Molecular Candidates Research Articles

Multitarget Potential Drug Candidates for High-Grade Gliomas Identified by Multiple Reaction Monitoring Coupled with In Silico Drug Repurposing.

High-grade gliomas (HGGs) are extremely aggressive primary brain tumors with high mortality rates. Despite notable progress achieved by clinical research and biomarkers emerging from proteomics studies, efficacious drugs and therapeutic targets are limited. This study used targeted proteomics, in silico molecular docking, and simulation-based drug repurposing to identify potential drug candidates for HGGs. Importantly, we performed multiple reaction monitoring (MRM) on differentially expressed proteins with putative roles in the development and progression of HGGs based on our previous work and the published literature. Furthermore, in silico molecular docking-based drug repurposing was performed with a customized library of FDA-approved drugs to identify multitarget-directed ligands. The top drug candidates such as Pazopanib, Icotinib, Entrectinib, Regorafenib, and Cabozantinib were explored for their drug-likeness properties using the SwissADME. Pazopanib exhibited binding affinities with a maximum number of proteins and was considered for molecular dynamic simulations and cell toxicity assays. HGG cell lines showed enhanced cytotoxicity and cell proliferation inhibition with Pazopanib and Temozolomide combinatorial treatment compared to Temozolomide alone. To the best of our knowledge, this is the first study combining MRM with molecular docking and simulation-based drug repurposing to identify potential drug candidates for HGG. While the present study identified five multitarget-directed potential drug candidates, future clinical studies in larger cohorts are crucial to evaluate the efficacy of these molecular candidates. The research strategy and methodology used in the present study offer new avenues for innovation in drug discovery and development which may prove useful, particularly for cancers with low cure rates.

Surveying the mass spectra and the electromagnetic properties of the Ξc(′,*)D(*) molecular pentaquarks

Motivated by the observed PψsΛ(4459)/PψsΛ(4338) and Tcc(3875)+ states as the ΞcD¯*/ΞcD¯ and DD* molecular candidates, respectively, in this work we investigate the Ξc(′,*)D(*) molecular systems. We obtain the mass spectra and the corresponding spatial wave functions of the Ξc(′,*)D(*)-type double-charm molecular pentaquark candidates with single strangeness, where we utilize the one-boson-exchange model and take into account both the S−D wave-mixing effect and the coupled-channel effect. Our results show that the most promising candidates of the double-charm molecular pentaquarks with single strangeness include the ΞcD state with I(JP)=0(1/2−), the Ξc′D state with I(JP)=0(1/2−), the ΞcD* states with I(JP)=0(1/2−,3/2−), the Ξc*D state with I(JP)=0(3/2−), the Ξc′D* states with I(JP)=0(1/2−,3/2−), and the Ξc*D* states with I(JP)=0(1/2−,3/2−,5/2−). To gain further insight into the inner structures and the properties of the isoscalar Ξc(′,*)D(*) molecular candidates, we utilize the constituent quark model to analyze their M1 radiative decay behaviors and magnetic moments based on the obtained mass spectra and spatial wave functions, which can offer significant information to determine their spin-parity quantum numbers and configurations in the forthcoming experiments. We suggest that our experimental colleagues search for the predicted Ξc(′,*)D(*) molecular states. Published by the American Physical Society 2024

An integrated QTL mapping and transcriptome sequencing provides further molecular insights and candidate genes for stem strength in rapeseed (Brassica napus L.).

We detected the major QTL- qSR.A07, which regulated stem strength and was fine-mapped to 490 kb. BnaA07G0302800ZS and BnaA07G0305700ZS as the candidate functional genes were identified at qSR.A07 locus. The stem's mechanical properties reflect its ability to resist lodging. In rapeseed (Brassica napus L.), although stem lodging negatively affects yield and generates harvesting difficulties, the molecular regulation of stem strength remains elusive. Hence, this study aimed to unravel the main loci and molecular mechanisms governing rapeseed stem strength. A mapping population consisting of 267 RILs (recombinant inbred lines) was developed from the crossed between ZS11 (high stem strength) and 4D122 (low stem strength), and two mechanical properties of stems including stem breaking strength and stem rind penetrometer resistance were phenotyped in four different environments. Four pleiotropic QTLs that were stable in at least two environments were detected. qSR.A07, the major one, was fine-mapped to a 490kb interval between markers SA7-2711 and SA7-2760 on chromosome 7. It displayed epistatic interaction with qRPR.A09-2. Comparative transcriptome sequencing and analysis unveiled methionine/S-adenosylmethionine cycle (Met/SAM cycle), cytoskeleton organization, sulfur metabolism and phenylpropanoid biosynthesis as the main pathways associated with high stem strength. Further, we identified two candidate genes, BnaA07G0302800ZS and BnaA07G0305700ZS, at qSR.A07 locus. Gene sequence alignment identified a number of InDels, SNPs and amino acid variants in sequences of these genes between ZS11 and 4D122. Finally, based on these genetic variants, we developed three SNP markers of these genes to facilitate future genetic selection and functional studies. These findings offer important genetic resources for the molecular-assisted breeding of novel rapeseed stem lodging-resistant varieties.

Long-Read Sequencing Genome Assembly of Ceratocystis fimbriata Enables Development of Molecular Diagnostics for Sweetpotato Black Rot.

Ceratocystis fimbriata is a destructive fungal pathogen of sweetpotato (Ipomoea batatas) that leads to losses at all stages of sweetpotato production. Accurate detection of C. fimbriata would allow for more efficient deployment of management tactics in sweetpotato production. To develop a diagnostic assay, a hybrid genome assembly of C. fimbriata isolate AS236 was generated. The resulting 31.7-MB assembly was near-chromosome level, with 18 contigs, 6,481 predicted genes, and a BUSCO completion score of 98.4% when compared with the fungus-specific lineage database. Additional Illumina DNA reads from C. manginecans, C. platani, and a second C. fimbriata isolate (C1421) were then mapped to the assembled genome using BOWTIE2 and counted using HTSeq, which identified 148 genes present only within C. fimbriata as molecular diagnostic candidates; 6 single-copy and 35 highly multi-copy (>40 BLAST hits), as determined through a self-BLAST-P alignment. Primers for PCR were designed in the 200-bp flanking region of the first exon for each candidate, and the candidates were validated against a diverse DNA panel containing Ceratocystis species, sweetpotato pathogens, and plants. After validation, two diagnostic candidates amplified only C. fimbriata DNA and were considered to be highly specific to the species. These genetic markers will serve as valuable diagnostic tools with multiple applications including the detection of C. fimbriata in seed, soil, and wash water in sweetpotato production.

Identification of shared pathogenetic mechanisms between COVID-19 and IC through bioinformatics and system biology

COVID-19 increased global mortality in 2019. Cystitis became a contributing factor in SARS-CoV-2 and COVID-19 complications. The complex molecular links between cystitis and COVID-19 are unclear. This study investigates COVID-19-associated cystitis (CAC) molecular mechanisms and drug candidates using bioinformatics and systems biology. Obtain the gene expression profiles of IC (GSE11783) and COVID-19 (GSE147507) from the Gene Expression Omnibus (GEO) database. Identified the common differentially expressed genes (DEGs) in both IC and COVID-19, and extracted a number of key genes from this group. Subsequently, conduct Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on the DEGs. Additionally, design a protein–protein interaction (PPI) network, a transcription factor gene regulatory network, a TF miRNA regulatory network, and a gene disease association network using the DEGs. Identify and extract hub genes from the PPI network. Then construct Nomogram diagnostic prediction models based on the hub genes. The DSigDB database was used to forecast many potential molecular medicines that are associated with common DEGs. Assess the precision of hub genes and Nomogram models in diagnosing IC and COVID-19 by employing Receiver Operating Characteristic (ROC) curves. The IC dataset (GSE57560) and the COVID-19 dataset (GSE171110) were selected to validate the models' diagnostic accuracy. A grand total of 198 DEGs that overlapped were found and chosen for further research. FCER1G, ITGAM, LCP2, LILRB2, MNDA, SPI1, and TYROBP were screened as the hub genes. The Nomogram model, built using the seven hub genes, demonstrates significant utility as a diagnostic prediction model for both IC and COVID-19. Multiple potential molecular medicines associated with common DEGs have been discovered. These pathways, hub genes, and models may provide new perspectives for future research into mechanisms and guide personalised and effective therapeutics for IC patients infected with COVID-19.

Three chemosensory proteins enriched in antennae and tarsi of Rhaphuma horsfieldi differentially contribute to the binding of insecticides

Antennae and legs (primarily the tarsal segments) of insects are the foremost sensory organs that contact a diverse range of toxic chemicals including insecticides. Binding proteins expressed in the two tissues are potential molecular candidates serving as the binding and sequestering of insecticides, like chemosensory proteins (CSPs). Insect CSPs endowed with multiple roles have been suggested to participate in insecticide resistance, focusing mainly on moths, aphids and mosquitos. Yet, the molecular underpinnings underlying the interactions of cerambycid CSPs and insecticides remain unexplored. Here, we present binding properties of three antenna- and tarsus-enriched RhorCSPs (RhorCSP1, CSP2 and CSP3) in Rhaphuma horsfieldi to eight insecticide classes totaling 15 chemicals. From the transcriptome of this beetle, totally 16 CSP-coding genes were found, with seven full-length sequences. In phylogeny, these RhorCSPs were distributed dispersedly in different clades. Expression profiles revealed the abundant expression of RhorCSP1, CSP2 and CSP3 in antennae and tarsi, thus as representatives for studying the protein-insecticide interactions. Binding assays showed that the three RhorCSPs were tuned differentially to insecticides but exhibited the highest affinities with hexaflumuron, chlorpyrifos and rotenone (dissociation constants <13 μM). In particular, RhorCSP3 could interact strongly with 10 of tested insecticides, of which four residues (Tyr25, Phe42, Val65 and Phe68) contributed significantly to the binding of six, four, three and four ligands, respectively. Of these, the binding of four mutated RhorCSP3s to a botanical insecticide rotenone was significantly weakened compared to the wildtype protein. Furthermore, we also evidenced that RhorCSP3 was a broadly-tuned carrier protein in response to a wide variety of plant odorants outside insecticides. Altogether, our findings shed light on different binding mechanisms and odorant-tuning profiles of three RhorCSPs in R. horsfieldi and identify key residues of the RhorCSP3-insecticide interactions.

MicroRNA-124 plays an inhibitory role in cutaneous squamous cell carcinoma cells via targeting SNAI2, an immunotherapy determinant

PurposeMicroRNAs (miRs) play multiple roles during cutaneous squamous cell carcinoma (CSCC) progression. Previous studies suggest miR-124 could inhibit cancer development in CSCC. Methods: Obtained 63 pairs of CSCC and adjacent tissues for analysis. Cultured HaCaT and two CSCC cell lines (A431 and SCL-1) in DMEM (10 % FBS). Transfected cells using Lipofectamine 2000 with various miR-124 mimics, inhibitors, or Snail family transcriptional repressor 2 (SNAI2) expression plasmid. Performed a series of assays, including real-time quantitative PCR, Western blot, CCK8, wound healing, transwell, and luciferase reporter gene assay, to examine the effects of miR-124 on CSCC cells. Results: An evident downregulation of miR-124 in CSCC tissues, which was related to advanced disease stage and nodal metastasis. Overexpressing miR-124 could reduce the proliferation, migration, and invasion abilities of CSCC cells. It was verified that miR-124 targets the SNAI2 in CSCC cells. Moreover, ectopic expression of SNAI2 rescued the suppressive effects on CSCC cells induced by miR-124 overexpression. Furthermore, miR-124 increased cell sensitivity to cisplatin. Besides, SNAI2 is a critical factor in the immune-related aspects of CSCC and its modulation may influence the response to immunotherapy. Conclusion: We demonstrate that miR-124 inhibits CSCC progression through downregulating SNAI2, and thus it may be a molecular candidate for treating CSCC in the clinic.

