Weak population regulation in ecological time series

How strongly natural populations are regulated has a long history of debate in ecology. Here, we discuss concepts of population regulation appropriate for stochastic population dynamics. We then analyse two large collections of data sets with autoregressive-moving average (ARMA) models, using model selection techniques to find best-fitting models. We estimated two metrics of population regulation: the characteristic return rate of populations to stationarity and the variability of the stationary distribution (the long-term distribution of population abundance). Empirically, longer time series were more likely to show weakly regulated population dynamics. For data sets of length ≥ 20, more than 35% had characteristic return times > 6 years, and more than 29% had stationary distributions whose coefficients of variation were more than two times greater than would be the case if they were maximally regulated. These results suggest that many natural populations are weakly regulated.
Ecology Letters (2010) 13: 21–31     

A comprehensive biomedical variant catalogue based on whole genome sequences of 582 dogs and eight wolves

The domestic dog serves as an excellent model to investigate the genetic basis of disease. More than 400 heritable traits analogous to human diseases have been described in dogs. To further canine medical genetics research, we established the Dog Biomedical Variant Database Consortium (DBVDC) and present a comprehensive list of functionally annotated genome variants that were identified with whole genome sequencing of 582 dogs from 126 breeds and eight wolves. The genomes used in the study have a minimum coverage of 10× and an average coverage of ~24×. In total, we identified 23 133 692 single‐nucleotide variants (SNVs) and 10 048 038 short indels, including 93% undescribed variants. On average, each individual dog genome carried ～4.1 million single‐nucleotide and ~1.4 million short‐indel variants with respect to the reference genome assembly. About 2% of the variants were located in coding regions of annotated genes and loci. Variant effect classification showed 247 141 SNVs and 99 562 short indels having moderate or high impact on 11 267 protein‐coding genes. On average, each genome contained heterozygous loss‐of‐function variants in 30 potentially embryonic lethal genes and 97 genes associated with developmental disorders. More than 50 inherited disorders and traits have been unravelled using the DBVDC variant catalogue, enabling genetic testing for breeding and diagnostics. This resource of annotated variants and their corresponding genotype frequencies constitutes a highly useful tool for the identification of potential variants causative for rare inherited disorders in dogs.     

SimShiftDB; local conformational restraints derived from chemical shift similarity searches on a large synthetic database

We present SimShiftDB, a new program to extract conformational data from protein chemical shifts using structural alignments. The alignments are obtained in searches of a large database containing 13,000 structures and corresponding back-calculated chemical shifts. SimShiftDB makes use of chemical shift data to provide accurate results even in the case of low sequence similarity, and with even coverage of the conformational search space. We compare SimShiftDB to HHSearch, a state-of-the-art sequence-based search tool, and to TALOS, the current standard tool for the task. We show that for a significant fraction of the predicted similarities, SimShiftDB outperforms the other two methods. Particularly, the high coverage afforded by the larger database often allows predictions to be made for residues not involved in canonical secondary structure, where TALOS predictions are both less frequent and more error prone. Thus SimShiftDB can be seen as a complement to currently available methods.     

LambdaPP: Fast and accessible protein-specific phenotype predictions

The availability of accurate and fast artificial intelligence (AI) solutions predicting aspects of proteins are revolutionizing experimental and computational molecular biology. The webserver LambdaPP aspires to supersede PredictProtein, the first internet server making AI protein predictions available in 1992. Given a protein sequence as input, LambdaPP provides easily accessible visualizations of protein 3D structure, along with predictions at the protein level (GeneOntology, subcellular location), and the residue level (binding to metal ions, small molecules, and nucleotides; conservation; intrinsic disorder; secondary structure; alpha-helical and beta-barrel transmembrane segments; signal-peptides; variant effect) in seconds. The structure prediction provided by LambdaPP—leveraging ColabFold and computed in minutes—is based on MMseqs2 multiple sequence alignments. All other feature prediction methods are based on the pLM ProtT5. Queried by a protein sequence, LambdaPP computes protein and residue predictions almost instantly for various phenotypes, including 3D structure and aspects of protein function. LambdaPP is freely available for everyone to use under embed.predictprotein.org , the interactive results for the case study can be found under https://embed.predictprotein.org/o/Q9NZC2 . The frontend of LambdaPP can be found on GitHub ( github.com/sacdallago/embed.predictprotein.org ), and can be freely used and distributed under the academic free use license (AFL-2). For high-throughput applications, all methods can be executed locally via the bio-embeddings ( bioembeddings.com ) python package, or docker image at ghcr.io/bioembeddings/bio_embeddings , which also includes the backend of LambdaPP.     

Genome-wide DNA methylation profiling of stomach cancer in the ethnic population of Mizoram,North East India

Stomach cancer is the fifth most common cancer in terms of prevalence and incidence and the fourth leading cause of mortality in men and women worldwide. It is well-established that aberrant DNA methylation in cells can lead to carcinogenesis. The primary objective of our study was to investigate the aberrant DNA methylation status of genes associated with stomach cancer with a particular reference to the ethnic population of Mizoram, North East India. The site-level analysis identified 2883 CpG sites differentially methylated, representing ～922 genes. Out of which 476 Differentially Methylated Positions (DMPs) were promoter-associated, 452 DMPs were hypermethylated, and 24 were hypomethylated. The region-level analysis identified 462 Differentially Methylated Regions (DMRs) corresponding to ～320 genes, of which ～281 genes were hypermethylated and ～40 genes were hypomethylated. TCGA analysis showed that some of the genes had been previously implicated in other cancers including stomach cancer. Five hypermethylated genes were selected as candidate genes for further investigations and they have shown to be novel and could serve as candidate hypermethylation biomarkers for stomach cancer in this particular ethnic group.     

Risk of Breast Cancer in Females With Hypothyroidism: A Nationwide,Population-Based,Cohort Study

ObjectivesThe results of studies investigating the relationship between breast cancer and hypothyroidism vary greatly from study to study. In this study, we analyzed a large and reliable, population-based database to gain a better understanding of the correlation.MethodsThis retrospective cohort study analyzed patients with hypothyroidism between January 1, 2000 and December 31, 2012 (hypothyroidism cohort) from the Longitudinal Health Insurance Database 2000 in Taiwan. For each woman with hypothyroidism, 1 woman without a history of breast cancer was randomly selected from the Longitudinal Health Insurance Database 2000 and frequency matched (1:4) with women without hypothyroidism by age and index year of hypothyroidism. The study outcome was the diagnosis of breast cancer during a 12-year follow-up period.ResultsIn this study, 6665 women with hypothyroidism and 26 660 women without hypothyroidism were identified. The hypothyroidism cohort had a significantly higher risk of breast cancer than the nonhypothyroidism cohort (adjusted hazard ratio [aHR] 1.69 [95% CI, 1.15-2.49]; P = .01), especially in the group aged 40 to 64 years (aHR 2.07 [95% CI, 1.32-3.23]; P = .01). Women in the hypothyroidism cohort taking levothyroxine for a duration ˃588 days showed a significantly decreased risk of breast cancer (aHR 0.37 [95% CI, 0.19-0.71]; P = .003).ConclusionWomen with hypothyroidism are at a higher risk of breast cancer than those without hypothyroidism. Levothyroxine may reduce the risk of breast cancer in a woman with hypothyroidism.     

Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology

There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases.