Male-specific age prediction based on Y-chromosome DNA methylation with blood using pyrosequencing.

Age prediction is an important aspect of forensic science that offers valuable insight into identification. In recent years, extensive studies have been conducted on age prediction based on DNA methylation, and numerous studies have demonstrated that DNA methylation is a reliable biomarker for age prediction. However, almost all studies on age prediction based on DNA methylation have focused on age-related CpG sites in autosomes, which are concentrated on single-source DNA samples. Mixed samples, especially male-female mixed samples, are common in forensic casework. The application of Y-STRs and Y-SNPs can provide clues for the genetic typing of male individuals in male-female mixtures, but they cannot provide the age information of male individuals. Studies on Y-chromosome DNA methylation can address this issue. In this study, we identified five age-related CpG sites on the Y chromosome (Y-CpGs) and developed a male-specific age prediction model using pyrosequencing combined with a support vector machine algorithm. The mean absolute deviation of the model was 5.50 years in the training set and 6.74 years in the testing set. When we used a male blood sample to predict age, the deviation between the predicted and chronological age was 1.18 years. Then, we mixed the genomic DNA of the male and a female at ratios of 1:1, 1:5, 1:10, and 1:50, the range of deviation between the predicted and chronological age of the male in the mixture was 1.16-1.74 years. In addition, there was no significant difference between the methylation values of bloodstains and blood in the same sample, which indicates that our model is also suitable for bloodstain samples. Overall, our results show that age prediction using DNA methylation of the Y chromosome has potential applications in forensic science and can be of great help in predicting the age of males in male-female mixtures. Furthermore, this work lays the foundation for future research on age-related applications of Y-CpGs.

Depression-associated gut microbes, metabolites and clinical trials.

Depression is one of the most prevalent mental disorders today. Over the past decade, there has been considerable attention given to the field of gut microbiota associated with depression. A substantial body of research indicates a bidirectional communication pathway between gut microbiota and the brain. In this review, we extensively detail the correlation between gut microbiota, including Lactobacillus acidophilus and Bifidobacterium longum, and metabolites such as short-chain fatty acids (SCFAs) and 5-hydroxytryptamine (5-HT) concerning depression. Furthermore, we delve into the potential health benefits of microbiome-targeted therapies, encompassing probiotics, prebiotics, and synbiotics, in alleviating depression. Lastly, we underscore the importance of employing a constraint-based modeling framework in the era of systems medicine to contextualize metabolomic measurements and integrate multi-omics data. This approach can offer valuable insights into the complex metabolic host-microbiota interactions, enabling personalized recommendations for potential biomarkers, novel drugs, and treatments for depression.

MMiKG: a knowledge graph-based platform for path mining of microbiota-mental diseases interactions.

The microbiota-gut-brain axis denotes a two-way system of interactions between the gut and the brain, comprising three key components: (1) gut microbiota, (2) intermediates and (3) mental ailments. These constituents communicate with one another to induce changes in the host's mood, cognition and demeanor. Knowledge concerning the regulation of the host central nervous system by gut microbiota is fragmented and mostly confined to disorganized or semi-structured unrestricted texts. Such a format hinders the exploration and comprehension of unknown territories or the further advancement of artificial intelligence systems. Hence, we collated crucial information by scrutinizing an extensive body of literature, amalgamated the extant knowledge of the microbiota-gut-brain axis and depicted it in the form of a knowledge graph named MMiKG, which can be visualized on the GraphXR platform and the Neo4j database, correspondingly. By merging various associated resources and deducing prospective connections between gut microbiota and the central nervous system through MMiKG, users can acquire a more comprehensive perception of the pathogenesis of mental disorders and generate novel insights for advancing therapeutic measures. As a free and open-source platform, MMiKG can be accessed at http://yangbiolab.cn:8501/ with no login requirement.

WMSA 2: a multiple DNA/RNA sequence alignment tool implemented with accurate progressive mode and a fast win-win mode combining the center star and progressive strategies.

Multiple sequence alignment is widely used for sequence analysis, such as identifying important sites and phylogenetic analysis. Traditional methods, such as progressive alignment, are time-consuming. To address this issue, we introduce StarTree, a novel method to fast construct a guide tree by combining sequence clustering and hierarchical clustering. Furthermore, we develop a new heuristic similar region detection algorithm using the FM-index and apply the k-banded dynamic program to the profile alignment. We also introduce a win-win alignment algorithm that applies the central star strategy within the clusters to fast the alignment process, then uses the progressive strategy to align the central-aligned profiles, guaranteeing the final alignment's accuracy. We present WMSA 2 based on these improvements and compare the speed and accuracy with other popular methods. The results show that the guide tree made by the StarTree clustering method can lead to better accuracy than that of PartTree while consuming less time and memory than that of UPGMA and mBed methods on datasets with thousands of sequences. During the alignment of simulated data sets, WMSA 2 can consume less time and memory while ranking at the top of Q and TC scores. The WMSA 2 is still better at the time, and memory efficiency on the real datasets and ranks at the top on the average sum of pairs score. For the alignment of 1 million SARS-CoV-2 genomes, the win-win mode of WMSA 2 significantly decreased the consumption time than the former version. The source code and data are available at https://github.com/malabz/WMSA2.

DNA methylation-based age prediction with bloodstains using pyrosequencing and random forest regression.

The use of DNA methylation to predict chronological age has shown promising potential for obtaining additional information in forensic investigations. To date, several studies have reported age prediction models based on DNA methylation in body fluids with high DNA content. However, it is often difficult to apply these existing methods in practice due to the low amount of DNA present in stains of body fluids that are part of a trace material. In this study, we present a sensitive and rapid test for age prediction with bloodstains based on pyrosequencing and random forest regression. This assay requires only 0.1 ng of genomic DNA and the entire procedure can be completed within 10 h, making it practical for forensic investigations that require a short turnaround time. We examined the methylation levels of 46 CpG sites from six genes using bloodstain samples from 128 males and 113 females aged 10-79 years. A random forest regression model was then used to construct an age prediction model for males and females separately. The final age prediction models were developed with seven CpG sites (three for males and four for females) based on the performance of the random forest regression. The mean absolute deviation was less than 3 years for each model. Our results demonstrate that DNA methylation-based age prediction using pyrosequencing and random forest regression has potential applications in forensics to accurately predict the biological age of a bloodstain donor.

HAlign 3: Fast Multiple Alignment of Ultra-Large Numbers of Similar DNA/RNA Sequences.

HAlign is a cross-platform program that performs multiple sequence alignments based on the center star strategy. Here we present two major updates of HAlign 3, which helped improve the time efficiency and the alignment quality, and made HAlign 3 a specialized program to process ultra-large numbers of similar DNA/RNA sequences, such as closely related viral or prokaryotic genomes. HAlign 3 can be easily installed via the Anaconda and Java release package on macOS, Linux, Windows subsystem for Linux, and Windows systems, and the source code is available on GitHub (https://github.com/malabz/HAlign-3).

Heavy metals in soil from gangue stacking areas increases children health risk and causes developmental neurotoxicity in zebrafish larvae.

Coal is the main energy source in China, with 4.5 billion metric tons of coal gangue accumulating near the mining areas in the process of coal mining. The objectives of the present study were to identify the health risks to children from soil pollution caused by coal gangue accumulation and to clarify the possible developmental neurotoxicity caused by this accumulation using zebrafish (Danio rerio) as a model. The results reveal that As and seven other heavy metals in soil samples from the gangue dumping area to the downstream villages exhibited distance-dependent concentration variations and posed substantial potential non-carcinogenic risks to local children. Additionally, soil leachate could affect the key processes of early neurodevelopment in zebrafish at critical windows, mainly including the alterations of cytoskeleton regulation (α1-tubulin), axon growth (gap43), neuronal myelination (mbp) and synapse formation (sypa, sypb, and psd95), eventually leading to hypoactivity in the zebrafish larvae. These findings suggest the possible health risks of soil pollution in the coal gangue stacking areas to children, particularly affecting their early neurodevelopment.