Structure-based drug design for protein arginine deiminase Type IV (PAD4) receptor: Chemoinformatics approach

Rheumatoid Arthritis (RA) is the second most common type of arthritis with symptoms first appearing in patients between 40 and 60 years of age. The number of older persons is projected to double to 1.5 billion in 2050, globally. Peptidylarginine deiminase Type 4 (PAD4), which catalyzes the conversion of peptidyl-arginine to peptidyl-citrulline, is widely believed to play a causative role in RA disease. Nonsteroidal anti-inflammatories (NSAIDs) and corticosteroids encompass a large group of clinically effective compounds whose mode of action is well established, these compounds relieve pain and reduce inflammation by preventing prostaglandin synthesis through inhibition of cyclooxygenase 2 and the production of arachidonic acid, respectively. We developed a computational protocol/pipeline, using virtual screening approaches to search for new chemical agents (NCA), capable of inhibiting the action of PAD4, in potential, in view of the treatment of RA. Our results allowed the selection of structures with suitable indices of pharmacokinetic properties and estimated low toxicological, in potential, important evidence for selection of more promising molecular candidates against the studied disease. Molecules ZINC20452582 and ZINC67673633 presented better results, as well as the drug-receptor interactions were similar to those observed between the crystallographic compound (GSK147) here used. They presented excellent results for predictions of metabolites, and they can be indicated as promising molecules, resulting from virtual screening approaches, as new PAD4 receptor inhibitors with activity against RA, in potential.

Integration of multi-omics technologies for molecular diagnosis in ataxia patients.

Background: Episodic ataxias are rare neurological disorders characterized by recurring episodes of imbalance and coordination difficulties. Obtaining definitive molecular diagnoses poses challenges, as clinical presentation is highly heterogeneous, and literature on the underlying genetics is limited. While the advent of high-throughput sequencing technologies has significantly contributed to Mendelian disorders genetics, interpretation of variants of uncertain significance and other limitations inherent to individual methods still leaves many patients undiagnosed. This study aimed to investigate the utility of multi-omics for the identification and validation of molecular candidates in a cohort of complex cases of ataxia with episodic presentation. Methods: Eight patients lacking molecular diagnosis despite extensive clinical examination were recruited following standard genetic testing. Whole genome and RNA sequencing were performed on samples isolated from peripheral blood mononuclear cells. Integration of expression and splicing data facilitated genomic variants prioritization. Subsequently, long-read sequencing played a crucial role in the validation of those candidate variants. Results: Whole genome sequencing uncovered pathogenic variants in four genes (SPG7, ATXN2, ELOVL4, PMPCB). A missense and a nonsense variant, both previously reported as likely pathogenic, configured in trans in individual #1 (SPG7: c.2228T>C/p.I743T, c.1861C>T/p.Q621*). An ATXN2 microsatellite expansion (CAG32) in another late-onset case. In two separate individuals, intronic variants near splice sites (ELOVL4: c.541 + 5G>A; PMPCB: c.1154 + 5G>C) were predicted to induce loss-of-function splicing, but had never been reported as disease-causing. Long-read sequencing confirmed the compound heterozygous variants configuration, repeat expansion length, as well as splicing landscape for those pathogenic variants. A potential genetic modifier of the ATXN2 expansion was discovered in ZFYVE26 (c.3022C>T/p.R1008*). Conclusion: Despite failure to identify pathogenic variants through clinical genetic testing, the multi-omics approach enabled the molecular diagnosis in 50% of patients, also giving valuable insights for variant prioritization in remaining cases. The findings demonstrate the value of long-read sequencing for the validation of candidate variants in various scenarios. Our study demonstrates the effectiveness of leveraging complementary omics technologies to unravel the underlying genetics in patients with unresolved rare diseases such as ataxia. Molecular diagnoses not only hold significant promise in improving patient care management, but also alleviates the burden of diagnostic odysseys, more broadly enhancing quality of life.

Genome-wide identification and functional analysis of mRNA m6A writers in soybean under abiotic stress.

