Adenosine mediates the amelioration of social novelty deficits during rhythmic light treatment of 16p11.2 deletion female mice.

Non-invasive brain stimulation therapy for autism spectrum disorder (ASD) has shown beneficial effects. Recently, we and others demonstrated that visual sensory stimulation using rhythmic 40 Hz light flicker effectively improved cognitive deficits in mouse models of Alzheimer's disease and stroke. However, whether rhythmic visual 40 Hz light flicker stimulation can ameliorate behavioral deficits in ASD remains unknown. Here, we show that 16p11.2 deletion female mice exhibit a strong social novelty deficit, which was ameliorated by treatment with a long-term 40 Hz light stimulation. The elevated power of local-field potential (LFP) in the prefrontal cortex (PFC) of 16p11.2 deletion female mice was also effectively reduced by 40 Hz light treatment. Importantly, the 40 Hz light flicker reversed the excessive excitatory neurotransmission of PFC pyramidal neurons without altering the firing rate and the number of resident PFC neurons. Mechanistically, 40 Hz light flicker evoked adenosine release in the PFC to modulate excessive excitatory neurotransmission of 16p11.2 deletion female mice. Elevated adenosine functioned through its cognate A1 receptor (A1R) to suppress excessive excitatory neurotransmission and to alleviate social novelty deficits. Indeed, either blocking the A1R using a specific antagonist DPCPX or knocking down the A1R in the PFC using a shRNA completely ablated the beneficial effects of 40 Hz light flicker. Thus, this study identified adenosine as a novel neurochemical mediator for ameliorating social novelty deficit by reducing excitatory neurotransmission during 40 Hz light flicker treatment. The 40 Hz light stimulation warrants further development as a non-invasive ASD therapeutics.

Human-derived fecal microbiota transplantation alleviates social deficits of the BTBR mouse model of autism through a potential mechanism involving vitamin B6 metabolism.

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition characterized by social communication deficiencies and stereotypic behaviors influenced by hereditary and/or environmental risk factors. There are currently no approved medications for treating the core symptoms of ASD. Human fecal microbiota transplantation (FMT) has emerged as a potential intervention to improve autistic symptoms, but the underlying mechanisms are not fully understood. In this study, we evaluated the effects of human-derived FMT on behavioral and multi-omics profiles of the BTBR mice, an established model for ASD. FMT effectively alleviated the social deficits in the BTBR mice and normalized their distinct plasma metabolic profile, notably reducing the elevated long-chain acylcarnitines. Integrative analysis linked these phenotypic changes to specific Bacteroides species and vitamin B6 metabolism. Indeed, vitamin B6 supplementation improved the social behaviors in BTBR mice. Collectively, these findings shed new light on the interplay between FMT and vitamin B6 metabolism and revealed a potential mechanism underlying the therapeutic role of FMT in ASD.IMPORTANCEAccumulating evidence supports the beneficial effects of human fecal microbiota transplantation (FMT) on symptoms associated with autism spectrum disorder (ASD). However, the precise mechanism by which FMT induces a shift in the microbiota and leads to symptom improvement remains incompletely understood. This study integrated data from colon-content metagenomics, colon-content metabolomics, and plasma metabolomics to investigate the effects of FMT treatment on the BTBR mouse model for ASD. The analysis linked the amelioration of social deficits following FMT treatment to the restoration of mitochondrial function and the modulation of vitamin B6 metabolism. Bacterial species and compounds with beneficial roles in vitamin B6 metabolism and mitochondrial function may further contribute to improving FMT products and designing novel therapies for ASD treatment.

Design and Synthesis of Pyrimidine Amine Containing Isothiazole Coumarins for Fungal Control.

Developing new fungicides is always crucial to protecting crops. A series of 4-(3,4-dichloroisothiazol-5-yl)-7-(2-((5-(5-pyrimidin-4-yl)amino)ethoxy)-8-methyl) coumarin derivatives were designed and synthesized by Williamson ether condensation and substitution reactions. Structure determinations were clarified by 1H NMR, 13C NMR, and HRMS, and compound 4h crystallized by the fusion method for further structural confirmation. The in vitro bioassay results showed that the target compounds displayed good fungicidal activity against Alternaria solani, Botrytis cinerea, Cercospora arachidicola, Fusarium graminearum, Physalospora piricola, Rhizoctonia solani, and Sclerotinia sclerotiorum. Among them, compounds 4b and 4d showed higher inhibitory activity against R. solani, with EC50 values of 11.3 and 13.7 µg/mL, respectively, and they were more active than the positive control diflumetorim with an EC50 value of 19.8 µg/mL. Molecular docking suggested that compound 4b and diflumetorim may have similar interactions with complex I NADH oxidoreductase. Density functional theory calculation and pesticide-likeness analysis studies gave a rational explanation of their fungicidal activity. These results indicated that compounds 4b and 4d deserved further optimization according to the principle of pesticide-likeness.

Hypothermia evoked by stimulation of medial preoptic nucleus protects the brain in a mouse model of ischaemia.

