Role of conventional and molecular techniques in soybean yield and quality improvement: A critical review

The soybean is one of the most significant legume crops around the globe and serves as a source of dietary components for humans and animals. It has a higher percentage of protein compared to any other crop. Soybean yield and quality have been affected by many environmental factors. The genetic mechanism of yield and quality is still not clearly understood. Hence there is still a need to investigate the major potent factors to shed light on the mechanism behind yield and quality traits in soybean. Recently, a lot of significant work, including novel QTL, genes, and CRISPR-based genome editing in soybeans, has been done, which opened new doors of hope. The current review has presented detailed work done previously. We have also discussed the role of different breeding techniques in the conventional way of soybean improvement. The genetic factors regulating yield, quality, and disease resistance could be further cloned and transferred into elite cultivars to attain higher output in the current situation of changing environment. The integrated use of several techniques, like CRISPR/Cas9, next-generation sequencing, omics approaches, would be a fruitful way to improve soybean yield and quality. Besides this, hybridization, mass selection, pure line selection, backcross breeding, and pedigree selection should be adopted to develop novel soybean cultivars. This review concluded that soybean yield and quality improvement could be enhanced by exploring its genetic mechanism using several molecular and conventional methods.

Characteristics and Genetic Mechanism of Pore Throat Structure of Shale Oil Reservoir in Saline Lake—A Case Study of Shale Oil of the Lucaogou Formation in Jimsar Sag, Junggar Basin

The shale oil reservoir of the Lucaogou Formation in the Jimsar Sag has undergone tectonic movement, regional deposition and complex diagenesis processes. Therefore, various reservoir space types and complex combination patterns of pores have developed, resulting in an intricate pore throat structure. The complex pore throat structure brings great challenges to the classification and evaluation of reservoirs and the efficient development of shale oil. The methods of scanning electron microscopy, high-pressure mercury injection, low-temperature adsorption experiments and thin-slice analysis were used in this study. Mineral, petrology, pore throat structure and evolution process characteristics of the shale oil reservoir were analyzed and discussed qualitatively and quantitatively. Based on these studies, the evolution characteristics and formation mechanisms of different pore throat structures were revealed, and four progressions were made. The reservoir space of the Lucaogou Formation is mainly composed of residual intergranular pores, dissolved pores, intercrystalline pores and fractures. Four types of pore throat structures in the shale oil reservoir of the Lucaogou Formation were quantitatively characterized. Furthermore, the primary pore throat structure was controlled by a sedimentary environment. The pores and throats were reduced and blocked by compaction and cementation, which deteriorates the physical properties of the reservoirs. However, the dissolution of early carbonate, feldspar and tuffaceous minerals and a small amount of carbonate cements by organic acids are the key factors to improve the pore throat structure of the reservoirs. The genetic evolution model of pore throat structures in the shale oil reservoir of the Lucaogou Formation are divided into two types. The large-pore medium-fine throat and medium-pore medium-throat reservoirs are mainly located in the delta front-shallow lake facies and are characterized by the diagenetic assemblage types of weak compaction–weak carbonate cementation–strong dissolution, early medium compaction–medium calcite and dolomite cementation–weak dissolution. The medium-pore fine throats and fine-pore fine throats are mainly developed in shallow lakes and semi-deep lakes. They are characterized by the diagenetic assemblage type of strong compaction–strong calcite cementation–weak dissolution diagenesis. This study provides a comprehensive understanding of the pore throat structure and the genetic mechanism of a complex shale oil reservoir and benefits the exploration and development of shale oil.

Bioinformatic Analysis Identified Hub Genes Associated with Heterocyclic Amines Induced Cytotoxicity of Peripheral Blood Mononuclear Cells

Heterocyclic amines (HCAs) are a set of food contaminants that may exert a cytotoxic effect on human peripheral blood mononuclear cells (PBMC). However, the genetic mechanism underlying the cytotoxicity of HCAs on PBMC has not been investigated. In the study, bioinformatic analysis on gene dataset GSE19078 was performed. The results of weighted correlation network analysis and linear models for microarray and RNA-seq data analysis showed that four gene modules were relevant to 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) exposure while one gene module was correlated with 2-amino-3-methyl-3H-imidazo[4,5f]quinoline (IQ) exposure. Gene functional analysis showed that the five modules were annotated mainly with mRNA transcriptional regulation, mitochondrial function, RNA catabolic process, protein targeting, and immune function. Five genes, MIER1, NDUFA4, MLL3, CD53 and CSF3 were recognized as the feature genes for each hub gene network of the corresponding gene module, and the expression of feature genes was observed with a significant difference between the PhIP/IQ samples and the other samples. Our results provide novel genes and promising mechanisms for exploration on the genetic mechanism of HCAs on PBMC.

Whole Genome Sequencing Identifies Microdeletions Affecting TET2 and RUNX1 with Clinical Impact in Myeloid Malignancies

Background: The current routine genetic work-up in hematological malignancies includes chromosome banding analysis (CBA) to detect complete or partial chromosomal deletions and fusions, and the identification of point mutations and small deletions or insertions by sequencing panels (max. length ~50 bp). Deletions of individual genes (e.g. IKZF1 in ALL) are only detected by specifically designed molecular tools. Therefore, those microdeletions might be overlooked by the current gold standard despite their clinical relevance. We established a bioinformatic pipeline to screen for microdeletions in whole genome sequencing (WGS) data of myeloid malignancies. Aim: (1) Screen for recurrent microdeletions in myeloid malignancies with a normal karyotype, and (2) characterize a patient specific profile of microdeletions in genes with known clinical and/or prognostic relevance. Patients and Methods: We analyzed 1356 cases (M/F: 778/578) of myeloid malignancies with a normal karyotype according to CBA (aCML: n=47; AML: n=251; CMML: n=165, mastocytosis: n=90; MDS: n=415, MDS/MPN-RS-T: n=69; MDS/MPN-U: n=42; MPN: n=250; PNH: n=27) using WGS. Median age was 71 [20-94] years. Amplification-free WGS was performed on the NovaSeq or HiSeq system with a median coverage of 103x (Illumina, San Diego, CA). Reads were aligned to the human reference genome (GRCh37, Ensembl annotation, Isaac aligner) and somatic copy number variant (CNV) discovery was performed with GATK (v 4.0.2.1), following best practice guidelines. Only gene overlapping CNV calls were considered for analysis (gene coordinates biomaRt (v 2.42.1), GRCh37 Ensembl). Results: On average, 38 genes per patient were partially or completely deleted and the size of the deletions ranged from 0.9 kb to 32 Mb (median 399 kb). The microdeletions affected a broad list of genes, but no gene was present in >5% of myeloid malignancies. As technical validation, we used 36 B-ALL samples (normal karyotype) and identified the known deletions of IKZF1 (42%); PAX5 (25%) and CDKN2A/CDKN2B (22%) with expected incidences. We focused on a patient-by-patient analysis of genes (n=47) with known clinical relevance in myeloid malignancies. We identified deleted genes in 46 out of 1356 patients (3.4%). In aCML 13% of patients had one of the above-mentioned genes deleted (6/47), in mastocytosis only 1% (1/90). The most frequently deleted genes were TET2 (20/1356, 1.5%) and RUNX1 (9/1356, 0.7%). Other deletions also affected transcription factors (e.g. GATA2) or epigenetic regulators (e.g. DNMT3A, figure 1). No deletion of splicing factors, RAS genes or cohesion complex regulators was observed. We found only two deletions of kinases, which are predominantly affected by activating mutations (both FLT3). Instead, the deletions in 41 patients involved genes with a known loss-of-function mutation profile in myeloid malignancies. This corresponds to 89% (41/46) of patients with microdeletions or 3% (41/1356) of all analyzed patients with myeloid malignancies. Microdeletions are thus another genetic element that can lead to loss of gene activity. Deletions and mutations are either alternative genetic mechanisms or co-operate as double hits to affect the same gene. We found additional mutations present in 18 of the 46 patients with microdeletions (39%, figure 1). The majority of these (n=14) involved TET2. TET2 mutations had a median variant allele frequency of 82% [9-100%] indicative of a mutation on the non-deleted allele. For the remaining genes (incl. RUNX1), deletions are predominantly an alternative genetic mechanism to mutations. For validation of WGS results we applied interphase FISH and identified 6/9 RUNX1 deletions. The remaining three microdeletions were only detectable by WGS and too small to be identified by FISH. Conclusions: (1) WGS data unrevealed a plethora of microdeletions, which can be an alternative genetic mechanism to mutations, but are not detected with today's standard diagnostic tools. (2) In the light of increasingly personalized therapy and diagnostics, all genetic mechanisms should be considered, which impact the function of clinically relevant genes. (3) Bioinformatic pipelines for WGS as a potential diagnostic tool in the near future should address microdeletions in genes with relevance for patients' diagnosis, prognosis and hopefully targeted treatment. Figure 1 Figure 1. Disclosures Kern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership.