The genetic distances among primate lineages estimated from orthologous noncoding nucleotide sequences of beta-type globin loci and their flanking and intergenic DNA agree closely with the distances (delta T50H values) estimated by cross hybridization of total genomic single-copy DNAs. These DNA distances and the maximum parsimony tree constructed for the nucleotide sequence orthologues depict a branching pattern of primate lineages that is essentially congruent with the picture from phylogenetic analyses of morphological characters. The molecular evidence, however, resolves ambiguities in the morphological picture and provides an objective view of the cladistic position of humans among the primates. The molecular data group humans with chimpanzees in subtribe Hominina, with gorillas in tribe Hominini, orangutans in subfamily Homininae, gibbons in family Hominidae, Old World monkeys in infraorder Catarrhini, New World monkeys in semisuborder Anthropoidea, tarsiers in suborder Haplorhini, and strepsirhines (lemuriforms and lorisiforms) in order Primates. A seeming incongruency between organismal and molecular levels of evolution, namely that morphological evolution appears to have speeded up in higher primates, especially in the lineage to humans, while molecular evolution has slowed down, may have the trivial explanation that relatively small genetic changes may sometimes result in marked phenotypic changes.

The genetic alterations that underlie prostate tumorigenesis are assumed to comprise gain or loss of specific chromosomal regions, whole chromosomes, or sequence-specific mutations. Existing data have not demonstrated clear specificity of whole chromosome or regional chromosomal gain or loss that characterizes entire individual malignant lesions, or all malignant lesions, within a cancerous prostate. We have analyzed tissues from 13 patients for target sequences by using PCR and FISH techniques on paired malignant or prostatic intraepithelial neoplastic (PIN) and benign samples (usually from different areas of the same paraffin section). We exercised stringent histologic control over these samples by examining small (< 5 mm2), discrete regions of sectioned benign, malignant, and PIN tissue. The same histologic region was examined on serial sections by FISH and PCR analysis. The tissues were examined for numerical aberrations involving chromosomes 4 (as a control), 7, 8, 10, and the Y by FISH analysis, and for gain or loss of chromosome 7 and chromosomal arms 8p, 10q, and Yp by PCR analysis. The concurrent application of PCR and FISH to microdissected prostatic tissues yielded evidence of higher frequencies of genetic aberration in prostate cancers than those found with either method alone or by other approaches. These results indicate the power of simultaneous genetic assays that are closely linked to specific tumor histology.

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Highly malignant brain tumors harbor the aberrant propensity for aerobic glycolysis, the excessive conversion of glucose to lactic acid even in the presence of ample tissue oxygen. Lactic acid is rapidly effluxed to the tumor microenvironment via a group of plasma-membrane transporters denoted monocarboxylate transporters (MCTs) to prevent “self-poisoning.” One isoform, MCT2, has the highest affinity for lactate and thus should have the ability to respond to microenvironment conditions such as hypoxia, lactate, and pH to help maintain high glycolytic flux in the tumor. Yet, MCT2 is considered to not respond to hypoxia, which is counterintuitive. Its response to tumor lactate has not been reported. In this report, we experimentally identify the transcription initiation site/s for MCT2 in astrocytes (normal) and glioma (tumor). We then use a BACmid library to isolate a 4.2-kbp MCT2 promoter-exon I region and examine promoter response to glycolysis-mediated stimuli in glioma cells. Reporter analysis of nested-promoter constructs indicated response of MCT2 to hypoxia, pH, lactate, and glucose, the major physiological “players” that facilitate a tumor's growth and proliferation. Immunoblot analysis of native MCT2 expression under altered pH and hypoxia reflected the reporter data. The pH-mediated gene-regulation studies we describe are the first to record H+-based reporter studies for any mammalian system and demonstrate the exquisite response of the MCT2 gene to minute changes in tumor pH. Identical promoter usage also provides the first evidence of astrocytes harnessing the same gene regulatory regions to facilitate astrocyte-neuron lactate shuttling, a metabolic feature of normal brain.

Cells are exposed to a plethora of influences that can cause damage to DNA and alter the genome, often with detrimental consequences for health. Cells mitigate this damage through a variety of repair protein pathways, and accurate measurement of the accumulation, action, and dissipation timescales of these repair proteins is required to fully understand the DNA damage response. Recently, we described the Q-FADD (Quantitation of Fluorescence Accumulation after DNA Damage) method, which enhances the analytical power of the widely used laser microirradiation technique. In that study, Q-FADD and its preprocessing operations required licensed software and a significant amount of user overhead to find the model of best fit. Here, we present "qFADD.py", an open-source implementation of the Q-FADD algorithm that is available as both a stand-alone software package and on a publicly accessible webserver (https://qfadd.colorado.edu/). Furthermore, we describe significant improvements to the fitting and preprocessing methods that include corrections for nuclear drift and an automated grid-search for the model of best fit. To improve statistical rigor, the grid-search algorithm also includes automated simulation of replicates. As an example, we discuss the recruitment dynamics of the signaling protein PARP1 to DNA damage sites, and we show how to compare different populations of qFADD.py models.

Eukaryotic cells are constantly subject to DNA damage, often with detrimental consequences for the health of the organism. Cells mitigate this DNA damage through a variety of repair pathways involving a diverse and large number of different proteins. To better understand the cellular response to DNA damage, one needs accurate measurements of the accumulation, retention, and dissipation timescales of these repair proteins. Here, we describe an automated implementation of the "quantitation of fluorescence accumulation after DNA damage" method that greatly enhances the analysis and quantitation of the widely used technique known as laser microirradiation, which is used to study the recruitment of DNA repair proteins to sites of DNA damage. This open-source implementation ("qFADD.py") is available as a stand-alone software package that can be run on laptops or computer clusters. Our implementation includes corrections for nuclear drift, an automated grid search for the model of a best fit, and the ability to model both horizontal striping and speckle experiments. To improve statistical rigor, the grid-search algorithm also includes automated simulation of replicates. As a practical example, we present and discuss the recruitment dynamics of the early responder PARP1 to DNA damage sites. Impact StatementLive-cell microscopy and data analysis have been particularly impactful in the study of complex DNA repair processes. Over recent years, innovations in microscopy have allowed for imaging of processes occurring inside cells or even inside nuclei. These improvements have been particularly impactful in the study of DNA repair processes. For example, the method known as laser microirradiation has become a popular in vivo technique to study how fluorescently tagged DNA repair proteins accumulate, reside, and then dissipate from sites of DNA damage induced by laser damage of the DNA in live cells. Unfortunately, analysis of laser microirradiation data is often performed incorrectly or incompletely, thus precluding comparisons between different studies or with other methods. Because the correct and quantitative analysis of biological processes is essential for a more complete understanding of life processes, we previously developed the "quantitation of fluorescence accumulation after DNA damage" method, which allows for the determination of true diffusion coefficients and thus enables comparison with other methods. Here, we describe the automated and improved implementation known as qFADD.py, which allows any biologist to accurately quantitate their data from laser microirradiation.

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RNAs can act as potential drug targets in different diseases as their dysregulated expression or misfolding can alter various cellular processes. Noncoding RNAs account for $70% of the human genome and can have complex