The Homeodomain Resource: a comprehensive collection of sequence, structure, interaction, genomic and functional information on the homeodomain protein family

The Homeodomain Resource is a curated collection of sequence, structure, interaction, genomic and functional information on the homeodomain family. The current version builds upon previous versions by the addition of new, complete sets of homeodomain sequences from fully sequenced genomes, the expansion of existing curated homeodomain information and the improvement of data accessibility through better search tools and more complete data integration. This release contains 1534 full-length homeodomain-containing sequences, 93 experimentally derived homeodomain structures, 101 homeodomain protein–protein interactions, 107 homeodomain DNA-binding sites and 206 homeodomain proteins implicated in human genetic disorders

Causality analysis detects the regulatory role of maternal effect genes in the early Drosophila embryo

In developmental studies, inferring regulatory interactions of segmentation genetic network play a vital role in unveiling the mechanism of pattern formation. As such, there exists an opportune demand for theoretical developments and new mathematical models which can result in a more accurate illustration of this genetic network. Accordingly, this paper seeks to extract the meaningful regulatory role of the maternal effect genes using a variety of causality detection techniques and to explore whether these methods can suggest a new analytical view to the gene regulatory networks. We evaluate the use of three different powerful and widely-used models representing time and frequency domain Granger causality and convergent cross mapping technique with the results being thoroughly evaluated for statistical significance. Our findings show that the regulatory role of maternal effect genes is detectable in different time classes and thereby the method is applicable to infer the possible regulatory interactions present among the other genes of this network

Whistle classification ofsympatric false killer whale populations in Hawaiian waters yields low accuracy rates

Funding for passive acoustic data collection during the shipboard cetacean line-transect surveys was provided by PIFSC, SWFSC, NOAA Fisheries Pacific Islands Regional Office, and NOAA Fisheries Office of Protected Resources (OPR) for HICEAS 2010, PIFSC for PICEAS, PACES and HITEC, and PIFSC, OPR, NOAA Fisheries Office of Science and Technology, Chief of Naval Operation Environmental Readiness Division and Pacific Fleet, and Bureau of Ocean Energy Management for HICEAS 2017. Funding for passive acoustic data analysis was provided by PIFSC and the National Science Foundation Graduate Research Fellowships Program.Cetaceans are ecologically important marine predators, and designating individuals to distinct populations can be challenging. Passive acoustic monitoring provides an approach to classify cetaceans to populations using their vocalizations. In the Hawaiian Archipelago, three genetically distinct, sympatric false killer whale (Pseudorca crassidens) populations coexist: a broadly distributed pelagic population and two island-associated populations, an endangered main Hawaiian Islands (MHI) population and a Northwestern Hawaiian Islands (NWHI) population. The mechanisms that sustain the genetic separation between these overlapping populations are unknown but previous studies suggest that the acoustic diversity between populations may correspond to genetic differences. Here, we investigated whether false killer whale whistles could be correctly classified to population based on their characteristics to serve as a method of identifying populations when genetic or photographic-identification data are unavailable. Acoustic data were collected during line-transect surveys using towed hydrophone arrays. We measured 50 time and frequency parameters from whistles in 16 false killer whale encounters identified to population and used those measures to train and test random forest classification models. Random forest models that included three populations correctly classified 42% of individual whistles overall and resulted in a low kappa coefficient, κ = 0.15, indicating low agreement between models, and the true population. Whistles from the MHI population showed the highest correct classification rate (52%) compared to pelagic and NWHI whistles (42 and 36%, respectively). Pairwise random forest models classifying pelagic and MHI whistles proved slightly more accurate (62% accuracy, κ = 0.24), though a similar pelagic-NWHI model did not (56% accuracy, κ = 0.12). Results suggest that the time-frequency whistle characteristics are not suitable to confidently classify encounters to a specific false killer whale population, although certain features of whistles produced by the endangered MHI population allow for overall higher classification accuracy. Inclusion of other vocalization types, such as echolocation clicks, and alternative whistle variables may improve correct classification success for these sympatric populations.Publisher PDFPeer reviewe

Hyperpolarized 13C-MRI of Tumor Metabolism Demonstrates Early Metabolic Response to Neoadjuvant Chemotherapy in Breast Cancer

