TY  - JOUR
A1  - Omolaoye, Temidayo S.
A1  - Omolaoye, Victor Adelakun
A1  - Kandasamy, Richard K.
A1  - Hachim, Mahmood Yaseen
A1  - Du Plessis, Stefan S.
T1  - Omics and male infertility
BT  - highlighting the application of transcriptomic data
JF  - Life : open access journal
N2  - Male infertility is a multifaceted disorder affecting approximately 50% of male partners in infertile couples. 
Over the years, male infertility has been diagnosed mainly through semen analysis, hormone evaluations, medical records and physical examinations, which of course are fundamental, but yet inefficient, because 30% of male infertility cases remain idiopathic. This dilemmatic status of the unknown needs to be addressed with more sophisticated and result-driven technologies and/or techniques. 
Genetic alterations have been linked with male infertility, thereby unveiling the practicality of investigating this disorder from the "omics" perspective. 
Omics aims at analyzing the structure and functions of a whole constituent of a given biological function at different levels, including the molecular gene level (genomics), transcript level (transcriptomics), protein level (proteomics) and metabolites level (metabolomics). In the current study, an overview of the four branches of omics and their roles in male infertility are briefly discussed; the potential usefulness of assessing transcriptomic data to understand this pathology is also elucidated. 
After assessing the publicly obtainable transcriptomic data for datasets on male infertility, a total of 1385 datasets were retrieved, of which 10 datasets met the inclusion criteria and were used for further analysis. 
These datasets were classified into groups according to the disease or cause of male infertility. 
The groups include non-obstructive azoospermia (NOA), obstructive azoospermia (OA), non-obstructive and obstructive azoospermia (NOA and OA), spermatogenic dysfunction, sperm dysfunction, and Y chromosome microdeletion. 
Findings revealed that 8 genes (LDHC, PDHA2, TNP1, TNP2, ODF1, ODF2, SPINK2, PCDHB3) were commonly differentially expressed between all disease groups. 
Likewise, 56 genes were common between NOA versus NOA and OA (ADAD1, BANF2, BCL2L14, C12orf50, C20orf173, C22orf23, C6orf99, C9orf131, C9orf24, CABS1, CAPZA3, CCDC187, CCDC54, CDKN3, CEP170, CFAP206, CRISP2, CT83, CXorf65, FAM209A, FAM71F1, FAM81B, GALNTL5, GTSF1, H1FNT, HEMGN, HMGB4, KIF2B, LDHC, LOC441601, LYZL2, ODF1, ODF2, PCDHB3, PDHA2, PGK2, PIH1D2, PLCZ1, PROCA1, RIMBP3, ROPN1L, SHCBP1L, SMCP, SPATA16, SPATA19, SPINK2, TEX33, TKTL2, TMCO2, TMCO5A, TNP1, TNP2, TSPAN16, TSSK1B, TTLL2, UBQLN3). 
These genes, particularly the above-mentioned 8 genes, are involved in diverse biological processes such as germ cell development, spermatid development, spermatid differentiation, regulation of proteolysis, spermatogenesis and metabolic processes. 
Owing to the stage-specific expression of these genes, any mal-expression can ultimately lead to male infertility. 
Therefore, currently available data on all branches of omics relating to male fertility can be used to identify biomarkers for diagnosing male infertility, which can potentially help in unravelling some idiopathic cases.
KW  - male infertility
KW  - omics
KW  - genomics
KW  - transcriptomics
KW  - proteomics
KW  - metabolomics
Y1  - 2022
U6  - https://doi.org/10.3390/life12020280
SN  - 2075-1729
VL  - 12
IS  - 2
PB  - MDPI
CY  - Basel
ER  - 
TY  - JOUR
A1  - de Abreu e Lima, Francisco Anastacio
A1  - Li, Kun
A1  - Wen, Weiwei
A1  - Yan, Jianbing
A1  - Nikoloski, Zoran
A1  - Willmitzer, Lothar
A1  - Brotman, Yariv
T1  - Unraveling lipid metabolism in maize with time-resolved multi-omics data
JF  - The plant journal
N2  - Maize is the cereal crop with the highest production worldwide, and its oil is a key energy resource. Improving the quantity and quality of maize oil requires a better understanding of lipid metabolism. To predict the function of maize genes involved in lipid biosynthesis, we assembled transcriptomic and lipidomic data sets from leaves of B73 and the high-oil line By804 in two distinct time-series experiments. The integrative analysis based on high-dimensional regularized regression yielded lipid-transcript associations indirectly validated by Gene Ontology and promoter motif enrichment analyses. The co-localization of lipid-transcript associations using the genetic mapping of lipid traits in leaves and seedlings of a B73 x By804 recombinant inbred line population uncovered 323 genes involved in the metabolism of phospholipids, galactolipids, sulfolipids and glycerolipids. The resulting association network further supported the involvement of 50 gene candidates in modulating levels of representatives from multiple acyl-lipid classes. Therefore, the proposed approach provides high-confidence candidates for experimental testing in maize and model plant species.
KW  - Zea mays
KW  - lipid metabolism
KW  - omics
KW  - GFLASSO
KW  - QTL
Y1  - 2018
U6  - https://doi.org/10.1111/tpj.13833
SN  - 0960-7412
SN  - 1365-313X
VL  - 93
IS  - 6
SP  - 1102
EP  - 1115
PB  - Wiley
CY  - Hoboken
ER  -