a large amount of space and equipment. The technique is difficult to master and not conducive to demonstration to more than a few medical students. Therefore, neurophysiological education is mostly limited to classroom-based pedagogies such as lectures. However, the demonstration of concepts such as changes in membrane potential and ion channel activity is best achieved with hands-on approaches. This article details an in silico activity suitable for large groups of medical students that demonstrates the key concepts in neurophysiology using the LabAXON simulation software. Learning activities in our practical include 1) measurements of voltage and time parameters of the neuronal action potential and its relationship to the Nernst potentials of Na+ and K+; 2) determination of the stimulus threshold to evoke action potentials; 3) demonstration of the refractory period of an action potential; and 4) voltage-clamp experiments to determine the current-voltage relationship of voltage-gated Na+ and K+ channels and the voltage dependence of, and recovery from, inactivation of voltage-gated Na+ channels. We emphasized the accuracy of quantitative measurements as well as the correct use of units. The level of difficulty of the activity can be altered through different multiple choice questions relating to material introduced in the associated lectures. This practical activity is suitable for different class sizes and is adaptable for delivery with online platforms. Student feedback showed that the students felt the activity helped them consolidate their understanding of the lecture material.

Computer-based virtual laboratory simulations: LabHEART cardiac physiology practical.

Practical demonstration of cardiomyocyte function requires substantial preparation, a source of freshly isolated animal hearts, and specialized equipment. Even where such resources are available, it is not conducive for demonstration to any more than a few students at a time. These approaches are also not consistent with the 3R principle (replacement, reduction, and refinement) of ethical use of animals. We present an implementation of the LabHEART software, developed by Donald Bers and Jose Puglisi, for medical students. Prior to the activity, students had lectures covering the physiological and pharmacological aspects of cardiac excitation-contraction (EC) coupling. We used this problem-based activity to help students consolidate their knowledge and to allow a hands-on approach to explore the key features of EC coupling. Students simulate and measure action potentials, intracellular calcium changes, and cardiomyocyte contraction. They also apply drugs that target ion channels (e.g., nifedipine or tetrodotoxin) or sympathetic input (using isoproterenol) and explore changes to EC coupling. Furthermore, by modifying the biophysical parameters of key ion channels involved in the electrical activity of the heart, students also explore the effect of channelopathies such as long QT syndromes. We describe approaches to implement this activity in a flipped classroom format, with recorded lecture materials provided ahead of the practical to facilitate active learning. We also describe our experiences implementing this activity online. The content and difficulty of the activity can be altered to suit individual courses and is also amenable to promote peer-driven learning.

Inferring Relevant Cell Types for Complex Traits by Using Single-Cell Gene Expression.

Previous studies have prioritized trait-relevant cell types by looking for an enrichment of genome-wide association study (GWAS) signal within functional regions. However, these studies are limited in cell resolution by the lack of functional annotations from difficult-to-characterize or rare cell populations. Measurement of single-cell gene expression has become a popular method for characterizing novel cell types, and yet limited work has linked single-cell RNA sequencing (RNA-seq) to phenotypes of interest. To address this deficiency, we present RolyPoly, a regression-based polygenic model that can prioritize trait-relevant cell types and genes from GWAS summary statistics and gene expression data. RolyPoly is designed to use expression data from either bulk tissue or single-cell RNA-seq. In this study, we demonstrated RolyPoly's accuracy through simulation and validated previously known tissue-trait associations. We discovered a significant association between microglia and late-onset Alzheimer disease and an association between schizophrenia and oligodendrocytes and replicating fetal cortical cells. Additionally, RolyPoly computes a trait-relevance score for each gene to reflect the importance of expression specific to a cell type. We found that differentially expressed genes in the prefrontal cortex of individuals with Alzheimer disease were significantly enriched with genes ranked highly by RolyPoly gene scores. Overall, our method represents a powerful framework for understanding the effect of common variants on cell types contributing to complex traits.

HSV-1-induced disruption of transcription termination resembles a cellular stress response but selectively increases chromatin accessibility downstream of genes.