The phytotoxicities of agricultural soil samples from a coal gangue stacking area to several maize cultivars (Zea mays L.).

In Shanxi, a major energy province in China, environmental pollution caused by coal gangue accumulation is becoming an increasingly serious problem. In addition, crops are the first trophic level in the human food chain, and the security and production of crops are closely related to human well-being. The objective of this study was to estimate the phytotoxicities of agricultural soil samples contaminated by coal gangue accumulation using maize (Zea mays L.) as a model organism. Finally, a tolerant maize cultivar was screened for coal gangue stacking areas. Seven cultivars of maize seeds were treated with agricultural soil leachate around the coal gangue stacking area at various concentrations of 0, 1:27, 1:9, 1:3, and 1:1. The results revealed that the agricultural soil leachate treatment could inhibit seed germination and the growth of roots and shoots and that the soil leachate-induced phytotoxicities were cultivar-dependent. At the same exposure concentration, tolerant maize cultivar displayed lower toxicity symptoms than sensitive maize cultivar in terms of growth inhibition, oxidative damage, and DNA damage. Stronger activities of antioxidant enzymes were observed in the tolerant maize cultivar than in the sensitive maize cultivar, indicating that the difference between cultivars in antioxidant capacity is one reason for the difference in plant tolerance. Our study provides experimental evidence for the ecological risk assessment of soil and the selection of maize cultivars with high environmental pollutant tolerance for use in coal gangue stacking areas.

DisBalance: a platform to automatically build balance-based disease prediction models and discover microbial biomarkers from microbiome data.

How best to utilize the microbial taxonomic abundances in regard to the prediction and explanation of human diseases remains appealing and challenging, and the relative nature of microbiome data necessitates a proper feature selection method to resolve the compositional problem. In this study, we developed an all-in-one platform to address a series of issues in microbiome-based human disease prediction and taxonomic biomarkers discovery. We prioritize the interpretation, runtime and classification accuracy of the distal discriminative balances analysis (DBA-distal) method in selecting a set of distal discriminative balances, and develop DisBalance, a comprehensive platform, to integrate and streamline the workflows of disease model building, disease risk prediction and disease-related biomarker discovery for microbiome-based binary classifications. DisBalance allows the de novo model-building and disease risk prediction in a very fast and convenient way. To facilitate the model-driven and knowledge-driven discoveries, DisBalance dedicates multiple strategies for the mining of microbial biomarkers. The independent validation of the models constructed by the DisBalance pipeline is performed on seven microbiome datasets from the original article of DBA-distal. The implementation of the DisBalance platform is demonstrated by a complete analysis of a shotgun metagenomic dataset of Ulcerative Colitis (UC). As a free and open-source, DisBlance can be accessed at http://lab.malab.cn/soft/DisBalance. The source code and demo data for Disbalance are available at https://github.com/yangfenglong/DisBalance.

A comprehensive review of the imbalance classification of protein post-translational modifications.

Post-translational modifications (PTMs) play significant roles in regulating protein structure, activity and function, and they are closely involved in various pathologies. Therefore, the identification of associated PTMs is the foundation of in-depth research on related biological mechanisms, disease treatments and drug design. Due to the high cost and time consumption of high-throughput sequencing techniques, developing machine learning-based predictors has been considered an effective approach to rapidly recognize potential modified sites. However, the imbalanced distribution of true and false PTM sites, namely, the data imbalance problem, largely effects the reliability and application of prediction tools. In this article, we conduct a systematic survey of the research progress in the imbalanced PTMs classification. First, we describe the modeling process in detail and outline useful data imbalance solutions. Then, we summarize the recently proposed bioinformatics tools based on imbalanced PTM data and simultaneously build a convenient website, ImClassi_PTMs (available at lab.malab.cn/∼dlj/ImbClassi_PTMs/), to facilitate the researchers to view. Moreover, we analyze the challenges of current computational predictors and propose some suggestions to improve the efficiency of imbalance learning. We hope that this work will provide comprehensive knowledge of imbalanced PTM recognition and contribute to advanced predictors in the future.