N6-methyladenosine (m6A), a well-characterized RNA modification, is involved in regulating multiple biological processes; however, genome-wide identification and functional characterization of the m6A modification in legume plants, including soybean (Glycine max (L.) Merr.), remains lacking. In this study, we utilized bioinformatics tools to perform comprehensive analyses of molecular writer candidates associated with the RNA m6A modification in soybean, characterizing their conserved domains, motifs, gene structures, promoters, and spatial expression patterns. Thirteen m6A writer complex genes in soybean were identified, which were assigned to four families: MT-A70, WTAP, VIR, and HAKAI. It also can be identified that multiple cis elements in the promoters of these genes, which were classified into five distinct groups, including elements responsive to light, phytohormone regulation, environmental stress, development, and others, suggesting that these genes may modulate various cellular and physiological processes in plants. Importantly, the enzymatic activities of two identified m6A writers, GmMTA1 and GmMTA2, were confirmed in vitro. Furthermore, we analyzed the expression patterns of the GmMTAs and GmMTBs under different abiotic stresses, revealing their potential involvement in stress tolerance, especially in the response to alkalinity or darkness. Overexpressing GmMTA2 and GmMTB1 in soybean altered the tolerance of the plants to alkalinity and long-term darkness, further confirming their effect on the stress response. Collectively, our findings identified the RNA m6A writer candidates in leguminous plants and highlighted the potential roles of GmMTAs and GmMTBs in the response to abiotic stress in soybean.

Combining multiomics to analyze the molecular mechanism of hair follicle cycle change in cashmere goats from Inner Mongolia.

Sheep body size can directly reflect the growth rates and fattening rates of sheep and is also an important index for measuring the growth performance of meat sheep.Inner Mongolia Cashmere Goat is a local excellent breed of cashmere and meat dual-purpose, which is a typical heterogeneous indumentum. The hair follicles cycle through periods of vigorous growth (anagen), a regression caused by apoptosis (catagen), and relative rest (telogen). At present, it is not clear which genes affect the cycle transformation of hair follicles and unclear how proteins impact the creation and expansion of hair follicles.we using multi-omics joint analysis methodologies to investigated the possible pathways of transformation and apoptosis in goat hair follicles. The results showed that 917,1,187, and 716 proteins were specifically expressed in anagen, catagen andtelogen. The result of gene ontology (GO) annotation showed that differentially expressed proteins (DEPs) are in different growth cycle periods, and enriched GO items are mostly related to the transformation of cells and proteins. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment result indicated that the apoptosis process has a great impact on hair follicle's growth cycle. The results of the protein interaction network of differential proteins showed that the ribosomal protein family (RPL4, RPL8, RPS16, RPS18, RPS2, RPS27A, RPS3) was the core protein in the network. The results of combined transcriptome and proteomics analysis showed that there were 16,34, and 26 overlapped DEGs and DEPs in the comparison of anagen VS catagen, catagen VS telogen and anagen VS telogen, of which API5 plays an important role in regulating protein and gene expression levels. We focused on API5 and Ribosomal protein and found that API5 affected the apoptosis process of hair follicles, and ribosomal protein was highly expressed in the resting stage of hair follicles. They are both useful as molecular marker candidate genes to study hair follicle growth and apoptosis,and they both have an essential function in the cycle transition process of hair follicles. The results of this study may provide a theoretical basis for further research on the growth and development of hair follicles in Inner Mongolian Cashmere goats.

Recent advances in organic molecule reactions on metal surfaces.

Chemical reactions of organic molecules on metal surfaces have been intensively investigated in the past decades, where metals play the role of catalysts in many cases. In this review, first, we summarize recent works on spatial molecules, small H2O, O2, CO, CO2 molecules, and the molecules carrying silicon groups as the new trends of molecular candidates for on-surface chemistry applications. Then, we introduce spectroscopy and DFT study advances in on-surface reactions. Especially, in situ spectroscopy technologies, such as electron spectroscopy, force spectroscopy, X-ray photoemission spectroscopy, STM-induced luminescence, tip-enhanced Raman spectroscopy, temperature-programmed desorption spectroscopy, and infrared reflection adsorption spectroscopy, are important to confirm the occurrence of organic reactions and analyze the products. To understand the underlying mechanism, the DFT study provides detailed information about reaction pathways, conformational evolution, and organometallic intermediates. Usually, STM/nc-AFM topological images, in situ spectroscopy data, and DFT studies are combined to describe the mechanism behind on-surface organic reactions.