Lytic herpes simplex virus 1 (HSV-1) infection triggers disruption of transcription termination (DoTT) of most cellular genes, resulting in extensive intergenic transcription. Similarly, cellular stress responses lead to gene-specific transcription downstream of genes (DoG). In this study, we performed a detailed comparison of DoTT/DoG transcription between HSV-1 infection, salt and heat stress in primary human fibroblasts using 4sU-seq and ATAC-seq. Although DoTT at late times of HSV-1 infection was substantially more prominent than DoG transcription in salt and heat stress, poly(A) read-through due to DoTT/DoG transcription and affected genes were significantly correlated between all three conditions, in particular at earlier times of infection. We speculate that HSV-1 either directly usurps a cellular stress response or disrupts the transcription termination machinery in other ways but with similar consequences. In contrast to previous reports, we found that inhibition of Ca2+ signaling by BAPTA-AM did not specifically inhibit DoG transcription but globally impaired transcription. Most importantly, HSV-1-induced DoTT, but not stress-induced DoG transcription, was accompanied by a strong increase in open chromatin downstream of the affected poly(A) sites. In its extent and kinetics, downstream open chromatin essentially matched the poly(A) read-through transcription. We show that this does not cause but rather requires DoTT as well as high levels of transcription into the genomic regions downstream of genes. This raises intriguing new questions regarding the role of histone repositioning in the wake of RNA Polymerase II passage downstream of impaired poly(A) site recognition.

Zooming past the coronavirus lockdown: online spirometry practical demonstration with student involvement in analysis by remote control.

This paper describes the process involved in conducting an online spirometry practical through Zoom. The teacher demonstrated the practical from the medical school, and the students observed the procedure from the comfort of their own homes. Students were able to analyze the graphs captured in the teacher's laptop by remotely controlling the teacher's laptop. This method may be useful for places where face-to-face classes are suspended due to the COVID-19 pandemic.

Mutation Rate Variation is a Primary Determinant of the Distribution of Allele Frequencies in Humans.

The site frequency spectrum (SFS) has long been used to study demographic history and natural selection. Here, we extend this summary by examining the SFS conditional on the alleles found at the same site in other species. We refer to this extension as the "phylogenetically-conditioned SFS" or cSFS. Using recent large-sample data from the Exome Aggregation Consortium (ExAC), combined with primate genome sequences, we find that human variants that occurred independently in closely related primate lineages are at higher frequencies in humans than variants with parallel substitutions in more distant primates. We show that this effect is largely due to sites with elevated mutation rates causing significant departures from the widely-used infinite sites mutation model. Our analysis also suggests substantial variation in mutation rates even among mutations involving the same nucleotide changes. In summary, we show that variable mutation rates are key determinants of the SFS in humans.

Efficient inference of population size histories and locus-specific mutation rates from large-sample genomic variation data.

With the recent increase in study sample sizes in human genetics, there has been growing interest in inferring historical population demography from genomic variation data. Here, we present an efficient inference method that can scale up to very large samples, with tens or hundreds of thousands of individuals. Specifically, by utilizing analytic results on the expected frequency spectrum under the coalescent and by leveraging the technique of automatic differentiation, which allows us to compute gradients exactly, we develop a very efficient algorithm to infer piecewise-exponential models of the historical effective population size from the distribution of sample allele frequencies. Our method is orders of magnitude faster than previous demographic inference methods based on the frequency spectrum. In addition to inferring demography, our method can also accurately estimate locus-specific mutation rates. We perform extensive validation of our method on simulated data and show that it can accurately infer multiple recent epochs of rapid exponential growth, a signal that is difficult to pick up with small sample sizes. Lastly, we use our method to analyze data from recent sequencing studies, including a large-sample exome-sequencing data set of tens of thousands of individuals assayed at a few hundred genic regions.

Novel probabilistic models of spatial genetic ancestry with applications to stratification correction in genome-wide association studies.

Motivation: Genetic variation in human populations is influenced by geographic ancestry due to spatial locality in historical mating and migration patterns. Spatial population structure in genetic datasets has been traditionally analyzed using either model-free algorithms, such as principal components analysis (PCA) and multidimensional scaling, or using explicit spatial probabilistic models of allele frequency evolution. We develop a general probabilistic model and an associated inference algorithm that unify the model-based and data-driven approaches to visualizing and inferring population structure. Our spatial inference algorithm can also be effectively applied to the problem of population stratification in genome-wide association studies (GWAS), where hidden population structure can create fictitious associations when population ancestry is correlated with both the genotype and the trait. Results: Our algorithm Geographic Ancestry Positioning (GAP) relates local genetic distances between samples to their spatial distances, and can be used for visually discerning population structure as well as accurately inferring the spatial origin of individuals on a two-dimensional continuum. On both simulated and several real datasets from diverse human populations, GAP exhibits substantially lower error in reconstructing spatial ancestry coordinates compared to PCA. We also develop an association test that uses the ancestry coordinates inferred by GAP to accurately account for ancestry-induced correlations in GWAS. Based on simulations and analysis of a dataset of 10 metabolic traits measured in a Northern Finland cohort, which is known to exhibit significant population structure, we find that our method has superior power to current approaches. Availability and Implementation: Our software is available at https://github.com/anand-bhaskar/gap . Contacts: abhaskar@stanford.edu or ajavanma@usc.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Distortion of genealogical properties when the sample is very large.