GutBalance: a server for the human gut microbiome-based disease prediction and biomarker discovery with compositionality addressed.

The compositionality of the microbiome data is well-known but often neglected. The compositional transformation pertains to the supervised learning of microbiome data and is a critical step that decides the performance and reliability of the disease classifiers. We value the excellent performance of the distal discriminative balance analysis (DBA) method, which selects distal balances of pairs and trios of bacteria, in addressing the classification of high-dimensional microbiome data. By applying this method to the species-level abundances of all the disease phenotypes in the GMrepo database, we build a balance-based model repository for the classification of human gut microbiome-related diseases. The model repository supports the prediction of disease risks for new sample(s). More importantly, we highlight the concept of balance-disease associations rather than the conventional microbe-disease associations and develop the human Gut Balance-Disease Association Database (GBDAD). Each predictable balance for each disease model indicates a potential biomarker-disease relationship and can be interpreted as a bacteria ratio positively or negatively correlated with the disease. Furthermore, by linking the balance-disease associations to the evidenced microbe-disease associations in MicroPhenoDB, we surprisingly found that most species-disease associations inferred from the shotgun metagenomic datasets can be validated by external evidence beyond MicroPhenoDB. The balance-based species-disease association inference will accelerate the generation of new microbe-disease association hypotheses in gastrointestinal microecology research and clinical trials. The model repository and the GBDAD database are deployed on the GutBalance server, which supports interactive visualization and systematic interrogation of the disease models, disease-related balances and disease-related species of interest.

[Toxicity of Soil Leachate from Coal Gangue and Its Surrounding Village of Barley(Hordeum vulgare)].

Coal gangue is a harmful solid waste product of coal mining. When it accumulates for a long time, it becomes harmful to the surroundings. To investigate the adverse effect of coal gangue on the surrounding environment, this study investigated the effects of coal gangue and its downstream village on the growth toxicity and genotoxicity of barley at different dilution concentrations (1：27, 1：9, 1：3, and 1：1) via hydroponic experiments. As a result, low concentration coal gangue showed a slight promotion effect on the growth of roots and shoots of barley, while coal gangue and village soil, which have a high concentration, could seriously inhibit their germination and growth. At the same time, with the increase of the concentration of coal gangue, malondialdehyde (MDA) in barley leaves increased, and chlorophyll (Chl) increased first and then decreased, while the village soil showed a lower toxic effect. In addition, our results showed that higher concentrations of coal gangue and village soil could decrease the mitotic index and increase the micronucleus rate in root tip cells, indicating that the toxicity mechanism of coal gangue to barley may be involved in genotoxicity. These results provide experimental evidence for the ecological risk assessment of the coal gangue and its surrounding environment.

mAML: an automated machine learning pipeline with a microbiome repository for human disease classification.

Due to the concerted efforts to utilize the microbial features to improve disease prediction capabilities, automated machine learning (AutoML) systems aiming to get rid of the tediousness in manually performing ML tasks are in great demand. Here we developed mAML, an ML model-building pipeline, which can automatically and rapidly generate optimized and interpretable models for personalized microbiome-based classification tasks in a reproducible way. The pipeline is deployed on a web-based platform, while the server is user-friendly and flexible and has been designed to be scalable according to the specific requirements. This pipeline exhibits high performance for 13 benchmark datasets including both binary and multi-class classification tasks. In addition, to facilitate the application of mAML and expand the human disease-related microbiome learning repository, we developed GMrepo ML repository (GMrepo Microbiome Learning repository) from the GMrepo database. The repository involves 120 microbiome-based classification tasks for 85 human-disease phenotypes referring to 12 429 metagenomic samples and 38 643 amplicon samples. The mAML pipeline and the GMrepo ML repository are expected to be important resources for researches in microbiology and algorithm developments. Database URL: http://lab.malab.cn/soft/mAML.