Therapeutic hypothermia at 32-34 °C during or after cerebral ischaemia is neuroprotective. However, peripheral cold sensor-triggered hypothermia is ineffective and evokes vigorous counteractive shivering thermogenesis and complications that are difficult to tolerate in awake patients. Here, we show in mice that deep brain stimulation (DBS) of warm-sensitive neurones (WSNs) in the medial preoptic nucleus (MPN) produces tolerable hypothermia. In contrast to surface cooling-evoked hypothermia, DBS mice exhibit a torpor-like state without counteractive shivering. Like hypothermia evoked by chemogenetic activation of WSNs, DBS in free-moving mice elicits a rapid lowering of the core body temperature to 32-34 °C, which confers significant brain protection and motor function reservation. Mechanistically, activation of WSNs contributes to DBS-evoked hypothermia. Inhibition of WSNs prevents DBS-evoked hypothermia. Maintaining the core body temperature at normothermia during DBS abolishes DBS-mediated brain protection. Thus, the MPN is a DBS target to evoke tolerable therapeutic hypothermia for stroke treatment.

Improving the topology prediction of α-helical transmembrane proteins with deep transfer learning.

Transmembrane proteins (TMPs) are essential for cell recognition and communication, and they serve as important drug targets in humans. Transmembrane proteins' 3D structures are critical for determining their functions and drug design but are hard to determine even by experimental methods. Although some computational methods have been developed to predict transmembrane helices (TMHs) and orientation, there is still room for improvement. Considering that the pre-trained language model can make full use of massive unlabeled protein sequences to obtain latent feature representation for TMPs and reduce the dependence on evolutionary information, we proposed DeepTMpred, which used pre-trained self-supervised language models called ESM, convolutional neural networks, attentive neural network and conditional random fields for alpha-TMP topology prediction. Compared with the current state-of-the-art tools on a non-redundant dataset of TMPs, DeepTMpred demonstrated superior predictive performance in most evaluation metrics, especially at the TMH level. Furthermore, DeepTMpred could also obtain reliable prediction results for TMPs without much evolutionary feature in a few seconds. A tutorial on how to use DeepTMpred can be found in the colab notebook (https://colab.research.google.com/github/ISYSLAB-HUST/DeepTMpred/blob/master/notebook/test.ipynb).

Correlation Between Hypothyroidism During Pregnancy and Glucose and Lipid Metabolism in Pregnant Women and Its Influence on Pregnancy Outcome and Fetal Growth and Development.

Purpose: To observe the correlation between hypothyroidism during pregnancy and glucose and lipid metabolism in pregnant women and its influence on a pregnancy outcome and fetal growth and development. Methods: About 152 patients with hypothyroidism during pregnancy in our hospital from June 2017 to June 2020 were selected as the observation group and divided into the overt hypothyroidism (OH) group, the subclinical hypothyroidism (SCH) group, and the low T4 group. Another 60 pregnant women with normal antenatal examination and normal thyroid function were selected as the normal group. The glucose and lipid metabolism indexes of each group were compared. The pregnant women in the OH group and the SCH group were given levothyroxine intervention, and the pregnancy outcome and infant development of the two groups were compared. Results: The fasting blood glucose and hemoglobin A1c, triglyceride and low-density lipoprotein of the OH group and the SCH group were higher than the low T4 group and the normal group, and the OH group was higher than the SCH group (p < 0.05). The incidence of premature delivery and premature rupture of membranes at term (PROM at term) in the hypothyroidism non-control group was higher than the hypothyroidism control group (p < 0.05). The mental development index and the psychomotor development index in the hypothyroidism non-control group were lower than the hypothyroidism control group (p < 0.05). Conclusion: Pregnant women with hypothyroidism during pregnancy are more prone to glucose and lipid metabolism disorder, which increases the risk of premature delivery and PROM at term, and has certain influence on the intellectual development and psychomotor development of infants.

Res-Dom: predicting protein domain boundary from sequence using deep residual network and Bi-LSTM.

Motivation: Protein domains are the basic units of proteins that can fold, function and evolve independently. Protein domain boundary partition plays an important role in protein structure prediction, understanding their biological functions, annotating their evolutionary mechanisms and protein design. Although there are many methods that have been developed to predict domain boundaries from protein sequence over the past two decades, there is still much room for improvement. Results: In this article, a novel domain boundary prediction tool called Res-Dom was developed, which is based on a deep residual network, bidirectional long short-term memory (Bi-LSTM) and transfer learning. We used deep residual neural networks to extract higher-order residue-related information. In addition, we also used a pre-trained protein language model called ESM to extract sequence embedded features, which can summarize sequence context information more abundantly. To improve the global representation of these deep residual networks, a Bi-LSTM network was also designed to consider long-range interactions between residues. Res-Dom was then tested on an independent test set including 342 proteins and generated correct single-domain and multi-domain classifications with a Matthew's correlation coefficient of 0.668, which was 17.6% higher than the second-best compared method. For domain boundaries, the normalized domain overlapping score of Res-Dom was 0.849, which was 5% higher than the second-best compared method. Furthermore, Res-Dom required significantly less time than most of the recently developed state-of-the-art domain prediction methods. Availability and implementation: All source code, datasets and model are available at http://isyslab.info/Res-Dom/.

