Clinical genomics expands the link between erroneous cell division, primary microcephaly and intellectual disability.

Primary microcephaly is a rare neurogenic and genetically heterogeneous disorder characterized by significant brain size reduction that results in numerous neurodevelopmental disorders (NDD) problems, including mild to severe intellectual disability (ID), global developmental delay (GDD), seizures and other congenital malformations. This disorder can arise from a mutation in genes involved in various biological pathways, including those within the brain. We characterized a recessive neurological disorder observed in nine young adults from five independent consanguineous Pakistani families. The disorder is characterized by microcephaly, ID, developmental delay (DD), early-onset epilepsy, recurrent infection, hearing loss, growth retardation, skeletal and limb defects. Through exome sequencing, we identified novel homozygous variants in five genes that were previously associated with brain diseases, namely CENPJ (NM_018451.5: c.1856A > G; p.Lys619Arg), STIL (NM_001048166.1: c.1235C > A; p.(Pro412Gln), CDK5RAP2 (NM_018249.6 c.3935 T > G; p.Leu1312Trp), RBBP8 (NM_203291.2 c.1843C > T; p.Gln615*) and CEP135 (NM_025009.5 c.1469A > G; p.Glu490Gly). These variants were validated by Sanger sequencing across all family members, and in silico structural analysis. Protein 3D homology modeling of wild-type and mutated proteins revealed substantial changes in the structure, suggesting a potential impact on function. Importantly, all identified genes play crucial roles in maintaining genomic integrity during cell division, with CENPJ, STIL, CDK5RAP2, and CEP135 being involved in centrosomal function. Collectively, our findings underscore the link between erroneous cell division, particularly centrosomal function, primary microcephaly and ID.

Analysis and prediction of interactions between transmembrane and non-transmembrane proteins.

BACKGROUND: Most of the important biological mechanisms and functions of transmembrane proteins (TMPs) are realized through their interactions with non-transmembrane proteins(nonTMPs). The interactions between TMPs and nonTMPs in cells play vital roles in intracellular signaling, energy metabolism, investigating membrane-crossing mechanisms, correlations between disease and drugs. RESULTS: Despite the importance of TMP-nonTMP interactions, the study of them remains in the wet experimental stage, lacking specific and comprehensive studies in the field of bioinformatics. To fill this gap, we performed a comprehensive statistical analysis of known TMP-nonTMP interactions and constructed a deep learning-based predictor to identify potential interactions. The statistical analysis describes known TMP-nonTMP interactions from various perspectives, such as distributions of species and protein families, enrichment of GO and KEGG pathways, as well as hub proteins and subnetwork modules in the PPI network. The predictor implemented by an end-to-end deep learning model can identify potential interactions from protein primary sequence information. The experimental results over the independent validation demonstrated considerable prediction performance with an MCC of 0.541. CONCLUSIONS: To our knowledge, we were the first to focus on TMP-nonTMP interactions. We comprehensively analyzed them using bioinformatics methods and predicted them via deep learning-based solely on their sequence. This research completes a key link in the protein network, benefits the understanding of protein functions, and helps in pathogenesis studies of diseases and associated drug development.

Exome sequencing identifies homozygous variants in MBOAT7 associated with neurodevelopmental disorder.

Intellectual disability (ID) is a large group of neurodevelopmental disorders characterized by a congenital limitation in intellectual functioning (reasoning, learning, and problem solving), adaptive behavior (conceptual, social, and practical skills), originated at birth and manifested before the age of 18. By whole exome sequencing of five consanguineous Pakistani families presenting hallmark features of ID, global developmental delay, aggressive and self-injurious behaviors, microcephaly, febrile seizures and facial dysmorphic features, we identified three novel homozygous missense variants (NM_024298.5: c.588G > T; p.Trp196Cys, c.736 T > C; p.Tyr246His and c.524A > C; p. Asp175Ala) and one rare homozygous in-frame deletion variant (c.758_778del;p.Glu253_Ala259del) in membrane-bound O-acyltransferase family member 7 (MBOAT7) gene previously associated with autosomal recessive neurodevelopmental disorder. The segregation of the variants was validated by Sanger sequencing in all family members. In silico homology modeling of wild-type and mutated proteins revealed substantial changes in the structure of both proteins, indicating a possible effect on function. The identification and validation of new pathogenic MBOAT7 variants in five cases of autosomal recessive ID further highlight the importance of this genes in proper brain function and development.