Embryonic exposure to soil samples from a gangue stacking area induces thyroid hormone disruption in zebrafish.

The total accumulative stockpiles of gangue from long-term coal mining exceed 1 billion tons and occupy 182 square kilometers, and 50 million tons of additional gangue are generated per year in Shanxi, a major energy province in China. The objective of this study was to examine whether exposure to village soils affected by gangue stacking would disrupt thyroid hormone system homeostasis and eventually affect endocrine system and development, using zebrafish (Danio rerio) as a model organism. The zebrafish embryos were exposed to village soil leachates at 0, 1:9, 1:3 and 1:1 from 1 to 120 h postfertilization (hpf), and the sample caused a dose-dependent increase in the mortality and malformation rate, and decrease in the heart rate, hatching rate and body length of zebrafish larvae. Importantly, the soil leachate alleviated the whole-body triiodothyronine (T3) and thyroxine (T4) levels at higher concentrations, and altered the expression of the hypothalamic-pituitary-thyroid (HPT) axis-regulating genes crh, trh, tshß, nis, tg, nkx2.1, pax8, hhex, ttr, dio1, dio2, ugt1ab, trα, and trß and the PAH exposure-related genes ahr2 and cyp1a. These findings highlight the potential risk of thyroid hormone disruption and developmental toxicity from soil samples around coal gangue stacking areas.

Biodegradation of BTEX Aromatics by a Haloduric Microbial Consortium Enriched from a Sediment of Bohai Sea, China.

This study focused on a haloduric BTEX-degrading microbial consortium EC20 enriched from Bohai Sea sediment. EC20 degraded 87% of BTEX at 435 mg L-1 initial concentration (benzene, toluene, ethylbenzene, and xylenes in equal proportions) in the presence of 3.4% NaCl. 16S rRNA gene-based PCR-DGGE profiles revealed that the dominant bacteria in EC20 were Proteobacteria, Actinobacteria, Bacteroidetes, and Firmicutes at the phylum level, and Pseudomonas, Mesorhizobium, Achromobacter, Stenotrophomonas, and Halomonas at the genus level. PCR detection of genes coding the key enzymes which participated in BTEX degradation pathways showed that the enriched consortium EC20 contained TOL pathway and TOD pathway to initiate biodegradation of BTEX.

A proposal of "core enzyme" bioindicator in long-term Pb-Zn ore pollution areas based on topsoil property analysis.

To study the effects of long-term mining activities on the agricultural soil quality of Mengnuo town in Yunnan province, China, the heavy metal and soil enzyme activities of soil samples from 47 sites were examined. The results showed that long-term mining processes led to point source heavy metal pollution and Pb, Cd, Zn and As were the primary metal pollutants. Polyphenoloxidase was found the most sensitive soil enzyme activity and significantly correlated with almost all the metals (P < 0.05). Amylase (for C cycling), acid phosphatase (for P cycling) and catalase (for redox reaction) activities showed significantly positive correlations (P < 0.05) with Pb, Cd, Zn and As contents. The correlations between soil enzymes activities and Cd, Pb and Zn contents were verified in microcosm experiments, it was found that catalase activity had significant correlations (P < 0.05) with these three metals in short-term experiments using different soils under different conditions. Based on both field investigation and microcosm simulation analysis, oxidoreductases activities (rather than a specific enzyme activity) were suggested to be used as "core enzyme", which could simply and universally indicate the heavy metal pollution degrees of different environments. And hydrolases (for C, N, P and S recycling) could be used as a supplement to improve correlation accuracy for heavy metal indication in various polluted environments.