Two-Body Hadronic Weak Decays of Bottomed Hadrons

The structure of light diquarks plays a crucial role in formation of exotic hadrons beyond the conventional quark model, especially with regard to the line shapes of bottomed hadron decays. We study the two-body hadronic weak decays of bottomed baryons and bottomed mesons to probe the light diquark structure and to pin down the quark–quark correlations in the diquark picture. It is found that the light diquark does not favor a compact structure. For instance, the isoscalar diquark [ud] in Λb0 can be easily split and rearranged to form Σc(*)D¯(*) via the color-suppressed transition. This provides a hint that the hidden charm pentaquark states produced in Λb0 decays could be the Σc(*)D¯(*) hadronic molecular candidates. This quantitative study resolves the apparent conflicts between the production mechanism and the molecular nature of these Pc states observed in experiment.

MSdeCIpher: A Tool to Link Data from Complementary Ionization Techniques in High-Resolution GC-MS to Identify Molecular Ions.

Electron ionization (EI) and molecular ion-generating techniques like chemical ionization (CI) are complementary ionization methods in gas chromatography (GC)-mass spectrometry (MS). However, manual curation effort and expert knowledge are required to correctly assign molecular ions to fragment spectra. MSdeCIpher is a software tool that enables the combination of two separate datasets from fragment-rich spectra, like EI-spectra, and soft ionization spectra containing molecular ion candidates. Using high-resolution GC-MS data, it identifies and assigns molecular ions based on retention time matching, user-defined adduct/neutral loss criteria, and sum formula matching. To our knowledge, no other freely available or vendor tool is currently capable of combining fragment-rich and soft ionization datasets in this manner. The tool's performance was evaluated on three test datasets. When molecular ions are present, MSdeCIpher consistently ranks the correct molecular ion for each fragment spectrum in one of the top positions, with average ranks of 1.5, 1, and 1.2 in the three datasets, respectively. MSdeCIpher effectively reduces candidate molecular ions for each fragment spectrum and thus enables the usage of compound identification tools that require molecular masses as input. It paves the way towards rapid annotations in untargeted analysis with high-resolution GC-MS.

Current Therapeutic Strategies and Possible Effective Drug Delivery Strategies against COVID-19.

COVID-19 pandemic is the biggest global crisis. The frequent mutations in coronavirus to generate new mutants are of major concern. The pathophysiology of SARS-CoV-2 infection has been well studied to find suitable molecular targets and candidate drugs for effective treatment. FDArecommended etiotropic therapies are currently followed along with mass vaccination. The drug delivery system and the route of administration have a great role in enhancing the efficacy of therapeutic agents and vaccines. Since COVID-19 primarily infects the lungs in the affected individuals, pulmonary administration may be the best possible route for the treatment of COVID-19. Liposomes, solid lipid nanoparticles, polymeric nanoparticles, porous microsphere, dendrimers, and nanoparticles encapsulated microparticles are the most suitable drug delivery systems for targeted drug delivery. The solubility, permeability, chemical stability, and biodegradability of drug molecules are the key factors for the right selection of suitable nanocarriers. The application of nanotechnology has been instrumental in the successful development of mRNA, DNA and subunit vaccines, as well as the delivery of COVID-19 therapeutic agents.

Transient Receptor Potential channels (TRP) in GtoPdb v.2023.3

Transient Receptor Potential channels (TRP) in GtoPdb v.2023.3

Evolutionary Monte Carlo of QM Properties in Chemical Space: Electrolyte Design.

Optimizing a target function over the space of organic molecules is an important problem appearing in many fields of applied science but also a very difficult one due to the vast number of possible molecular systems. We propose an evolutionary Monte Carlo algorithm for solving such problems which is capable of straightforwardly tuning both exploration and exploitation characteristics of an optimization procedure while retaining favorable properties of genetic algorithms. The method, dubbed MOSAiCS (Metropolis Optimization by Sampling Adaptively in Chemical Space), is tested on problems related to optimizing components of battery electrolytes, namely, minimizing solvation energy in water or maximizing dipole moment while enforcing a lower bound on the HOMO-LUMO gap; optimization was carried out over sets of molecular graphs inspired by QM9 and Electrolyte Genome Project (EGP) data sets. MOSAiCS reliably generated molecular candidates with good target quantity values, which were in most cases better than the ones found in QM9 or EGP. While the optimization results presented in this work sometimes required up to 106 QM calculations and were thus feasible only thanks to computationally efficient ab initio approximations of properties of interest, we discuss possible strategies for accelerating MOSAiCS using machine learning approaches.