Multiple sequence alignment-based RNA language model and its application to structural inference.

Compared with proteins, DNA and RNA are more difficult languages to interpret because four-letter coded DNA/RNA sequences have less information content than 20-letter coded protein sequences. While BERT (Bidirectional Encoder Representations from Transformers)-like language models have been developed for RNA, they are ineffective at capturing the evolutionary information from homologous sequences because unlike proteins, RNA sequences are less conserved. Here, we have developed an unsupervised multiple sequence alignment-based RNA language model (RNA-MSM) by utilizing homologous sequences from an automatic pipeline, RNAcmap, as it can provide significantly more homologous sequences than manually annotated Rfam. We demonstrate that the resulting unsupervised, two-dimensional attention maps and one-dimensional embeddings from RNA-MSM contain structural information. In fact, they can be directly mapped with high accuracy to 2D base pairing probabilities and 1D solvent accessibilities, respectively. Further fine-tuning led to significantly improved performance on these two downstream tasks compared with existing state-of-the-art techniques including SPOT-RNA2 and RNAsnap2. By comparison, RNA-FM, a BERT-based RNA language model, performs worse than one-hot encoding with its embedding in base pair and solvent-accessible surface area prediction. We anticipate that the pre-trained RNA-MSM model can be fine-tuned on many other tasks related to RNA structure and function.

Ribocentre-switch: a database of riboswitches.

Riboswitches are regulatory elements found in the untranslated regions (UTRs) of certain mRNA molecules. They typically comprise two distinct domains: an aptamer domain that can bind to specific small molecules, and an expression platform that controls gene expression. Riboswitches work by undergoing a conformational change upon binding to their specific ligand, thus activating or repressing the genes downstream. This mechanism allows gene expression regulation in response to metabolites or small molecules. To systematically summarise riboswitch structures and their related ligand binding functions, we present Ribocentre-switch, a comprehensive database of riboswitches, including the information as follows: sequences, structures, functions, ligand binding pockets and biological applications. It encompasses 56 riboswitches and 26 orphan riboswitches from over 430 references, with a total of 89 591 sequences. It serves as a good resource for comparing different riboswitches and facilitating the identification of potential riboswitch candidates. Therefore, it may facilitate the understanding of RNA structural conformational changes in response to ligand signaling. The database is publicly available at https://riboswitch.ribocentre.org.

Optimisation of the Mechanical Properties and Mix Proportion of Multiscale-Fibre-Reinforced Engineered Cementitious Composites.

Engineered cementitious composites (ECCs) are cement-based composite materials with strain-hardening and multiple-cracking characteristics. ECCs have multiscale defects, including nanoscale hydrated silicate gels, micron-scale capillary pores, and millimetre-scale cracks. By using millimetre-scale polyethylene (PE) fibres, microscale calcium carbonate whiskers (CWs), and nanoscale carbon nanotubes (CNTs) as exo-doped fibres, a multiscale enhancement system was formed, and the effects of multiscale fibres on the mechanical properties of ECCs were tested. The Box-Behnken experimental design method, which is a response surface methodology, was used to construct a quadratic polynomial regression equation to optimise ECC design and provide an optimisation of ECC mix proportions. The results of this study showed that a multiscale reinforcement system consisting of PE fibres, CWs, and CNTs enhanced the mechanical properties of ECCs. CWs had the greatest effect on the compressive strengths of highly ductile-fibre-reinforced cementitious composites, followed by CNTs and PE fibres. PE fibres had the greatest effect on the flexural and tensile strengths of high-ductility fibre-reinforced cementitious composites, followed by CWs and CNTs. The final optimisation results showed that when the ECC matrix was doped with 1.55% PE fibres, 2.17% CWs, and 0.154% CNTs, the compressive strength, flexural strength, and tensile strength of the matrix were optimal.

Optimal Design of Multi-Scale Fibre-Reinforced Cement-Matrix Composites Based on an Orthogonal Experimental Design.