Study sample sizes in human genetics are growing rapidly, and in due course it will become routine to analyze samples with hundreds of thousands, if not millions, of individuals. In addition to posing computational challenges, such large sample sizes call for carefully reexamining the theoretical foundation underlying commonly used analytical tools. Here, we study the accuracy of the coalescent, a central model for studying the ancestry of a sample of individuals. The coalescent arises as a limit of a large class of random mating models, and it is an accurate approximation to the original model provided that the population size is sufficiently larger than the sample size. We develop a method for performing exact computation in the discrete-time Wright-Fisher (DTWF) model and compare several key genealogical quantities of interest with the coalescent predictions. For recently inferred demographic scenarios, we find that there are a significant number of multiple- and simultaneous-merger events under the DTWF model, which are absent in the coalescent by construction. Furthermore, for large sample sizes, there are noticeable differences in the expected number of rare variants between the coalescent and the DTWF model. To balance the trade-off between accuracy and computational efficiency, we propose a hybrid algorithm that uses the DTWF model for the recent past and the coalescent for the more distant past. Our results demonstrate that the hybrid method with only a handful of generations of the DTWF model leads to a frequency spectrum that is quite close to the prediction of the full DTWF model.

Depletion of the Trypanosome Pumilio domain protein PUF2 or of some other essential proteins causes transcriptome changes related to coding region length.

Pumilio domain RNA-binding proteins are known mainly as posttranscriptional repressors of gene expression that reduce mRNA translation and stability. Trypanosoma brucei has 11 PUF proteins. We show here that PUF2 is in the cytosol, with roughly the same number of molecules per cell as there are mRNAs. Although PUF2 exhibits a low level of in vivo RNA binding, it is not associated with polysomes. PUF2 also decreased reporter mRNA levels in a tethering assay, consistent with a repressive role. Depletion of PUF2 inhibited growth of bloodstream-form trypanosomes, causing selective loss of mRNAs with long open reading frames and increases in mRNAs with shorter open reading frames. Reexamination of published RNASeq data revealed the same trend in cells depleted of some other proteins. We speculate that these length effects could be caused by inhibition of the elongation phase of transcription or by an influence of translation status or polysomal conformation on mRNA decay.

DESCARTES' RULE OF SIGNS AND THE IDENTIFIABILITY OF POPULATION DEMOGRAPHIC MODELS FROM GENOMIC VARIATION DATA.

The sample frequency spectrum (SFS) is a widely-used summary statistic of genomic variation in a sample of homologous DNA sequences. It provides a highly efficient dimensional reduction of large-scale population genomic data and its mathematical dependence on the underlying population demography is well understood, thus enabling the development of efficient inference algorithms. However, it has been recently shown that very different population demographies can actually generate the same SFS for arbitrarily large sample sizes. Although in principle this nonidentifiability issue poses a thorny challenge to statistical inference, the population size functions involved in the counterexamples are arguably not so biologically realistic. Here, we revisit this problem and examine the identifiability of demographic models under the restriction that the population sizes are piecewise-defined where each piece belongs to some family of biologically-motivated functions. Under this assumption, we prove that the expected SFS of a sample uniquely determines the underlying demographic model, provided that the sample is sufficiently large. We obtain a general bound on the sample size sufficient for identifiability; the bound depends on the number of pieces in the demographic model and also on the type of population size function in each piece. In the cases of piecewise-constant, piecewise-exponential and piecewise-generalized-exponential models, which are often assumed in population genomic inferences, we provide explicit formulas for the bounds as simple functions of the number of pieces. Lastly, we obtain analogous results for the "folded" SFS, which is often used when there is ambiguity as to which allelic type is ancestral. Our results are proved using a generalization of Descartes' rule of signs for polynomials to the Laplace transform of piecewise continuous functions.