Genome-Wide Association Study of Egg Production Traits in Shuanglian Chickens Using Whole Genome Sequencing.

Egg production is the most important economic trait in laying hens. To identify molecular markers and candidate genes associated with egg production traits, such as age at first egg (AFE), weight at first egg (WFE), egg weight (EW), egg number (EN), and maximum consecutive egg laying days (MCD), a genome-wide analysis by whole genome sequencing was performed in Shuanglian chickens. Through whole genome sequencing and quality control, a total of 11,006,178 SNPs were obtained for further analysis. Heritability estimates ranged from moderate to high for EW (0.897) and MCD (0.632), and from low to moderate (0.193~0.379) for AFE, WFE, and EN. The GWAS results showed 11 genome-wide significant SNPs and 23 suggestive significant SNPs were identified to be associated with EN, MCD, WFE, and EW. Linkage disequilibrium analysis revealed twenty-seven SNPs associated with EN were located in a 0.57 Mb region on GGA10, and clustered into five blocks. Through functional annotation, three candidate genes NEO1, ADPGK, and CYP11A1, were identified to be associated with EN, while the S1PR4, LDB2, and GRM8 genes was linked to MCD, WFE, and EW, respectively. These findings may help us to better understand the molecular mechanisms underlying egg production traits in chickens and contribute to genetic improvement of these traits.

Genome-wide single nucleotide polymorphism (SNP) data reveal potential candidate genes for litter traits in a Yorkshire pig population.

The litter trait is one of the most important economic traits, and increasing litter size is of great economic value in the pig industry. However, the molecular mechanisms underlying pig litter traits remain elusive. To identify molecular markers and candidate genes for pig litter traits, a genome-wide association study (GWAS) and selection signature analysis were conducted in a Yorkshire pig population. A total of 518 producing sows were genotyped with Illumina Porcine SNP 50 BeadChip, and 1969 farrowing records for the total number born (TNB), the number born alive (NBA), piglets born dead (PBD), and litter weight born alive (LWB) were collected. Then, a GWAS was performed for the four litter traits using a repeatability model. Based on the estimated breeding values (EBVs) of TNB, 15 high- and 15 low-prolificacy individuals were selected from the 518 sows to implement selection signature analysis. Subsequently, the selection signatures affecting the litter traits of sows were detected by using two methods including the fixation index (FST) and . Combining the results of the GWAS and selection signature analysis, 20 promising candidate genes (NKAIN2, IGF1R, KISS1R, TYRO3, SPINT1, ADGRF5, APC2, PTBP1, CLCN3, CBR4, HPF1, FAM174A, SCP2, CLIC1, ZFYVE9, SPATA33, KIF5C, EPC2, GABRA2, and GABRA4) were identified. These findings provide novel insights into the genetic basis of pig litter traits and will be helpful for improving the reproductive performances of sows in pig breeding.

Data-Driven Discovery of Organic Electronic Materials Enabled by Hybrid Top-Down/Bottom-Up Design.

The high-throughput exploration and screening of molecules for organic electronics involves either a 'top-down' curation and mining of existing repositories, or a 'bottom-up' assembly of user-defined fragments based on known synthetic templates. Both are time-consuming approaches requiring significant resources to compute electronic properties accurately. Here, 'top-down' is combined with 'bottom-up' through automatic assembly and statistical models, thus providing a platform for the fragment-based discovery of organic electronic materials. This study generates a top-down set of 117K synthesized molecules containing structures, electronic and topological properties and chemical composition, and uses them as building blocks for bottom-up design. A tool is developed to automate the coupling of these building blocks at their C(sp2/sp)-H bonds, providing a fundamental link between the two dataset construction philosophies. Statistical models are trained on this dataset and a subset of resulting top-down/bottom-up compounds, enabling on-the-fly prediction of ground and excited state properties with high accuracy across organic compound space. With access to ab initio-quality optical properties, this bottom-up pipeline may be applied to any materials design campaign using existing compounds as building blocks. To illustrate this, over a million molecules are screened for singlet fission. tThe leading candidates provide insight into the features promoting this multiexciton-generatingprocess.