Cement-matrix composite are typical multi-scale composite materials, the failure process has the characteristics of gradual, multi-scale and multi-stage damage. In order to delay the multi-stage damage process of cement-matrix composites, the defects of different scales are suppressed by using different scales of fibres and fly ash (FA), and the overall performance of cement-matrix composites is improved, a new multi-scale fibre-reinforced cement-based composite composed of millimetre-scale polyvinyl alcohol fibre (PVA), micron-scale calcium carbonate whisker (CW), and nano-scale carbon nanotubes (CNTs) was designed in this study. The compressive strength, flexural strength, splitting tensile strength, and chloride ion permeability coefficient were used as assessment indices by the orthogonal test design. The impacts of the three fibre scales and fly ash on each individual index were examined, and the overall performance of the multi-scale fibre-reinforced cementitious materials (MSFRCC) was then optimized using grey correlation analysis. The optimized mix ratio for overall performance was PVA: 1.5%, CW: 2%, CNTs: 0.1%, FA: 40%. Compared with the optimal results for each group, the compressive strength of the final optimized MSFRCC group decreased by 8.9%, the flexural strength increased by 28.4%, the splitting tensile strength increased by 10%, and the chloride ion permeability coefficient decreased by 5.7%. The results show that the compressive performance and resistance to chloride ion penetration of the optimized group are slightly worse than those of the optimal group in the orthogonal test, but its flexural performance and splitting tensile performance are significantly improved.

Maternal blood inflammatory marker levels increased in fetuses with ventriculomegaly.

Background: Fetal ventriculomegaly (VM) is one of the most common abnormalities of the central nervous system (CNS), which can be significantly identified by brain anomalies prenatally by magnetic resonance imaging (MRI). Aberrant white blood cells (WBCs) levels indicate that the maternal is suffering from the infection. Previous studies have confirmed that prenatal infection can affect fetal brain structure, but there is no research revealed the association between maternal blood parameters with fetal VM until now. Methods: We measured the width of the lateral ventricle of 142 fetuses, which were divided into the fetal VM group (n = 70) and the normal lateral ventricle group (n = 72). We compared maternal blood cell levels between the two groups and investigate potential biomarkers of fetal VM. Result: High levels of maternal WBC and neutrophil (NE#) levels were observed in fetuses with VM (p < 0.001), while lymphocyte percentage, monocytes (MO#), neutrophil/lymphocyte ratio (NLR), and platelet were also increased in the fetal VM group (p = 0.033, 0.027, 0.034, and 0.025, respectively). receiver-operator curve (ROC) analysis suggested that WBC and NE# counts might be useful to distinguish fetuses with enlarged lateral ventricles (AUC = 0.688, 0.678, respectively). Conclusion: The current study emphasizes the importance of maternal infection for fetal brain growth, which could provide important information for prenatal diagnosis of CNS anomalies. Future research needs longitudinal analysis and exploration of the influence of maternal blood inflammatory marker levels on fetal brain development.

Study of SPRC Impact Resistance Based on the Weibull Distribution and the Response Surface Method.

Silica-fume-polyvinyl-alcohol-fiber-reinforced concrete (SPRC) is a green and environmentally friendly composite material incorporating silica fume and polyvinyl alcohol fiber into concrete. To study the impact resistance of SPRC, compressive-strength and drop hammer impact tests were conducted on SPRC with different silica-fume and polyvinyl-alcohol-fiber contents. The mechanical and impact resistance properties of the SPRC were comprehensively analyzed in terms of the compressive strength, ductility ratio and impact-energy-dissipation variation. Based on the impact resistance of the SPRC, the impact life of SPRC with different failure probabilities was predicted by incorporating the Weibull distribution model, and an impact damage evolution equation for SPRC was established. The impact life of SPRC under the action of silica-fume content, polyvinyl-alcohol-fiber content and failure probability was analyzed in depth by the response surface method (RSM). The research results show that, when the content of silica fume is 10% and the content of polyvinyl alcohol fiber is 1%, the compressive strength and impact resistance of SPRC are the best. The RSM response model can effectively predict and describe the impact life of SPRC specimens under the action of three factors.