Purpose: To compare hyperpolarized carbon-13 (13C)-MRI with dynamic contrast-enhanced MRI (DCE-MRI) for detecting early treatment response in breast cancer. Materials and Methods: In this institutional review board-approved prospective study, one woman with triple-negative breast cancer (age 49) underwent 13C-MRI following injection of hyperpolarized [1-13C]pyruvate and DCE-MRI at 3 T at baseline and after a single cycle of neoadjuvant therapy. The 13C-lactate/13C-pyruvate ratio derived from hyperpolarized 13C-MRI and the pharmacokinetic parameters Ktrans and kep derived from DCE-MRI were compared, before and after treatment. Results: Exchange of the 13C-label between injected hyperpolarized [1-13C]pyruvate and the endogenous lactate pool was demonstrated, catalyzed by the enzyme lactate dehydrogenase. After one cycle of neoadjuvant chemotherapy, a 34% reduction in the 13C-lactate/13C-pyruvate ratio was shown to correctly identify the patient as a responder to therapy, which was subsequently confirmed by a complete pathologic response. However, DCE-MRI showed an increase in the pharmacokinetic parameters Ktrans (132%) and kep (31%), which could be incorrectly interpreted as a poor response to treatment. Conclusion: Hyperpolarized 13C-MRI successfully identified response in breast cancer after a single cycle of neoadjuvant chemotherapy and may improve response prediction when used in conjunction with multiparametric proton MRI.This work was supported by a Wellcome Trust Strategic Award, Cancer Research UK (CRUK; Grants C8742/A18097, C19212/ A16628, C19212/A911376, and C197/A16465), the Austrian Science Fund (Grant J4025-B26), the CRUK Cambridge Centre, the CRUK & Engineering and Physical Sciences Research Council Cancer Imaging Centre in Cambridge and Manchester, the Mark Foundation for Cancer Research and Cancer Research UK Cambridge Centre (Grant C9685/A25177), CRUK National Cancer Imaging Translational Accelerator Award, Addenbrooke’s Charitable Trust, the National Institute for Health Research Cambridge Biomedical Research Centre, Cambridge Experimental Cancer Medicine Centre, and Cambridge University Hospitals National Health Service Foundation Trust

Imaging breast cancer using hyperpolarized carbon-13 MRI.

Our purpose is to investigate the feasibility of imaging tumor metabolism in breast cancer patients using 13C magnetic resonance spectroscopic imaging (MRSI) of hyperpolarized 13C label exchange between injected [1-13C]pyruvate and the endogenous tumor lactate pool. Treatment-naïve breast cancer patients were recruited: four triple-negative grade 3 cancers; two invasive ductal carcinomas that were estrogen and progesterone receptor-positive (ER/PR+) and HER2/neu-negative (HER2-), one grade 2 and one grade 3; and one grade 2 ER/PR+ HER2- invasive lobular carcinoma (ILC). Dynamic 13C MRSI was performed following injection of hyperpolarized [1-13C]pyruvate. Expression of lactate dehydrogenase A (LDHA), which catalyzes 13C label exchange between pyruvate and lactate, hypoxia-inducible factor-1 (HIF1α), and the monocarboxylate transporters MCT1 and MCT4 were quantified using immunohistochemistry and RNA sequencing. We have demonstrated the feasibility and safety of hyperpolarized 13C MRI in early breast cancer. Both intertumoral and intratumoral heterogeneity of the hyperpolarized pyruvate and lactate signals were observed. The lactate-to-pyruvate signal ratio (LAC/PYR) ranged from 0.021 to 0.473 across the tumor subtypes (mean ± SD: 0.145 ± 0.164), and a lactate signal was observed in all of the grade 3 tumors. The LAC/PYR was significantly correlated with tumor volume (R = 0.903, P = 0.005) and MCT 1 (R = 0.85, P = 0.032) and HIF1α expression (R = 0.83, P = 0.043). Imaging of hyperpolarized [1-13C]pyruvate metabolism in breast cancer is feasible and demonstrated significant intertumoral and intratumoral metabolic heterogeneity, where lactate labeling correlated with MCT1 expression and hypoxia

Lysine Side-Chain Dynamics in the Binding Site of Homeodomain/DNA Complexes As Observed by NMR Relaxation Experiments and Molecular Dynamics Simulations

An important but poorly characterized contribution to the thermodynamics of protein–DNA interactions is the loss of entropy that occurs from restricting the conformational freedom of amino acid side chains. The effect of restricting the flexibility of several side chains at a protein–DNA interface may be comparable in many cases to the other factors that determine the binding thermodynamics and may, therefore, play a key role in dictating the binding affinity and/or specificity. Because the entropic contributions, including the presence and influence of side-chain dynamics, are especially difficult to estimate based on structural information, it is important to pursue experimental and theoretical studies that can provide direct information regarding these issues. We report on studies of a model system, the homeodomain/DNA complex, focusing on the Lys50 class of homeodomains where a key lysine residue in position 50 was shown previously to be critical for binding site specificity. NMR methodology was employed for determining the dynamics of lysine side-chain amino groups via ¹⁵N relaxation measurements in the Lys50-class homeodomains from the <i>Drosophila</i> protein Bicoid and the human protein Pitx2. In the case of Pitx2, complexes with both a consensus and a nonconsensus DNA binding site were examined. NMR-derived order parameters indicated moderate to substantial conformational freedom for the lysine NH₃⁺ group in the complexes studied. To complement the experimental NMR measurements, molecular dynamics simulations were performed for the consensus complexes to gain further, detailed insights regarding the dynamics of the Lys50 side chain and other important residues in the protein–DNA interface