The Distinct Properties of the Consecutive Disordered Regions Inside or Outside Protein Domains and Their Functional Significance.

The consecutive disordered regions (CDRs) are the basis for the formation of intrinsically disordered proteins, which contribute to various biological functions and increasing organism complexity. Previous studies have revealed that CDRs may be present inside or outside protein domains, but a comprehensive analysis of the property differences between these two types of CDRs and the proteins containing them is lacking. In this study, we investigated this issue from three viewpoints. Firstly, we found that in-domain CDRs are more hydrophilic and stable but have less stickiness and fewer post-translational modification sites compared with out-domain CDRs. Secondly, at the protein level, we found that proteins with only in-domain CDRs originated late, evolved rapidly, and had weak functional constraints, compared with the other two types of CDR-containing proteins. Proteins with only in-domain CDRs tend to be expressed spatiotemporal specifically, but they tend to have higher abundance and are more stable. Thirdly, we screened the CDR-containing protein domains that have a strong correlation with organism complexity. The CDR-containing domains tend to be evolutionarily young, or they changed from a domain without CDR to a CDR-containing domain during evolution. These results provide valuable new insights about the evolution and function of CDRs and protein domains.

Protein domain identification methods and online resources.

Protein domains are the basic units of proteins that can fold, function, and evolve independently. Knowledge of protein domains is critical for protein classification, understanding their biological functions, annotating their evolutionary mechanisms and protein design. Thus, over the past two decades, a number of protein domain identification approaches have been developed, and a variety of protein domain databases have also been constructed. This review divides protein domain prediction methods into two categories, namely sequence-based and structure-based. These methods are introduced in detail, and their advantages and limitations are compared. Furthermore, this review also provides a comprehensive overview of popular online protein domain sequence and structure databases. Finally, we discuss potential improvements of these prediction methods.

Intrinsic disorder in protein domains contributes to both organism complexity and clade-specific functions.

Interestingly, some protein domains are intrinsically disordered (abbreviated as IDD), and the disorder degree of same domains may differ in different contexts. However, the evolutionary causes and biological significance of these phenomena are unclear. Here, we address these issues by genome-wide analyses of the evolutionary and functional features of IDDs in 1,870 species across the three superkingdoms. As the result, there is a significant positive correlation between the proportion of IDDs and organism complexity with some interesting exceptions. These phenomena may be due to the high disorder of clade-specific domains and the different disorder degrees of the domains shared in different clades. The functions of IDDs are clade-specific and the higher proportion of post-translational modification sites may contribute to their complex functions. Compared with metazoans, fungi have more IDDs with a consecutive disorder region but a low disorder ratio, which reflects their different functional requirements. As for disorder variation, it's greater for domains among different proteins than those within the same proteins. Some clade-specific 'no-variation' or 'high-variation' domains are involved in clade-specific functions. In sum, intrinsic domain disorder is related to both the organism complexity and clade-specific functions. These results deepen the understanding of the evolution and function of IDDs.

Risk stratification of cervical lesions using capture sequencing and machine learning method based on HPV and human integrated genomic profiles.

From initial human papillomavirus (HPV) infection and precursor stages, the development of cervical cancer takes decades. High-sensitivity HPV DNA testing is currently recommended as primary screening method for cervical cancer, whereas better triage methodologies are encouraged to provide accurate risk management for HPV-positive women. Given that virus-driven genomic variation accumulates during cervical carcinogenesis, we designed a 39 Mb custom capture panel targeting 17 HPV types and 522 mutant genes related to cervical cancer. Using capture-based next-generation sequencing, HPV integration status, somatic mutation and copy number variation were analyzed on 34 paired samples, including 10 cases of HPV infection (HPV+), 10 cases of cervical intraepithelial neoplasia (CIN) grade and 14 cases of CIN2+ (CIN2: n = 1; CIN2-3: n = 3; CIN3: n = 9; squamous cell carcinoma: n = 1). Finally, the machine learning algorithm (Random Forest) was applied to build the risk stratification model for cervical precursor lesions based on CIN2+ enriched biomarkers. Generally, HPV integration events (11 in HPV+, 25 in CIN1 and 56 in CIN2+), non-synonymous mutations (2 in CIN1, 12 in CIN2+) and copy number variations (19.1 in HPV+, 29.4 in CIN1 and 127 in CIN2+) increased from HPV+ to CIN2+. Interestingly, 'common' deletion of mitochondrial chromosome was significantly observed in CIN2+ (P = 0.009). Together, CIN2+ enriched biomarkers, classified as HPV information, mutation, amplification, deletion and mitochondrial change, successfully predicted CIN2+ with average accuracy probability score of 0.814, and amplification and deletion ranked as the most important features. Our custom capture sequencing combined with machine learning method effectively stratified the risk of cervical lesions and provided valuable integrated triage strategies.