Expression of the RNA recognition motif protein RBP10 promotes a bloodstream-form transcript pattern in Trypanosoma brucei.

When Trypanosoma brucei differentiates from the bloodstream form to the procyclic form, there are decreases in the levels of many mRNAs encoding proteins required for the glycolytic pathway, and the mRNA encoding the RNA recognition motif protein RBP10 decreases in parallel. We show that RBP10 is a cytoplasmic protein that is specific to bloodstream-form trypanosomes, where it is essential. Depletion of RBP10 caused decreases in many bloodstream-form-specific mRNAs, with increases in mRNAs associated with the early stages of differentiation. The changes were similar to, but more extensive than, those caused by glucose deprivation. Conversely, forced RBP10 expression in procyclics induced a switch towards bloodstream-form mRNA expression patterns, with concomitant growth inhibition. Forced expression of RBP10 prevented differentiation of bloodstream forms in response to cis-aconitate, but did not prevent expression of key differentiation markers in response to glucose deprivation. RBP10 was not associated with heavy polysomes, showed no detectable in vivo binding to RNA, and was not stably associated with other proteins. Tethering of RBP10 to a reporter mRNA inhibited translation, and halved the abundance of the bound mRNA. We suggest that RBP10 may prevent the expression of regulatory proteins that are specific to the procyclic form.

A comprehensive genome-wide map of autonomously replicating sequences in a naive genome.

Eukaryotic chromosomes initiate DNA synthesis from multiple replication origins. The machinery that initiates DNA synthesis is highly conserved, but the sites where the replication initiation proteins bind have diverged significantly. Functional comparative genomics is an obvious approach to study the evolution of replication origins. However, to date, the Saccharomyces cerevisiae replication origin map is the only genome map available. Using an iterative approach that combines computational prediction and functional validation, we have generated a high-resolution genome-wide map of DNA replication origins in Kluyveromyces lactis. Unlike other yeasts or metazoans, K. lactis autonomously replicating sequences (KlARSs) contain a 50 bp consensus motif suggestive of a dimeric structure. This motif is necessary and largely sufficient for initiation and was used to dependably identify 145 of the up to 156 non-repetitive intergenic ARSs projected for the K. lactis genome. Though similar in genome sizes, K. lactis has half as many ARSs as its distant relative S. cerevisiae. Comparative genomic analysis shows that ARSs in K. lactis and S. cerevisiae preferentially localize to non-syntenic intergenic regions, linking ARSs with loci of accelerated evolutionary change.

Retinal fundus image classification for diabetic retinopathy using SVM predictions.

Diabetic Retinopathy (DR) is one of the leading causes of blindness in all age groups. Inadequate blood supply to the retina, retinal vascular exudation, and intraocular hemorrhage cause DR. Despite recent advances in the diagnosis and treatment of DR, this complication remains a challenging task for physicians and patients. Hence, a comprehensive and automated technique for DR screening is necessary, which will give early detection of this disease. The proposed work focuses on 16 class classification method using Support Vector Machine (SVM) that predict abnormalities individually or in combination based on the selected class. Our proposed work comprises Gaussian mixture model (GMM), K-means, Maximum a Posteriori (MAP) algorithm, Principal Component Analysis (PCA), Grey level co-occurrence matrix (GLCM), and SVM for disease diagnosis using DR. The proposed method provides an accuracy of 77.3% on DIARETDB1 dataset. We expect this low computational cost will be helpful in the medicine and diagnosis of DR.

The Prevalence and Pathophysiology of Chemical Sense Disorder Caused by the Novel Coronavirus.

Emerging viral infections are a ceaseless challenge and remain a global public health concern. The world has not yet come back to normal from the devastating effects of the highly contagious and pathogenic novel coronavirus, or Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Olfactory and taste dysfunction is common in patients infected by the novel coronavirus. In light of the emergence of different coronavirus variants, it is important to update the prevalence and pathophysiology of these side effects. In this review, articles published on the prevalence of olfactory and taste dysfunction from coronavirus disease (COVID-19) and their possible pathophysiologic mechanisms have been reviewed and reported. The modulatory role of different SARS-CoV-2 variants on the chemical senses is then described. The clinical relevance of chemical sense disorder and its long-term morbidity and management is also discussed.